Microbat activity in vegetable crops relative to insect, landscape, and abiotic influences

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Fardell, Luke M. Noble, Rani Davis, Greg Ford, Brad S. Law, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7871928/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Context As natural landscapes continue to be modified, there is growing evidence that some wildlife can persist in disturbed areas that have adequate resources. Invertebrates synonymous with crop farming may be one such reliable and attractive resource to extant insectivorous wildlife, such as microbats. Objectives To build on existing knowledge bases of microbats using complex agroecosystems globally, we evaluated if microbats were also active across the less researched, relatively open vegetable crops. Methods We assessed three vegetable farms in southeast Queensland, Australia, testing relationships between the number of microbat call sequences and microbat richness to landscape, season, and moon phase dynamics. We also tested relationships between crop invertebrate abundances and richness, as collected on sticky traps, to landscape dynamics and microbat call sequences. Microbat scats from the area were assessed to identify the invertebrates consumed using metabarcoding. Results We found that the open, mixed vegetable crop farm configuration supported the activity of 55% of the microbat species known to occupy the region (17/31). Microbat activity was continually observed across both the typically high (autumn) and low (winter) activity periods, though was higher in autumn. Microbat activity was also continually observed during waning gibbous and waxing gibbous to new moon phases, though was higher during waning gibbous. Of the landscape features, creek edges supported the highest microbat species richness and call activity. Invertebrate surveys confirmed high abundance and richness across the farm landscape area. Metabarcoding analysis revealed that nearly one-third of the invertebrate species identified in microbat scats were also present on farm sticky traps, with the most frequently consumed orders matching those most abundant on the farms. Conclusions We suggest that targeted wildlife-friendly management could enhance resource use of vegetable crops to help conserve microbats and benefit crops. Chiroptera disturbed landscape wildlife-friendly farming crop invertebrate biocontrol moon phase Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Habitat reduction and land modification pressures on native fauna continue to increase due to human needs (Kehoe et al., 2015 ). Often this can force wildlife populations into isolated pockets of remnant habitat separated by areas that are not viable for occupation or use, which has the potential to increase competition pressures (Bartlett et al., 2016 ; Rybicki et al., 2020 ). However, it is also the case that modified landscapes can maintain or create new resources that some wildlife species can exploit (McKinney, 2006 ; Kennedy et al., 2018 ). Consequently, agricultural and urban landscapes, despite the additional stressors (Fardell et al., 2020 ), can provide supplementary resources, and on occasion become the best or only available habitat with adequate resources to sustain wildlife (Ives et al., 2016 ; Maclagan et al., 2018 ). To increase the chance of disturbed habitats supporting the long-term coexistence of wildlife, wildlife-friendly practices need to be implemented (Apfelbeck et al., 2020 ), and the value of disturbed landscapes as potential novel habitats or resource sources for wildlife should be encouraged (Valdez et al., 2021 ; Littlefair et al., 2024 ). This is especially relevant in agricultural habitats, which currently cover 37% of the world’s terrestrial surface (FAO, 2023 ) and, amongst other resources, host an abundance of invertebrates (Symondson et al., 2002 ; Segoli and Rosenheim, 2012 ) that can be attractive food sources to insectivores (Orlowski et al., 2014). Microbats are highly mobile insectivores capable of consuming 30–100% of their body weight in invertebrates a night (Kunz and Stern, 1995 ; Kunz et al., 2011 ) and have been observed to exploit agricultural invertebrate resources globally (Tuneu-Corral et al., 2023 ). Considering the somewhat controversial decline in invertebrates that is due largely to climate impacts and land modification (Cardoso et al., 2020 ), agricultural invertebrates could be helping to support native insectivore diversity and preservation. This resource could be particularly pertinent for microbats, as they are facing the increasing and potentially compounding threats of reduced habitat, disease, introduced toxins, climate-change impacts, and human-wildlife conflicts (Mickleburgh et al., 2002 ; Stahlschmidt et al., 2017 ; Frick et al., 2020 ). Microbats are known to use agricultural landscapes interspersed with patches of remnant vegetation, although comparatively more activity can be evident within the surrounding remnant vegetation (Lumsden and Bennett, 2005 ; Law and Chidel, 2006 ). However, division of activity between these habitats may vary depending upon broader landscape pressures, resource availability, the extent of remaining native vegetation and the energetic costs associated with foraging in simpler compared to more complex habitats (Patriquin and Barclay, 2003 ; Suarez-Rubio et al., 2018 ). Nevertheless, agricultural management practices that increase vegetation heterogeneity and structural complexity are generally key drivers of increased wildlife activity and diversity (Benton et al., 2003 ; Tuneu-Corral et al., 2023 ) and need to be assessed at the local scale to design the best wildlife supportive management. For microbats, retaining hedgerows, scattered trees, and remnant vegetation can increase their activity in agroecosystems (Law and Chidel, 2006 ; Fisher et al., 2010; Boughey et al., 2011 ; Lentini et al., 2012 ; Graham et al., 2018 : Froidevaux et al., 2019 ; Froidevaux et al., 2022a ). Intercropping with rotated planting has also been observed to support higher microbat activity by promoting greater invertebrate availability (Monck-Whipp et al., 2018 ; Tortosa et al., 2023 ). The position on a farm where invertebrates are most abundant, typically crop edges (Nguyen and Nansen, 2018 ), could potentially result in localised increased microbat activity, and should also be considered in designing wildlife-friendly crop management. As should permanent water sources, which likely drive increased microbat activity due to water needs, increased prey abundance, and the acoustic properties of water surfaces that can aid microbat navigation across the predominantly open landscapes (Greif and Siemers, 2010 ; Stahlschmidt et al., 2012 ; Korine et al., 2016 ; Froidevaux et al., 2017 ; Froidevaux et al., 2022b ). Similarly, the introduction of vertical structures such as poles, posts or buildings could also aid microbats to navigate across the open landscapes and increase their activity (Vandevelde et al., 2014 ; Warnecke et al., 2018 ) – though this has not been tested extensively. An important part of understanding the value of a disturbed landscape to wildlife is assessing use across seasonal periods of high and low resource needs and shifting abiotic conditions, especially those that in the disturbed landscape have the potential to be a stressor. For microbats in disturbed landscapes in Australia, activity is generally higher in autumn and lulls in winter (Gonsalves and Law, 2017 ). Higher foraging rates in autumn are likely due to juveniles of some species beginning to forage independently (Law et al., 2015 ), and the need for some species to build fat stores before low-activity winter torpor and emerging reproductive support for females (Stawski et al., 2014 ). Lower foraging rates in winter are likely due to reduced invertebrate activity during colder temperatures (Stawski et al., 2010). Microbat activity has also been observed to be influenced by moon phase and light intensity changes in landscapes without artificial light, with brighter nights influencing reduced activity or foraging in areas of dense cover (Saldaña-Vázquez and Munguía-Rosas, 2013 ) – though responses are generally species specific relative to morphology and behaviour (Holland et al., 2011 ; Appel et al., 2017 ; Li and Wilkins, 2022 ). Similarly, in urban and complex agricultural landscapes, natural and artificial high illumination conditions have been observed to have both negative and positive impacts on microbat activity, dependent upon species (Vásquez et al., 2020 ) and habitat structural complexity (Roeleke et al., 2018 ), and likely relative to increased prey activity (Li and Wilkins, 2022 ). Structurally open agricultural landscapes create the potential stressor of increased exposure under high levels of artificial or natural light, which could impact microbat activities further. Observations of microbat activity in agricultural cropping landscapes have predominantly been conducted in crops with some structural complexity, particularly tree crops or tall stands of crop vegetation with service spaces between, such as apples (Jay et al., 2012 ; Murphy and Ament, 2022 ), cotton (Cohen et al., 2020 ; Kolkert et al., 2020a , 2021 ), corn (Maine and Boyles, 2015 ), rice (Puig-Montserrat et al., 2015 , 2020 ), pecan (Brown et al., 2015 ), macadamia (Crisol‐Martínez et al., 2017; Taylor et al., 2018 ), cacao (Maas et al., 2013 ; Vansynghel et al., 2022 ), coffee (Schmitt et al., 2021 ), and vineyards (Froidevaux et al., 2017 ; Rodríguez-San Pedro et al., 2020 ; Baroja et al., 2021 ). Less research has been undertaken in more structurally simplified, open crops, such as vegetables (Tuneu‐Corral et al., 2023). However, microbat species can exhibit plasticity in adjusting their primary foraging behaviours to optimise prey capture efficiency in similar structurally open areas (Jung and Kalko, 2010 ; Raposeira et al., 2023 ; Vescera et al., 2024 ). Given the need to better understand how microbats use open crop landscapes to determine their resource value and begin to inform appropriate wildlife-friendly management practices, we conducted surveys across vegetable farms in South East Queensland, Australia. We assessed the influence of season, moon phase, and landscape dynamics – including distance to vertical structures, on the total number of microbat call sequences recorded as a measure for microbat activity. We also assessed the influence of landscape dynamics and number of microbat call sequences on crop invertebrate abundances, as collected on invertebrate sticky traps. To better understand the value of these habitats to a diversity of these species, we also assessed the influence of these factors on microbat and invertebrate richness. To further understand the resource value, we also assessed microbat diet using opportunistic scat collection from night-roosts in the vegetable crop wider area. We specifically tested the questions: Are microbats continually active across the temporal and abiotic changes of season and moon phase (autumn compared to winter; and new moon to waxing gibbous compared to waning gibbous)? Is microbat activity or richness influenced by farm landscape areas (creek edge, centre, road edge), crop areas (edge, inner), and distances to vertical structures? Is invertebrate abundance or richness influenced farm landscape areas (creek edge, centre, road edge), crop areas (edge, inner), and increased microbat activity? Are the invertebrates captured on farms present in microbat scats at proportionally similar levels, with those most captured in crops also being the most frequently observed in microbat scats? Methods Survey Area We conducted surveys to investigate microbat activity across small vegetable farms in the Lockyer Valley region of southeast Queensland, Australia (Fig. 1 ), c. 70 km west of the state capital, Brisbane (Powell et al., 2002 ). The Lockyer Valley region has a subtropical climate with an average annual rainfall of 780 mm that falls largely in summer (Powell et al., 2002 ). The Lockyer catchment area has major southern tributaries that drain north into Lockyer Creek, which in turn drains northeast into the Brisbane River (Powell et al., 2002 ). The Lockyer Valley region spans an area of 227,200 km 2 and of this c. 23% of the low-lying area is dedicated to agricultural practices, making it one of Queensland’s most productive regions (RDAIWM, 2018). Biodiversity of the region includes 203 terrestrial invertebrates and 662 terrestrial vertebrates, including 31 species of microbats (Department of Environment and Science, 2013). Agricultural crops in the Lockyer Valley maximise their land use and plant crops to property edges, with very few road verges of extant trees, and low, widely spaced fencing or no fencing between the neighbouring properties. Our surveys were conducted in the Glenore Grove area of the Lockyer Valley across three separate properties (Farm 1, Farm 2, Farm 3) that were spaced 0.2-2 km apart, all with the Lockyer Creek at one boundary, a public sealed road at the parallel boundary, and managed by the same farm group (Fig. 1 ). Integrated Pest Management was the primary management method used by the farm group. This included intercropping – planting different crops adjacently and routinely rotating the types of crops planted across the farm, as well as introducing key invertebrates for biological control, and routine pesticide use. All properties had sparse non-crop vegetation surrounding the water-filled section of Lockyer Creek at the rear, and a water-filled dam toward the opposite edge near the sealed-road. The mixed intercrops included adjacent rotations of potato, carrot, broccoli, mung bean, pumpkin, and grain, and each at varied growth stages during our research, which added a level of randomisation to our surveys. Each area within the three farms that we surveyed was separated by a 1–2 m path, or 4 m wide management road, with 5–8 m between the neighbouring crops that were not surveyed, and an open unsealed road running the length of the property at each side of the farm (Fig. 1 ). All surveys were conducted within the same sized area and at the same time (Fig. 1 ). Microbat Activity - Acoustic Surveys Surveys to detect microbat call sequences across Farms 1 to 3 were conducted using six passive ultrasonic audio detectors (Anabat Chorus – Titley Scientific) per farm. Detectors were secured to posts at a height of 1.5 m (minimum 1 m from crop apex), at 1 m into the crop, for three consecutive survey nights, at each farm simultaneously. Two survey sessions were in autumn (April – May 2024) and two were in winter (July 2024). One replicate each season was under new moon to waxing crescent conditions (0–4% illumination) when moon light increased in the first half of the night, and the other under waning gibbous conditions (50–71% illumination) when moonlight was restricted to the period after midnight. At each survey session detectors were arranged in the same linear transect positions of one 25 m from the crop edge and another 75 m into the crop width, each at 10–15 m from the creek edge, the centre of the farm, and 20–30 m from the sealed road edge, with microphones positioned horizontally towards the crop edge or inner crop – respective to their position (Fig. 1 ). Transect positions were spaced equally across each farm at 400–500 m apart, depending on the farm. There was a minimum 230 m between transects at Farms 1 and 2 traversing Lockyer Creek, and a maximum 3,537 m between the creek edge at Farm 3 and the road edge at Farm 2. Detectors were set to record microbat call sequences from 30 mins before sunset till 30 mins after sunrise using the night setting formatted with GPS fixtures, with a sampling rate of 256 kHz, a minimum trigger of 8 kHz and maximum file length of 10 seconds. Recordings were confirmed as microbat species using the automated Anascheme system (Adams et al., 2010 ). Confirmation of species identification was made by considering known distribution records (Atlas of Living Australia; Ausbat) and using Anabat Insight (Titley Scientific) to check call metrics against reference guides (Reinhold et al., 2001 ; Pennay et al., 2004 ) and to reference calls recorded during the study. Further confirmation was sought by professional expertise (Greg Ford) as needed. Each survey station had varying distances to structures that could potentially be used by microbats in orientation, such as poles, posts, houses or sheds, for which, the distance to the nearest structure was measured using ArcMap 10.8.2 (Esri) and GPS coordinates taken in-field. Crop Invertebrates – Sticky Traps To assess the invertebrates present relative to each crop (edge, inner) and farm (creek edge, centre, road edge) position, one yellow and one blue sticky trap (Bugs for Bugs ®) was secured vertically at 0–20 cm from the crop apex, 1 m into the crop, between plants, alternating along the crop c. 75 m between each microbat audio detector. Traps were left open both day and night during each 3-night microbat activity survey period, and upon collection were frozen at -20° C. This passive collection method was used to reduce any bias unrepresentative sampling that lures may have supported (Busse et al., 2022 ). Invertebrates captured on the sticky traps were analysed to Amplicon Sequence Variant (ASVs) by EnviroDNA using metabarcoding (as detailed below). To facilitate the time and financial budgets of this project, traps were pooled by season (autumn, winter) at each survey station (crop and farm position) across the three farms. The identified ASVs were assessed at the lowest taxonomic level and grouped to the order (or next highest available) taxonomic level for ease of comparisons. Invertebrates were also classified to functional types relative to crop management, using established understandings of their impacts to food crops based on Google Scholar searches of the species (or higher level) scientific name. From this, invertebrates were grouped in to six types: 1) beneficial – positive crop impacts including pest depredation; 2) damaging – negative crop impacts including by identified pest species; 3) both – group or species can have both positive and negative impacts; 4) potential – group or species can cause damage and negative impacts under the right conditions and could also be neutral/unknown but no positive impacts known; 5) neutral – does not have any known impact; and 6) unknown – no research or enough detail has been established yet to know impacts. Invertebrates in Microbat Diet – Near-Crop Night-Roost Scat Collection To assess microbat diet in the Lockyer Valley vegetable crop regions, microbat capture surveys were performed using mist nets (75 denier 2 ply 2.6 x 9/6 m: Avinet, USA) and harp traps (2 bank: Austbat, Australia) for the first five hours after sunset. However, capture in the open areas proved challenging, even when employing audio lures; therefore, opportunistic collections from near-crop night-roosts were used instead. Clean plastic tarps were secured on the ground, three days before each scat collection session, under a house on Farm 1 and another unoccupied farmhouse – which we will refer to as Farm 4 that was 6.5 km from Farm 3 but was adjacent to Lockyer Creek and a vegetable farm that had similar crop rotations. At each scat collection session, 5–7 scats (size dependent) were collected using sterilised stainless-steel forceps to place them in 2 ml screw cap tubes that contained 0.5 ml of Qiagen ATL buffer. A sealed sterile single-use swab was then used to homogenize the sample in the tube and was broken off at a suitable height to seal in the tube with the scats for processing. A negative control was collected at each collection session, using these methods in the same area, but without touching any scats. Samples were stored in a -20°C freezer until processing (maximum 7 months). Scats were analysed to identify the invertebrates ingested, to ASVs by EnviroDNA using metabarcoding (as detailed below). The identified ASVs were assessed at the lowest taxonomic level and grouped to the order (or next highest available) taxonomic level for ease of comparisons. The identified invertebrates were also classified to the above detailed functional types ( beneficial, damaging, both, potential, neutral , and unknown ). DNA Metabarcoding – Invertebrates in Sticky Traps and Microbat Scats All extraction and metabarcoding processes were undertaken by EnviroDNA (Melbourne, Australia). Sticky traps were processed by first removing all insects using hot orange oil solvent followed by washing in 100% ethanol (Butterwort et al., 2022 ). Samples were dried and weighed before DNA was extracted with a non-destructive lysis followed by Qiagen PowerFecal DNA extraction kits on a QIASymphony platform (Qiagen, Australia). DNA was extracted from the microbat scats using Qiagen PowerFecal DNA extraction kits on a QIASymphony platform (Qiagen, Australia). Following extraction, metabarcoding libraries were prepared targeting the mitochondrial COI locus using a two-step PCR method. The first PCR amplified the target region using primers fwhF1/fwhR1 designed for macroinvertebrates (Vamos et al., 2017 ) that perform well for insectivore diet analyses (Tournayre et al., 2020 ; 2021 ) and also amplify host DNA. The second PCR stage added sequencing adapters and unique barcodes for each sample. Extraction and PCR negative controls were used for all 96-well plates, and field negative controls were also used for scat samples (in-field collection process using materials without scat included). Sequencing was performed across two runs of an Illumina iSeq 100 (Illumina, USA) with 2x150bp paired-end reads. Bioinformatic analysis was performed following quality control filtering that removed primer sequences (Martin, 2011 ) and truncated and low-frequency reads, the remaining sequences were denoised (Rognes et al., 2016 ), deduplicated, and unique sequences were assigned an amplicon sequencing variant (ASV) identifier. Filtering at the ASV level removed detections with less than 30 reads or a per-sample proportion of less than 0.3%. Species assignment was performed using VSEARCH at a 90% bootstrap threshold by comparing to a reference sequence database based on NCBI nt mitochondrial sequences (updated 20240903) followed by global alignment with minimap2 (Li, 2018 ). Species rank was assigned for unique perfect matches (100% coverage and identity), genus rank at 99% coverage 97% identity, family rank at 95% coverage 95% identity and order level at 90% coverage 90% identity. Statistical Analyses All statistical assessments were performed using R statistical software (R Core Team, 2024 ). Microbat Activity To determine if there were differences in the total number of microbat call sequences between the seasons (autumn, winter), and moon phases (new moon – waxing crescent, and waning gibbous), independently, we used non-parametric Friedman tests that considered the survey station to account for the repeated measures, with the R base package (R Core Team, 2024 ). Kendall’s coefficient of concordance was used to determine the effect size statistic, and Namenyi-Wilcoxon-Wilcox post-hoc tests were used to make pair-wise comparisons using the DescTools and PMCMRplus packages respectively (Pohlert, 2023 ; Signorell, 2024 ). To better understand microbat activity across the vegetable farm landscapes, we investigated the relationships of the total number of all microbat call sequences (all species across three consecutive survey nights each session) to the position on the farm (creek edge, centre, road edge), within the crop (edge, inner), and the distance to the nearest structure (poles, posts, houses or sheds), using mixed-effects models (estimated using REML) with the lme4 package (Bates et al., 2015 ). To account for the repeated surveys across time at the same station locations, station identification (unique to the farm and location within) nested into the survey session (1,2,3,4) was incorporated as a random effect (Zuur et al., 2015 ). Crop type was also incorporated as a random effect, to better balance the residual errors. The intercept was the creek edge farm position, the edge crop position, and the smallest distance to the closest structure. To stabilize the variance and linearize relationships, considering the left skew of a density plot of the response, the response was log transformed within the mixed-effects model, as it was the most suitable distribution shape for the data (Lee, 2020 ). Model selection was based on logic from study design but was also informed by likelihood ratio tests and AICc that compared models with all measured components in a stepwise manner. Model fit was established by assessing the residuals and theoretical quantiles, testing for overdispersion and outliers, and assessing the general deviation and dispersion, using the DHARMa package (Hartig, 2022 ). Beta estimates were back-transformed for ease of interpretation. Pairwise comparisons were made using back-transferred estimated marginal means, based on model predictions, comprising all factor combinations, using the emmeans package (Lenth, 2024). Bonferroni correction of p-values was used to account for multiple simultaneous hypotheses testing. This process and model design was also used with microbat species richness (total number of species observed across the three consecutive survey nights each session) as the response. Crop Invertebrates To better understand where invertebrates were present across the vegetable farm landscapes, we investigated relationships between the total number of all invertebrate ASVs each season (across two, three consecutive survey nights sessions) and the position on the farm (creek edge, centre, road edge), within the crop (edge, inner), and microbat activity (total number of call sequences, for all species), using mixed-effects models (estimated using REML) with the lme4 package (Bates et al., 2015 ). To account for the repeated surveys across time at the same station locations, station identification (unique to the farm and location within) nested into the survey season (autumn, winter – that the invertebrate sticky traps were pooled across) was incorporated as a random effect (Zuur et al., 2015 ). Invertebrate richness was also incorporated as a random effect, to better balance the residual errors – considering the richness to abundance relationship that had a greater impact on data spread than crop type did. The intercept was the creek edge farm position, the edge crop position, and the lowest number of microbat call sequences recorded. To stabilize the variance and linearize relationships, considering the left skew of a density plot of the response, the response was log transformed within the mixed-effects model, as it was the most suitable distribution shape for the data (Lee, 2020 ). Model selection, fit and pairwise comparisons were assessed as detailed above. This process and same model design was also used with invertebrate ASV richness (total number of ASVs identified across the two, three consecutive survey nights each season) as the response, and removing it from the random effects. To understand the relative abundance of invertebrates observed, we calculated the proportion of the total ASVs and order groupings of invertebrates in sticky traps, pooled by season for each crop and farm location per farm, and in microbat scats, pooled as survey session at each location. For the ASVs and order groupings of invertebrates observed in the microbat scats, we further calculated the Frequency Of Occurrence (FOO) – that is the proportion of samples in which the target is detected as a measure of how often a particular invertebrate is present in a diet. Similarities between the proportions and FOOs from the sticky traps and the microbat scats were only able to be inferentially assessed considering differences in collection times and locations. Results Microbat Activity Across the four survey sessions of three consecutive nights, at the three farms with six detectors each (total 216 detector nights), 24,064 confirmed microbat 10 second call sequences were recorded, of which we were able to identify 16,350 to species or genus (68%). This included 17 species from four families, and three genus complexes of species that could not reliably be separated (Table 1 ). The total number of microbat call sequences was relatively similar across all three farms surveyed, and the same five species were most active at all three farms (Table 1 ). Table 1 Total microbat call sequences identified to species or species complex across the three farm survey locations in the Lockyer Valley, QLD. Count of species by farm (all sessions all stations) Species Farm 1 Farm 2 Farm 3 Total Austronomus australis * 1485 1741 826 4052 Chalinolobus gouldii * 388 527 993 1908 Chalinolobus morio 9 19 28 56 Chalinolobus nigrogriseus 53 99 221 373 Chalinolobus picatus 8 5 4 17 Miniopterus australis 100 206 91 397 Micronomus norfolkensis 33 16 76 125 Miniopterus orianae oceanensis * 707 392 419 1518 Myotis species or Nyctophilus species complex 3 7 15 25 Myotis species 29 40 36 105 Nyctophilus species 46 64 64 174 Ozimops lumsdenae 153 204 261 618 Ozimops ridei * 898 971 1405 3274 Saccolaimus flaviventris * 912 589 708 2209 Scotorepens orion 41 67 36 144 Scotorepens species (Parnaby) or S. greyii complex 36 68 140 244 Vespadelus darlingtoni 24 10 10 44 Vespadelus pumilus 23 26 9 58 Vespadelus troughtoni 219 99 188 506 V. troughtoni or V. pumilus complex 46 327 130 503 Total 5213 5477 5660 16350 * Species results in bold are the five most active species at all three farms Season and Moon Phase Impacts on Microbat Activity Season had a large and significant effect on the combined microbat activity (X 2 = 14.22, df = 1, p-value = 0.0002; KendallW = 0.79, CI:0.444-1), with more activity in autumn (71% of total calls) compared to winter (29% of total call sequences) (Fig. 2 A). In autumn we recorded an average 1,902 microbat call sequences nightly (11,411 total), and in winter we recorded an average 823 microbat call sequences nightly (4,939 total). Moon phase had a large and significant effect on the combined microbat activity (X 2 = 10.89, df = 1, p-value = 0.001; KendallW = 0.605, CI:0.198-1), with more activity evident in the waning gibbous stage (50–71% illumination; 66.5% of total call sequences) compared to the new moon – waxing crescent stage (0–4% illumination; 33.5% of total call sequences) (Fig. 2 B). Under waning gibbous conditions, we recorded an average 1,819 microbat call sequences nightly (10,916 total), and under new moon – waxing crescent conditions we recorded an average 906 microbat call sequences nightly (5,434 total). Farm and Crop Position, and Distance to Structure Impacts on Microbat Activity The explanatory power of the mixed-effects model that assessed the total microbat activity (all species call sequences) across farm and crop positions and distance to closest structure was substantial (conditional R2 = 0.74, marginal R2 0.10). The effect of farm position, considering creek edge as the intercept, on microbat activity was statistically significant and negative for road edge (beta = 0.49, SE = 0.26, df = 16.91, t-value = -2.74, p = 0.014, p-adjusted = 0.07), and non-significant and negative for centre (beta = 0.24, SE = 0.70, df = 9.14, t-value = -2.04, p = 0.071, p-adjusted = 0.357) (Fig. 3 A). The highest number of call sequences were recorded at the creek edge farm position, based on pairwise comparisons (creek edge: farm centre ratio = 4.136, SE = 3.66, df = 9.67, t = 1.605, p.adjusted = 0.4221; creek edge: road edge ratio = 2.038, SE = 0.61, df = 15.89, t = 2.377, p.adjusted = 0.091) (Fig. 3 A). There was no significant effect of crop position on microbat activity, considering edge as the intercept (beta = 1.03, SE = 0.20, df = 17.91, t-value = 0.174, p = 0.864, p-adjusted = 1) (Fig. 3 A). The inner crop position generally had marginally higher call activity, based on pairwise comparisons (crop edge: inner ratio = 0.966, SE = 0.191, df = 17.8, t =-0.173, p.adjusted = 0.8648). There was no significant effect or general pattern of distance to closest structure on microbat activity, considering the smallest distance as the intercept (beta = 1, SE = 0.0001, df = 7.37, t-value = 1.03 p = 0.336, p-adjusted = 1). The explanatory power of the mixed-effects model that assessed microbat richness (the number of microbat species/genus complex identified at a location per survey session) across farm and crop positions and distance to closest structure was substantial (conditional R2 = 0.52, marginal R2 0.19). The effect of farm position on microbat richness, considering creek edge as the intercept, was negative for road edge and centre but non-significant (beta = 0.85, SE = 0.09, df = 22.68, t-value = -0.91 p = 0.069, p-adjusted = 0.347; and beta = 0.71, SE = 0.26, df = 20.71, t-value = -1.30 p = 0.207, p-adjusted = 1, respectively) (Fig. 3 B). The highest number of microbat species/genus complex identified at a location per survey session were recorded at the creek edge farm position (Table 1 ), based on pairwise comparisons (creek edge: farm centre ratio = 1.403, SE = 0.435, df = 18.6, t = 1.093, p.adjusted = 0.8655; creek edge: road edge ratio = 1.177, SE = 0.113, df = 20.9, t = 1.698, p.adjusted = 0.3132) (Fig. 3 B). There was no significant effect of crop position on microbat richness, considering edge as the intercept (beta = 0.95, SE = 0.06, df = 18.44, t-value = -0.75 p = 0.462, p-adjusted = 1) (Fig. 3 B). The crop edge position generally had marginally higher numbers of microbat species/genus complex, based on pairwise comparisons (crop edge: inner ratio = 1.05, SE = 0.0642, df = 18, t = 0.746, p.adjusted = 0.4655). There was no significant effect or general pattern of distance to closest structure on microbat richness, considering the smallest distance as the intercept, (beta = 1, SE = 0.0005, df = 17.12, t-value = 0.213 p = 0.8338, p-adjusted = 1). Crop Invertebrates Invertebrate sticky traps (a yellow and blue at each location) captured a total of 1,356,984 invertebrates. These were pooled for DNA metabarcoding analyses across the two season replicates (total 144 individual traps; in 36 pools). From these, we were able to identify them to 84 ASVs (45 species, 14 genera, 18 families, 5 orders, 1 class, and 1 phylum). More invertebrates were captured in autumn (885,038) compared to in winter (471,946). When grouped to order level, there were similarly high number of ASVs assigned to orders observed at Farms 1 and 2 (52, and 50 respectively) with slightly less at Farm 3 (44). Across all farms combined, the highest number of classifications to an order was observed for Diptera (32), followed by Coleoptera and Lepidoptera (12 and 10 respectively). The highest proportion of identifications to an order was observed for Diptera (0.42), and Coleoptera (0.43) (Table 2 ). When grouped to functional type, both and damaging , were observed in highest quantities and potential in the lowest (Table 2 ). Table 2 Invertebrates observed via eDNA methods of invertebrate sticky traps across the six survey positions of each farm (creek-edge crop inner/edge, farm-centre crop inner/edge, road-edge crop inner/edge), and in microbat scats collected at Farm 1 and Farm 4 – further removed but along the same creek line with similar crops grown on opposite bank. For each eDNA type the total count, proportion of the total (Pi) and Frequency of Occurrence (FOO; the Pi of all samples that the order/type is detected in). Invertebrates have been grouped by Order (or higher) and as Type – representing the perceived impact on crops (beneficial, both – damaging and beneficial, potential – to be damaging, unknown – impacts, neutral – no impact). Invertebrates detected via eDNA Grouped by Order Insect Sticky Trap Total Insect Sticky Trap Pi Microbat Scat Farm 1 Total Microbat Scat Farm 1 Pi Microbat Scat Farm 1 FOO Microbat Scat Farm 4 Total Microbat Scat Farm 4 Pi Microbat Scat Farm 4 FOO Araucariales 0 0 0 0 0% 317 0.002 6% Arthropoda (Phylum) 4 0.000003 21414 0.30 33% 2147 0.01 38% Blattodea 0 0 0 0 0% 4 0.00002 6% Coleoptera 626726 0.42 3904 0.06 44% 12273 0.07 63% Diptera 587120 0.43 28719 0.40 89% 66369 0.36 94% Entomobryomorpha 131 0.0001 0 0 0% 0 0 0% Ephemeroptera 425 0.0003 47 0.001 11% 0 0 0% Hemiptera 79288 0.05 173 0.002 11% 2181 0.01 31% Hymenoptera 143 0.0001 0 0 0% 0 0 0% Insecta (Class) 14642 0.01 2798 0.04 11% 5195 0.03 44% Lepidoptera 57295 0.04 1491 0.02 56% 14493 0.08 50% Mesostigmata 0 0 7 0.0001 11% 0 0 0% Metazoa (Kingdom) 0 0 0 0 0% 45 0.0002 6% Neuroptera 61 0.00004 3879 0.05 11% 51 0.0003 13% Orthoptera 392 0.0003 0 0 0% 963 0.01 13% Trichoptera 426 0.0003 0 0 0% 141 0.001 6% Trombidiformes 0 0 4874 0.07 33% 78085 0.42 44% Unknown 0 0 3627 0.05 56% 3795 0.02 75% Total Individuals per Type Beneficial 56214 0.04 6568 0.09 22% 247 0.001 19% Both 1065790 0.78 40621 0.57 89% 29130 0.16 94% Damaging 152869 0.11 2246 0.03 56% 9684 0.05 75% Potential 16815 0.01 3712 0.05 89% 30680 0.16 75% Unknown 74965 0.05 17786 0.25 89% 116001 0.62 100% Neutral 0 0 0 0 0% 317 0.002 6% Total 1366653 70933 186059 Farm and Crop Position, and Microbat Activity Impacts on Invertebrates The explanatory power of the mixed-effects model that assessed the total number of invertebrates across farm and crop positions and the total number of microbat calls, was substantial (conditional R2 = 0.58, marginal R2 0.10). The effect of farm position on invertebrate abundance, considering creek edge as the intercept, was positive for road edge and centre but non-significant (beta = 3.14, SE = 0.50, df = 18.25, t-value = 2.31, p = 0.033, p-adjusted = 0.165; and beta = 1.42, SE = 0.48, df = 18.28, t-value = 0.73, p = 0.477, p-adjusted = 1, respectively) (Fig. 4 A). The highest number of invertebrates were recorded at the road edge farm positions, based on pairwise comparisons (creek edge: road ratio = 0.319, SE = 0.171, df = 20.8, t =-2.125, p.adjusted = 0.1372; farm centre: road edge ratio = 0.453, SE = 0.231, df = 23.6, t =-1.555, p.adjusted = 0.3997). The effect of crop position on invertebrate abundance, considering edge as the intercept, was positive but non-significant (beta = 1.12, SE = 0.38, df = 19.20, t-value = 0.30, p = 0.769, p-adjusted = 1) (Fig. 4 A). The inner crop position generally had marginally higher invertebrate abundance, based on pairwise comparisons (crop edge: inner ratio = 0.893, SE = 0.356, df = 21.5, t =-0.283, p.adjusted = 0.78). The effect of the total number of microbat call sequences on invertebrate abundance, considering the lowest number of calls as the intercept, was non-significant (beta = 1, SE = 0.0006, df = 15.21, t-value = 0.90, p = 0.383, p-adjusted = 1), though the slope of the relationship appeared to differ between seasons being more positive in autumn and negative in winter (Fig. 4 C). The explanatory power of the mixed-effects model that assessed invertebrate ASVs richness (the number of species or higher level of taxa identified per location and season) across farm and crop positions and the total number of microbat call sequences, was weak (conditional R2 = 0.12, marginal R2 0.04). The effect of farm position on invertebrate richness, considering creek edge as the intercept, was positive and non-significant for both centre and road edge (beta = 1, SE = 0.17, df = 29.03, t-value = 0.40, p = 0.692, p-adjusted = 1; and beta = 1.13, SE = 0.17, df = 29.02, t-value = 0.73, p = 0.474, p-adjusted = 1, respectively) (Fig. 4 B). The highest number of invertebrate ASVs observed at a location per season were recorded at the road edge farm positions, based on pairwise comparisons (creek edge: road edge ratio = 0.884, SE = 0.15, df = 29, t =-0.726, p.adjusted = 1; farm centre: road edge ratio = 0.947, SE = 0.141, df = 29, t =-0.366, p.adjusted = 1. The effect of crop position on invertebrate richness, considering edge as the intercept, was positive but non-significant (beta = 1.14, SE = 0.12, df = 29.00, t-value = 1.07, p = 0.294, p-adjusted = 1) (Fig. 4 B). The inner crop position generally had marginally higher invertebrate abundance, based on pairwise comparisons (crop edge: inner ratio = 0.877, SE = 0.108, df = 29, t =-1.069, p.adjusted = 0.294). The effect of the total number of microbat call sequences on invertebrate richness, considering the lowest number of calls as the intercept, was non-significant (beta = 1, SE = 0.045, df = 29.10, t-value = 0.26, p = 0.793, p-adjusted = 1), though the slope was slightly negative in both autumn and winter (Fig. 4 D). Invertebrates in Microbat Scats Microbat scats were opportunistically collected over nine survey sessions at Farm 1 (spring and winter) and 16 sessions at Farm 4 (all seasons). All scats belonged to the Vespertilionidae Family, and 23% of the identifications from Farm 4 were further assigned to genus Chalinolobus . A total of 70,933 invertebrate detections were obtained from the microbat scats from Farm 1, with 95% identified to 31 ASVs (17 species, 6 genera, 5 families, 2 orders, and 1 phylum). At Farm 4, 186,059 invertebrate detections were obtained from the microbat scats, with 98% identified to 61 ASVs (35 species, 10 genera, 10 families, 4 orders, 1 class, and 1 phylum). There were almost double the number of orders at Farm 4 compared to Farm 1 (63 and 33 respectively). At Farm 1, the highest number of classifications to an order were for Diptera (14), followed by Lepidoptera and Coleoptera (7 and 4, respectively). The highest proportion of identifications to an order at Farm 1 were for Diptera (0.40) and the Arthropoda phylum (0.30), and the highest FOO were for Diptera (89%), Lepidoptera (56%), unknown (56%), and Coleoptera (44%) (Table 2 ). Similarly, at Farm 4, the highest number of classifications to an order were for Diptera (19), followed by Lepidoptera, Coleoptera, and Hemiptera (14, 10, and 8, respectively). The highest proportion of identifications to an order at Farm 4 were for Diptera (0.36) and Trombidiformes (0.42), and the highest FOO were for Diptera (94%), unknown (75%), Coleoptera (63%), and Lepidoptera (50%) (Table 2 ). When grouped to functional type, scats from Farm 1 had lower damaging , unknown and potential , but more both and beneficial than observed in scats from Farm 4 (Table 2 ). The highest consumed type at Farm 1 was both and the lowest was damaging , and at Farm 4 the highest consumed type was unknown and the lowest was beneficial (Table 2 ). Similarities in Invertebrates at Crops and in Microbat Scats There were 24 invertebrate ASVs commonly identified across the farms in sticky traps and in the microbat scats collected from Farm 1 and 4, these common ASVs made up 33% of the total observed on the sticky traps and 29% of total observed in microbat scats. Resultantly, 60 invertebrate ASVs identified in the microbat scats were not identified in the sticky traps, and 49 invertebrate ASVs identified in the sticky traps were not identified in the microbat scats. Of the invertebrates both collected on the sticky traps in the crops and in microbat scats from both farms, Diptera was commonly the highest observed order and had the highest FOO in scats. The most observed functional type on the sticky traps in the crops and microbat scats from Farm 1 was both , but unknown was the most observed functional type in the scats from the further away Farm 4. When assessed by species/group, out of the five most abundant observed on the sticky traps – Psilopa spp, Arthropoda Phylum, Lycaenidae Family, Eleale Genus , and Crambidae Family , only Arthropoda Phylum was also within the highest FOO at Farm 1. Discussion The aim of our study was to understand how insectivorous microbats use predominantly open vegetable crop landscapes to better understand the value of the habitat as a resource, and to begin to inform appropriate wildlife-friendly management practices. We found that a high number of microbat species (17) used the three vegetable farms in southeast Queensland, with the same five species most active across all farms. Microbat activity was sustained across seasonal and moon phase changes, but was higher during autumn, and during waning moon phase conditions. This indicates that the simplified agroecosystems provide some ongoing resource value. Microbats were active across all farm and crop positions, but there was a notably higher number of microbat species and calls at the creek edges, which suggests that increasing vegetation complexity may enhance habitat suitability. However, proximity to structures did not appear to impact microbat activity in any way. Microbat activity did not have an obvious interactive impact on invertebrate abundances and richness; however, lower invertebrate abundances were observed at the farm and crop locations that the highest number of microbat call sequences were recorded at. Invertebrate prey was abundant and diverse across the vegetable crop landscapes, and almost one-third of the invertebrates present in microbat scats were also caught in the sticky traps, with the most consumed invertebrate orders being the most abundant on the farms. Therefore, demonstrating the current resource value of the vegetable crops and the potential that the invertebrate resources could support more foraging. Consequentially, we describe the most relevant wildlife-friendly farming practices that should be further investigated to confirm their benefit to microbat conservation in open agricultural landscapes. Season and Moon Phase Impacts on Microbat Activity The vegetable crop landscapes that we observed were used by microbats during both the typically higher and lower activity periods of autumn and winter accordingly (Turbill, 2008 ; Stawski et al., 2014 ; Law et al., 2015 ; Gonsalves and Law, 2017 ). During these periods it has been established that prey availability is particularly important to support microbat juvenile growth, torpor, and emerging reproductive needs (Stawski et al., 2014 ). Though activity was higher in autumn, as expected (Turbill, 2008 ; Stawski et al., 2014 ; Gonsalves and Law, 2017 ), the continual activity of microbats throughout winter suggests that the open vegetable farms of southeast Queensland are helping to meet microbat nutritional needs. However, further research into microbat species specific seasonal activity and diet, including prey nutritional assessments, for species in agroecosystems compared to extant natural ecosystems will help to clarify this importance. Microbat call activity was influenced by moon phase, being higher during the waning gibbous moon phase (50–71% illumination) when moonlight is restricted to after midnight, and lower during the new moon to waxing crescent phase (0–4% illumination) when the moon is rising steadily higher in the sky at dusk and remaining present until around midnight. In fact, despite the open structure of the vegetable crops not providing any shelter from moonlight, and similar to previous findings (Roeleke et al., 2018 ), we observed more microbat call activity under the waning gibbous moon phase. Further investigation into species specific activity across the hours of the night will help determine potential drivers of this behaviour. However, higher microbat call activity under later but brighter illumination conditions is likely driven by optimal foraging theory in disturbed habitats, with the predators hunting where effort is lower due to abundant prey (Stephens and Krebs, 1986 ). This could be relative to higher invertebrate activity at periods of increased illumination, which has been observed for Hemiptera species in Australian cotton crops, which correlated to higher rates of microbat call activity (Kolkert et al., 2020b ). As Hemiptera and Lepidoptera use moonlight to navigate (Nowinszky and Puskas, 2017; Owens and Lewis, 2018 ) and are key prey for Australian microbats (Kolkert et al., 2020a , b ), this opportunity of increased hunting success and ease of escape movement that the mostly open areas accommodate (Roeleke et al., 2018 ) could be driving the activity that we observed under the later, brighter moon phase, especially considering the abundances of these invertebrates that we observed. Farm and Crop Position Impacts on Microbat Activity and Invertebrates Our results are similar to those in more complex crop habitats; having positive relationships between microbat activity and heterogenous vegetation, non-crop vegetation, edge microhabitats, and water bodies (Tuneu-Corral et al., 2023 ). Within our study, the creek edge location was the sole forest-edge-like habitat on the farms, having tall grass, shrubs, and trees surrounding the creek, and an unsealed open road area transecting the crop edge and creek vegetation. The higher number of microbat call sequences and richness that we observed could be because such edge habitats can host increased invertebrate (microbat prey) activity (Tscharntke et al., 2002 ; Verboom and Spoelstra, 1999 ). We observed higher abundances and richness of invertebrates at the road edge locations on the farms, which was also the area of lowest microbat call activity – for which the inverse was observed too (less invertebrates where there was more microbat calls recorded). Though these were not significant differences or relationships, it could be indicative of microbat hunting pressure impacts. Microbat exclusion studies could help validate this theory (Bouarakia et al., 2023 ; Korányi et al., 2025 ). Another driver of the increased microbat activity and richness at the creek edge could be that Lockyer Creek, which spans across much of the Lockyer Valley region could support orientation via continued vertical edges and reflective surfaces for echolocating edge-space species (Greif and Siemers, 2010 ; Morris et al., 2010 ; Denzinger and Schnitzler, 2013 ). It could also be used by microbats as a water source and potential additional foraging area (Stahlschmidt et al., 2012 ; Korine et al., 2016 ; Froidevaux et al., 2017 ). The higher microbat activity observed at the creek edge could be indicative of more efficient hunting for edge-space species using echolocation along the trees, and indicates that retaining small diverse vegetation patches and scattered trees in agricultural landscapes could be beneficial (Law and Chidel, 2006 ; Manning et al., 2006 ; Fisher et al., 2010; Lindborg et al., 2014 ; Froidevaux et al., 2022a ). Wildlife-friendly farming practices that allow more complex non-crop vegetation edges, which could include planting or leaving diverse non-crop vegetation cover patches in crops or on non-arable land like around roads, water bodies, and dams could also be beneficial (Benton et al., 2003 ; Boughey et al., 2011 ; Meena et al., 2017 ; Graham et al., 2018 : Froidevaux et al., 2019 ; Tscharntke et al., 2021 ) and warrant further investigation in the vegetable crop landscapes. Considering there is some evidence for native tree plantations adding limited value to supporting microbat activity 4–11 years after plantation, especially in comparison to remnant native trees (Law et al., 2011 ), site-specific investigations are crucial. Whilst increasing refuge could indeed be beneficial to increasing or sustaining microbat activity, we recorded microbats across all of the farm and crop locations, in the small, open crop landscapes, suggesting that limitations to using these areas for resources were not evident. The rates of microbat call sequences that we observed were largely similar in the edge and inner crop locations and despite the creek edge area of the farms hosting significantly more microbat call activity (45% of all calls), microbat call activity was also observed in the centre and road edge farm positions (29% and 26% of all calls respectively). Therefore, microbats were still active, though to a lesser degree, 400-1,100 m from the nearest complex vegetation (i.e., trees, shrubs, and grass at creek edge). These results support previous findings of widespread microbat activity across open areas of human-disturbed landscapes (Law and Chidel, 2006 ; Heim et al., 2016 ). The microbat activity that we observed across all locations on the farm could have been supported by the intercrops that the vegetable farms were structured in, as they can support a high diversity of invertebrates across all seasons (Tortosa et al., 2023 ). The contrasting results from investigations into fruit, corn, rice, nut, cacao, coffee, vineyards and cotton crops (Tuneu-Corral et al., 2023 ) could be influenced by them being large expanses of monoculture crops that are typically at the same growth stage with only seasonal fluxes of invertebrates. Our results justify further investigation into benefits for microbats that may be facilitated by reducing crop size or altering crop shape (e.g., Clough et al., 2020 ; Tscharntke et al., 2021 ), and incorporating the wildlife-friendly practices of intercropping, or in crops where intercropping is not feasible, introducing diversified lower stratum, edge vegetation, or flowers (Meena et al., 2017 ; Tscharntke et al., 2021 ; Krings et al., 2022 ). We found that distance to introduced structures that could be used in orientation had no effect on rates of microbat activity, and consequentially we do not advocate introducing more structures to support microbat activity in such small, open, agricultural cropping systems. However, another applicable wildlife-friendly management practice that could increase support for microbat activity is the installation of artificial roost boxes. Considering the largely modified expanses of habitat in cropped areas, roost availability is likely a primary driver of sustaining and increasing microbat activity on farms, with microbat activity commonly observed to be higher when near roosts (Bontadina et al., 2002 ; Rainho and Palmeirim, 2011 ; Hunninck et al., 2022 ). Attaching roost boxes to the external of structures, such as sheds, in disturbed landscapes has proven more successful in housing microbats than if secured to a distant tree, which could redirect microbats from using the sheds and reduce the human-wildlife conflict potential (Flaquer et al., 2006 ). Given the dearth of data on microbats using artificial roosts in subtropical Australia, further investigation into the structure that may best suit the microbat species in an area, relative to what they may currently be using on or nearby farms, needs to be considered (Rueegger, 2016 ). However, before implementing any wildlife-friendly farming practices, particularly those that provide roost habitats, cost-benefit analysis considering negative impacts is needed to ensure an ecological trap is not inadvertently created (Russo et al., 2024 ). Invertebrates in Crops and Microbat Scats Indirect comparisons of microbat scats and the invertebrates captured across the farms show that more than a quarter of the ASVs observed were common and that consumption of crop invertebrates by microbats occurred. The orders that were most observed and had the highest frequency of occurrences in microbat scats: Diptera, Coleoptera, and Lepidoptera; were also the most abundant across the crop sticky traps. Species belonging to these orders found in the scats and sticky traps are known to be damaging to crops, particularly those within Lepidoptera, for cotton in Australia (Kolkert et al., 2020a , 2021 ) and for the more complex crops in the Americas, Europe, and South East Asia (cotton, rice, cacao, coffee, nuts, corn, apples, vineyards: Tuneu-Corral et al., 2023 ). Therefore, the feeding behaviour of microbats in vegetable crops could be beneficial in assisting to manage invertebrate pests. Wildlife-friendly farming practices could be used to support more microbat foraging activity, and thus biological control, which could be a motivating factor to establishing such management practices. For example, the use of insect pheromones that are effective microbat lures could be beneficial in directing microbats to target key areas in agriculture to help lower invertebrate pest populations and reduce pesticide reliance (Korine et al., 2022 ). A reduction in pesticide application could also be valuable to supporting microbat persistence, given that insecticide toxicity can occur via secondary consumption (Tooker and Pearsons, 2021 ) and environmental contact, with up to 25 pesticide pollutants evident on microbats swabbed in agroecosystems (Schanzer et al., 2022 ). Conclusion Our study demonstrates that open, intercropped vegetable farms incorporating creek-lined corridors and narrow patches of heterogeneous, non-crop vegetation can support sustained microbat activity across a range of environmental conditions, including those considered potentially disruptive (i.e., high illumination with no cover available). We documented high levels of insect prey availability in these landscapes and continually observed a proportion of them in microbat scats. This suggests that microbats regularly exploit these resources, but that there is also potential for further exploitation. While no clear barriers to microbat use of cropped areas were identified, our findings highlight that creek edge areas with non-crop vegetation hosted the highest levels of activity. These results underscore the ecological value of integrating structurally diverse, wildlife-friendly features within agricultural landscapes. Future research should focus on species-specific responses to seasonal and abiotic variation and landscape dynamics, nutritional assessments of available prey with comparisons to those in extant surrounding natural areas, and long-term studies of roosting dynamics within agricultural matrices. A deeper understanding of how structural and management variables influence microbat behaviour and activity will help guide the development and support of more effective and context-specific wildlife-friendly farming practices to help sustain microbat populations. Permits Research was conducted under a University of Queensland Animal Ethics Permit (2023/AE000560) and Biosafety Permit (IBC/648B/SENV/VET/2023), and Queensland State Licence Permits (WA0055666, P-PTC-100498443, P-PTUKI-100498441). Declarations Author Contribution All authors contributed to the study conception and design. L.L.F. led the fieldwork, assisted by R.D., G.F. and A.E.R. L.N. led the DNA metabarcoding. G.F. and B.S.L. provided assistance with bat call ID. L.L.F. performed the analyses and led the writing of the manuscript. All authors contributed to the drafting of the manuscript. All authors read and approved the final manuscript. Acknowledgement We extend our sincere gratitude to the volunteers and landholders who made the fieldwork for this study possible, particularly Helen Mayfield. Annabel Smith assisted with the design of the project, John Gould gave constructive insight for shaping the manuscript and Eric Vanderduys assisted with the figures. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References ABARES. (2023). Snapshot of Australian Agriculture 2023, ABARES Insights, Canberra, March, DOI: https://doi.org/10.25814/rk1z-qm36 . CC BY 4.0. Accessed online 16th October 2023: https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1034541/0 Adams, M.D., Law, B.S. and French, K.O. (2009). Vegetation structure influences the vertical stratification of open-and edge-space aerial-foraging bats in harvested forests. Forest Ecology and Management , 258 (9), pp.2090–2100. Adams, M.D., Law, B.S. and Gibson, M.S. (2010). Reliable automation of bat call identification for eastern New South Wales, Australia, using classification trees and AnaScheme software. Acta Chiropterologica , 12 (1), pp.231–245. Ajzen, I. (1991). The Theory of planned behavior. Organizational Behavior and Human Decision Processes , 50, pp.179–211. Amoabeng, B.W., Stevenson, P.C., Mochiah, M.B., Asare, K.P. and Gurr, G.M. (2021). Economic analysis of habitat manipulation in Brassica pest management: Wild plant species suppress cabbage webworm. Crop Protection , 150 , p.105788. Ancillotto, L., Ariano, A., Nardone, V., Budinski, I., Rydell, J. and Russo, D. (2017). Effects of free-ranging cattle and landscape complexity on bat foraging: implications for bat conservation and livestock management. Agriculture, Ecosystems & Environment , 241 , pp.54–61. Apfelbeck, B., Snep, R.P., Hauck, T.E., Ferguson, J., Holy, M., Jakoby, C., MacIvor, J.S., Schär, L., Taylor, M. and Weisser, W.W. (2020). Designing wildlife-inclusive cities that support human-animal co-existence. Landscape and Urban Planning , 200 , p.103817. Appel, G., López-Baucells, A., Ernest-Magnusson, W. and Bobrowiec, P.E.D. (2017). Aerial insectivorous bat activity in relation to moonlight intensity. Mammalian Biology , 85 (1), pp.37–46. Baroja, U., Garin, I., Vallejo, N., Aihartza, J., Rebelo, H. and Goiti, U. (2021). Bats actively track and prey on grape pest populations. Ecological Indicators , 126 , p.107718. Bartlett, L.J., Newbold, T., Purves, D.W., Tittensor, D.P. and Harfoot, M.B. (2016). Synergistic impacts of habitat loss and fragmentation on model ecosystems. Proceedings of the Royal Society B: Biological Sciences , 283 (1839), p.20161027. Bates, D., Maechler, M., Bolker, B. and Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1–48. doi: 10.18637/jss.v067.i01 . Belton, P. and Kempster, R.H. (1962). A field test on the use of sound to repel the European corn borer. Entomologia Experimentalis et Applicata , 5 (4), pp.281–288. Benton, T.G., Vickery, J.A. and Wilson, J.D. (2003). Farmland biodiversity: is habitat heterogeneity the key? Trends in ecology & evolution , 18 (4), pp.182–188. Bleicher, S.S. (2017). The landscape of fear conceptual framework: definition and review of current applications and misuses. PeerJ , 5 , p.e3772. Bontadina, F., Schofield, H. and Naef-Daenzer, B. (2002). Radio-tracking reveals that lesser horseshoe bats (Rhinolophus hipposideros) forage in woodland. Journal of Zoology , 258 (3), pp.281–290. Børresen, S.T., Ulimboka, R., Nyahongo, J., Ranke, P.S., Skjaervø, G.R. and Røskaft, E. (2023). The role of education in biodiversity conservation: Can knowledge and understanding alter locals’ views and attitudes towards ecosystem services?. Environmental Education Research , 29 (1), pp.148–163. Boughey, K.L., Lake, I.R., Haysom, K.A. and Dolman, P.M. (2011). Improving the biodiversity benefits of hedgerows: how physical characteristics and the proximity of foraging habitat affect the use of linear features by bats. Biological conservation , 144 (6), pp.1790–1798. Bouarakia, O., Linden, V.M., Joubert, E., Weier, S.M., Grass, I., Tscharntke, T., Foord, S.H. and Taylor, P.J. (2023). Bats and birds control tortricid pest moths in South African macadamia orchards. Agriculture, Ecosystems & Environment , 352 , p.108527. Boyles, J.G., Cryan, P.M., McCracken, G.F. and Kunz, T.H. (2011). Economic importance of bats in agriculture. Science , 332 (6025), pp.41–42. Bradshaw, C.J., Leroy, B., Bellard, C., Roiz, D., Albert, C., Fournier, A., Barbet-Massin, M., Salles, J.M., Simard, F. and Courchamp, F. (2016). Massive yet grossly underestimated global costs of invasive insects. Nature communications , 7 (1), p.12986. Bradshaw, C. J. A., Hoskins, A. J., Haubrock, P. J., Cuthbert, R. N., Diagne, C., Leroy, B., Andrews, L., Page, B., Cassey, P., Sheppard, A. W. and Courchamp, F. (2021). Detailed assessment of the reported economic costs of invasive species in australia. NeoBiota , 67, pp.511–550. Brown, V.A., de Torrez, E.B. and McCracken, G.F. (2015). Crop pests eaten by bats in organic pecan orchards. Crop protection , 67 , pp.66–71. Busse, A., Bässler, C., Brandl, R., Friess, N., Hacker, H., Heidrich, L., Hilmers, T., Merkel-Wallner, G., Schmid-Egger, C., Seifert, L. and Müller, J. (2022) Light and Malaise traps tell different stories about the spatial variations in arthropod biomass and method-specific insect abundance. Insect Conservation and Diversity , 15(6), pp.655–665. Butterwort, V., Dansby, H., Zink, F.A., Tembrock, L.R., Gilligan, T.M., Godoy, A., Braswell, W.E. and Kawahara, A.Y. (2022). A DNA extraction method for insects from sticky traps: targeting a low abundance pest, Phthorimaea absoluta (Lepidoptera: Gelechiidae), in mixed species communities. Journal of Economic Entomology , 115 (3), pp.844–851. Cardoso, P., Barton, P.S., Birkhofer, K., Chichorro, F., Deacon, C., Fartmann, T., Fukushima, C.S., Gaigher, R., Habel, J.C., Hallmann, C.A. and Hill, M.J. (2020). Scientists' warning to humanity on insect extinctions. Biological conservation , 242 , p.108426. Chambers, C.L., Cushman, S.A., Medina-Fitoria, A., Martínez-Fonseca, J. and Chávez-Velásquez, M. (2016). Influences of scale on bat habitat relationships in a forested landscape in Nicaragua. Landscape Ecology , 31 , pp.1299–1318. Clough, Y., Kirchweger, S. and Kantelhardt, J. (2020). Field sizes and the future of farmland biodiversity in European landscapes. Conservation letters , 13 (6), p.e12752. Cohen, Y., Bar-David, S., Nielsen, M., Bohmann, K. and Korine, C. (2020). An appetite for pests: Synanthropic insectivorous bats exploit cotton pest irruptions and consume various deleterious arthropods. Molecular ecology , 29 (6), pp.1185–1198. Crisol-Martínez, E., Ford, G., Horgan, F.G., Brown, P.H. and Wormington, K.R. (2017). Ecology and conservation of insectivorous bats in fragmented areas of macadamia production in eastern Australia. Austral ecology , 42 (5), pp.597–610. Damalas, C.A. and Eleftherohorinos, I.G. (2011). Pesticide exposure, safety issues, and risk assessment indicators. International journal of environmental research and public health , 8 (5), pp.1402–1419. Denzinger, A. and Schnitzler, H.U. (2013). Bat guilds, a concept to classify the highly diverse foraging and echolocation behaviors of microchiropteran bats. Frontiers in physiology , 4 , p.164. Department of Environment and Science, Queensland. (2013). Wildlife of Lockyer Valley local government area, Wetland Info website. Accessed online, 1st August 2023: https://wetlandinfo.des.qld.gov.au/wetlands/facts-maps/wildlife/?AreaID=lga-lockyer-valley FAO (2023). Land use statistics and indicators 2000–2021. Global, regional and country trends. FAOSTAT Analytical Briefs Series No. 71. Rome. Accessed online 19th October 2023: https://doi.org/10.4060/cc6907en Fardell, L.L., Pavey, C.R. and Dickman, C.R. (2020). Fear and stressing in predator–prey ecology: considering the twin stressors of predators and people on mammals. PeerJ , 8 , p.e9104. Fischer, J., Stott, J. and Law, B.S. (2010). The disproportionate value of scattered trees. Biological Conservation , 143 (6), pp.1564–1567. Flaquer, C., Torre, I. and Ruiz-Jarillo, R. (2006). The value of bat-boxes in the conservation of Pipistrellus pygmaeus in wetland rice paddies. Biological conservation , 128 (2), pp.223–230. Frick, W.F., Kingston, T. and Flanders, J. (2020). A review of the major threats and challenges to global bat conservation. Annals of the new York Academy of Sciences , 1469 (1), pp.5–25. Froidevaux, J.S., Louboutin, B. and Jones, G. (2017). Does organic farming enhance biodiversity in Mediterranean vineyards? A case study with bats and C. nigrogriseus . Agriculture, ecosystems & environment , 249 , pp.112–122. Froidevaux, J.S., Boughey, K.L., Hawkins, C.L., Broyles, M. and Jones, G. (2019). Managing hedgerows for nocturnal wildlife: Do bats and their insect prey benefit from targeted agri-environment schemes? Journal of Applied Ecology , 56 (7), pp.1610–1623. Froidevaux, J.S., Laforge, A., Larrieu, L., Barbaro, L., Park, K., Fialas, P.C. and Jones, G. (2022a). Tree size, microhabitat diversity and landscape structure determine the value of isolated trees for bats in farmland. Biological Conservation, 267 , p.109476. Froidevaux, J.S., Duarte, G., Fonseca, A., Zina, V., Conde, S., Ferreira, M.T. and Fernandes, M.R. (2022b). The location and vegetation physiognomy of ecological infrastructures determine bat activity in Mediterranean floodplain landscapes. Agriculture, Ecosystems & Environment, 332 , p.107929. Gonsalves, L. and Law, B. (2017). Seasonal activity patterns of bats in North Sydney, New South Wales: implications for urban bat monitoring programs. Australian Mammalogy , 40 (2), pp.220–229. Gorman, K.M., Barr, E.L., Ries, L., Nocera, T. and Ford, W.M. (2021). Bat activity patterns relative to temporal and weather effects in a temperate coastal environment. Global Ecology and Conservation , 30 , p.e01769. Graham, L., Gaulton, R., Gerard, F. and Staley, J.T. (2018). The influence of hedgerow structural condition on wildlife habitat provision in farmed landscapes. Biological Conservation , 220 , pp.122–131. Greif, S. and Siemers, B.M. (2010). Innate recognition of water bodies in echolocating bats. Nature communications , 1 (1), p.107. Gurr, G.M., Lu, Z., Zheng, X., Xu, H., Zhu, P., Chen, G., Yao, X., Cheng, J., Zhu, Z., Catindig, J.L. and Villareal, S. (2016). Multi-country evidence that crop diversification promotes ecological intensification of agriculture. Nature plants , 2 (3), pp.1–4. Hartig F (2022). _DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models_. R package version 0.4.6, %3Chttps://CRAN.R-project.org/package=DHARMa%3E . Hawkins, N.J., Bass, C., Dixon, A. and Neve, P. (2019). The evolutionary origins of pesticide resistance. Biological Reviews , 94 (1), pp.135–155. Hecker, K.R. and Brigham, R.M. (1999). Does moonlight change vertical stratification of activity by forest-dwelling insectivorous bats?. Journal of Mammalogy , 80 (4), pp.1196–1201. Heim, O., Schröder, A., Eccard, J., Jung, K. and Voigt, C.C. (2016). Seasonal activity patterns of European bats above intensively used farmland. Agriculture, Ecosystems & Environment , 233 , pp.130–139. Hoffmaster, E., Vonk, J. and Mies, R. (2016). Education to action: Improving public perception of bats. Animals , 6 (1), p.6. Holland, R.A., Meyer, C.F., Kalko, E.K., Kays, R. and Wikelski, M. (2011). Emergence time and foraging activity in Pallas' mastiff bat, Molossus molossus (Chiroptera: Molossidae) in relation to sunset/sunrise and phase of the moon. Acta chiropterologica , 13 (2), pp.399–404. Hort Innovation. (2024). Australian Horticulture Statistics Handbook. Hort Innovation: Sydney, Australia . Accessed online 2nd February 2024: https://www.horticulture.com.au/growers/help-your-business-grow/research-reports-publications-fact-sheets-and-more/australian-horticulture-statistics-handbook/ Huang, F. and Subramanyam, B. (2004). Behavioral and reproductive effects of ultrasound on the Indian meal moth, Plodia interpunctella . Entomologia experimentalis et applicata , 113 (3), pp.157–164. Huang, F., Subramanyam, B. and Taylor, R. (2003). Ultrasound affects spermatophore transfer, larval numbers, and larval weight of Plodia interpunctella (Hübner)(Lepidoptera: Pyralidae). Journal of Stored Products Research , 39 (4), pp.413–422. Hunninck, L., Coleman, K., Boman, M. and O’Keefe, J. (2022). Far from home: Bat activity and diversity in row crop agriculture decreases with distance to potential roost habitat. Global Ecology and Conservation , 39 , p.e02297. Ives, C.D., Lentini, P.E., Threlfall, C.G., Ikin, K., Shanahan, D.F., Garrard, G.E., Bekessy, S.A., Fuller, R.A., Mumaw, L., Rayner, L. and Rowe, R. (2016). Cities are hotspots for threatened species. Global Ecology and biogeography , 25 (1), pp.117–126. Jackson, K., Kerr, J., Kilpatrick, J., Henderson, C. and Lovatt, J. (1997). Potato Information Kit. Agrilink, your growing guide to better farming guide. Jay, M., Roincé, C.D., Ricard, J.M., Garcin, A., Mandrin, J.F., Lavigne, C., Bouvier, J.C., Tupinier, Y. and Puechmaille, S. (2012). Functional biodiversity in apple orchards: are the bats eating the pests? Jung, K. and Kalko, E.K. (2010). Where forest meets urbanization: foraging plasticity of aerial insectivorous bats in an anthropogenically altered environment. Journal of Mammalogy , 91 (1), pp.144–153. Kehoe, L., Kuemmerle, T., Meyer, C., Levers, C., Václavík, T. and Kreft, H. (2015). Global patterns of agricultural land-use intensity and vertebrate diversity. Diversity and Distributions , 21 (11), pp.1308–1318. Kennedy, P.L., Fontaine, J.B., Hobbs, R.J., Johnson, T.N., Boyle, R. and Lueders, A.S. (2018). Do novel ecosystems provide habitat value for wildlife? Revisiting the physiognomy vs. floristics debate. Ecosphere , 9 (3), p.e02172. Kleijn, D., Rundlöf, M., Scheper, J., Smith, H.G. and Tscharntke, T. (2011). Does conservation on farmland contribute to halting the biodiversity decline? Trends in ecology & evolution , 26 (9), pp.474–481. Klingbeil, B.T. and Willig, M.R. (2009). Guild-specific responses of bats to landscape composition and configuration in fragmented Amazonian rainforest. Journal of applied ecology , pp.203–213. Kolkert, H., Andrew, R., Smith, R., Rader, R. and Reid, N. (2020a). Insectivorous bats selectively source moths and eat mostly pest insects on dryland and irrigated cotton farms. Ecology and Evolution 10:371–388. Kolkert, H., Smith, R., Rader, R. and Reid, N. (2020b). Insectivorous bats foraging in cotton crop interiors is driven by moon illumination and insect abundance, but diversity benefits from woody vegetation cover. Agriculture, Ecosystems & Environment , 302 , p.107068. Kolkert, H., Smith, R., Rader, R. and Reid, N. (2021). Insectivorous bats provide significant economic value to the Australian cotton industry. Ecosystem Services , 49 , p.101280. Korányi, D., Zsebők, S., Báldi, A., Brambilla, M., Varga, M. and Batáry, P. (2025). Forest cover enhances pest control by birds and bats independently of vineyard management intensity. Journal of Applied Ecology . Korine, C., Adams, R., Russo, D., Fisher-Phelps, M. and Jacobs, D. (2016). Bats and water: anthropogenic alterations threaten global bat populations. Bats in the Anthropocene: Conservation of bats in a changing world , pp.215–241. Korine, C., Cohen, Y. and Kahnonitch, I. (2022). Insect Pest Pheromone Lures May Enhance the Activity of Insectivorous Bats in Mediterranean Vineyards and Apple Orchards. Sustainability , 14 (24), p.16566. Krings, C.H., Darras, K., Hass, A., Batáry, P. and Fabian, Y. (2022). Not only hedgerows, but also flower fields can enhance bat activity in intensively used agricultural landscapes. Basic and Applied Ecology , 63 , pp.23–35. Kunz, T. H. and Stern, A. A. (1995). Maternal investment and post-natal growth in bats. Symposia of the Zoological Society of London 67:123–138. Kunz, T. H., Braun de Torrez, E., Bauer, D., Lobova, T. and Fleming, T. H. (2011). Ecosystem services provided by bats. Annals of the New York Academy of Sciences 1223:1–38. Laundré, J.W., Hernández, L. and Altendorf, K.B. (2001). Wolves, elk, and bison: reestablishing the" landscape of fear" in Yellowstone National Park, USA. Canadian Journal of Zoology , 79 (8), pp.1401–1409. Laundré, J.W., Hernández, L., Medina, P.L., Campanella, A., López-Portillo, J., González-Romero, A., Grajales-Tam, K.M., Burke, A.M., Gronemeyer, P. and Browning, D.M. (2014). The landscape of fear: the missing link to understand top-down and bottom‐up controls of prey abundance?. Ecology , 95 (5), pp.1141–1152. Law, B. and Chidel, M. (2002). Tracks and riparian zones facilitate the use of Australian regrowth forest by insectivorous bats. Journal of Applied Ecology , 39 : 605–617. https://doi.org/10.1046/j.1365-2664.2002.00739.x Law B. and Chidel M. (2006). Eucalypt plantings on farms: use by insectivorous bats. Biological Conservation 133 : 236–49. Law, B.S., Chidel, M. and Penman, T. (2011). Do young eucalypt plantations benefit bats in an intensive agricultural landscape?. Wildlife Research , 38 (3), pp.173–187. Law, B., Gonsalves, L., Tap, P., Penman, T. and Chidel, M. (2015). Optimizing ultrasonic sampling effort for monitoring forest bats. Austral Ecology , 40 (8), pp.886–897. Lee, D.K. (2020). Data transformation: a focus on the interpretation. Korean journal of anesthesiology , 73 (6), pp.503–508. Lentini, P.E., Gibbons, P., Fischer, J., LAW, B., Hanspach, J. and Martin, T.G. (2012) Bats in a farming landscape benefit from linear remnants and unimproved pastures. PLoS ONE 7 , e48201. doi: 10.1371/journal.pone.0048201 . Li, H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics , 34 (18), pp.3094–3100. Li, H. and Wilkins, K.T. (2022). Predator-prey relationship between urban bats and insects impacted by both artificial light at night and spatial clutter. Biology , 11 (6), p.829. Lindborg, R., Plue, J., Andersson, K. and Cousins, S.A. (2014). Function of small habitat elements for enhancing plant diversity in different agricultural landscapes. Biological Conservation , 169 , pp.206–213. Lindell, C., Eaton, R.A., Howard, P.H., Roels, S.M. and Shave, M.E. (2018). Enhancing agricultural landscapes to increase crop pest reduction by vertebrates. Agriculture, Ecosystems & Environment , 257 , pp.1–11. Linden, V.M., Grass, I., Joubert, E., Tscharntke, T., Weier, S.M. and Taylor, P.J. (2019). Ecosystem services and disservices by birds, bats and monkeys change with macadamia landscape heterogeneity. Journal of Applied Ecology , 56 (8), pp.2069–2078. Littlefair, M., Scheele, B.C., Westgate, M. and Lindenmayer, D. (2024). The ecological and biodiversity conservation values of farm dams: A systematic review. Plos one , 19 (5), p.e0303504. Lumsden, L.F. and Bennett, A.F. (2005). Scattered trees in rural landscapes: foraging habitat for insectivorous bats in south-eastern Australia. Biological Conservation , 122 (2), pp.205–222. Maas, B., Clough, Y. and Tscharntke, T. (2013). Bats and birds increase crop yield in tropical agroforestry landscapes. Ecology letters , 16 (12), pp.1480–1487. Maclagan, S.J., Coates, T. and Ritchie, E.G. (2018). Don't judge habitat on its novelty: Assessing the value of novel habitats for an endangered mammal in a peri-urban landscape. Biological Conservation , 223 , pp.11–18. Maine, J.J. and Boyles, J.G. (2015). Bats initiate vital agroecological interactions in corn. Proceedings of the National Academy of sciences , 112 (40), pp.12438–12443. Manning, A.D., Fischer, J. and Lindenmayer, D.B. (2006). Scattered trees are keystone structures–implications for conservation. Biological conservation , 132 (3), pp.311–321. Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. journal , 17 (1), pp.10–12. Matzek, V. and Wilson, K.A. (2021). Public support for restoration: Does including ecosystem services as a goal engage a different set of values and attitudes than biodiversity protection alone?. Plos one , 16 (1), p.e0245074. McKinney, M.L. (2006). Urbanization as a major cause of biotic homogenization. Biological conservation , 127 (3), pp.247–260. Meena, A., Meena, M., Kumar, R. and Meena, B.M. (2017). Farmscaping: An ecological approach to insect pest management in agroecosystem. Journal of Entomology and Zoology Studies , 5 (3), pp.598–603. Mickleburgh, S.P., Hutson, A.M. and Racey, P.A. (2002). A review of the global conservation status of bats. Oryx , 36 (1), pp.18–34. Milne, D.J., Armstrong, M., Fisher, A., Flores, T. and Pavey, C.R. (2004). A comparison of three survey methods for collecting bat echolocation calls and species-accumulation rates from nightly Anabat recordings. Wildlife Research , 31 (1), pp.57–63. Monck-Whipp, L., Martin, A.E., Francis, C.M. and Fahrig, L. (2018). Farmland heterogeneity benefits bats in agricultural landscapes. Agriculture, ecosystems & environment , 253 , pp.131–139. Morris, A.D., Miller, D.A. and Kalcounis-Rueppell, M.C. (2010). Use of forest edges by bats in a managed pine forest landscape. The Journal of Wildlife Management , 74 (1), pp.26–34. Murphy, F. and Ament, J. (2022). Pluralistic valuation of codling moth regulation by brown long-eared bats in English apple orchards. Sustainability , 14 (19), p.11966. Nash, M., Severtson, D. and Macfadyen, S. (2019). New approaches to manage invertebrate pests in conservation agriculture systems–Uncoupling intensification. Australian Agriculture in 2020: From conservation to automation , pp.189–202. Nguyen, H.D.D. and Nansen, C. (2018). Edge-biased distributions of insects. A review. Agronomy for sustainable development , 38 , pp.1–13. Nowinszky, L. and Puskás, J. (2014). Light-trap catch of Lygus sp.(Heteroptera: Miridae) in connection with the polarized moonlight, the collecting distance and the staying of the Moon above horizon. Journal of Advanced Laboratory Research in Biology , 5 (4), pp.102–107. Orłowski, G., Karg, J. and Karg, G. (2014). Functional invertebrate prey groups reflect dietary responses to phenology and farming activity and pest control services in three sympatric species of aerially foraging insectivorous birds. PLoS One , 9 (12), p.e114906. Owens, A.C. and Lewis, S.M. (2018). The impact of artificial light at night on nocturnal insects: a review and synthesis. Ecology and evolution , 8 (22), pp.11337–11358. Patriquin, K.J. and Barclay, R.M. (2003). Foraging by bats in cleared, thinned and unharvested boreal forest. Journal of Applied Ecology , 40 (4), pp.646–657. Pe'er, G., Zinngrebe, Y., Hauck, J., Schindler, S., Dittrich, A., Zingg, S., Tscharntke, T., Oppermann, R., Sutcliffe, L.M., Sirami, C. and Schmidt, J. (2017). Adding some green to the greening: Improving the EU's Ecological Focus Areas for biodiversity and farmers. Conservation letters , 10 (5), pp.517–530. Pennay, M., Law, B. and Reinhold, L. (2004). Bat calls of New South Wales: region based guide to the echolocation calls of microchiropteran bats . NSW Department of Environment and Conservation. Pohlert T (2023). PMCMRplus: Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended_. R package version 1.9.10, %3Chttps://CRAN.Rproject.org/package=PMCMRplus%3E . Powell, B., Loi, J. and Christianos, N.G. (2002). Soils and irrigated land suitability of the Lockyer Valley alluvial plains, South-East Queensland . Department of Natural Resources and Mines. Puckett, H.L., Brandle, J.R., Johnson, R.J. and Blankenship, E.E. (2009). Avian foraging patterns in crop field edges adjacent to woody habitat. Agriculture, Ecosystems & Environment , 131 (1–2), pp.9–15. Puig-Montserrat, X., Flaquer, C., Gómez‐Aguilera, N., Burgas, A., Mas, M., Tuneu, C., Marquès, E. and López‐Baucells, A. (2020). Bats actively prey on mosquitoes and other deleterious insects in rice paddies: Potential impact on human health and agriculture. Pest Management Science , 76 (11), pp.3759–3769. Puig-Montserrat, X., Torre, I., López-Baucells, A., Guerrieri, E., Monti, M.M., Ràfols-García, R., Ferrer, X., Gisbert, D. and Flaquer, C. (2015). Pest control service provided by bats in Mediterranean rice paddies: linking agroecosystems structure to ecological functions. Mammalian Biology , 80 (3), pp.237–245. Pywell, R.F., Heard, M.S., Woodcock, B.A., Hinsley, S., Ridding, L., Nowakowski, M. and Bullock, J.M. (2015). Wildlife-friendly farming increases crop yield: evidence for ecological intensification. Proceedings of the Royal Society B: Biological Sciences , 282 (1816), p.20151740. R Core Team (2024). R: A language and environment for statistical computing . R Foundation for Statistical Computing, Vienna, Austria. Rainho, A. and Palmeirim, J.M. (2011). The importance of distance to resources in the spatial modelling of bat foraging habitat. PloS one , 6 (4), p.e19227. Raposeira, H., Horta, P., Heleno, R. and Rebelo, H. (2023). Changing with the times: Seasonal environmental gradients unveil dynamic bat assemblages and vulnerability. Ecology and Evolution , 13 (7), p.e10246. Reinhold, L., Law, B., Ford, G. and Pennay, M. (2001). Key to the bat calls of south-east Queensland and north-east New South Wales. Forest Ecosystem Research and Assessment TeC. nigrogriseuscal Paper , 7 . Reside, A. E., and Lumsden, L. F. (2011). Resource partitioning by two closely-related sympatric freetail bats, Mormopterus spp. In: The Biology and Conservation of Australian Bats (Eds. Law, B., Eby, P., Lunney, D., and Lumsden, L. F.). Royal Zoological Society of NSW, Mosman, NSW, Australia. pp 155–166. Rizvi, S.Z., Reynolds, O.L., Haque, A., Furlong, M.J., Mo, J., Melo, M.C., Akter, S., Sandoval-Gomez, V.E., Johnson, A.C. and Gurr, G.M. (2022). Prospects for habitat management to suppress vegetable pests in Australia. Austral Entomology , 61 (1), pp.3–28. Rodríguez-San Pedro, A., Allendes, J.L., Beltrán, C.A., Chaperon, P.N., Saldarriaga-Córdoba, M.M., Silva, A.X. and Grez, A.A. (2020). Quantifying ecological and economic value of pest control services provided by bats in a vineyard landscape of central Chile. Agriculture, Ecosystems & Environment , 302 , p.107063. Roeleke, M., Teige, T., Hoffmeister, U., Klingler, F. and Voigt, C.C. (2018). Aerial-hawking bats adjust their use of space to the lunar cycle. Movement Ecology , 6 , pp.1–10. Rognes, T., Flouri, T., Nichols, B., Quince, C. and Mahé, F. (2016). VSEARCH: a versatile open source tool for metagenomics. PeerJ , 4 , p.e2584. Rueegger, N. (2016). Bat boxes—a review of their use and application, past, present and future. Acta Chiropterologica , 18 (1), pp.279–299. Russo, D., Bosso, L. and Ancillotto, L. (2018). Novel perspectives on bat insectivory highlight the value of this ecosystem service in farmland: Research frontiers and management implications. Agriculture, Ecosystems & Environment , 266 , pp.31–38. Russo, D., Tanalgo, K., Rebelo, H. and Cistrone, L. (2024). To improve or not to improve? The dilemma of “bat-friendly” farmland potentially becoming an ecological trap. Agriculture, Ecosystems & Environment , 375 , p.109215. Rybicki, J., Abrego, N. and Ovaskainen, O. (2020). Habitat fragmentation and species diversity in competitive communities. Ecology letters , 23 (3), pp.506–517. Saldaña-Vázquez, R.A. and Munguía-Rosas, M.A. (2013). Lunar phobia in bats and its ecological correlates: a meta-analysis. Mammalian Biology , 78 (3), pp.216–219. Schanzer, S., Koch, M., Kiefer, A., Jentke, T., Veith, M., Bracher, F., Bracher, J. and Müller, C. (2022). Analysis of pesticide and persistent organic pollutant residues in German bats. Chemosphere , 305 , p.135342. Schmitt, L., Greenberg, R., Ibarra-Núñez, G., Bichier, P., Gordon, C.E. and Perfecto, I. (2021). Cascading effects of birds and bats in a shaded coffee agroforestry system. Frontiers in Sustainable Food Systems , 5 , p.512998. Segoli, M. and Rosenheim, J.A. (2012). Should increasing the field size of monocultural crops be expected to exacerbate pest damage?. Agriculture, ecosystems & environment , 150 , pp.38–44. Signorell A (2024). DescTools: Tools for Descriptive Statistics. R package version 0.99.55, %3Chttps://CRAN.R-project.org/package=DescTools%3E . Snyder, W.E. (2019). Give predators a complement: Conserving natural enemy biodiversity to improve biocontrol. Biological control , 135 , pp.73–82. Stahlschmidt, P., Pätzold, A., Ressl, L., Schulz, R. and Brühl, C.A. (2012). Constructed wetlands support bats in agricultural landscapes. Basic and Applied Ecology , 13 (2), pp.196–203. Stahlschmidt, P., Hahn, M. and Brühl, C.A. (2017). Nocturnal risks-high bat activity in the agricultural landscape indicates potential pesticide exposure. Frontiers in Environmental Science , 5 , p.62. Stawski, C. and Geiser, F. (2010). Seasonality of torpor patterns and physiological variables of a free-ranging subtropical bat. Journal of Experimental Biology , 213 (3), pp.393–399. Stawski, C., Willis, C.K.R. and Geiser, F. (2014). The importance of temporal heterothermy in bats. Journal of Zoology , 292 (2), pp.86–100. Stephens, D.W. and Krebs, J.R. (1986). Foraging theory (Vol. 6). Princeton university press. Suarez-Rubio, M., Ille, C. and Bruckner, A. (2018). Insectivorous bats respond to vegetation complexity in urban green spaces. Ecology and Evolution , 8 (6), pp.3240–3253. Symondson, W.O.C., Sunderland, K.D. and Greenstone, M.H. (2002). Can generalist predators be effective biocontrol agents?. Annual review of entomology , 47 (1), pp.561–594. Taylor, P.J., Grass, I., Alberts, A.J., Joubert, E. and Tscharntke, T. (2018). Economic value of bat predation services–A review and new estimates from macadamia orchards. Ecosystem Services , 30 , pp.372–381. TEEB. The Economics of Ecosystems and Biodiversity for Agriculture and Food: Scientific and Economic Foundations. (2018). UN Environment, Geneva, Switzerland. Therneau, T., Atkinson, B. (2023). rpart: Recursive Partitioning and Regression Trees. R package version 4.1.23, < https://CRAN.R-project.org/package=rpart%3E . Tooker, J.F. and Pearsons, K.A. (2021). Newer characters, same story: neonicotinoid insecticides disrupt food webs through direct and indirect effects. Current Opinion in Insect Science , 46 , pp.50–56. Tortosa, A., Giffard, B., Barbaro, L., Froidevaux, J.S., Ladet, S., Delhommel, J. and Vialatte, A., (2023). Diverse agricultural landscapes increase bat activity and diversity: Implications for biological pest control. Agriculture, Ecosystems & Environment, 345 , p.108318. Tournayre, O., Leuchtmann, M., Filippi-Codaccioni, O., Trillat, M., Piry, S., Pontier, D., Charbonnel, N. and Galan, M. (2020). In silico and empirical evaluation of twelve metabarcoding primer sets for insectivorous diet analyses. Ecology and Evolution , 10 (13), pp.6310–6332. Tournayre, O., Leuchtmann, M., Galan, M., Trillat, M., Piry, S., Pinaud, D., Filippi-Codaccioni, O., Pontier, D. and Charbonnel, N. (2021). eDNA metabarcoding reveals a core and secondary diets of the greater horseshoe bat with strong spatio‐temporal plasticity. Environmental DNA , 3 (1), pp.277–296. Tscharntke, T., Steffan-Dewenter, I., Kruess, A. and Thies, C. (2002). Contribution of small habitat fragments to conservation of insect communities of grassland–cropland landscapes. Ecological applications , 12 (2), pp.354–363. Tscharntke, T., Clough, Y., Wanger, T.C., Jackson, L., Motzke, I., Perfecto, I., Vandermeer, J. and Whitbread, A. (2012). Global food security, biodiversity conservation and the future of agricultural intensification. Biological conservation , 151 (1), pp.53–59. Tscharntke, T., Grass, I., Wanger, T.C., Westphal, C. and Batáry, P. (2021). Beyond organic farming–harnessing biodiversity-friendly landscapes. Trends in ecology & evolution , 36 (10), pp.919–930. Tuneu-Corral, C., Puig‐Montserrat, X., Riba‐Bertolín, D., Russo, D., Rebelo, H., Cabeza, M. and López‐Baucells, A. (2023). Pest suppression by bats and management strategies to favour it: a global review. Biological Reviews , 98 (5), pp.1564–1582. Turbill, C. (2008). Winter activity of Australian tree-roosting bats: Influence of temperature and climatic patterns. Journal of Zoology , 276 (3), pp.285–290. Umina, P.A., McDonald, G., Maino, J., Edwards, O. and Hoffmann, A.A. (2019). Escalating insecticide resistance in Australian grain pests: contributing factors, industry trends and management opportunities. Pest Management Science , 75 (6), pp.1494–1506. Valdez, J.W., Gould, J. and Garnham, J.I. (2021). Global assessment of artificial habitat use by amphibian species. Biological Conservation , 257 , p.109129. Vamos, E.E., Elbrecht, V. and Leese, F. (2017). Short COI markers for freshwater macroinvertebrate metabarcoding (No. e3037v2). PeerJ Preprints. Vandevelde, J.C., Bouhours, A., Julien, J.F., Couvet, D. and Kerbiriou, C. (2014). Activity of European common bats along railway verges. Ecological Engineering , 64 , pp.49–56. Vansynghel, J., Ocampo-Ariza, C., Maas, B., Martin, E.A., Thomas, E., Hanf-Dressler, T., Schumacher, N.C., Ulloque-Samatelo, C., Yovera, F.F., Tscharntke, T. and Steffan-Dewenter, I. (2022). Quantifying services and disservices provided by insects and vertebrates in cacao agroforestry landscapes. Proceedings of the Royal Society B , 289 (1982), p.20221309. Vásquez, D.A., Grez, A.A. and Rodríguez-San Pedro, A. (2020). Species-specific effects of moonlight on insectivorous bat activity in central Chile. Journal of Mammalogy , 101 (5), pp.1356–1363. Verboom, B. and Spoelstra, K. (1999). Effects of food abundance and wind on the use of tree lines by an insectivorous bat, Pipistrellus pipistrellus . Canadian Journal of Zoology , 77 (9), pp.1393–1401. Vescera, C., Van Vyve, C., Smits, Q. and Michaux, J.R. (2024). All-you-can-eat buffet: A spider-specialized bat species ( Myotis emarginatus ) turns into a pest fly eater around cattle. Plos one , 19 (5), p.e0302028. Wanger, T.C., Darras, K., Bumrungsri, S., Tscharntke, T. and Klein, A.M. (2014). Bat pest control contributes to food security in Thailand. Biological Conservation , 171 , pp.220–223. Warnecke, M., Macías, S., Falk, B. and Moss, C.F. (2018). Echo interval and not echo intensity drives bat flight behavior in structured corridors. Journal of Experimental Biology , 221 (24), p .jeb 191155. Wine Australia. (2023). Bats could be the perfect pairing for vineyard insect control . Australian Government. Accessed 20th July 2023: https://www.wineaustralia.com/news/articles/bats-could-be-the-perfect-pairing-for-vineyard-insect-control Wittman, H., Chappell, M.J., Abson, D.J., Kerr, R.B., Blesh, J., Hanspach, J., Perfecto, I. and Fischer, J. (2017). A social–ecological perspective on harmonizing food security and biodiversity conservation. Regional Environmental Change , 17 , pp.1291–1301. Woinarski, J.C., Burbidge, A.A. and Harrison, P.L. (2015). Ongoing unraveling of a continental fauna: decline and extinction of Australian mammals since European settlement. Proceedings of the National Academy of Sciences , 112 (15), pp.4531–4540. Woinarski, J.C., Braby, M.F., Burbidge, A.A., Coates, D., Garnett, S.T., Fensham, R.J., Legge, S.M., McKenzie, N.L., Silcock, J.L. and Murphy, B.P. (2019). Reading the black book: The number, timing, distribution and causes of listed extinctions in Australia. Biological Conservation , 239 , p.108261. Zuur, A.F., Hilbe, J.M., and Leno, E.N. (2015) A Beginner’s guide to GLM and GLMM with R: a frequentist. and Bayesian perspective for ecologists. Highland Statistics Ltd., Newburgh. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7871928","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535722760,"identity":"ba38c3b8-c478-41c7-9960-44c685276406","order_by":0,"name":"Loren L. Fardell","email":"","orcid":"","institution":"the University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Loren","middleName":"L.","lastName":"Fardell","suffix":""},{"id":535722761,"identity":"2c4f317e-fc2d-432b-9756-60ed00846d14","order_by":1,"name":"Luke M. 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09:17:40","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":364264,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7871928/v1/080bfbcf0167aeb296d646b6.html"},{"id":94648904,"identity":"468a1735-c88d-4005-9b02-bf59028a5587","added_by":"auto","created_at":"2025-10-29 09:17:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":951311,"visible":true,"origin":"","legend":"\u003cp\u003eThree farms researched in the Lockyer Valley, QLD. The squares depict the trap locations that within each row alternated between edge and inner crop positions to make a parallel line of call detectors ranging from creek edge to the centre of the farm, to the sealed road edge of the farm. The northern most property is referred to as ‘Farm 1’, the property across the creek and to the south of this is referred to as ‘Farm 2’, and the western most property is referred to as ‘Farm 3’.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7871928/v1/fc5dce911b6f1631ed25fcba.png"},{"id":94648909,"identity":"64d9570b-fd90-48ad-90b7-ab1af60cccb7","added_by":"auto","created_at":"2025-10-29 09:17:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":364875,"visible":true,"origin":"","legend":"\u003cp\u003eThe Average number of call sequences recorded for all microbat call sequences for the two, 3-night survey sessions each a) Season (autumn, and winter) and b) Moon phase (new moon – waxing crescent, and waning gibbous) at three vegetable farms in South East Queensland.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7871928/v1/8b4ce1468866c731178b6e25.png"},{"id":94648908,"identity":"49240352-800f-4799-89ae-0e75eb4af622","added_by":"auto","created_at":"2025-10-29 09:17:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":405770,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobat activity as the a) total for all call sequences and b) species/genus complex richness, relative to position in crop, as edge (red) and inner (blue) and position on farm (creek edge, centre, road edge) across four, three-night surveys.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7871928/v1/d0e383707a26946905e7036c.png"},{"id":94672172,"identity":"f1d1638a-defe-4802-96ae-65f22e658c1d","added_by":"auto","created_at":"2025-10-29 13:39:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":463383,"visible":true,"origin":"","legend":"\u003cp\u003eInvertebrates identified in the crop as the a) abundance, and b) richness, and comparing c) total invertebrates against total microbats call sequences, and d) invertebrate order richness against the microbat richness identified across four, three-night surveys.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7871928/v1/01422b9ccf689b6e1fdf18b2.png"},{"id":99797753,"identity":"1c75e58a-d700-4328-8167-b8919992dc87","added_by":"auto","created_at":"2026-01-08 13:46:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3912197,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7871928/v1/cc4ae0cd-e4fe-4ecc-a461-e302293ea198.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microbat activity in vegetable crops relative to insect, landscape, and abiotic influences","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHabitat reduction and land modification pressures on native fauna continue to increase due to human needs (Kehoe et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Often this can force wildlife populations into isolated pockets of remnant habitat separated by areas that are not viable for occupation or use, which has the potential to increase competition pressures (Bartlett et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rybicki et al., \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, it is also the case that modified landscapes can maintain or create new resources that some wildlife species can exploit (McKinney, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kennedy et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, agricultural and urban landscapes, despite the additional stressors (Fardell et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), can provide supplementary resources, and on occasion become the best or only available habitat with adequate resources to sustain wildlife (Ives et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Maclagan et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To increase the chance of disturbed habitats supporting the long-term coexistence of wildlife, wildlife-friendly practices need to be implemented (Apfelbeck et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and the value of disturbed landscapes as potential novel habitats or resource sources for wildlife should be encouraged (Valdez et al., \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Littlefair et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is especially relevant in agricultural habitats, which currently cover 37% of the world\u0026rsquo;s terrestrial surface (FAO, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and, amongst other resources, host an abundance of invertebrates (Symondson et al., \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Segoli and Rosenheim, \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) that can be attractive food sources to insectivores (Orlowski et al., 2014). Microbats are highly mobile insectivores capable of consuming 30\u0026ndash;100% of their body weight in invertebrates a night (Kunz and Stern, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Kunz et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and have been observed to exploit agricultural invertebrate resources globally (Tuneu-Corral et al., \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Considering the somewhat controversial decline in invertebrates that is due largely to climate impacts and land modification (Cardoso et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), agricultural invertebrates could be helping to support native insectivore diversity and preservation. This resource could be particularly pertinent for microbats, as they are facing the increasing and potentially compounding threats of reduced habitat, disease, introduced toxins, climate-change impacts, and human-wildlife conflicts (Mickleburgh et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Stahlschmidt et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Frick et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMicrobats are known to use agricultural landscapes interspersed with patches of remnant vegetation, although comparatively more activity can be evident within the surrounding remnant vegetation (Lumsden and Bennett, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Law and Chidel, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, division of activity between these habitats may vary depending upon broader landscape pressures, resource availability, the extent of remaining native vegetation and the energetic costs associated with foraging in simpler compared to more complex habitats (Patriquin and Barclay, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Suarez-Rubio et al., \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nevertheless, agricultural management practices that increase vegetation heterogeneity and structural complexity are generally key drivers of increased wildlife activity and diversity (Benton et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Tuneu-Corral et al., \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and need to be assessed at the local scale to design the best wildlife supportive management. For microbats, retaining hedgerows, scattered trees, and remnant vegetation can increase their activity in agroecosystems (Law and Chidel, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fisher et al., 2010; Boughey et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lentini et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Graham et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e: Froidevaux et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Froidevaux et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Intercropping with rotated planting has also been observed to support higher microbat activity by promoting greater invertebrate availability (Monck-Whipp et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tortosa et al., \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The position on a farm where invertebrates are most abundant, typically crop edges (Nguyen and Nansen, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), could potentially result in localised increased microbat activity, and should also be considered in designing wildlife-friendly crop management. As should permanent water sources, which likely drive increased microbat activity due to water needs, increased prey abundance, and the acoustic properties of water surfaces that can aid microbat navigation across the predominantly open landscapes (Greif and Siemers, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Stahlschmidt et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Korine et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Froidevaux et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Froidevaux et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Similarly, the introduction of vertical structures such as poles, posts or buildings could also aid microbats to navigate across the open landscapes and increase their activity (Vandevelde et al., \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Warnecke et al., \u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) \u0026ndash; though this has not been tested extensively.\u003c/p\u003e\u003cp\u003eAn important part of understanding the value of a disturbed landscape to wildlife is assessing use across seasonal periods of high and low resource needs and shifting abiotic conditions, especially those that in the disturbed landscape have the potential to be a stressor. For microbats in disturbed landscapes in Australia, activity is generally higher in autumn and lulls in winter (Gonsalves and Law, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Higher foraging rates in autumn are likely due to juveniles of some species beginning to forage independently (Law et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and the need for some species to build fat stores before low-activity winter torpor and emerging reproductive support for females (Stawski et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Lower foraging rates in winter are likely due to reduced invertebrate activity during colder temperatures (Stawski et al., 2010). Microbat activity has also been observed to be influenced by moon phase and light intensity changes in landscapes without artificial light, with brighter nights influencing reduced activity or foraging in areas of dense cover (Salda\u0026ntilde;a-V\u0026aacute;zquez and Mungu\u0026iacute;a-Rosas, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) \u0026ndash; though responses are generally species specific relative to morphology and behaviour (Holland et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Appel et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li and Wilkins, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, in urban and complex agricultural landscapes, natural and artificial high illumination conditions have been observed to have both negative and positive impacts on microbat activity, dependent upon species (V\u0026aacute;squez et al., \u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and habitat structural complexity (Roeleke et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and likely relative to increased prey activity (Li and Wilkins, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Structurally open agricultural landscapes create the potential stressor of increased exposure under high levels of artificial or natural light, which could impact microbat activities further.\u003c/p\u003e\u003cp\u003eObservations of microbat activity in agricultural cropping landscapes have predominantly been conducted in crops with some structural complexity, particularly tree crops or tall stands of crop vegetation with service spaces between, such as apples (Jay et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Murphy and Ament, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), cotton (Cohen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kolkert et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), corn (Maine and Boyles, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), rice (Puig-Montserrat et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), pecan (Brown et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), macadamia (Crisol‐Mart\u0026iacute;nez et al., 2017; Taylor et al., \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), cacao (Maas et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Vansynghel et al., \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), coffee (Schmitt et al., \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and vineyards (Froidevaux et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rodr\u0026iacute;guez-San Pedro et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Baroja et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Less research has been undertaken in more structurally simplified, open crops, such as vegetables (Tuneu‐Corral et al., 2023). However, microbat species can exhibit plasticity in adjusting their primary foraging behaviours to optimise prey capture efficiency in similar structurally open areas (Jung and Kalko, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Raposeira et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vescera et al., \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given the need to better understand how microbats use open crop landscapes to determine their resource value and begin to inform appropriate wildlife-friendly management practices, we conducted surveys across vegetable farms in South East Queensland, Australia. We assessed the influence of season, moon phase, and landscape dynamics \u0026ndash; including distance to vertical structures, on the total number of microbat call sequences recorded as a measure for microbat activity. We also assessed the influence of landscape dynamics and number of microbat call sequences on crop invertebrate abundances, as collected on invertebrate sticky traps. To better understand the value of these habitats to a diversity of these species, we also assessed the influence of these factors on microbat and invertebrate richness. To further understand the resource value, we also assessed microbat diet using opportunistic scat collection from night-roosts in the vegetable crop wider area. We specifically tested the questions:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAre microbats continually active across the temporal and abiotic changes of season and moon phase (autumn compared to winter; and new moon to waxing gibbous compared to waning gibbous)?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIs microbat activity or richness influenced by farm landscape areas (creek edge, centre, road edge), crop areas (edge, inner), and distances to vertical structures?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIs invertebrate abundance or richness influenced farm landscape areas (creek edge, centre, road edge), crop areas (edge, inner), and increased microbat activity?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAre the invertebrates captured on farms present in microbat scats at proportionally similar levels, with those most captured in crops also being the most frequently observed in microbat scats?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSurvey Area\u003c/h2\u003e\u003cp\u003eWe conducted surveys to investigate microbat activity across small vegetable farms in the Lockyer Valley region of southeast Queensland, Australia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), c. 70 km west of the state capital, Brisbane (Powell et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The Lockyer Valley region has a subtropical climate with an average annual rainfall of 780 mm that falls largely in summer (Powell et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The Lockyer catchment area has major southern tributaries that drain north into Lockyer Creek, which in turn drains northeast into the Brisbane River (Powell et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The Lockyer Valley region spans an area of 227,200 km\u003csup\u003e2\u003c/sup\u003e and of this c. 23% of the low-lying area is dedicated to agricultural practices, making it one of Queensland\u0026rsquo;s most productive regions (RDAIWM, 2018). Biodiversity of the region includes 203 terrestrial invertebrates and 662 terrestrial vertebrates, including 31 species of microbats (Department of Environment and Science, 2013). Agricultural crops in the Lockyer Valley maximise their land use and plant crops to property edges, with very few road verges of extant trees, and low, widely spaced fencing or no fencing between the neighbouring properties.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOur surveys were conducted in the Glenore Grove area of the Lockyer Valley across three separate properties (Farm 1, Farm 2, Farm 3) that were spaced 0.2-2 km apart, all with the Lockyer Creek at one boundary, a public sealed road at the parallel boundary, and managed by the same farm group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Integrated Pest Management was the primary management method used by the farm group. This included intercropping \u0026ndash; planting different crops adjacently and routinely rotating the types of crops planted across the farm, as well as introducing key invertebrates for biological control, and routine pesticide use. All properties had sparse non-crop vegetation surrounding the water-filled section of Lockyer Creek at the rear, and a water-filled dam toward the opposite edge near the sealed-road. The mixed intercrops included adjacent rotations of potato, carrot, broccoli, mung bean, pumpkin, and grain, and each at varied growth stages during our research, which added a level of randomisation to our surveys. Each area within the three farms that we surveyed was separated by a 1\u0026ndash;2 m path, or 4 m wide management road, with 5\u0026ndash;8 m between the neighbouring crops that were not surveyed, and an open unsealed road running the length of the property at each side of the farm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All surveys were conducted within the same sized area and at the same time (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMicrobat Activity - Acoustic Surveys\u003c/h3\u003e\n\u003cp\u003eSurveys to detect microbat call sequences across Farms 1 to 3 were conducted using six passive ultrasonic audio detectors (Anabat Chorus \u0026ndash; Titley Scientific) per farm. Detectors were secured to posts at a height of 1.5 m (minimum 1 m from crop apex), at 1 m into the crop, for three consecutive survey nights, at each farm simultaneously. Two survey sessions were in autumn (April \u0026ndash; May 2024) and two were in winter (July 2024). One replicate each season was under new moon to waxing crescent conditions (0\u0026ndash;4% illumination) when moon light increased in the first half of the night, and the other under waning gibbous conditions (50\u0026ndash;71% illumination) when moonlight was restricted to the period after midnight. At each survey session detectors were arranged in the same linear transect positions of one 25 m from the crop edge and another 75 m into the crop width, each at 10\u0026ndash;15 m from the creek edge, the centre of the farm, and 20\u0026ndash;30 m from the sealed road edge, with microphones positioned horizontally towards the crop edge or inner crop \u0026ndash; respective to their position (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Transect positions were spaced equally across each farm at 400\u0026ndash;500 m apart, depending on the farm. There was a minimum 230 m between transects at Farms 1 and 2 traversing Lockyer Creek, and a maximum 3,537 m between the creek edge at Farm 3 and the road edge at Farm 2. Detectors were set to record microbat call sequences from 30 mins before sunset till 30 mins after sunrise using the \u003cem\u003enight\u003c/em\u003e setting formatted with GPS fixtures, with a sampling rate of 256 kHz, a minimum trigger of 8 kHz and maximum file length of 10 seconds. Recordings were confirmed as microbat species using the automated \u003cem\u003eAnascheme\u003c/em\u003e system (Adams et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Confirmation of species identification was made by considering known distribution records (Atlas of Living Australia; Ausbat) and using \u003cem\u003eAnabat Insight\u003c/em\u003e (Titley Scientific) to check call metrics against reference guides (Reinhold et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Pennay et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and to reference calls recorded during the study. Further confirmation was sought by professional expertise (Greg Ford) as needed. Each survey station had varying distances to structures that could potentially be used by microbats in orientation, such as poles, posts, houses or sheds, for which, the distance to the nearest structure was measured using \u003cem\u003eArcMap 10.8.2\u003c/em\u003e (Esri) and GPS coordinates taken in-field.\u003c/p\u003e\n\u003ch3\u003eCrop Invertebrates – Sticky Traps\u003c/h3\u003e\n\u003cp\u003eTo assess the invertebrates present relative to each crop (edge, inner) and farm (creek edge, centre, road edge) position, one yellow and one blue sticky trap (Bugs for Bugs \u0026reg;) was secured vertically at 0\u0026ndash;20 cm from the crop apex, 1 m into the crop, between plants, alternating along the crop c. 75 m between each microbat audio detector. Traps were left open both day and night during each 3-night microbat activity survey period, and upon collection were frozen at -20\u0026deg; C. This passive collection method was used to reduce any bias unrepresentative sampling that lures may have supported (Busse et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Invertebrates captured on the sticky traps were analysed to Amplicon Sequence Variant (ASVs) by \u003cem\u003eEnviroDNA\u003c/em\u003e using metabarcoding (as detailed below). To facilitate the time and financial budgets of this project, traps were pooled by season (autumn, winter) at each survey station (crop and farm position) across the three farms. The identified ASVs were assessed at the lowest taxonomic level and grouped to the order (or next highest available) taxonomic level for ease of comparisons. Invertebrates were also classified to functional types relative to crop management, using established understandings of their impacts to food crops based on \u003cem\u003eGoogle Scholar\u003c/em\u003e searches of the species (or higher level) scientific name. From this, invertebrates were grouped in to six types: 1) \u003cem\u003ebeneficial\u003c/em\u003e \u0026ndash; positive crop impacts including pest depredation; 2) \u003cem\u003edamaging\u003c/em\u003e \u0026ndash; negative crop impacts including by identified pest species; 3) \u003cem\u003eboth\u003c/em\u003e \u0026ndash; group or species can have both positive and negative impacts; 4) \u003cem\u003epotential\u003c/em\u003e \u0026ndash; group or species can cause damage and negative impacts under the right conditions and could also be neutral/unknown but no positive impacts known; 5) \u003cem\u003eneutral\u003c/em\u003e \u0026ndash; does not have any known impact; and 6) \u003cem\u003eunknown\u003c/em\u003e \u0026ndash; no research or enough detail has been established yet to know impacts.\u003c/p\u003e\n\u003ch3\u003eInvertebrates in Microbat Diet – Near-Crop Night-Roost Scat Collection\u003c/h3\u003e\n\u003cp\u003eTo assess microbat diet in the Lockyer Valley vegetable crop regions, microbat capture surveys were performed using mist nets (75 denier 2 ply 2.6 x 9/6 m: Avinet, USA) and harp traps (2 bank: Austbat, Australia) for the first five hours after sunset. However, capture in the open areas proved challenging, even when employing audio lures; therefore, opportunistic collections from near-crop night-roosts were used instead. Clean plastic tarps were secured on the ground, three days before each scat collection session, under a house on Farm 1 and another unoccupied farmhouse \u0026ndash; which we will refer to as Farm 4 that was 6.5 km from Farm 3 but was adjacent to Lockyer Creek and a vegetable farm that had similar crop rotations. At each scat collection session, 5\u0026ndash;7 scats (size dependent) were collected using sterilised stainless-steel forceps to place them in 2 ml screw cap tubes that contained 0.5 ml of Qiagen ATL buffer. A sealed sterile single-use swab was then used to homogenize the sample in the tube and was broken off at a suitable height to seal in the tube with the scats for processing. A negative control was collected at each collection session, using these methods in the same area, but without touching any scats. Samples were stored in a -20\u0026deg;C freezer until processing (maximum 7 months). Scats were analysed to identify the invertebrates ingested, to ASVs by \u003cem\u003eEnviroDNA\u003c/em\u003e using metabarcoding (as detailed below). The identified ASVs were assessed at the lowest taxonomic level and grouped to the order (or next highest available) taxonomic level for ease of comparisons. The identified invertebrates were also classified to the above detailed functional types (\u003cem\u003ebeneficial, damaging, both, potential, neutral\u003c/em\u003e, and \u003cem\u003eunknown\u003c/em\u003e).\u003c/p\u003e\n\u003ch3\u003eDNA Metabarcoding – Invertebrates in Sticky Traps and Microbat Scats\u003c/h3\u003e\n\u003cp\u003eAll extraction and metabarcoding processes were undertaken by \u003cem\u003eEnviroDNA\u003c/em\u003e (Melbourne, Australia). Sticky traps were processed by first removing all insects using hot orange oil solvent followed by washing in 100% ethanol (Butterwort et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Samples were dried and weighed before DNA was extracted with a non-destructive lysis followed by Qiagen PowerFecal DNA extraction kits on a QIASymphony platform (Qiagen, Australia). DNA was extracted from the microbat scats using Qiagen PowerFecal DNA extraction kits on a QIASymphony platform (Qiagen, Australia). Following extraction, metabarcoding libraries were prepared targeting the mitochondrial COI locus using a two-step PCR method. The first PCR amplified the target region using primers fwhF1/fwhR1 designed for macroinvertebrates (Vamos et al., \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) that perform well for insectivore diet analyses (Tournayre et al., \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and also amplify host DNA. The second PCR stage added sequencing adapters and unique barcodes for each sample. Extraction and PCR negative controls were used for all 96-well plates, and field negative controls were also used for scat samples (in-field collection process using materials without scat included). Sequencing was performed across two runs of an Illumina iSeq 100 (Illumina, USA) with 2x150bp paired-end reads. Bioinformatic analysis was performed following quality control filtering that removed primer sequences (Martin, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and truncated and low-frequency reads, the remaining sequences were denoised (Rognes et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), deduplicated, and unique sequences were assigned an amplicon sequencing variant (ASV) identifier. Filtering at the ASV level removed detections with less than 30 reads or a per-sample proportion of less than 0.3%. Species assignment was performed using VSEARCH at a 90% bootstrap threshold by comparing to a reference sequence database based on NCBI nt mitochondrial sequences (updated 20240903) followed by global alignment with minimap2 (Li, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Species rank was assigned for unique perfect matches (100% coverage and identity), genus rank at 99% coverage 97% identity, family rank at 95% coverage 95% identity and order level at 90% coverage 90% identity.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analyses\u003c/h2\u003e\u003cp\u003eAll statistical assessments were performed using \u003cem\u003eR\u003c/em\u003e statistical software (R Core Team, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMicrobat Activity\u003c/h3\u003e\n\u003cp\u003eTo determine if there were differences in the total number of microbat call sequences between the seasons (autumn, winter), and moon phases (new moon \u0026ndash; waxing crescent, and waning gibbous), independently, we used non-parametric Friedman tests that considered the survey station to account for the repeated measures, with the \u003cem\u003eR base\u003c/em\u003e package (R Core Team, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Kendall\u0026rsquo;s coefficient of concordance was used to determine the effect size statistic, and Namenyi-Wilcoxon-Wilcox post-hoc tests were used to make pair-wise comparisons using the \u003cem\u003eDescTools\u003c/em\u003e and \u003cem\u003ePMCMRplus\u003c/em\u003e packages respectively (Pohlert, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Signorell, \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo better understand microbat activity across the vegetable farm landscapes, we investigated the relationships of the total number of all microbat call sequences (all species across three consecutive survey nights each session) to the position on the farm (creek edge, centre, road edge), within the crop (edge, inner), and the distance to the nearest structure (poles, posts, houses or sheds), using mixed-effects models (estimated using REML) with the \u003cem\u003elme4\u003c/em\u003e package (Bates et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To account for the repeated surveys across time at the same station locations, station identification (unique to the farm and location within) nested into the survey session (1,2,3,4) was incorporated as a random effect (Zuur et al., \u003cspan citationid=\"CR168\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Crop type was also incorporated as a random effect, to better balance the residual errors. The intercept was the creek edge farm position, the edge crop position, and the smallest distance to the closest structure. To stabilize the variance and linearize relationships, considering the left skew of a density plot of the response, the response was log transformed within the mixed-effects model, as it was the most suitable distribution shape for the data (Lee, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Model selection was based on logic from study design but was also informed by likelihood ratio tests and AICc that compared models with all measured components in a stepwise manner. Model fit was established by assessing the residuals and theoretical quantiles, testing for overdispersion and outliers, and assessing the general deviation and dispersion, using the \u003cem\u003eDHARMa\u003c/em\u003e package (Hartig, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Beta estimates were back-transformed for ease of interpretation. Pairwise comparisons were made using back-transferred estimated marginal means, based on model predictions, comprising all factor combinations, using the \u003cem\u003eemmeans\u003c/em\u003e package (Lenth, 2024). Bonferroni correction of p-values was used to account for multiple simultaneous hypotheses testing. This process and model design was also used with microbat species richness (total number of species observed across the three consecutive survey nights each session) as the response.\u003c/p\u003e\n\u003ch3\u003eCrop Invertebrates\u003c/h3\u003e\n\u003cp\u003eTo better understand where invertebrates were present across the vegetable farm landscapes, we investigated relationships between the total number of all invertebrate ASVs each season (across two, three consecutive survey nights sessions) and the position on the farm (creek edge, centre, road edge), within the crop (edge, inner), and microbat activity (total number of call sequences, for all species), using mixed-effects models (estimated using REML) with the \u003cem\u003elme4\u003c/em\u003e package (Bates et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To account for the repeated surveys across time at the same station locations, station identification (unique to the farm and location within) nested into the survey season (autumn, winter \u0026ndash; that the invertebrate sticky traps were pooled across) was incorporated as a random effect (Zuur et al., \u003cspan citationid=\"CR168\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Invertebrate richness was also incorporated as a random effect, to better balance the residual errors \u0026ndash; considering the richness to abundance relationship that had a greater impact on data spread than crop type did. The intercept was the creek edge farm position, the edge crop position, and the lowest number of microbat call sequences recorded. To stabilize the variance and linearize relationships, considering the left skew of a density plot of the response, the response was log transformed within the mixed-effects model, as it was the most suitable distribution shape for the data (Lee, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Model selection, fit and pairwise comparisons were assessed as detailed above. This process and same model design was also used with invertebrate ASV richness (total number of ASVs identified across the two, three consecutive survey nights each season) as the response, and removing it from the random effects.\u003c/p\u003e\u003cp\u003e To understand the relative abundance of invertebrates observed, we calculated the proportion of the total ASVs and order groupings of invertebrates in sticky traps, pooled by season for each crop and farm location per farm, and in microbat scats, pooled as survey session at each location. For the ASVs and order groupings of invertebrates observed in the microbat scats, we further calculated the \u003cem\u003eFrequency Of Occurrence\u003c/em\u003e (FOO) \u0026ndash; that is the proportion of samples in which the target is detected as a measure of how often a particular invertebrate is present in a diet. Similarities between the proportions and FOOs from the sticky traps and the microbat scats were only able to be inferentially assessed considering differences in collection times and locations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMicrobat Activity\u003c/h2\u003e\u003cp\u003eAcross the four survey sessions of three consecutive nights, at the three farms with six detectors each (total 216 detector nights), 24,064 confirmed microbat 10 second call sequences were recorded, of which we were able to identify 16,350 to species or genus (68%). This included 17 species from four families, and three genus complexes of species that could not reliably be separated (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The total number of microbat call sequences was relatively similar across all three farms surveyed, and the same five species were most active at all three farms (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTotal microbat call sequences identified to species or species complex across the three farm survey locations in the Lockyer Valley, QLD.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eCount of species by farm (all sessions all stations)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFarm 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFarm 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFarm 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAustronomus australis *\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1485\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1741\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e826\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4052\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChalinolobus gouldii *\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e388\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e527\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e993\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1908\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChalinolobus morio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChalinolobus nigrogriseus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e373\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eChalinolobus picatus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMiniopterus australis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e397\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMicronomus norfolkensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMiniopterus orianae oceanensis *\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e707\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e392\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e419\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1518\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMyotis species or Nyctophilus species complex\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMyotis species\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNyctophilus species\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOzimops lumsdenae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e618\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOzimops ridei *\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e898\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e971\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1405\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e3274\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSaccolaimus flaviventris *\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e912\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e589\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e708\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2209\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eScotorepens orion\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eScotorepens species (Parnaby) or S. greyii complex\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e244\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVespadelus darlingtoni\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVespadelus pumilus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVespadelus troughtoni\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e506\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eV. troughtoni or V. pumilus complex\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e503\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16350\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003e* Species results in bold are the five most active species at all three farms\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSeason and Moon Phase Impacts on Microbat Activity\u003c/h2\u003e\u003cp\u003eSeason had a large and significant effect on the combined microbat activity (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;14.22, df\u0026thinsp;=\u0026thinsp;1, p-value\u0026thinsp;=\u0026thinsp;0.0002; KendallW\u0026thinsp;=\u0026thinsp;0.79, CI:0.444-1), with more activity in autumn (71% of total calls) compared to winter (29% of total call sequences) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In autumn we recorded an average 1,902 microbat call sequences nightly (11,411 total), and in winter we recorded an average 823 microbat call sequences nightly (4,939 total).\u003c/p\u003e\u003cp\u003eMoon phase had a large and significant effect on the combined microbat activity (X\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.89, df\u0026thinsp;=\u0026thinsp;1, p-value\u0026thinsp;=\u0026thinsp;0.001; KendallW\u0026thinsp;=\u0026thinsp;0.605, CI:0.198-1), with more activity evident in the waning gibbous stage (50\u0026ndash;71% illumination; 66.5% of total call sequences) compared to the new moon \u0026ndash; waxing crescent stage (0\u0026ndash;4% illumination; 33.5% of total call sequences) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Under waning gibbous conditions, we recorded an average 1,819 microbat call sequences nightly (10,916 total), and under new moon \u0026ndash; waxing crescent conditions we recorded an average 906 microbat call sequences nightly (5,434 total).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eFarm and Crop Position, and Distance to Structure Impacts on Microbat Activity\u003c/h2\u003e\u003cp\u003eThe explanatory power of the mixed-effects model that assessed the total microbat activity (all species call sequences) across farm and crop positions and distance to closest structure was substantial (conditional R2\u0026thinsp;=\u0026thinsp;0.74, marginal R2 0.10). The effect of farm position, considering creek edge as the intercept, on microbat activity was statistically significant and negative for road edge (beta\u0026thinsp;=\u0026thinsp;0.49, SE\u0026thinsp;=\u0026thinsp;0.26, df\u0026thinsp;=\u0026thinsp;16.91, t-value = -2.74, p\u0026thinsp;=\u0026thinsp;0.014, p-adjusted\u0026thinsp;=\u0026thinsp;0.07), and non-significant and negative for centre (beta\u0026thinsp;=\u0026thinsp;0.24, SE\u0026thinsp;=\u0026thinsp;0.70, df\u0026thinsp;=\u0026thinsp;9.14, t-value = -2.04, p\u0026thinsp;=\u0026thinsp;0.071, p-adjusted\u0026thinsp;=\u0026thinsp;0.357) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The highest number of call sequences were recorded at the creek edge farm position, based on pairwise comparisons (creek edge: farm centre ratio\u0026thinsp;=\u0026thinsp;4.136, SE\u0026thinsp;=\u0026thinsp;3.66, df\u0026thinsp;=\u0026thinsp;9.67, t\u0026thinsp;=\u0026thinsp;1.605, p.adjusted\u0026thinsp;=\u0026thinsp;0.4221; creek edge: road edge ratio\u0026thinsp;=\u0026thinsp;2.038, SE\u0026thinsp;=\u0026thinsp;0.61, df\u0026thinsp;=\u0026thinsp;15.89, t\u0026thinsp;=\u0026thinsp;2.377, p.adjusted\u0026thinsp;=\u0026thinsp;0.091) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). There was no significant effect of crop position on microbat activity, considering edge as the intercept (beta\u0026thinsp;=\u0026thinsp;1.03, SE\u0026thinsp;=\u0026thinsp;0.20, df\u0026thinsp;=\u0026thinsp;17.91, t-value\u0026thinsp;=\u0026thinsp;0.174, p\u0026thinsp;=\u0026thinsp;0.864, p-adjusted\u0026thinsp;=\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The inner crop position generally had marginally higher call activity, based on pairwise comparisons (crop edge: inner ratio\u0026thinsp;=\u0026thinsp;0.966, SE\u0026thinsp;=\u0026thinsp;0.191, df\u0026thinsp;=\u0026thinsp;17.8, t =-0.173, p.adjusted\u0026thinsp;=\u0026thinsp;0.8648). There was no significant effect or general pattern of distance to closest structure on microbat activity, considering the smallest distance as the intercept (beta\u0026thinsp;=\u0026thinsp;1, SE\u0026thinsp;=\u0026thinsp;0.0001, df\u0026thinsp;=\u0026thinsp;7.37, t-value\u0026thinsp;=\u0026thinsp;1.03 p\u0026thinsp;=\u0026thinsp;0.336, p-adjusted\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\u003cp\u003eThe explanatory power of the mixed-effects model that assessed microbat richness (the number of microbat species/genus complex identified at a location per survey session) across farm and crop positions and distance to closest structure was substantial (conditional R2\u0026thinsp;=\u0026thinsp;0.52, marginal R2 0.19). The effect of farm position on microbat richness, considering creek edge as the intercept, was negative for road edge and centre but non-significant (beta\u0026thinsp;=\u0026thinsp;0.85, SE\u0026thinsp;=\u0026thinsp;0.09, df\u0026thinsp;=\u0026thinsp;22.68, t-value = -0.91 p\u0026thinsp;=\u0026thinsp;0.069, p-adjusted\u0026thinsp;=\u0026thinsp;0.347; and beta\u0026thinsp;=\u0026thinsp;0.71, SE\u0026thinsp;=\u0026thinsp;0.26, df\u0026thinsp;=\u0026thinsp;20.71, t-value = -1.30 p\u0026thinsp;=\u0026thinsp;0.207, p-adjusted\u0026thinsp;=\u0026thinsp;1, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The highest number of microbat species/genus complex identified at a location per survey session were recorded at the creek edge farm position (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), based on pairwise comparisons (creek edge: farm centre ratio\u0026thinsp;=\u0026thinsp;1.403, SE\u0026thinsp;=\u0026thinsp;0.435, df\u0026thinsp;=\u0026thinsp;18.6, t\u0026thinsp;=\u0026thinsp;1.093, p.adjusted\u0026thinsp;=\u0026thinsp;0.8655; creek edge: road edge ratio\u0026thinsp;=\u0026thinsp;1.177, SE\u0026thinsp;=\u0026thinsp;0.113, df\u0026thinsp;=\u0026thinsp;20.9, t\u0026thinsp;=\u0026thinsp;1.698, p.adjusted\u0026thinsp;=\u0026thinsp;0.3132) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). There was no significant effect of crop position on microbat richness, considering edge as the intercept (beta\u0026thinsp;=\u0026thinsp;0.95, SE\u0026thinsp;=\u0026thinsp;0.06, df\u0026thinsp;=\u0026thinsp;18.44, t-value = -0.75 p\u0026thinsp;=\u0026thinsp;0.462, p-adjusted\u0026thinsp;=\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The crop edge position generally had marginally higher numbers of microbat species/genus complex, based on pairwise comparisons (crop edge: inner ratio\u0026thinsp;=\u0026thinsp;1.05, SE\u0026thinsp;=\u0026thinsp;0.0642, df\u0026thinsp;=\u0026thinsp;18, t\u0026thinsp;=\u0026thinsp;0.746, p.adjusted\u0026thinsp;=\u0026thinsp;0.4655). There was no significant effect or general pattern of distance to closest structure on microbat richness, considering the smallest distance as the intercept, (beta\u0026thinsp;=\u0026thinsp;1, SE\u0026thinsp;=\u0026thinsp;0.0005, df\u0026thinsp;=\u0026thinsp;17.12, t-value\u0026thinsp;=\u0026thinsp;0.213 p\u0026thinsp;=\u0026thinsp;0.8338, p-adjusted\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eCrop Invertebrates\u003c/h2\u003e\u003cp\u003eInvertebrate sticky traps (a yellow and blue at each location) captured a total of 1,356,984 invertebrates. These were pooled for DNA metabarcoding analyses across the two season replicates (total 144 individual traps; in 36 pools). From these, we were able to identify them to 84 ASVs (45 species, 14 genera, 18 families, 5 orders, 1 class, and 1 phylum). More invertebrates were captured in autumn (885,038) compared to in winter (471,946). When grouped to order level, there were similarly high number of ASVs assigned to orders observed at Farms 1 and 2 (52, and 50 respectively) with slightly less at Farm 3 (44). Across all farms combined, the highest number of classifications to an order was observed for \u003cem\u003eDiptera\u003c/em\u003e (32), followed by \u003cem\u003eColeoptera\u003c/em\u003e and \u003cem\u003eLepidoptera\u003c/em\u003e (12 and 10 respectively). The highest proportion of identifications to an order was observed for \u003cem\u003eDiptera\u003c/em\u003e (0.42), and \u003cem\u003eColeoptera\u003c/em\u003e (0.43) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When grouped to functional type, \u003cem\u003eboth\u003c/em\u003e and \u003cem\u003edamaging\u003c/em\u003e, were observed in highest quantities and \u003cem\u003epotential\u003c/em\u003e in the lowest (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInvertebrates observed via eDNA methods of invertebrate sticky traps across the six survey positions of each farm (creek-edge crop inner/edge, farm-centre crop inner/edge, road-edge crop inner/edge), and in microbat scats collected at Farm 1 and Farm 4 \u0026ndash; further removed but along the same creek line with similar crops grown on opposite bank. For each eDNA type the total count, proportion of the total (Pi) and Frequency of Occurrence (FOO; the Pi of all samples that the order/type is detected in). Invertebrates have been grouped by Order (or higher) and as Type \u0026ndash; representing the perceived impact on crops (beneficial, both \u0026ndash; damaging and beneficial, potential \u0026ndash; to be damaging, unknown \u0026ndash; impacts, neutral \u0026ndash; no impact).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"38\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c26\" colnum=\"26\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c27\" colnum=\"27\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c28\" colnum=\"28\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c29\" colnum=\"29\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c30\" colnum=\"30\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c31\" colnum=\"31\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c32\" colnum=\"32\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c33\" colnum=\"33\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c34\" colnum=\"34\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c35\" colnum=\"35\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c36\" colnum=\"36\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c37\" colnum=\"37\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c38\" colnum=\"38\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"37\" nameend=\"c37\" namest=\"c1\"\u003e\u003cp\u003eInvertebrates detected via eDNA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c38\" namest=\"c38\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGrouped by Order\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eInsect Sticky Trap Total\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e\u003cp\u003eInsect Sticky Trap Pi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c18\" namest=\"c14\"\u003e\u003cp\u003eMicrobat Scat Farm 1 Total\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c22\" namest=\"c19\"\u003e\u003cp\u003eMicrobat Scat Farm 1 Pi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c26\" namest=\"c23\"\u003e\u003cp\u003eMicrobat Scat Farm 1 FOO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c29\" namest=\"c27\"\u003e\u003cp\u003eMicrobat Scat Farm 4 Total\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c33\" namest=\"c30\"\u003e\u003cp\u003eMicrobat Scat Farm 4 Pi\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c38\" namest=\"c34\"\u003e\u003cp\u003eMicrobat Scat Farm 4 FOO\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAraucariales\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArthropoda (Phylum)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0.000003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e21414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e33%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e2147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e38%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBlattodea\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.00002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eColeoptera\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e626726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e3904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e44%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e12273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e63%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiptera\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e587120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e28719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e89%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e66369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e94%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEntomobryomorpha\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEphemeroptera\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e11%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHemiptera\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e79288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e11%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e2181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e31%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHymenoptera\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInsecta (Class)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e14642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e2798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e11%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e5195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e44%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLepidoptera\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e57295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e1491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e56%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e14493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMesostigmata\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e11%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMetazoa (Kingdom)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeuroptera\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0.00004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e3879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e11%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e13%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOrthoptera\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e13%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTrichoptera\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTrombidiformes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e4874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e33%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e78085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e44%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e\u003cp\u003e3627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c25\" namest=\"c22\"\u003e\u003cp\u003e56%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c28\" namest=\"c26\"\u003e\u003cp\u003e3795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c32\" namest=\"c29\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c36\" namest=\"c33\"\u003e\u003cp\u003e75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c38\" namest=\"c37\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Individuals per Type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c17\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c23\" namest=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c27\" namest=\"c24\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c30\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c34\" namest=\"c31\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c37\" namest=\"c35\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c38\" namest=\"c38\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBeneficial\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e56214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c15\" namest=\"c11\"\u003e\u003cp\u003e6568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c20\" namest=\"c16\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c24\" namest=\"c21\"\u003e\u003cp\u003e22%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c28\" namest=\"c25\"\u003e\u003cp\u003e247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c31\" namest=\"c29\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c35\" namest=\"c32\"\u003e\u003cp\u003e19%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c38\" namest=\"c36\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBoth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e1065790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c15\" namest=\"c11\"\u003e\u003cp\u003e40621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c20\" namest=\"c16\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c24\" namest=\"c21\"\u003e\u003cp\u003e89%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c28\" namest=\"c25\"\u003e\u003cp\u003e29130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c31\" namest=\"c29\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c35\" namest=\"c32\"\u003e\u003cp\u003e94%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c38\" namest=\"c36\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDamaging\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e152869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c15\" namest=\"c11\"\u003e\u003cp\u003e2246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c20\" namest=\"c16\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c24\" namest=\"c21\"\u003e\u003cp\u003e56%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c28\" namest=\"c25\"\u003e\u003cp\u003e9684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c31\" namest=\"c29\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c35\" namest=\"c32\"\u003e\u003cp\u003e75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c38\" namest=\"c36\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePotential\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e16815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c15\" namest=\"c11\"\u003e\u003cp\u003e3712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c20\" namest=\"c16\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c24\" namest=\"c21\"\u003e\u003cp\u003e89%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c28\" namest=\"c25\"\u003e\u003cp\u003e30680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c31\" namest=\"c29\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c35\" namest=\"c32\"\u003e\u003cp\u003e75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c38\" namest=\"c36\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e74965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c15\" namest=\"c11\"\u003e\u003cp\u003e17786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c20\" namest=\"c16\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c24\" namest=\"c21\"\u003e\u003cp\u003e89%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c28\" namest=\"c25\"\u003e\u003cp\u003e116001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c31\" namest=\"c29\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c35\" namest=\"c32\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c38\" namest=\"c36\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeutral\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c15\" namest=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c20\" namest=\"c16\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c24\" namest=\"c21\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c28\" namest=\"c25\"\u003e\u003cp\u003e317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c31\" namest=\"c29\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c35\" namest=\"c32\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c38\" namest=\"c36\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003e1366653\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c15\" namest=\"c11\"\u003e\u003cp\u003e\u003cem\u003e70933\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c20\" namest=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c24\" namest=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c28\" namest=\"c25\"\u003e\u003cp\u003e\u003cem\u003e186059\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c31\" namest=\"c29\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c35\" namest=\"c32\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c38\" namest=\"c36\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eFarm and Crop Position, and Microbat Activity Impacts on Invertebrates\u003c/h2\u003e\u003cp\u003eThe explanatory power of the mixed-effects model that assessed the total number of invertebrates across farm and crop positions and the total number of microbat calls, was substantial (conditional R2\u0026thinsp;=\u0026thinsp;0.58, marginal R2 0.10). The effect of farm position on invertebrate abundance, considering creek edge as the intercept, was positive for road edge and centre but non-significant (beta\u0026thinsp;=\u0026thinsp;3.14, SE\u0026thinsp;=\u0026thinsp;0.50, df\u0026thinsp;=\u0026thinsp;18.25, t-value\u0026thinsp;=\u0026thinsp;2.31, p\u0026thinsp;=\u0026thinsp;0.033, p-adjusted\u0026thinsp;=\u0026thinsp;0.165; and beta\u0026thinsp;=\u0026thinsp;1.42, SE\u0026thinsp;=\u0026thinsp;0.48, df\u0026thinsp;=\u0026thinsp;18.28, t-value\u0026thinsp;=\u0026thinsp;0.73, p\u0026thinsp;=\u0026thinsp;0.477, p-adjusted\u0026thinsp;=\u0026thinsp;1, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The highest number of invertebrates were recorded at the road edge farm positions, based on pairwise comparisons (creek edge: road ratio\u0026thinsp;=\u0026thinsp;0.319, SE\u0026thinsp;=\u0026thinsp;0.171, df\u0026thinsp;=\u0026thinsp;20.8, t =-2.125, p.adjusted\u0026thinsp;=\u0026thinsp;0.1372; farm centre: road edge ratio\u0026thinsp;=\u0026thinsp;0.453, SE\u0026thinsp;=\u0026thinsp;0.231, df\u0026thinsp;=\u0026thinsp;23.6, t =-1.555, p.adjusted\u0026thinsp;=\u0026thinsp;0.3997). The effect of crop position on invertebrate abundance, considering edge as the intercept, was positive but non-significant (beta\u0026thinsp;=\u0026thinsp;1.12, SE\u0026thinsp;=\u0026thinsp;0.38, df\u0026thinsp;=\u0026thinsp;19.20, t-value\u0026thinsp;=\u0026thinsp;0.30, p\u0026thinsp;=\u0026thinsp;0.769, p-adjusted\u0026thinsp;=\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The inner crop position generally had marginally higher invertebrate abundance, based on pairwise comparisons (crop edge: inner ratio\u0026thinsp;=\u0026thinsp;0.893, SE\u0026thinsp;=\u0026thinsp;0.356, df\u0026thinsp;=\u0026thinsp;21.5, t =-0.283, p.adjusted\u0026thinsp;=\u0026thinsp;0.78). The effect of the total number of microbat call sequences on invertebrate abundance, considering the lowest number of calls as the intercept, was non-significant (beta\u0026thinsp;=\u0026thinsp;1, SE\u0026thinsp;=\u0026thinsp;0.0006, df\u0026thinsp;=\u0026thinsp;15.21, t-value\u0026thinsp;=\u0026thinsp;0.90, p\u0026thinsp;=\u0026thinsp;0.383, p-adjusted\u0026thinsp;=\u0026thinsp;1), though the slope of the relationship appeared to differ between seasons being more positive in autumn and negative in winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eThe explanatory power of the mixed-effects model that assessed invertebrate ASVs richness (the number of species or higher level of taxa identified per location and season) across farm and crop positions and the total number of microbat call sequences, was weak (conditional R2\u0026thinsp;=\u0026thinsp;0.12, marginal R2 0.04). The effect of farm position on invertebrate richness, considering creek edge as the intercept, was positive and non-significant for both centre and road edge (beta\u0026thinsp;=\u0026thinsp;1, SE\u0026thinsp;=\u0026thinsp;0.17, df\u0026thinsp;=\u0026thinsp;29.03, t-value\u0026thinsp;=\u0026thinsp;0.40, p\u0026thinsp;=\u0026thinsp;0.692, p-adjusted\u0026thinsp;=\u0026thinsp;1; and beta\u0026thinsp;=\u0026thinsp;1.13, SE\u0026thinsp;=\u0026thinsp;0.17, df\u0026thinsp;=\u0026thinsp;29.02, t-value\u0026thinsp;=\u0026thinsp;0.73, p\u0026thinsp;=\u0026thinsp;0.474, p-adjusted\u0026thinsp;=\u0026thinsp;1, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The highest number of invertebrate ASVs observed at a location per season were recorded at the road edge farm positions, based on pairwise comparisons (creek edge: road edge ratio\u0026thinsp;=\u0026thinsp;0.884, SE\u0026thinsp;=\u0026thinsp;0.15, df\u0026thinsp;=\u0026thinsp;29, t =-0.726, p.adjusted\u0026thinsp;=\u0026thinsp;1; farm centre: road edge ratio\u0026thinsp;=\u0026thinsp;0.947, SE\u0026thinsp;=\u0026thinsp;0.141, df\u0026thinsp;=\u0026thinsp;29, t =-0.366, p.adjusted\u0026thinsp;=\u0026thinsp;1. The effect of crop position on invertebrate richness, considering edge as the intercept, was positive but non-significant (beta\u0026thinsp;=\u0026thinsp;1.14, SE\u0026thinsp;=\u0026thinsp;0.12, df\u0026thinsp;=\u0026thinsp;29.00, t-value\u0026thinsp;=\u0026thinsp;1.07, p\u0026thinsp;=\u0026thinsp;0.294, p-adjusted\u0026thinsp;=\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The inner crop position generally had marginally higher invertebrate abundance, based on pairwise comparisons (crop edge: inner ratio\u0026thinsp;=\u0026thinsp;0.877, SE\u0026thinsp;=\u0026thinsp;0.108, df\u0026thinsp;=\u0026thinsp;29, t =-1.069, p.adjusted\u0026thinsp;=\u0026thinsp;0.294). The effect of the total number of microbat call sequences on invertebrate richness, considering the lowest number of calls as the intercept, was non-significant (beta\u0026thinsp;=\u0026thinsp;1, SE\u0026thinsp;=\u0026thinsp;0.045, df\u0026thinsp;=\u0026thinsp;29.10, t-value\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;=\u0026thinsp;0.793, p-adjusted\u0026thinsp;=\u0026thinsp;1), though the slope was slightly negative in both autumn and winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eInvertebrates in Microbat Scats\u003c/h2\u003e\u003cp\u003eMicrobat scats were opportunistically collected over nine survey sessions at Farm 1 (spring and winter) and 16 sessions at Farm 4 (all seasons). All scats belonged to the \u003cem\u003eVespertilionidae\u003c/em\u003e Family, and 23% of the identifications from Farm 4 were further assigned to genus \u003cem\u003eChalinolobus\u003c/em\u003e. A total of 70,933 invertebrate detections were obtained from the microbat scats from Farm 1, with 95% identified to 31 ASVs (17 species, 6 genera, 5 families, 2 orders, and 1 phylum). At Farm 4, 186,059 invertebrate detections were obtained from the microbat scats, with 98% identified to 61 ASVs (35 species, 10 genera, 10 families, 4 orders, 1 class, and 1 phylum). There were almost double the number of orders at Farm 4 compared to Farm 1 (63 and 33 respectively). At Farm 1, the highest number of classifications to an order were for \u003cem\u003eDiptera\u003c/em\u003e (14), followed by \u003cem\u003eLepidoptera\u003c/em\u003e and \u003cem\u003eColeoptera\u003c/em\u003e (7 and 4, respectively). The highest proportion of identifications to an order at Farm 1 were for \u003cem\u003eDiptera\u003c/em\u003e (0.40) and the \u003cem\u003eArthropoda phylum\u003c/em\u003e (0.30), and the highest FOO were for \u003cem\u003eDiptera\u003c/em\u003e (89%), \u003cem\u003eLepidoptera\u003c/em\u003e (56%), unknown (56%), and \u003cem\u003eColeoptera\u003c/em\u003e (44%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, at Farm 4, the highest number of classifications to an order were for \u003cem\u003eDiptera\u003c/em\u003e (19), followed by \u003cem\u003eLepidoptera, Coleoptera, and Hemiptera\u003c/em\u003e (14, 10, and 8, respectively). The highest proportion of identifications to an order at Farm 4 were for \u003cem\u003eDiptera\u003c/em\u003e (0.36) and \u003cem\u003eTrombidiformes\u003c/em\u003e (0.42), and the highest FOO were for \u003cem\u003eDiptera\u003c/em\u003e (94%), unknown (75%), \u003cem\u003eColeoptera\u003c/em\u003e (63%), and \u003cem\u003eLepidoptera\u003c/em\u003e (50%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When grouped to functional type, scats from Farm 1 had lower \u003cem\u003edamaging\u003c/em\u003e, \u003cem\u003eunknown\u003c/em\u003e and \u003cem\u003epotential\u003c/em\u003e, but more \u003cem\u003eboth\u003c/em\u003e and \u003cem\u003ebeneficial\u003c/em\u003e than observed in scats from Farm 4 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The highest consumed type at Farm 1 was \u003cem\u003eboth\u003c/em\u003e and the lowest was \u003cem\u003edamaging\u003c/em\u003e, and at Farm 4 the highest consumed type was \u003cem\u003eunknown\u003c/em\u003e and the lowest was \u003cem\u003ebeneficial\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eSimilarities in Invertebrates at Crops and in Microbat Scats\u003c/h2\u003e\u003cp\u003eThere were 24 invertebrate ASVs commonly identified across the farms in sticky traps and in the microbat scats collected from Farm 1 and 4, these common ASVs made up 33% of the total observed on the sticky traps and 29% of total observed in microbat scats. Resultantly, 60 invertebrate ASVs identified in the microbat scats were not identified in the sticky traps, and 49 invertebrate ASVs identified in the sticky traps were not identified in the microbat scats. Of the invertebrates both collected on the sticky traps in the crops and in microbat scats from both farms, \u003cem\u003eDiptera\u003c/em\u003e was commonly the highest observed order and had the highest FOO in scats. The most observed functional type on the sticky traps in the crops and microbat scats from Farm 1 was \u003cem\u003eboth\u003c/em\u003e, but \u003cem\u003eunknown\u003c/em\u003e was the most observed functional type in the scats from the further away Farm 4. When assessed by species/group, out of the five most abundant observed on the sticky traps \u0026ndash; \u003cem\u003ePsilopa spp, Arthropoda Phylum, Lycaenidae Family, Eleale Genus\u003c/em\u003e, and \u003cem\u003eCrambidae Family\u003c/em\u003e, only \u003cem\u003eArthropoda Phylum\u003c/em\u003e was also within the highest FOO at Farm 1.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of our study was to understand how insectivorous microbats use predominantly open vegetable crop landscapes to better understand the value of the habitat as a resource, and to begin to inform appropriate wildlife-friendly management practices. We found that a high number of microbat species (17) used the three vegetable farms in southeast Queensland, with the same five species most active across all farms. Microbat activity was sustained across seasonal and moon phase changes, but was higher during autumn, and during waning moon phase conditions. This indicates that the simplified agroecosystems provide some ongoing resource value. Microbats were active across all farm and crop positions, but there was a notably higher number of microbat species and calls at the creek edges, which suggests that increasing vegetation complexity may enhance habitat suitability. However, proximity to structures did not appear to impact microbat activity in any way. Microbat activity did not have an obvious interactive impact on invertebrate abundances and richness; however, lower invertebrate abundances were observed at the farm and crop locations that the highest number of microbat call sequences were recorded at. Invertebrate prey was abundant and diverse across the vegetable crop landscapes, and almost one-third of the invertebrates present in microbat scats were also caught in the sticky traps, with the most consumed invertebrate orders being the most abundant on the farms. Therefore, demonstrating the current resource value of the vegetable crops and the potential that the invertebrate resources could support more foraging. Consequentially, we describe the most relevant wildlife-friendly farming practices that should be further investigated to confirm their benefit to microbat conservation in open agricultural landscapes.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eSeason and Moon Phase Impacts on Microbat Activity\u003c/h2\u003e\u003cp\u003eThe vegetable crop landscapes that we observed were used by microbats during both the typically higher and lower activity periods of autumn and winter accordingly (Turbill, \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Stawski et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Law et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gonsalves and Law, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). During these periods it has been established that prey availability is particularly important to support microbat juvenile growth, torpor, and emerging reproductive needs (Stawski et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Though activity was higher in autumn, as expected (Turbill, \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Stawski et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gonsalves and Law, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the continual activity of microbats throughout winter suggests that the open vegetable farms of southeast Queensland are helping to meet microbat nutritional needs. However, further research into microbat species specific seasonal activity and diet, including prey nutritional assessments, for species in agroecosystems compared to extant natural ecosystems will help to clarify this importance.\u003c/p\u003e\u003cp\u003eMicrobat call activity was influenced by moon phase, being higher during the waning gibbous moon phase (50\u0026ndash;71% illumination) when moonlight is restricted to after midnight, and lower during the new moon to waxing crescent phase (0\u0026ndash;4% illumination) when the moon is rising steadily higher in the sky at dusk and remaining present until around midnight. In fact, despite the open structure of the vegetable crops not providing any shelter from moonlight, and similar to previous findings (Roeleke et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), we observed more microbat call activity under the waning gibbous moon phase. Further investigation into species specific activity across the hours of the night will help determine potential drivers of this behaviour. However, higher microbat call activity under later but brighter illumination conditions is likely driven by optimal foraging theory in disturbed habitats, with the predators hunting where effort is lower due to abundant prey (Stephens and Krebs, \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). This could be relative to higher invertebrate activity at periods of increased illumination, which has been observed for Hemiptera species in Australian cotton crops, which correlated to higher rates of microbat call activity (Kolkert et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). As Hemiptera and Lepidoptera use moonlight to navigate (Nowinszky and Puskas, 2017; Owens and Lewis, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and are key prey for Australian microbats (Kolkert et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003eb\u003c/span\u003e), this opportunity of increased hunting success and ease of escape movement that the mostly open areas accommodate (Roeleke et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) could be driving the activity that we observed under the later, brighter moon phase, especially considering the abundances of these invertebrates that we observed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eFarm and Crop Position Impacts on Microbat Activity and Invertebrates\u003c/h2\u003e\u003cp\u003eOur results are similar to those in more complex crop habitats; having positive relationships between microbat activity and heterogenous vegetation, non-crop vegetation, edge microhabitats, and water bodies (Tuneu-Corral et al., \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Within our study, the creek edge location was the sole forest-edge-like habitat on the farms, having tall grass, shrubs, and trees surrounding the creek, and an unsealed open road area transecting the crop edge and creek vegetation. The higher number of microbat call sequences and richness that we observed could be because such edge habitats can host increased invertebrate (microbat prey) activity (Tscharntke et al., \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Verboom and Spoelstra, \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). We observed higher abundances and richness of invertebrates at the road edge locations on the farms, which was also the area of lowest microbat call activity \u0026ndash; for which the inverse was observed too (less invertebrates where there was more microbat calls recorded). Though these were not significant differences or relationships, it could be indicative of microbat hunting pressure impacts. Microbat exclusion studies could help validate this theory (Bouarakia et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kor\u0026aacute;nyi et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Another driver of the increased microbat activity and richness at the creek edge could be that Lockyer Creek, which spans across much of the Lockyer Valley region could support orientation via continued vertical edges and reflective surfaces for echolocating edge-space species (Greif and Siemers, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Morris et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Denzinger and Schnitzler, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It could also be used by microbats as a water source and potential additional foraging area (Stahlschmidt et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Korine et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Froidevaux et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe higher microbat activity observed at the creek edge could be indicative of more efficient hunting for edge-space species using echolocation along the trees, and indicates that retaining small diverse vegetation patches and scattered trees in agricultural landscapes could be beneficial (Law and Chidel, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Manning et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fisher et al., 2010; Lindborg et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Froidevaux et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Wildlife-friendly farming practices that allow more complex non-crop vegetation edges, which could include planting or leaving diverse non-crop vegetation cover patches in crops or on non-arable land like around roads, water bodies, and dams could also be beneficial (Benton et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Boughey et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Meena et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Graham et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e: Froidevaux et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tscharntke et al., \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and warrant further investigation in the vegetable crop landscapes. Considering there is some evidence for native tree plantations adding limited value to supporting microbat activity 4\u0026ndash;11 years after plantation, especially in comparison to remnant native trees (Law et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), site-specific investigations are crucial. Whilst increasing refuge could indeed be beneficial to increasing or sustaining microbat activity, we recorded microbats across all of the farm and crop locations, in the small, open crop landscapes, suggesting that limitations to using these areas for resources were not evident.\u003c/p\u003e\u003cp\u003eThe rates of microbat call sequences that we observed were largely similar in the edge and inner crop locations and despite the creek edge area of the farms hosting significantly more microbat call activity (45% of all calls), microbat call activity was also observed in the centre and road edge farm positions (29% and 26% of all calls respectively). Therefore, microbats were still active, though to a lesser degree, 400-1,100 m from the nearest complex vegetation (i.e., trees, shrubs, and grass at creek edge). These results support previous findings of widespread microbat activity across open areas of human-disturbed landscapes (Law and Chidel, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Heim et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The microbat activity that we observed across all locations on the farm could have been supported by the intercrops that the vegetable farms were structured in, as they can support a high diversity of invertebrates across all seasons (Tortosa et al., \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The contrasting results from investigations into fruit, corn, rice, nut, cacao, coffee, vineyards and cotton crops (Tuneu-Corral et al., \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) could be influenced by them being large expanses of monoculture crops that are typically at the same growth stage with only seasonal fluxes of invertebrates. Our results justify further investigation into benefits for microbats that may be facilitated by reducing crop size or altering crop shape (e.g., Clough et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tscharntke et al., \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and incorporating the wildlife-friendly practices of intercropping, or in crops where intercropping is not feasible, introducing diversified lower stratum, edge vegetation, or flowers (Meena et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tscharntke et al., \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Krings et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe found that distance to introduced structures that could be used in orientation had no effect on rates of microbat activity, and consequentially we do not advocate introducing more structures to support microbat activity in such small, open, agricultural cropping systems. However, another applicable wildlife-friendly management practice that could increase support for microbat activity is the installation of artificial roost boxes. Considering the largely modified expanses of habitat in cropped areas, roost availability is likely a primary driver of sustaining and increasing microbat activity on farms, with microbat activity commonly observed to be higher when near roosts (Bontadina et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Rainho and Palmeirim, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hunninck et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Attaching roost boxes to the external of structures, such as sheds, in disturbed landscapes has proven more successful in housing microbats than if secured to a distant tree, which could redirect microbats from using the sheds and reduce the human-wildlife conflict potential (Flaquer et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Given the dearth of data on microbats using artificial roosts in subtropical Australia, further investigation into the structure that may best suit the microbat species in an area, relative to what they may currently be using on or nearby farms, needs to be considered (Rueegger, \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, before implementing any wildlife-friendly farming practices, particularly those that provide roost habitats, cost-benefit analysis considering negative impacts is needed to ensure an ecological trap is not inadvertently created (Russo et al., \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eInvertebrates in Crops and Microbat Scats\u003c/h2\u003e\u003cp\u003eIndirect comparisons of microbat scats and the invertebrates captured across the farms show that more than a quarter of the ASVs observed were common and that consumption of crop invertebrates by microbats occurred. The orders that were most observed and had the highest frequency of occurrences in microbat scats: Diptera, Coleoptera, and Lepidoptera; were also the most abundant across the crop sticky traps. Species belonging to these orders found in the scats and sticky traps are known to be damaging to crops, particularly those within Lepidoptera, for cotton in Australia (Kolkert et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and for the more complex crops in the Americas, Europe, and South East Asia (cotton, rice, cacao, coffee, nuts, corn, apples, vineyards: Tuneu-Corral et al., \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, the feeding behaviour of microbats in vegetable crops could be beneficial in assisting to manage invertebrate pests. Wildlife-friendly farming practices could be used to support more microbat foraging activity, and thus biological control, which could be a motivating factor to establishing such management practices. For example, the use of insect pheromones that are effective microbat lures could be beneficial in directing microbats to target key areas in agriculture to help lower invertebrate pest populations and reduce pesticide reliance (Korine et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A reduction in pesticide application could also be valuable to supporting microbat persistence, given that insecticide toxicity can occur via secondary consumption (Tooker and Pearsons, \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and environmental contact, with up to 25 pesticide pollutants evident on microbats swabbed in agroecosystems (Schanzer et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study demonstrates that open, intercropped vegetable farms incorporating creek-lined corridors and narrow patches of heterogeneous, non-crop vegetation can support sustained microbat activity across a range of environmental conditions, including those considered potentially disruptive (i.e., high illumination with no cover available). We documented high levels of insect prey availability in these landscapes and continually observed a proportion of them in microbat scats. This suggests that microbats regularly exploit these resources, but that there is also potential for further exploitation. While no clear barriers to microbat use of cropped areas were identified, our findings highlight that creek edge areas with non-crop vegetation hosted the highest levels of activity. These results underscore the ecological value of integrating structurally diverse, wildlife-friendly features within agricultural landscapes. Future research should focus on species-specific responses to seasonal and abiotic variation and landscape dynamics, nutritional assessments of available prey with comparisons to those in extant surrounding natural areas, and long-term studies of roosting dynamics within agricultural matrices. A deeper understanding of how structural and management variables influence microbat behaviour and activity will help guide the development and support of more effective and context-specific wildlife-friendly farming practices to help sustain microbat populations.\u003c/p\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003ePermits\u003c/h2\u003e\u003cp\u003eResearch was conducted under a University of Queensland Animal Ethics Permit (2023/AE000560) and Biosafety Permit (IBC/648B/SENV/VET/2023), and Queensland State Licence Permits (WA0055666, P-PTC-100498443, P-PTUKI-100498441).\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. L.L.F. led the fieldwork, assisted by R.D., G.F. and A.E.R. L.N. led the DNA metabarcoding. G.F. and B.S.L. provided assistance with bat call ID. L.L.F. performed the analyses and led the writing of the manuscript. All authors contributed to the drafting of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe extend our sincere gratitude to the volunteers and landholders who made the fieldwork for this study possible, particularly Helen Mayfield. Annabel Smith assisted with the design of the project, John Gould gave constructive insight for shaping the manuscript and Eric Vanderduys assisted with the figures.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eABARES. (2023). Snapshot of Australian Agriculture 2023, ABARES Insights, Canberra, March, DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.25814/rk1z-qm36\u003c/span\u003e\u003cspan address=\"10.25814/rk1z-qm36\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. CC BY 4.0. Accessed online 16th October 2023: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1034541/0\u003c/span\u003e\u003cspan address=\"https://daff.ent.sirsidynix.net.au/client/en_AU/search/asset/1034541/0\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdams, M.D., Law, B.S. and French, K.O. (2009). Vegetation structure influences the vertical stratification of open-and edge-space aerial-foraging bats in harvested forests. \u003cem\u003eForest Ecology and Management\u003c/em\u003e, \u003cem\u003e258\u003c/em\u003e(9), pp.2090\u0026ndash;2100.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdams, M.D., Law, B.S. and Gibson, M.S. (2010). Reliable automation of bat call identification for eastern New South Wales, Australia, using classification trees and AnaScheme software. \u003cem\u003eActa Chiropterologica\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), pp.231\u0026ndash;245.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAjzen, I. (1991). The Theory of planned behavior. \u003cem\u003eOrganizational Behavior and Human Decision Processes\u003c/em\u003e, 50, pp.179\u0026ndash;211.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmoabeng, B.W., Stevenson, P.C., Mochiah, M.B., Asare, K.P. and Gurr, G.M. (2021). Economic analysis of habitat manipulation in Brassica pest management: Wild plant species suppress cabbage webworm. \u003cem\u003eCrop Protection\u003c/em\u003e, \u003cem\u003e150\u003c/em\u003e, p.105788.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAncillotto, L., Ariano, A., Nardone, V., Budinski, I., Rydell, J. and Russo, D. (2017). Effects of free-ranging cattle and landscape complexity on bat foraging: implications for bat conservation and livestock management. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e241\u003c/em\u003e, pp.54\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eApfelbeck, B., Snep, R.P., Hauck, T.E., Ferguson, J., Holy, M., Jakoby, C., MacIvor, J.S., Sch\u0026auml;r, L., Taylor, M. and Weisser, W.W. (2020). Designing wildlife-inclusive cities that support human-animal co-existence. \u003cem\u003eLandscape and Urban Planning\u003c/em\u003e, \u003cem\u003e200\u003c/em\u003e, p.103817.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAppel, G., L\u0026oacute;pez-Baucells, A., Ernest-Magnusson, W. and Bobrowiec, P.E.D. (2017). Aerial insectivorous bat activity in relation to moonlight intensity. \u003cem\u003eMammalian Biology\u003c/em\u003e, \u003cem\u003e85\u003c/em\u003e(1), pp.37\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaroja, U., Garin, I., Vallejo, N., Aihartza, J., Rebelo, H. and Goiti, U. (2021). Bats actively track and prey on grape pest populations. \u003cem\u003eEcological Indicators\u003c/em\u003e, \u003cem\u003e126\u003c/em\u003e, p.107718.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBartlett, L.J., Newbold, T., Purves, D.W., Tittensor, D.P. and Harfoot, M.B. (2016). Synergistic impacts of habitat loss and fragmentation on model ecosystems. \u003cem\u003eProceedings of the Royal Society B: Biological Sciences\u003c/em\u003e, \u003cem\u003e283\u003c/em\u003e(1839), p.20161027.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBates, D., Maechler, M., Bolker, B. and Walker, S. (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1\u0026ndash;48. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18637/jss.v067.i01\u003c/span\u003e\u003cspan address=\"10.18637/jss.v067.i01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBelton, P. and Kempster, R.H. (1962). A field test on the use of sound to repel the European corn borer. \u003cem\u003eEntomologia Experimentalis et Applicata\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(4), pp.281\u0026ndash;288.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBenton, T.G., Vickery, J.A. and Wilson, J.D. (2003). Farmland biodiversity: is habitat heterogeneity the key? \u003cem\u003eTrends in ecology \u0026amp; evolution\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(4), pp.182\u0026ndash;188.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBleicher, S.S. (2017). The landscape of fear conceptual framework: definition and review of current applications and misuses. \u003cem\u003ePeerJ\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, p.e3772.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBontadina, F., Schofield, H. and Naef-Daenzer, B. (2002). Radio-tracking reveals that lesser horseshoe bats (Rhinolophus hipposideros) forage in woodland. \u003cem\u003eJournal of Zoology\u003c/em\u003e, \u003cem\u003e258\u003c/em\u003e(3), pp.281\u0026ndash;290.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eB\u0026oslash;rresen, S.T., Ulimboka, R., Nyahongo, J., Ranke, P.S., Skjaerv\u0026oslash;, G.R. and R\u0026oslash;skaft, E. (2023). The role of education in biodiversity conservation: Can knowledge and understanding alter locals\u0026rsquo; views and attitudes towards ecosystem services?. \u003cem\u003eEnvironmental Education Research\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(1), pp.148\u0026ndash;163.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoughey, K.L., Lake, I.R., Haysom, K.A. and Dolman, P.M. (2011). Improving the biodiversity benefits of hedgerows: how physical characteristics and the proximity of foraging habitat affect the use of linear features by bats. \u003cem\u003eBiological conservation\u003c/em\u003e, \u003cem\u003e144\u003c/em\u003e(6), pp.1790\u0026ndash;1798.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBouarakia, O., Linden, V.M., Joubert, E., Weier, S.M., Grass, I., Tscharntke, T., Foord, S.H. and Taylor, P.J. (2023). Bats and birds control tortricid pest moths in South African macadamia orchards. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e352\u003c/em\u003e, p.108527.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoyles, J.G., Cryan, P.M., McCracken, G.F. and Kunz, T.H. (2011). Economic importance of bats in agriculture. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e332\u003c/em\u003e(6025), pp.41\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBradshaw, C.J., Leroy, B., Bellard, C., Roiz, D., Albert, C., Fournier, A., Barbet-Massin, M., Salles, J.M., Simard, F. and Courchamp, F. (2016). Massive yet grossly underestimated global costs of invasive insects. \u003cem\u003eNature communications\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), p.12986.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBradshaw, C. J. A., Hoskins, A. J., Haubrock, P. J., Cuthbert, R. N., Diagne, C., Leroy, B., Andrews, L., Page, B., Cassey, P., Sheppard, A. W. and Courchamp, F. (2021). Detailed assessment of the reported economic costs of invasive species in australia. \u003cem\u003eNeoBiota\u003c/em\u003e, 67, pp.511\u0026ndash;550.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrown, V.A., de Torrez, E.B. and McCracken, G.F. (2015). Crop pests eaten by bats in organic pecan orchards. \u003cem\u003eCrop protection\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e, pp.66\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBusse, A., B\u0026auml;ssler, C., Brandl, R., Friess, N., Hacker, H., Heidrich, L., Hilmers, T., Merkel-Wallner, G., Schmid-Egger, C., Seifert, L. and M\u0026uuml;ller, J. (2022) Light and Malaise traps tell different stories about the spatial variations in arthropod biomass and method-specific insect abundance. \u003cem\u003eInsect Conservation and Diversity\u003c/em\u003e, 15(6), pp.655\u0026ndash;665.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eButterwort, V., Dansby, H., Zink, F.A., Tembrock, L.R., Gilligan, T.M., Godoy, A., Braswell, W.E. and Kawahara, A.Y. (2022). A DNA extraction method for insects from sticky traps: targeting a low abundance pest, \u003cem\u003ePhthorimaea absoluta\u003c/em\u003e (Lepidoptera: Gelechiidae), in mixed species communities. \u003cem\u003eJournal of Economic Entomology\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e(3), pp.844\u0026ndash;851.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCardoso, P., Barton, P.S., Birkhofer, K., Chichorro, F., Deacon, C., Fartmann, T., Fukushima, C.S., Gaigher, R., Habel, J.C., Hallmann, C.A. and Hill, M.J. (2020). Scientists' warning to humanity on insect extinctions. \u003cem\u003eBiological conservation\u003c/em\u003e, \u003cem\u003e242\u003c/em\u003e, p.108426.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChambers, C.L., Cushman, S.A., Medina-Fitoria, A., Mart\u0026iacute;nez-Fonseca, J. and Ch\u0026aacute;vez-Vel\u0026aacute;squez, M. (2016). Influences of scale on bat habitat relationships in a forested landscape in Nicaragua. \u003cem\u003eLandscape Ecology\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e, pp.1299\u0026ndash;1318.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClough, Y., Kirchweger, S. and Kantelhardt, J. (2020). Field sizes and the future of farmland biodiversity in European landscapes. \u003cem\u003eConservation letters\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(6), p.e12752.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCohen, Y., Bar-David, S., Nielsen, M., Bohmann, K. and Korine, C. (2020). An appetite for pests: Synanthropic insectivorous bats exploit cotton pest irruptions and consume various deleterious arthropods. \u003cem\u003eMolecular ecology\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(6), pp.1185\u0026ndash;1198.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrisol-Mart\u0026iacute;nez, E., Ford, G., Horgan, F.G., Brown, P.H. and Wormington, K.R. (2017). Ecology and conservation of insectivorous bats in fragmented areas of macadamia production in eastern Australia. \u003cem\u003eAustral ecology\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(5), pp.597\u0026ndash;610.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDamalas, C.A. and Eleftherohorinos, I.G. (2011). Pesticide exposure, safety issues, and risk assessment indicators. \u003cem\u003eInternational journal of environmental research and public health\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(5), pp.1402\u0026ndash;1419.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDenzinger, A. and Schnitzler, H.U. (2013). Bat guilds, a concept to classify the highly diverse foraging and echolocation behaviors of microchiropteran bats. \u003cem\u003eFrontiers in physiology\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, p.164.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDepartment of Environment and Science, Queensland. (2013). Wildlife of Lockyer Valley local government area, Wetland \u003cem\u003eInfo\u003c/em\u003e website. Accessed online, 1st August 2023: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wetlandinfo.des.qld.gov.au/wetlands/facts-maps/wildlife/?AreaID=lga-lockyer-valley\u003c/span\u003e\u003cspan address=\"https://wetlandinfo.des.qld.gov.au/wetlands/facts-maps/wildlife/?AreaID=lga-lockyer-valley\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFAO (2023). Land use statistics and indicators 2000\u0026ndash;2021. Global, regional and country trends. FAOSTAT Analytical Briefs Series No. 71. Rome. Accessed online 19th October 2023: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4060/cc6907en\u003c/span\u003e\u003cspan address=\"10.4060/cc6907en\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFardell, L.L., Pavey, C.R. and Dickman, C.R. (2020). Fear and stressing in predator\u0026ndash;prey ecology: considering the twin stressors of predators and people on mammals. \u003cem\u003ePeerJ\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, p.e9104.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFischer, J., Stott, J. and Law, B.S. (2010). The disproportionate value of scattered trees. \u003cem\u003eBiological Conservation\u003c/em\u003e, \u003cem\u003e143\u003c/em\u003e(6), pp.1564\u0026ndash;1567.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFlaquer, C., Torre, I. and Ruiz-Jarillo, R. (2006). The value of bat-boxes in the conservation of Pipistrellus pygmaeus in wetland rice paddies. \u003cem\u003eBiological conservation\u003c/em\u003e, \u003cem\u003e128\u003c/em\u003e(2), pp.223\u0026ndash;230.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrick, W.F., Kingston, T. and Flanders, J. (2020). A review of the major threats and challenges to global bat conservation. \u003cem\u003eAnnals of the new York Academy of Sciences\u003c/em\u003e, \u003cem\u003e1469\u003c/em\u003e(1), pp.5\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFroidevaux, J.S., Louboutin, B. and Jones, G. (2017). Does organic farming enhance biodiversity in Mediterranean vineyards? A case study with bats and \u003cem\u003eC. nigrogriseus\u003c/em\u003e. \u003cem\u003eAgriculture, ecosystems \u0026amp; environment\u003c/em\u003e, \u003cem\u003e249\u003c/em\u003e, pp.112\u0026ndash;122.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFroidevaux, J.S., Boughey, K.L., Hawkins, C.L., Broyles, M. and Jones, G. (2019). Managing hedgerows for nocturnal wildlife: Do bats and their insect prey benefit from targeted agri-environment schemes? \u003cem\u003eJournal of Applied Ecology\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(7), pp.1610\u0026ndash;1623.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFroidevaux, J.S., Laforge, A., Larrieu, L., Barbaro, L., Park, K., Fialas, P.C. and Jones, G. (2022a). Tree size, microhabitat diversity and landscape structure determine the value of isolated trees for bats in farmland. \u003cem\u003eBiological Conservation, 267\u003c/em\u003e, p.109476.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFroidevaux, J.S., Duarte, G., Fonseca, A., Zina, V., Conde, S., Ferreira, M.T. and Fernandes, M.R. (2022b). The location and vegetation physiognomy of ecological infrastructures determine bat activity in Mediterranean floodplain landscapes. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment, 332\u003c/em\u003e, p.107929.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGonsalves, L. and Law, B. (2017). Seasonal activity patterns of bats in North Sydney, New South Wales: implications for urban bat monitoring programs. \u003cem\u003eAustralian Mammalogy\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(2), pp.220\u0026ndash;229.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGorman, K.M., Barr, E.L., Ries, L., Nocera, T. and Ford, W.M. (2021). Bat activity patterns relative to temporal and weather effects in a temperate coastal environment. \u003cem\u003eGlobal Ecology and Conservation\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e, p.e01769.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGraham, L., Gaulton, R., Gerard, F. and Staley, J.T. (2018). The influence of hedgerow structural condition on wildlife habitat provision in farmed landscapes. \u003cem\u003eBiological Conservation\u003c/em\u003e, \u003cem\u003e220\u003c/em\u003e, pp.122\u0026ndash;131.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreif, S. and Siemers, B.M. (2010). Innate recognition of water bodies in echolocating bats. \u003cem\u003eNature communications\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(1), p.107.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGurr, G.M., Lu, Z., Zheng, X., Xu, H., Zhu, P., Chen, G., Yao, X., Cheng, J., Zhu, Z., Catindig, J.L. and Villareal, S. (2016). Multi-country evidence that crop diversification promotes ecological intensification of agriculture. \u003cem\u003eNature plants\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(3), pp.1\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHartig F (2022). _DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models_. R package version 0.4.6, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e%3Chttps://CRAN.R-project.org/package=DHARMa%3E\u003c/span\u003e\u003cspan address=\"http://%3Chttps://CRAN.R-project.org/package=DHARMa%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHawkins, N.J., Bass, C., Dixon, A. and Neve, P. (2019). The evolutionary origins of pesticide resistance. \u003cem\u003eBiological Reviews\u003c/em\u003e, \u003cem\u003e94\u003c/em\u003e(1), pp.135\u0026ndash;155.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHecker, K.R. and Brigham, R.M. (1999). Does moonlight change vertical stratification of activity by forest-dwelling insectivorous bats?. \u003cem\u003eJournal of Mammalogy\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e(4), pp.1196\u0026ndash;1201.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeim, O., Schr\u0026ouml;der, A., Eccard, J., Jung, K. and Voigt, C.C. (2016). Seasonal activity patterns of European bats above intensively used farmland. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e233\u003c/em\u003e, pp.130\u0026ndash;139.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoffmaster, E., Vonk, J. and Mies, R. (2016). Education to action: Improving public perception of bats. \u003cem\u003eAnimals\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), p.6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolland, R.A., Meyer, C.F., Kalko, E.K., Kays, R. and Wikelski, M. (2011). Emergence time and foraging activity in Pallas' mastiff bat, \u003cem\u003eMolossus molossus\u003c/em\u003e (Chiroptera: Molossidae) in relation to sunset/sunrise and phase of the moon. \u003cem\u003eActa chiropterologica\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(2), pp.399\u0026ndash;404.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHort Innovation. (2024). Australian Horticulture Statistics Handbook. \u003cem\u003eHort Innovation: Sydney, Australia\u003c/em\u003e. Accessed online 2nd February 2024: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.horticulture.com.au/growers/help-your-business-grow/research-reports-publications-fact-sheets-and-more/australian-horticulture-statistics-handbook/\u003c/span\u003e\u003cspan address=\"https://www.horticulture.com.au/growers/help-your-business-grow/research-reports-publications-fact-sheets-and-more/australian-horticulture-statistics-handbook/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang, F. and Subramanyam, B. (2004). Behavioral and reproductive effects of ultrasound on the Indian meal moth, \u003cem\u003ePlodia interpunctella\u003c/em\u003e. \u003cem\u003eEntomologia experimentalis et applicata\u003c/em\u003e, \u003cem\u003e113\u003c/em\u003e(3), pp.157\u0026ndash;164.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang, F., Subramanyam, B. and Taylor, R. (2003). Ultrasound affects spermatophore transfer, larval numbers, and larval weight of \u003cem\u003ePlodia interpunctella\u003c/em\u003e (H\u0026uuml;bner)(Lepidoptera: Pyralidae). \u003cem\u003eJournal of Stored Products Research\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(4), pp.413\u0026ndash;422.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHunninck, L., Coleman, K., Boman, M. and O\u0026rsquo;Keefe, J. (2022). Far from home: Bat activity and diversity in row crop agriculture decreases with distance to potential roost habitat. \u003cem\u003eGlobal Ecology and Conservation\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e, p.e02297.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIves, C.D., Lentini, P.E., Threlfall, C.G., Ikin, K., Shanahan, D.F., Garrard, G.E., Bekessy, S.A., Fuller, R.A., Mumaw, L., Rayner, L. and Rowe, R. (2016). Cities are hotspots for threatened species. \u003cem\u003eGlobal Ecology and biogeography\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), pp.117\u0026ndash;126.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJackson, K., Kerr, J., Kilpatrick, J., Henderson, C. and Lovatt, J. (1997). Potato Information Kit. Agrilink, your growing guide to better farming guide.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJay, M., Roinc\u0026eacute;, C.D., Ricard, J.M., Garcin, A., Mandrin, J.F., Lavigne, C., Bouvier, J.C., Tupinier, Y. and Puechmaille, S. (2012). Functional biodiversity in apple orchards: are the bats eating the pests?\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJung, K. and Kalko, E.K. (2010). Where forest meets urbanization: foraging plasticity of aerial insectivorous bats in an anthropogenically altered environment. \u003cem\u003eJournal of Mammalogy\u003c/em\u003e, \u003cem\u003e91\u003c/em\u003e(1), pp.144\u0026ndash;153.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKehoe, L., Kuemmerle, T., Meyer, C., Levers, C., V\u0026aacute;clav\u0026iacute;k, T. and Kreft, H. (2015). Global patterns of agricultural land-use intensity and vertebrate diversity. \u003cem\u003eDiversity and Distributions\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(11), pp.1308\u0026ndash;1318.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKennedy, P.L., Fontaine, J.B., Hobbs, R.J., Johnson, T.N., Boyle, R. and Lueders, A.S. (2018). Do novel ecosystems provide habitat value for wildlife? Revisiting the physiognomy vs. floristics debate. \u003cem\u003eEcosphere\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(3), p.e02172.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKleijn, D., Rundl\u0026ouml;f, M., Scheper, J., Smith, H.G. and Tscharntke, T. (2011). Does conservation on farmland contribute to halting the biodiversity decline? \u003cem\u003eTrends in ecology \u0026amp; evolution\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(9), pp.474\u0026ndash;481.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlingbeil, B.T. and Willig, M.R. (2009). Guild-specific responses of bats to landscape composition and configuration in fragmented Amazonian rainforest. \u003cem\u003eJournal of applied ecology\u003c/em\u003e, pp.203\u0026ndash;213.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKolkert, H., Andrew, R., Smith, R., Rader, R. and Reid, N. (2020a). Insectivorous bats selectively source moths and eat mostly pest insects on dryland and irrigated cotton farms. \u003cem\u003eEcology and Evolution\u003c/em\u003e 10:371\u0026ndash;388.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKolkert, H., Smith, R., Rader, R. and Reid, N. (2020b). Insectivorous bats foraging in cotton crop interiors is driven by moon illumination and insect abundance, but diversity benefits from woody vegetation cover. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e302\u003c/em\u003e, p.107068.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKolkert, H., Smith, R., Rader, R. and Reid, N. (2021). Insectivorous bats provide significant economic value to the Australian cotton industry. \u003cem\u003eEcosystem Services\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e, p.101280.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKor\u0026aacute;nyi, D., Zsebők, S., B\u0026aacute;ldi, A., Brambilla, M., Varga, M. and Bat\u0026aacute;ry, P. (2025). Forest cover enhances pest control by birds and bats independently of vineyard management intensity. \u003cem\u003eJournal of Applied Ecology\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKorine, C., Adams, R., Russo, D., Fisher-Phelps, M. and Jacobs, D. (2016). Bats and water: anthropogenic alterations threaten global bat populations. \u003cem\u003eBats in the Anthropocene: Conservation of bats in a changing world\u003c/em\u003e, pp.215\u0026ndash;241.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKorine, C., Cohen, Y. and Kahnonitch, I. (2022). Insect Pest Pheromone Lures May Enhance the Activity of Insectivorous Bats in Mediterranean Vineyards and Apple Orchards. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(24), p.16566.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKrings, C.H., Darras, K., Hass, A., Bat\u0026aacute;ry, P. and Fabian, Y. (2022). Not only hedgerows, but also flower fields can enhance bat activity in intensively used agricultural landscapes. \u003cem\u003eBasic and Applied Ecology\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e, pp.23\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKunz, T. H. and Stern, A. A. (1995). Maternal investment and post-natal growth in bats. \u003cem\u003eSymposia of the Zoological Society of London\u003c/em\u003e 67:123\u0026ndash;138.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKunz, T. H., Braun de Torrez, E., Bauer, D., Lobova, T. and Fleming, T. H. (2011). Ecosystem services provided by bats. \u003cem\u003eAnnals of the New York Academy of Sciences\u003c/em\u003e 1223:1\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaundr\u0026eacute;, J.W., Hern\u0026aacute;ndez, L. and Altendorf, K.B. (2001). Wolves, elk, and bison: reestablishing the\" landscape of fear\" in Yellowstone National Park, USA. \u003cem\u003eCanadian Journal of Zoology\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e(8), pp.1401\u0026ndash;1409.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaundr\u0026eacute;, J.W., Hern\u0026aacute;ndez, L., Medina, P.L., Campanella, A., L\u0026oacute;pez-Portillo, J., Gonz\u0026aacute;lez-Romero, A., Grajales-Tam, K.M., Burke, A.M., Gronemeyer, P. and Browning, D.M. (2014). The landscape of fear: the missing link to understand top-down and bottom‐up controls of prey abundance?. \u003cem\u003eEcology\u003c/em\u003e, \u003cem\u003e95\u003c/em\u003e(5), pp.1141\u0026ndash;1152.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaw, B. and Chidel, M. (2002). Tracks and riparian zones facilitate the use of Australian regrowth forest by insectivorous bats. \u003cem\u003eJournal of Applied Ecology\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e: 605\u0026ndash;617. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1365-2664.2002.00739.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1365-2664.2002.00739.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaw B. and Chidel M. (2006). Eucalypt plantings on farms: use by insectivorous bats. \u003cem\u003eBiological Conservation 133\u003c/em\u003e: 236\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaw, B.S., Chidel, M. and Penman, T. (2011). Do young eucalypt plantations benefit bats in an intensive agricultural landscape?. \u003cem\u003eWildlife Research\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(3), pp.173\u0026ndash;187.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaw, B., Gonsalves, L., Tap, P., Penman, T. and Chidel, M. (2015). Optimizing ultrasonic sampling effort for monitoring forest bats. \u003cem\u003eAustral Ecology\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(8), pp.886\u0026ndash;897.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee, D.K. (2020). Data transformation: a focus on the interpretation. \u003cem\u003eKorean journal of anesthesiology\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(6), pp.503\u0026ndash;508.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLentini, P.E., Gibbons, P., Fischer, J., LAW, B., Hanspach, J. and Martin, T.G. (2012) Bats in a farming landscape benefit from linear remnants and unimproved pastures. \u003cem\u003ePLoS ONE 7\u003c/em\u003e, e48201. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0048201\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0048201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, H. (2018). Minimap2: pairwise alignment for nucleotide sequences. \u003cem\u003eBioinformatics\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(18), pp.3094\u0026ndash;3100.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi, H. and Wilkins, K.T. (2022). Predator-prey relationship between urban bats and insects impacted by both artificial light at night and spatial clutter. \u003cem\u003eBiology\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(6), p.829.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLindborg, R., Plue, J., Andersson, K. and Cousins, S.A. (2014). Function of small habitat elements for enhancing plant diversity in different agricultural landscapes. \u003cem\u003eBiological Conservation\u003c/em\u003e, \u003cem\u003e169\u003c/em\u003e, pp.206\u0026ndash;213.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLindell, C., Eaton, R.A., Howard, P.H., Roels, S.M. and Shave, M.E. (2018). Enhancing agricultural landscapes to increase crop pest reduction by vertebrates. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e257\u003c/em\u003e, pp.1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLinden, V.M., Grass, I., Joubert, E., Tscharntke, T., Weier, S.M. and Taylor, P.J. (2019). Ecosystem services and disservices by birds, bats and monkeys change with macadamia landscape heterogeneity. \u003cem\u003eJournal of Applied Ecology\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(8), pp.2069\u0026ndash;2078.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLittlefair, M., Scheele, B.C., Westgate, M. and Lindenmayer, D. (2024). The ecological and biodiversity conservation values of farm dams: A systematic review. \u003cem\u003ePlos one\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(5), p.e0303504.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLumsden, L.F. and Bennett, A.F. (2005). Scattered trees in rural landscapes: foraging habitat for insectivorous bats in south-eastern Australia. \u003cem\u003eBiological Conservation\u003c/em\u003e, \u003cem\u003e122\u003c/em\u003e(2), pp.205\u0026ndash;222.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaas, B., Clough, Y. and Tscharntke, T. (2013). Bats and birds increase crop yield in tropical agroforestry landscapes. \u003cem\u003eEcology letters\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(12), pp.1480\u0026ndash;1487.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaclagan, S.J., Coates, T. and Ritchie, E.G. (2018). Don't judge habitat on its novelty: Assessing the value of novel habitats for an endangered mammal in a peri-urban landscape. \u003cem\u003eBiological Conservation\u003c/em\u003e, \u003cem\u003e223\u003c/em\u003e, pp.11\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaine, J.J. and Boyles, J.G. (2015). Bats initiate vital agroecological interactions in corn. \u003cem\u003eProceedings of the National Academy of sciences\u003c/em\u003e, \u003cem\u003e112\u003c/em\u003e(40), pp.12438\u0026ndash;12443.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManning, A.D., Fischer, J. and Lindenmayer, D.B. (2006). Scattered trees are keystone structures\u0026ndash;implications for conservation. \u003cem\u003eBiological conservation\u003c/em\u003e, \u003cem\u003e132\u003c/em\u003e(3), pp.311\u0026ndash;321.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. \u003cem\u003eEMBnet. journal\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), pp.10\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMatzek, V. and Wilson, K.A. (2021). Public support for restoration: Does including ecosystem services as a goal engage a different set of values and attitudes than biodiversity protection alone?. \u003cem\u003ePlos one\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), p.e0245074.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcKinney, M.L. (2006). Urbanization as a major cause of biotic homogenization. \u003cem\u003eBiological conservation\u003c/em\u003e, \u003cem\u003e127\u003c/em\u003e(3), pp.247\u0026ndash;260.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeena, A., Meena, M., Kumar, R. and Meena, B.M. (2017). Farmscaping: An ecological approach to insect pest management in agroecosystem. \u003cem\u003eJournal of Entomology and Zoology Studies\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(3), pp.598\u0026ndash;603.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMickleburgh, S.P., Hutson, A.M. and Racey, P.A. (2002). A review of the global conservation status of bats. \u003cem\u003eOryx\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(1), pp.18\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMilne, D.J., Armstrong, M., Fisher, A., Flores, T. and Pavey, C.R. (2004). A comparison of three survey methods for collecting bat echolocation calls and species-accumulation rates from nightly Anabat recordings. \u003cem\u003eWildlife Research\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(1), pp.57\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMonck-Whipp, L., Martin, A.E., Francis, C.M. and Fahrig, L. (2018). Farmland heterogeneity benefits bats in agricultural landscapes. \u003cem\u003eAgriculture, ecosystems \u0026amp; environment\u003c/em\u003e, \u003cem\u003e253\u003c/em\u003e, pp.131\u0026ndash;139.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorris, A.D., Miller, D.A. and Kalcounis-Rueppell, M.C. (2010). Use of forest edges by bats in a managed pine forest landscape. \u003cem\u003eThe Journal of Wildlife Management\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(1), pp.26\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurphy, F. and Ament, J. (2022). Pluralistic valuation of codling moth regulation by brown long-eared bats in English apple orchards. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(19), p.11966.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNash, M., Severtson, D. and Macfadyen, S. (2019). New approaches to manage invertebrate pests in conservation agriculture systems\u0026ndash;Uncoupling intensification. \u003cem\u003eAustralian Agriculture in 2020: From conservation to automation\u003c/em\u003e, pp.189\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen, H.D.D. and Nansen, C. (2018). Edge-biased distributions of insects. A review. \u003cem\u003eAgronomy for sustainable development\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e, pp.1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNowinszky, L. and Pusk\u0026aacute;s, J. (2014). Light-trap catch of Lygus sp.(Heteroptera: Miridae) in connection with the polarized moonlight, the collecting distance and the staying of the Moon above horizon. \u003cem\u003eJournal of Advanced Laboratory Research in Biology\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(4), pp.102\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrłowski, G., Karg, J. and Karg, G. (2014). Functional invertebrate prey groups reflect dietary responses to phenology and farming activity and pest control services in three sympatric species of aerially foraging insectivorous birds. \u003cem\u003ePLoS One\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(12), p.e114906.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOwens, A.C. and Lewis, S.M. (2018). The impact of artificial light at night on nocturnal insects: a review and synthesis. \u003cem\u003eEcology and evolution\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(22), pp.11337\u0026ndash;11358.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePatriquin, K.J. and Barclay, R.M. (2003). Foraging by bats in cleared, thinned and unharvested boreal forest. \u003cem\u003eJournal of Applied Ecology\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(4), pp.646\u0026ndash;657.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePe'er, G., Zinngrebe, Y., Hauck, J., Schindler, S., Dittrich, A., Zingg, S., Tscharntke, T., Oppermann, R., Sutcliffe, L.M., Sirami, C. and Schmidt, J. (2017). Adding some green to the greening: Improving the EU's Ecological Focus Areas for biodiversity and farmers. \u003cem\u003eConservation letters\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(5), pp.517\u0026ndash;530.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePennay, M., Law, B. and Reinhold, L. (2004). \u003cem\u003eBat calls of New South Wales: region based guide to the echolocation calls of microchiropteran bats\u003c/em\u003e. NSW Department of Environment and Conservation.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePohlert T (2023). PMCMRplus: Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended_. R package version 1.9.10, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e%3Chttps://CRAN.Rproject.org/package=PMCMRplus%3E\u003c/span\u003e\u003cspan address=\"http://%3Chttps://CRAN.Rproject.org/package=PMCMRplus%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePowell, B., Loi, J. and Christianos, N.G. (2002). \u003cem\u003eSoils and irrigated land suitability of the Lockyer Valley alluvial plains, South-East Queensland\u003c/em\u003e. Department of Natural Resources and Mines.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuckett, H.L., Brandle, J.R., Johnson, R.J. and Blankenship, E.E. (2009). Avian foraging patterns in crop field edges adjacent to woody habitat. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e131\u003c/em\u003e(1\u0026ndash;2), pp.9\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuig-Montserrat, X., Flaquer, C., G\u0026oacute;mez‐Aguilera, N., Burgas, A., Mas, M., Tuneu, C., Marqu\u0026egrave;s, E. and L\u0026oacute;pez‐Baucells, A. (2020). Bats actively prey on mosquitoes and other deleterious insects in rice paddies: Potential impact on human health and agriculture. \u003cem\u003ePest Management Science\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e(11), pp.3759\u0026ndash;3769.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePuig-Montserrat, X., Torre, I., L\u0026oacute;pez-Baucells, A., Guerrieri, E., Monti, M.M., R\u0026agrave;fols-Garc\u0026iacute;a, R., Ferrer, X., Gisbert, D. and Flaquer, C. (2015). Pest control service provided by bats in Mediterranean rice paddies: linking agroecosystems structure to ecological functions. \u003cem\u003eMammalian Biology\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e(3), pp.237\u0026ndash;245.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePywell, R.F., Heard, M.S., Woodcock, B.A., Hinsley, S., Ridding, L., Nowakowski, M. and Bullock, J.M. (2015). Wildlife-friendly farming increases crop yield: evidence for ecological intensification. \u003cem\u003eProceedings of the Royal Society B: Biological Sciences\u003c/em\u003e, \u003cem\u003e282\u003c/em\u003e(1816), p.20151740.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR Core Team (2024). \u003cem\u003eR: A language and environment for statistical computing\u003c/em\u003e. R Foundation for Statistical Computing, Vienna, Austria.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRainho, A. and Palmeirim, J.M. (2011). The importance of distance to resources in the spatial modelling of bat foraging habitat. \u003cem\u003ePloS one\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(4), p.e19227.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRaposeira, H., Horta, P., Heleno, R. and Rebelo, H. (2023). Changing with the times: Seasonal environmental gradients unveil dynamic bat assemblages and vulnerability. \u003cem\u003eEcology and Evolution\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(7), p.e10246.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReinhold, L., Law, B., Ford, G. and Pennay, M. (2001). Key to the bat calls of south-east Queensland and north-east New South Wales. \u003cem\u003eForest Ecosystem Research and Assessment TeC. nigrogriseuscal Paper\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReside, A. E., and Lumsden, L. F. (2011). Resource partitioning by two closely-related sympatric freetail bats, \u003cem\u003eMormopterus\u003c/em\u003e spp. In: \u003cem\u003eThe Biology and Conservation of Australian Bats\u003c/em\u003e (Eds. Law, B., Eby, P., Lunney, D., and Lumsden, L. F.). Royal Zoological Society of NSW, Mosman, NSW, Australia. pp 155\u0026ndash;166.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRizvi, S.Z., Reynolds, O.L., Haque, A., Furlong, M.J., Mo, J., Melo, M.C., Akter, S., Sandoval-Gomez, V.E., Johnson, A.C. and Gurr, G.M. (2022). Prospects for habitat management to suppress vegetable pests in Australia. \u003cem\u003eAustral Entomology\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e(1), pp.3\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez-San Pedro, A., Allendes, J.L., Beltr\u0026aacute;n, C.A., Chaperon, P.N., Saldarriaga-C\u0026oacute;rdoba, M.M., Silva, A.X. and Grez, A.A. (2020). Quantifying ecological and economic value of pest control services provided by bats in a vineyard landscape of central Chile. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e302\u003c/em\u003e, p.107063.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoeleke, M., Teige, T., Hoffmeister, U., Klingler, F. and Voigt, C.C. (2018). Aerial-hawking bats adjust their use of space to the lunar cycle. \u003cem\u003eMovement Ecology\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, pp.1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRognes, T., Flouri, T., Nichols, B., Quince, C. and Mah\u0026eacute;, F. (2016). VSEARCH: a versatile open source tool for metagenomics. \u003cem\u003ePeerJ\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, p.e2584.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRueegger, N. (2016). Bat boxes\u0026mdash;a review of their use and application, past, present and future. \u003cem\u003eActa Chiropterologica\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), pp.279\u0026ndash;299.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRusso, D., Bosso, L. and Ancillotto, L. (2018). Novel perspectives on bat insectivory highlight the value of this ecosystem service in farmland: Research frontiers and management implications. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e266\u003c/em\u003e, pp.31\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRusso, D., Tanalgo, K., Rebelo, H. and Cistrone, L. (2024). To improve or not to improve? The dilemma of \u0026ldquo;bat-friendly\u0026rdquo; farmland potentially becoming an ecological trap. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment\u003c/em\u003e, \u003cem\u003e375\u003c/em\u003e, p.109215.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRybicki, J., Abrego, N. and Ovaskainen, O. (2020). Habitat fragmentation and species diversity in competitive communities. \u003cem\u003eEcology letters\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(3), pp.506\u0026ndash;517.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalda\u0026ntilde;a-V\u0026aacute;zquez, R.A. and Mungu\u0026iacute;a-Rosas, M.A. (2013). Lunar phobia in bats and its ecological correlates: a meta-analysis. \u003cem\u003eMammalian Biology\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e(3), pp.216\u0026ndash;219.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchanzer, S., Koch, M., Kiefer, A., Jentke, T., Veith, M., Bracher, F., Bracher, J. and M\u0026uuml;ller, C. (2022). Analysis of pesticide and persistent organic pollutant residues in German bats. \u003cem\u003eChemosphere\u003c/em\u003e, \u003cem\u003e305\u003c/em\u003e, p.135342.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchmitt, L., Greenberg, R., Ibarra-N\u0026uacute;\u0026ntilde;ez, G., Bichier, P., Gordon, C.E. and Perfecto, I. (2021). Cascading effects of birds and bats in a shaded coffee agroforestry system. \u003cem\u003eFrontiers in Sustainable Food Systems\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, p.512998.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSegoli, M. and Rosenheim, J.A. (2012). Should increasing the field size of monocultural crops be expected to exacerbate pest damage?. \u003cem\u003eAgriculture, ecosystems \u0026amp; environment\u003c/em\u003e, \u003cem\u003e150\u003c/em\u003e, pp.38\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSignorell A (2024). DescTools: Tools for Descriptive Statistics. R package version 0.99.55, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e%3Chttps://CRAN.R-project.org/package=DescTools%3E\u003c/span\u003e\u003cspan address=\"http://%3Chttps://CRAN.R-project.org/package=DescTools%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSnyder, W.E. (2019). Give predators a complement: Conserving natural enemy biodiversity to improve biocontrol. \u003cem\u003eBiological control\u003c/em\u003e, \u003cem\u003e135\u003c/em\u003e, pp.73\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStahlschmidt, P., P\u0026auml;tzold, A., Ressl, L., Schulz, R. and Br\u0026uuml;hl, C.A. (2012). Constructed wetlands support bats in agricultural landscapes. \u003cem\u003eBasic and Applied Ecology\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(2), pp.196\u0026ndash;203.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStahlschmidt, P., Hahn, M. and Br\u0026uuml;hl, C.A. (2017). Nocturnal risks-high bat activity in the agricultural landscape indicates potential pesticide exposure. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, p.62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStawski, C. and Geiser, F. (2010). Seasonality of torpor patterns and physiological variables of a free-ranging subtropical bat. \u003cem\u003eJournal of Experimental Biology\u003c/em\u003e, \u003cem\u003e213\u003c/em\u003e(3), pp.393\u0026ndash;399.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStawski, C., Willis, C.K.R. and Geiser, F. (2014). The importance of temporal heterothermy in bats. \u003cem\u003eJournal of Zoology\u003c/em\u003e, \u003cem\u003e292\u003c/em\u003e(2), pp.86\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStephens, D.W. and Krebs, J.R. (1986). \u003cem\u003eForaging theory\u003c/em\u003e (Vol. 6). Princeton university press.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuarez-Rubio, M., Ille, C. and Bruckner, A. (2018). Insectivorous bats respond to vegetation complexity in urban green spaces. \u003cem\u003eEcology and Evolution\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(6), pp.3240\u0026ndash;3253.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSymondson, W.O.C., Sunderland, K.D. and Greenstone, M.H. (2002). Can generalist predators be effective biocontrol agents?. \u003cem\u003eAnnual review of entomology\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(1), pp.561\u0026ndash;594.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaylor, P.J., Grass, I., Alberts, A.J., Joubert, E. and Tscharntke, T. (2018). Economic value of bat predation services\u0026ndash;A review and new estimates from macadamia orchards. \u003cem\u003eEcosystem Services\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e, pp.372\u0026ndash;381.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTEEB. The Economics of Ecosystems and Biodiversity for Agriculture and Food: Scientific and Economic Foundations. (2018). UN Environment, Geneva, Switzerland.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTherneau, T., Atkinson, B. (2023). rpart: Recursive Partitioning and Regression Trees. R package version 4.1.23, \u0026lt;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=rpart%3E\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=rpart%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTooker, J.F. and Pearsons, K.A. (2021). Newer characters, same story: neonicotinoid insecticides disrupt food webs through direct and indirect effects. \u003cem\u003eCurrent Opinion in Insect Science\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e, pp.50\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTortosa, A., Giffard, B., Barbaro, L., Froidevaux, J.S., Ladet, S., Delhommel, J. and Vialatte, A., (2023). Diverse agricultural landscapes increase bat activity and diversity: Implications for biological pest control. \u003cem\u003eAgriculture, Ecosystems \u0026amp; Environment, 345\u003c/em\u003e, p.108318.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTournayre, O., Leuchtmann, M., Filippi-Codaccioni, O., Trillat, M., Piry, S., Pontier, D., Charbonnel, N. and Galan, M. (2020). In silico and empirical evaluation of twelve metabarcoding primer sets for insectivorous diet analyses. \u003cem\u003eEcology and Evolution\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(13), pp.6310\u0026ndash;6332.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTournayre, O., Leuchtmann, M., Galan, M., Trillat, M., Piry, S., Pinaud, D., Filippi-Codaccioni, O., Pontier, D. and Charbonnel, N. (2021). eDNA metabarcoding reveals a core and secondary diets of the greater horseshoe bat with strong spatio‐temporal plasticity. \u003cem\u003eEnvironmental DNA\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), pp.277\u0026ndash;296.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTscharntke, T., Steffan-Dewenter, I., Kruess, A. and Thies, C. (2002). Contribution of small habitat fragments to conservation of insect communities of grassland\u0026ndash;cropland landscapes. \u003cem\u003eEcological applications\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(2), pp.354\u0026ndash;363.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTscharntke, T., Clough, Y., Wanger, T.C., Jackson, L., Motzke, I., Perfecto, I., Vandermeer, J. and Whitbread, A. (2012). Global food security, biodiversity conservation and the future of agricultural intensification. \u003cem\u003eBiological conservation\u003c/em\u003e, \u003cem\u003e151\u003c/em\u003e(1), pp.53\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTscharntke, T., Grass, I., Wanger, T.C., Westphal, C. and Bat\u0026aacute;ry, P. (2021). Beyond organic farming\u0026ndash;harnessing biodiversity-friendly landscapes. \u003cem\u003eTrends in ecology \u0026amp; evolution\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(10), pp.919\u0026ndash;930.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTuneu-Corral, C., Puig‐Montserrat, X., Riba‐Bertol\u0026iacute;n, D., Russo, D., Rebelo, H., Cabeza, M. and L\u0026oacute;pez‐Baucells, A. (2023). Pest suppression by bats and management strategies to favour it: a global review. \u003cem\u003eBiological Reviews\u003c/em\u003e, \u003cem\u003e98\u003c/em\u003e(5), pp.1564\u0026ndash;1582.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurbill, C. (2008). Winter activity of Australian tree-roosting bats: Influence of temperature and climatic patterns. \u003cem\u003eJournal of Zoology\u003c/em\u003e, \u003cem\u003e276\u003c/em\u003e(3), pp.285\u0026ndash;290.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUmina, P.A., McDonald, G., Maino, J., Edwards, O. and Hoffmann, A.A. (2019). Escalating insecticide resistance in Australian grain pests: contributing factors, industry trends and management opportunities. \u003cem\u003ePest Management Science\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e(6), pp.1494\u0026ndash;1506.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eValdez, J.W., Gould, J. and Garnham, J.I. (2021). Global assessment of artificial habitat use by amphibian species. \u003cem\u003eBiological Conservation\u003c/em\u003e, \u003cem\u003e257\u003c/em\u003e, p.109129.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVamos, E.E., Elbrecht, V. and Leese, F. (2017). \u003cem\u003eShort COI markers for freshwater macroinvertebrate metabarcoding\u003c/em\u003e (No. e3037v2). PeerJ Preprints.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVandevelde, J.C., Bouhours, A., Julien, J.F., Couvet, D. and Kerbiriou, C. (2014). Activity of European common bats along railway verges. \u003cem\u003eEcological Engineering\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e, pp.49\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVansynghel, J., Ocampo-Ariza, C., Maas, B., Martin, E.A., Thomas, E., Hanf-Dressler, T., Schumacher, N.C., Ulloque-Samatelo, C., Yovera, F.F., Tscharntke, T. and Steffan-Dewenter, I. (2022). Quantifying services and disservices provided by insects and vertebrates in cacao agroforestry landscapes. \u003cem\u003eProceedings of the Royal Society B\u003c/em\u003e, \u003cem\u003e289\u003c/em\u003e(1982), p.20221309.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eV\u0026aacute;squez, D.A., Grez, A.A. and Rodr\u0026iacute;guez-San Pedro, A. (2020). Species-specific effects of moonlight on insectivorous bat activity in central Chile. \u003cem\u003eJournal of Mammalogy\u003c/em\u003e, \u003cem\u003e101\u003c/em\u003e(5), pp.1356\u0026ndash;1363.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerboom, B. and Spoelstra, K. (1999). Effects of food abundance and wind on the use of tree lines by an insectivorous bat, \u003cem\u003ePipistrellus pipistrellus\u003c/em\u003e. \u003cem\u003eCanadian Journal of Zoology\u003c/em\u003e, \u003cem\u003e77\u003c/em\u003e(9), pp.1393\u0026ndash;1401.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVescera, C., Van Vyve, C., Smits, Q. and Michaux, J.R. (2024). All-you-can-eat buffet: A spider-specialized bat species (\u003cem\u003eMyotis emarginatus\u003c/em\u003e) turns into a pest fly eater around cattle. \u003cem\u003ePlos one\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(5), p.e0302028.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWanger, T.C., Darras, K., Bumrungsri, S., Tscharntke, T. and Klein, A.M. (2014). Bat pest control contributes to food security in Thailand. \u003cem\u003eBiological Conservation\u003c/em\u003e, \u003cem\u003e171\u003c/em\u003e, pp.220\u0026ndash;223.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWarnecke, M., Mac\u0026iacute;as, S., Falk, B. and Moss, C.F. (2018). Echo interval and not echo intensity drives bat flight behavior in structured corridors. \u003cem\u003eJournal of Experimental Biology\u003c/em\u003e, \u003cem\u003e221\u003c/em\u003e(24), p\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.jeb\u003c/span\u003e\u003cspan address=\"http://.jeb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e191155.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWine Australia. (2023). \u003cem\u003eBats could be the perfect pairing for vineyard insect control\u003c/em\u003e. Australian Government. Accessed 20th July 2023: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wineaustralia.com/news/articles/bats-could-be-the-perfect-pairing-for-vineyard-insect-control\u003c/span\u003e\u003cspan address=\"https://www.wineaustralia.com/news/articles/bats-could-be-the-perfect-pairing-for-vineyard-insect-control\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWittman, H., Chappell, M.J., Abson, D.J., Kerr, R.B., Blesh, J., Hanspach, J., Perfecto, I. and Fischer, J. (2017). A social\u0026ndash;ecological perspective on harmonizing food security and biodiversity conservation. \u003cem\u003eRegional Environmental Change\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e, pp.1291\u0026ndash;1301.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWoinarski, J.C., Burbidge, A.A. and Harrison, P.L. (2015). Ongoing unraveling of a continental fauna: decline and extinction of Australian mammals since European settlement. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e112\u003c/em\u003e(15), pp.4531\u0026ndash;4540.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWoinarski, J.C., Braby, M.F., Burbidge, A.A., Coates, D., Garnett, S.T., Fensham, R.J., Legge, S.M., McKenzie, N.L., Silcock, J.L. and Murphy, B.P. (2019). Reading the black book: The number, timing, distribution and causes of listed extinctions in Australia. \u003cem\u003eBiological Conservation\u003c/em\u003e, \u003cem\u003e239\u003c/em\u003e, p.108261.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZuur, A.F., Hilbe, J.M., and Leno, E.N. (2015) A Beginner\u0026rsquo;s guide to GLM and GLMM with R: a frequentist. and Bayesian perspective for ecologists. Highland Statistics Ltd., Newburgh.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chiroptera, disturbed landscape, wildlife-friendly farming, crop invertebrate, biocontrol, moon phase","lastPublishedDoi":"10.21203/rs.3.rs-7871928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7871928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eContext\u003c/h2\u003e\u003cp\u003eAs natural landscapes continue to be modified, there is growing evidence that some wildlife can persist in disturbed areas that have adequate resources. Invertebrates synonymous with crop farming may be one such reliable and attractive resource to extant insectivorous wildlife, such as microbats.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003e To build on existing knowledge bases of microbats using complex agroecosystems globally, we evaluated if microbats were also active across the less researched, relatively open vegetable crops.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe assessed three vegetable farms in southeast Queensland, Australia, testing relationships between the number of microbat call sequences and microbat richness to landscape, season, and moon phase dynamics. We also tested relationships between crop invertebrate abundances and richness, as collected on sticky traps, to landscape dynamics and microbat call sequences. Microbat scats from the area were assessed to identify the invertebrates consumed using metabarcoding.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe found that the open, mixed vegetable crop farm configuration supported the activity of 55% of the microbat species known to occupy the region (17/31). Microbat activity was continually observed across both the typically high (autumn) and low (winter) activity periods, though was higher in autumn. Microbat activity was also continually observed during waning gibbous and waxing gibbous to new moon phases, though was higher during waning gibbous. Of the landscape features, creek edges supported the highest microbat species richness and call activity. Invertebrate surveys confirmed high abundance and richness across the farm landscape area. Metabarcoding analysis revealed that nearly one-third of the invertebrate species identified in microbat scats were also present on farm sticky traps, with the most frequently consumed orders matching those most abundant on the farms.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eWe suggest that targeted wildlife-friendly management could enhance resource use of vegetable crops to help conserve microbats and benefit crops.\u003c/p\u003e","manuscriptTitle":"Microbat activity in vegetable crops relative to insect, landscape, and abiotic influences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 09:17:34","doi":"10.21203/rs.3.rs-7871928/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8e7fa4b6-47b0-4166-83cf-de7cd644dbce","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T02:09:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 09:17:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7871928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7871928","identity":"rs-7871928","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}