Resource Limitation Shapes Platypus Spatial Ecology in Urban Streams

Resource Limitation Shapes Platypus Spatial Ecology in Urban Streams | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Resource Limitation Shapes Platypus Spatial Ecology in Urban Streams Amy Young, Gilad Bino, Tahneal Hawke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8168416/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 As urbanisation accelerates, freshwater ecosystems face growing threats, particularly for species reliant on riparian zones, like the platypus (Ornithorhynchus anatinus). This study examines platypus presence, distribution and habitat use along Moggill Creek in Queensland across an urban-rural gradient. Using environmental DNA (eDNA) sampling, live trapping and radio tracking, we assessed urban development influences on platypus home range and habitat preferences. Over six nights of trapping, we captured six adult platypuses (four males, two females). Cross study analysis of radiotracking data revealed that platypuses in urban environments maintain home ranges approximately 2.36 times larger than those in rural habitats (95% CI: 1.58 to 3.63, P<0.001), with the model explaining 57.5% of variance. Net type emerged as the dominant predictor of capture rates, with fyke nets capturing platypuses at 72% lower rates than mesh nets (P<0.001), representing a critical methodological consideration for comparative studies. We confirmed platypus DNA at 13 of 14 sites through eDNA sampling, with notable absence at the most downstream urban site suggesting potential habitat limitations. Analysis of macroinvertebrate communities revealed significant differences between urban and rural sites, driven by environmental factors including elevation and riparian vegetation, which correlated with higher biodiversity and water quality in rural areas. These findings underscore platypus capacity to persist in urban environments whilst revealing ecological costs, including substantially expanded home ranges likely driven by resource limitation. This research, the first to radio track platypuses in Queensland, emphasises the urgent need for conservation strategies targeting urban waterways to maintain habitat quality and support platypus populations amidst accelerating urbanisation pressures. Habitat selection Macroinvertebrate Ornithorhynchus anatinus Resource availability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The human population is increasing rapidly, expected to reach 8.5 billion by 2030 (United Nations 2022). Rising urbanisation rates have resulted in expansion of urban areas to accommodate growing populations (Güneralp et al. 2020 ). This has come at the expense of natural environments, impacting remaining green-spaces and the species that inhabit them (Faulkner 2004 ). In urban environments, riparian zones and adjacent waterways are often encroached by infrastructure as they provide a wide array of ecosystem services (Paul & Meyer 2001 ). Consequently, these environments undergo structural simplification and further alteration, predisposing them to an array of environmental pressures (McKinney 2008 ). Observed effects that urbanisation has on waterways includes rapid and extreme responses to rainfall, alterations to channel morphology, higher concentrations of effluents and anthropogenic pollution, increased runoff from infrastructure, increase in dominance of tolerant species, and a reduction of biotic richness (Beavan et al. 2001 ; Paul & Meyer 2001 ; Sabha et al. 2022 ). This continual degradation of urban waterways has been referred to as ‘urban stream syndrome’, the almost universal response of waterways to the urbanisation of surrounding catchments (Vietz et al. 2016 ). The complexity in which these pressures interact with the environment deems their consideration and understanding a critical aspect to conserving freshwater ecosystems (Palmer & Ruhi 2019 ). In Australia, freshwater ecosystems are a precious resource for humans, flora and fauna. On a world scale, the aquatic biodiversity of Australia is highly significant and is particularly renowned for supporting a high degree of endemic species (Dunn 2003 ). With freshwater ecosystems experiencing far greater declines in biodiversity than terrestrial ecosystems, it is paramount that the requirements of species inhabiting waterways are investigated and understood for the implementation of effective conservation management (Dudgeon et al. 2006 ). Current methods of determining species presence in waterways range from observational surveys to DNA metabarcoding. DNA that can be extracted from environmental samples is termed environmental DNA (eDNA) and is used to monitor populations that both occupy the water and the environment surrounding (Rees et al. 2014 ). This method of determining species presence involves the detection of mitochondrial DNA and as such allows the detection of target species without trapping or physically observing them (Hebert & Gregory 2005 ). This can be especially beneficial when targeting a species that is more difficult to locate or catch (Rees et al. 2014 ). Unlike eDNA sampling, live trapping of species allows for additional information to be collected from individuals along with measures of species richness, abundance, and composition (De Bondi et al. 2010 ). The close analysis of individual subjects granted by live trapping, provides the means to investigate population dynamics dependent on individuals and their characteristics (Croose et al. 2023 ). Live trapping provides the opportunity for subsequent monitoring of individuals to further understand their requirements (Nichols & Pollock 1983 ). Radio-tagging and tracking individuals means monitoring fine-scale movement of individuals through their environment is attainable, providing crucial data for evidence-based conservation initiatives (Paxton et al. 2022 ). Observing how wildlife interact with their surrounding environment can be a key indicator of where threats and environmental pressures may be present (Cooke 2008 ). Coupling tracking and species presence data with remotely sensed data allows for investigation into how environmental variation impacts species on a larger scale (Neumann et al. 2015 ). These methods of monitoring species and the environment are crucial aspects of evidence-based conservation and allow for effective conservation initiatives to be implemented to conserve iconic species of interest (Aarts et al. 2008 ). One of Australia’s most iconic freshwater species, which inhabits urban waterways, is the platypus ( Ornithorhynchus anatinus ). As the only remaining living representative of the Ornithorhynchidae family, platypuses are one of the most phylogenetically and evolutionary distinct species in the world (Bino et al. 2019 ; Grant & Temple-Smith 2003 ). Platypuses represent one of five remaining species of egg-laying mammals, belonging to the order Monotremata (Bino et al. 2019 ). Dependent on water for feeding, the distribution of platypuses is restricted to permanent water sources in eastern Australia. Platypuses will inhabit ephemeral streams in proximity to permanent pools, acting as refuges during periods of drought (Grant 2007 ). The irregular mosaic of Australia’s water bodies results in the discontinuity of platypus distributions across river catchments (Grant 2007 ). The distribution of platypuses encompasses most of Eastern Australia, including King Island and Tasmania, typically abundant to the east of the Great Dividing Range (Grant 1998 ). Additionally, platypuses can be found on Kangaroo Island (South Australia) due to the introduction of a small population (Bino et al. 2019 ). Occurring naturally in freshwater lakes, rivers, and streams (Gongora et al. 2012 ), platypuses spend most of their life in the water (Grant 2007 ). With home ranges varying between 0.5 and 15 km of linear river habitats, males tend to have larger home ranges than females, particularly as juveniles and in the lead up to the breeding season (Bino et al. 2019 ). Water depths between 1–5 m allow platypuses to forage efficiently while also avoiding predation (Bethge et al. 2001 ; Bino et al. 2019 ). Platypuses have foraging preference for coarser bottom substrate providing fixed habitats for the aquatic invertebrates on which they feed (Grant 2004 ). The burrows of platypuses are generally found in the banks of waterways that are consolidated by the roots of vegetation and have an undercut to protect burrow entrances (Serena et al. 1998 ). Movement outside of water is usually limited for platypuses due to the risks of predation (Bino et al. 2019 ), overheating (Grant & Dawson 1978 ) and associated excessive energy expenditure (Fish et al. 2001 ). Platypuses have been observed using logs and other debris for shelter instead of burrows, indicating behavioural flexibility (Serena 1994 ). The primary aim of this project is to assess the prevalence and fine-scale habitat use of platypuses along a gradient of development in Moggill Creek, Queensland (QLD). Through eDNA surveys, live-trapping, radio-tracking, analyses of remotely sensed data, and review of current literature, we pursued identifying the environmental predictors influencing platypuses and other freshwater species along a gradient of development. We hypothesised that the gradient of urban development would be a key driver for the presence of riparian vegetation and preferred food sources of platypuses, consequently influencing the presence and home-ranges of platypuses within the study area. Beyond these specific aims, this study also seeks to engage with broader ecological theory regarding the effects of urbanisation on aquatic biota. By considering the tenets of ‘urban stream syndrome’, this study investigates their hypothesised effect on key environmental factors, such as food availability, vegetation cover and biotic richness (Vietz et al. 2016 ), influencing urban platypus population viability. eDNA surveys were conducted to assess distribution and food availability throughout the study site and live trapping of platypuses was undertaken to estimate abundance across the sites. This study is the first to radio-track platypuses in QLD, addressing major gaps in knowledge surrounding their ecology in this region and contributing to the current body of knowledge on the effects of urbanisation on aquatic species. Materials and methods Study Area This study was undertaken on the first 16 km of Moggill Creek from its convergence with the Brisbane River and the first 10 km of Gold Creek from its convergence with Moggill Creek (Fig. 1 ). Originating in Brisbane Forest Park, Moggill Creek flows roughly 15 km southeast before meeting with the Brisbane River. The main tributaries of Moggill Creek are Gap Creek, Wonga Creek, and Gold Creek, forming the Moggill Creek catchment. Moggill Creek and its associated tributaries flow through varying landscapes and are exposed to several methods of land use and environmental conditions (He et al. 2020 ). The upper limits of the Moggill Creek catchment undergo a steep change in elevation as the creeks flow towards the Brisbane River. This results in high velocity runoff following rain events, causing major disruption to riparian vegetation and freshwater biodiversity (Vietz et al. 2016 ). Additionally, as the catchment approaches the Brisbane River, land use types change from agricultural and rural to urbanised areas of high recreational activity (He et al. 2020 ). The movement of these waterways through changing landscapes, suggest that there are varying environmental conditions throughout the Moggill Creek catchment impacting the ecological communities present (Pellissier et al. 2018 ). For the purposes of this study, we analyse data for three creek sections, dividing Moggill Creek into upstream (rural) and downstream (urban) sections associated with changes in land use, as well as Gold Creek (rural), (Fig. 1 ). Trapping and Tagging Live trapping of platypuses was conducted along Moggill Creek and Gold Creek for six consecutive nights from 29/07/2024–4/08/2024. Five nights of trapping were undertaken on Moggill Creek sites and one on Gold Creek. At sites along Moggill Creek, either unweighted mesh nets (80 mm multifilament nets 25m x 2m), fyke nets (30-mm knotless 20-ply nylon, 1m x 5m wings, and 0.8m x 5m wings) or a combination of both net types were used. At the Gold Creek site only fyke nets were used (Hawke et al. 2021a ). On nights where both mesh and fyke nets were used, two pairs of fyke nets, facing opposite directions (upstream/downstream), were set at either end of a pool which had an unweighted mesh net set in it. If the pool at the trapping site was over 50m long, two 25m mesh nets were tied together, otherwise a single 25m x 2m mesh net was used. On the night where only fyke nets were used, four pairs of fyke nets were set at four different sites along the creek (Fig. 2 a). Net set up commenced at 16:00 each day using a small 6-ft punt to set unweighted mesh nets along the pool, parallel to the bank. Fyke nets were set by hand in suitable shallow sections of the creek. Both fyke and mesh nets were anchored with either star pickets or tied to vegetation. The wings of fyke nets were additionally secured along the creek bed with rocks to eliminate gaps underneath the wing. Unweighted mesh nets were consistently monitored using a spotlight and any animal captures were removed immediately. Fyke nets were thoroughly checked every three hours (19:00, 22:00, 01:00, 04:00) and any animal caught was removed. Mesh nets remained set until 00:00 and fyke nets remained set until 04:00. Trapping success rate was calculated using a Catch Per Unit Effort (CPUE) metric to represent the number of platypuses caught per net effort. A 50m unweighted mesh net was defined as one unit of net effort and a pair of two fyke nets was defined as one unit of net effort. CPUE was calculated for the overall study, mesh nets and fyke nets. Following capture, platypuses were placed in pillowcases and moved to a quiet location until they were processed. For processing, platypuses were placed in a portable isolation chamber where isoflurane (Pharmachem 2–5%) in oxygen (2-3L/min) was administered to anaesthetise them (Chinnadurai et al. 2016 ; Fiorello et al. 2016 ). Body temperature, heart rate, and oxygen levels of platypuses was continuously monitored using a Darvall H100N (Bino et al. 2018 ). The tail volume index (TVI) of platypuses were determined through physical examination of their tail fat content and given a score from 1–5. Platypuses were weighed, sexed, measured (tail, body, bill), and aged based on the morphology of spurs (Williams et al. 2013 ). Each platypus was scanned for the presence of a microchip, and if absent, a Passive Integrated Transponder (PIT) (Trovan) was inserted between the shoulder blades for subsequent identification (Grant & Whittington 1991 ). Additional samples were taken from each platypus (mouth-cheek swabs, tissue biopsy, bloods, fur sample) for related studies. Once processed, each platypus was taken off anaesthetic and placed in a quiet place to recover. Radio Tracking All platypuses were fitted with radio transmitters (ATS F1540, battery life approximately 70 days). A small (2x2cm) patch of fur was shaved from the dorsal side of each platypus, anterior to the tail and fast-setting non-toxic superglue was used to secure the radio transmitter to the shaved area (Casper 2009 ). Platypuses were released back to their respective capture location once fully recovered. Lotek receivers and antennas were used to track radio tagged platypuses throughout the study site. Tracking was undertaken during the day to determine potential burrow sites and the monitor the movement of platypuses through altered and natural environments. Between the 30th of July 2024 and 30th of August 2024, a total of 25 days of tracking were conducted. Tracking was undertaken in two sessions, one in the rural upstream Moggill Creek site and the other in the urban downstream Moggill Creek site. The two sessions of day tracking were split into AM and PM sessions and each day the rural and urban sites would alternate between AM and PM surveys. AM tracking sessions were undertaken between 09:00 and 12:00 and PM tracking sessions were undertaken between 13:00 and 16:00. At the determined point of strongest signal for each frequency, coordinates were taken and stored with a GPS (Gamin GPSMap 65S) along with the date, time, and any notes on the location. Platypus locations were mapped using ‘sf’ (Pebesma 2018 ) and ‘mapview’ (Appelhans et al. 2023 ) packages using R statistical software (Fig. 2 b). Environmental DNA survey Environmental DNA (eDNA) surveys were undertaken along the 26 km study site at 2–3 km intervals (Fig. 1 ). A total of 14 sites, 10 along Moggill Creek and 4 along Gold Creek, were identified. At each site, two samples, two meters apart were collected using Wilderlab eDNA mini kits ( https://www.wilderlab.co.nz ). For each sample 50 mL of water was drawn into a 60 mL sterile syringe from just below the water’s surface and then passed through a filter. This process was repeated until the filter became clogged or had passed 1 L of water. The filter was capped and injected with a DNA/RNA Shield preservative buffer. Each filter was then put back into its respective bag and sealed to avoid contamination. New sampling equipment was used for each sample and care was taken to avoid contamination between sites. Samples were sent to Wilderlab for multispecies sample sequencing and analysis using the basic freshwater eDNA package. Wilderlab extracted DNA from the samples, PCR-amplifying the extracts using fusion-tag mitochondrial and nuclear rRNA assays for detecting the primer sequences of invertebrates and vertebrates. Fusion tag primers used were Illumina TruSeq™ sequencing primer bind site and Illumina P5 and P7 adapter sequences, respectively. Output sequences were de-multiplexed and filtered to produce a series of exact amplicon sequence variants (ASVs), which were then assigned to the lowest possible taxonomic rank (Wilkinson 2023 ). Environmental DNA analysis We filtered eDNA macroinvertebrate data to retain phyla: Annelida, Arthropoda, Cnidaria, Gastrotricha, Mollusca, Nemertea, Nematoda, and Platyhelminthes, known to contribute to the diet of platypuses (Hawke et al. 2022 ). Data was then summarised at the order level and classified as presence or absence for each sample. The total number of orders was then summarised for each sample. In addition, a modified Stream Invertebrate Grade Number – Average Level (SIGNAL) score was calculated at the family scale (Chessman 2003 ). The SIGNAL score is a metric used to evaluate the strength and importance of ecological indicators in assessing environmental health and ecosystem quality. It assigns scores to various macroinvertebrate taxa based on their sensitivity to pollution, with higher scores indicating greater sensitivity and, thus, better environmental conditions. Normally an abundance weighting is used to adjust the score, but given eDNA data provided presence/absence information, an equal abundance weighting was given to each detected family. We used the ‘biomonitoR’ package in R (Laini et al. 2022 ) to calculate the SIGNAL score. We then calculate a dissimilarity matrix based on the Bray-Curtis method using the ‘vegdist’ function from the ‘vegan’ package (Oksanen et al. 2024 ). To examine if environmental predictors influenced macroinvertebrate community composition between creeks, we used the ‘adonis2’ function from the ‘vegan’ package (Oksanen et al. 2024 ) to perform a Permutational Multivariate Analysis of Variance (PERMANOVA). The adjusted R 2 value, F-value and p-value were extracted from the results of each predictor to assess their contributions. Non-metric Multidimensional Scaling (NMDS) was then conducted on the macroinvertebrate data using the ‘metaMDS’ function from the ‘vegan’ package (Oksanen et al. 2024 ), with the jaccard distance metric and 1000 permutations, to explore patterns in the community structure. To assess correlations between environmental variables and NMDS axes, we used the ‘envfit’ function, testing significance with tested 999 permutations. NMDS sample coordinates were extracted and converted into a data frame. Creek NMDS scores were plotted against the NMDS axes, along with 68% confidence ellipses and the environmental vectors from the envfit results. Additionally, the NMDS scores for macroinvertebrate orders were plotted with 68% confidence ellipses and vectors representing macroinvertebrate orders. 68% confidence ellipses were used to represent one standard deviation either side of the mean. The ‘simper’ function from the ‘vegan’ package (Oksanen et al. 2024 ) was used to perform a similarity percentage (SIMPER) analysis of the response data (community composition) and predictor data (creek), running 10,000 permutations to assess the significance of the results. Three specific comparisons (Gold x Moggill upstream, Gold x Moggill downstream, Moggill upstream x Moggill downstream) were defined to determine which invertebrate orders were driving the dissimilarity in community composition between creeks. A Generalised Linear Model (GLM) was created by fitting a Poisson regression model to predict the effect of the environmental predictors on the number of invertebrate orders. The ‘dredge’ function from the ‘MuMIn’ package (Bartoń 2024 ) was used to generate models of all possible predictor combinations. A model averaging approach was applied to the generated models to select those with an Akaike Information Criterion ( \(\:{\Delta\:}\) AIC) of less than 2 for averaging. Models with cumulative \(\:{\Delta\:}\) AIC weights of 0.95 or less were used to create a set of 95% confidence models. These models were averaged to generate the predicted number of invertebrate orders and their standard errors. A fitted GLM containing all the predictor variables was created to visualise the relationship between SIGNAL and the predictors. Model selection was performed to generate all possible models and rank them based on their \(\:{\Delta\:}\) AIC value. A model averaging approach was applied to the generated models to select those with an \(\:{\Delta\:}\) AIC of less than 2 for averaging. The 95% confidence models were averaged further using the ‘model.avg’ function to produce the final model. Using the ‘geom_point’ function, the observed data was plotted against Elevation. A Gamma GLM of the effect of Elevation on predicted SIGNAL was added to the plot using the ‘geom_smooth’ function. We used the mvabund package (Wang et al. 2022 ) to model multiple response variables of GLMs. The response data was transformed into a multivariate format using the ‘mvabund’ function. The most influential predictors were visualised in coefficient plots by using the ‘coefplot.manyglm’ function. A final reduced statistical summary of the model, including the coefficients for the key predictors, was created to help visualise the effect of the predictors on invertebrate orders. Habitat metrics Remotely sensed data was collated across our study site to explore possible drivers of observed eDNA results. Elevation data was sourced from the Queensland LiDAR Data - Brisbane 2019 Project, collected through airborne laser scanning (ALS) using fixed-wing aircraft in 2019, and covers the greater Brisbane area. Processed data was used to generate a Digital Elevation Model (DEM) with a 1-meter grid. Data was accessed from the Elevation and Depth Foundation Spatial Data (ELVIS) portal ( https://elevation.fsdf.org.au/ ). Woody vegetation extent data from the 2021–22 Statewide Landcover and Trees Study (SLATS) based Sentinel-2 satellite imagery was used to assess vegetation clearing and regrowth (Queensland Government 2024). We used the seasonal fractional cover which provided the proportion of bare, green and non-green cover, created from a time series of Sentinel-2 imagery at medium resolution of 10 m. Using an image from July 2024, we focused on Band 1 which provides the estimated fractions of bare ground cover (Joint Remote Sensing Research Program & Department of Environment and Science (2017–2023) 2022). We collated land use data from the Catchment Scale Land Use of Australia – Update December 2023 (CLUM) dataset, a national compilation of land use information developed by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) as part of the Australian Collaborative Land Use and Management Program (ACLUMP). This dataset integrates vector-based land use data, satellite imagery, and field-collected information, producing a 50-meter resolution raster showing the dominant land use type per area. Land use was classified according to the Australian Land Use and Management (ALUM) Classification Version 8, facilitating standardized analysis across regions and timeframes (2008–2023). Using the CLUM 2023 dataset, land uses were grouped into categories reflecting key areas of interest: conservation (1.1.7 for preserved natural spaces), urban residential (5.4.1), rural residential (5.4.3), and recreational land use, which includes 5.5.2 (public services) and 5.5.3 (recreation and culture). For each site, the average land use area per category was calculated, representing the predominant land types and intensity of use per classification. For each of the four datasets, the total value within 1 km buffer surrounding each of the 14 eDNA survey sites was calculated using the ‘st_buffer’ function in the ‘sf’ package in R. Review of platypus home ranges A review of the literature on platypus radio tracking was conducted for comparative analyses with the findings of this study. Google Scholar, Environment Complete, and University of New South Wales Library databases were searched using the terms: ‘platypus’ OR ‘ Ornithorhynchus anatinus ’, ‘radio-tracking’ OR ‘radiotelemetry’, ‘home range’ OR ‘movement’ OR ‘activity’. These searches yielded six published articles (Boulton et al. 2022; Crane et al. 2022, Gardner & Serena 1995 ; Gust & Handasyde 1995 ; Serena 1994 ; Serena et al. 1998 ,). Data on locations, habitat type, overall home ranges, tracking duration, age class, and sex were recorded for each study (Appendix 1). Land use types for each study were obtained from the Land Use of Australia Web Map (Australian Bureau of Agricultural and Resource Economics and Sciences 2024 ) and used to determine habitat type. Studies in urban intensive use areas were classified as urban-dominated and studies in nature-conserved, rural residential, grazing native vegetation use areas, were classified as rural-dominated. Four studies were classified as undertaken in rural-dominated environments (Crane et al. 2022; Gardner & Serena 1995 ; Gust & Handasyde 1995 ; Serena 1994 ) and two in urban-dominated environments (Boulton et al. 2022; Serena et al. 1998 ). As juvenile platypuses can move significantly large distances during dispersal periods (Bino et al. 2015 ), only data from adult platypuses was used for comparison with the findings of this study. Tracking data from Crane et al. (2022) and Boulten et al. (2022) was imported into Google Earth Pro software (version 7.3.6) to calculate the extent of tracked home ranges for adult platypuses. We performed a Generalized Linear Model (GLM) analysis using the Gamma distribution with a log link function, appropriate for continuous, positively skewed data. The dependent variable considered in the model was home range size, and the explanatory variables included latitude, sex, duration of tracking (days), and rural\urban classification. Review of platypus density A literature review was undertaken to collate information relating to platypus abundance, measured as catch-per-unit-effort (CPUE) from previous studies. The same databases (Google Scholar, Environment Complete, and University of New South Wales Library) were used with search terms such as: ‘platypus’ OR ‘ Ornithorhynchus anatinus ’, ‘density’ OR ‘abundance’ OR ‘capture’ OR ‘CPUE’. Data from relevant studies were extracted, including study locations, sampling effort, habitat type, and reported CPUE values (Appendix 2). Where raw CPUE data was available, it was standardised to captures per net night to ensure comparability. A net night was defined for fyke nets as a single pair of fyke nets set and for mesh nets, as 50m length set. CPUE was then standardised for net hour, assuming 12 hours for fykes and 6 hours for mesh nets. Where raw CPUE data was unavailable, CPUE was calculated using the number of platypuses caught. CPUE calculations for major catchments across Greater Melbourne were provided by Melbourne Water (Coleman Pers. Comm.), using the most recent fyke netting data (2013–2024, 162 sites) from the Melbourne Urban Platypus Program (Melbourne Water ( 2020 ); Coleman et al. ( 2022 )). Other unpublished data was sourced from one PhD thesis (Brunt, 2023 ), and data from the Platypus Conservation Initiative (Bino Pers. Comm.). A study conducted on Dingo Creek was excluded from the review of platypus CPUE due to recent droughts and fires, suggested to significantly reduce platypus densities (Bino et al. 2021 ). We applied a Generalized Linear Model (GLM) using the Tweedie distribution with a log link function, suitable for modelling continuous data. The dependent variable considered in the model was CPUE, and the explanatory variables included latitude, net type (fyke\net), and rural\urban classification. Results Trapping and tracking Over six nights of trapping (14 fyke pairs and four mesh nets deployments), six platypuses were captured (Table 1 ). All platypuses were adults, four males and two females. Of the four adult males, three were captured using fyke nets and one was captured using mesh nets. Both adult females were captured using mesh nets in deep pools (Fig. 2 a). CPUE across all six trapping nights was 0.36, 0.25 for all 14 fyke pairs, and 0.5 for the four mesh deployments. In Gold Creek no platypuses were captured, while the overall CPUE in upstream Moggill Creek was 0.38 (0.5 fyke and 0.25 mesh) and the overall CPUE in downstream Moggill Creek was 0.44 (0.13 fyke and 0.75 mesh; Appendix 2). Table 1 The sex, age, weight (kg), total body length, TVI, tracking days, detections, and range (metres) of Platypuses trapped and tagged in Moggill Creek and Gold Creek, QLD. ID Sex Age Weight [kg] Total body length [mm] Tail volume index [TVI] Duration tracked [days] Number of detections Range [m] F1 F Adult 1.23 468 3 24 20 1,480 F2 F Adult 0.94 421 3 22 18 625 M1 M Adult 1.45 525 2 25 16 2,420 M2 M Adult 1.63 525 1 22 11 2,140 M3 M Adult 1.53 512 3 19 10 1,400 M4 M Adult 1.88 545 2 20 15 2,240 The mean weight of platypuses varied between females (1.08 kg ± 0.2 SD), and males (1.62 kg ± 0.18 SD). The mean total body length of platypuses also varied between females (444.5 mm ± 33.23 SD) and males (526.75 mm ± 13.6 SD). Both females had a TVI of 3 and male TVI scores ranged between 1 and 3, indicating overall good fat storages (Table 1 ). Radio tags remained attached to platypuses for the entirety of the tracking durations (Table 1 ). Platypuses were detected within the rural upstream Moggill Creek (F1, M2, M3) and urban downstream Moggill Creek (F2, M1, M4) sites during the 25 days of tracking (Fig. 2 b). The number of detections varied between platypuses (Table 1 ), and between females (19.0 average ± 1.4 SD) and males (13.0 ± 2.9 SD). The mean number of detections in the rural upstream Moggill Creek was 13.6 ± 5.5 SD and the mean number of detections in the urban downstream Moggill Creek was 16.3 ± 1.5 SD. Home range varied between individual platypuses (Table 1 ). The mean home range length for female platypuses was 1052.5 m ± 604.6 SD and mean home range for male platypuses was 2050.0 m ± 448.5 SD. Mean platypus home range length in the urban downstream Moggill Creek was 1761.6 m ± 988.5 and mean platypus home range length in the rural upstream Moggill Creek was 1673.3 m ± 406.1 SD. Environmental DNA From the 14 eDNA sites sampled (Fig. 1 ), 13 were positive for platypus DNA. Twenty-five out of 28 samples taken detected presence of platypus DNA. Both samples taken at the most downstream site on Moggill Creek were negative for platypus DNA. PERMANOVA model indicated that environmental predictors contributed significantly to macroinvertebrate community composition (Table 2 ). Creek had the largest effect (Adjusted R 2 = 0.33, P < 0.05), followed by elevation (Adjusted R 2 = 0.16, P < 0.05), area of recreation (Adjusted R 2 = 0.14, P < 0.05), proportion of bare ground (Adjusted R 2 = 0.12, P < 0.05), area of woody vegetation (Adjusted R 2 = 0.12, P < 0.05), area of urban residential (Adjusted R 2 = 0.12, P < 0.05), area of rural residential (Adjusted R 2 = 0.12, P < 0.05) and area of conserved habitat (Adjusted R 2 = 0.10, P < 0.05). Table 2 PERMANOVA results for macroinvertebrate prey species variance across sites Predictor Adjusted_R2 F P Creek 0.321 5.431 0.001 Elevation 0.165 4.757 0.001 Recreation 0.143 3.989 0.005 Band1 0.125 3.415 0.013 Woody 0.124 3.384 0.007 Rural 0.122 3.348 0.004 Urban 0.121 3.294 0.008 Conserved 0.103 2.751 0.020 NMDS analysis indicated significant differences in the community composition of macroinvertebrate orders between upstream rural and downstream urban sites on Moggill Creek (Fig. 3 a). Elevation was the strongest predictor of community composition (R 2 = 0.42, P < 0.05), followed by area of urban residential (R 2 = 0.36, P < 0.05), area of rural residential (R 2 = 0.37, P < 0.05), area of recreation (R 2 = 0.41, P < 0.05), area of Band1 vegetation (R 2 = 0.34, P < 0.05) and area of woody vegetation (R 2 = 0.23, P < 0.05) also had significant influence on community structure. Conserved areas showed a weaker, non-significant influence on macroinvertebrate community structure (R 2 = 0.21, P = 0.09). Differences in the community composition were driven by key orders (Fig. 3 b). The SIMPER analysis demonstrated the contribution of macroinvertebrate orders to the dissimilarity between creeks (Appendix 3). The order with the largest average contribution (AC) to variance between Gold Creek and downstream Moggill Creek was Amphipoda (crustaceans) (AC = 0.03, P = < 0.05), followed by Spongilida (freshwater sponges) (AC = 0.08, P = < 0.05). Together these two orders contributed to 14.2% of the cumulative differences between the sites. The order with the largest average contribution to variance between Gold Creek and upstream Moggill Creek was Diplostraca (water fleas) (AC = 0.04, P < 0.001), followed by Lumbriculida (worms) (AC = 0.03, P = 0.025), with these two orders contributing to 14.7% of the cumulative differences between the sites. The order with the largest average contribution to variance between upstream Moggill Creek and downstream Moggill Creek was Amphipoda (crustaceans) (AC = 0.05, P < 0.001), followed by Diplostraca (water fleas) (AC = 0.04, P < 0.001). Together these two orders contributed to 17.2% of the cumulative differences. The number of potential prey orders detected across all sites varied from 5 to 15 (10.3 ± 2.8 SD) but no significant associations were found between evaluated environmental predictors and the number of prey orders across sites (Table 3 , Appendix 4). SIGNAL score varied considerably more across sites, ranging from 4 to 56 (23.1 ± 15.8 SD) and was correlated with prey orders (Spearman r = 0.79, P < 0.001). Model averaging identified SIGNAL score was negatively associated with elevation (P = 0.001), Band 1 vegetation cover (P = 0.003), and Woody vegetation cover (P = 0.003), while positively associated with recreational area (P = 0.026) (Table 4 , Appendix 5). Multivariate GLM analysis did not identify any significant association between assessed predictors and the prevalence of any particular prey order (Appendix 6). Table 3 Model averaging results for environmental predictor influence on number of macroinvertebrate prey orders Predictor Estimate Std Error Z P Intercept -1.478 10.645 10.770 0.137 Band1 0.108 0.181 0.183 0.588 Woody 0.009 0.006 0.006 1.603 Conserved -0.002 0.001 0.001 1.616 Urban 0.004 0.003 0.003 1.556 Rural 0.001 0.002 0.002 0.332 Elevation 0.004 0.002 0.002 1.545 Table 4 Conditional averaged model coefficients for predicting SIGNAL based on environmental factors Variable Estimate Std Error Z P Intercept 2.378 2.146 1.095 0.273 Band1 -0.035 0.011 2.991 0.003 Recreation 0.002 0.001 2.234 0.026 Woody -0.001 0.000 3.014 0.003 Conserved 0.000 0.000 1.963 0.050 Elevation -0.001 0.000 3.242 0.001 Urban -0.000 0.000 1.356 0.175 Range and density in urban and rural environments Platypus home range sizes from six studies across the species' distribution ranged from 330 m to 7300 m (2,009 m ± 1,698 SD) (Appendix 1). We found significant associations between range sizes and several predictor variables (Table 5 ). The estimated baseline home range for a female in a rural habitat at Sydney's latitude (34°S) with zero tracking duration was 570 m (95% CI: 8 to 51,303, P < 0.001). Tracking duration exerted a small but significant positive effect on home range size, with each additional day of tracking associated with a 1% increase in range (95% CI: 1.00 to 1.01, P = 0.028, Fig. 4 a). Habitat type emerged as a strong predictor. Home ranges in urban environments were approximately 2.36 times larger than in rural habitats (95% CI: 1.58 to 3.63, P < 0.001, Fig. 4 b). Sex also significantly influenced home range, with males maintaining ranges 1.63 times larger than females (95% CI: 1.19 to 2.23, P = 0.002, Fig. 4 c). Latitude showed a negative effect on home range size, reducing it by a factor of 0.95 per degree increase (95% CI: 0.89 to 1.01, P = 0.101, Fig. 4 d), though this relationship did not reach statistical significance. The model explained 57.5% of variance in home range size, with habitat type and sex representing the most influential predictors. Table 5 Generalized Linear Model (GLM) results for home range size (top) and CPUE (bottom). Considered predictors for home range size included tracking duration, habitat type (reference: non-urban), latitude, and sex (reference: female). Considered predictors for CPUE included habitat type (reference: non-urban), latitude, and net type (reference: fyke). Home Range Predictors Estimates CI p (Intercept) 99.77 10.86–975.89 < 0.001 Lat 0.95 0.89–1.01 0.101 Sex [M] 1.63 1.19–2.23 0.002 Duration 1.01 1.00–1.01 0.028 Habitat [Urban] 2.36 1.58–3.63 < 0.001 Observations 56 R 2 Nagelkerke 0.575 CPUE Predictors Estimates CI p (Intercept) 0.15 0.02–1.14 0.054 Habitat [U] 0.56 0.24–1.29 0.179 net type [Fyke] 0.27 0.16–0.47 < 0.001 Latitude 0.99 0.94–1.05 0.802 Observations 63 R 2 Nagelkerke 0.511 Analysis of CPUE revealed that net type was a highly significant predictor, with fyke nets capturing platypuses at approximately 73% lower rates than mesh nets (Estimate = 0.27, 95% CI: 0.16 to 0.47, P < 0.001, Fig. 5 c). We observed a 44% reduction in CPUE within urban environments compared to rural habitats (Estimate = 0.56, 95% CI: 0.24 to 1.29, P = 0.179, Fig. 5 a), though this difference did not reach statistical significance. This result warrants cautious interpretation given the substantial sample size imbalance between rural (n = 52) and urban (n = 11) sites, which likely constrained statistical power to detect habitat effects. Latitude exerted negligible influence on CPUE (Estimate = 0.99, 95% CI: 0.94 to 1.05, P = 0.802, Fig. 5 b). The model explained 51.1% of variance in CPUE, indicating that net type accounts for a major proportion of variation in capture rates across study sites. Discussion This study provides critical insight into platypus spatial ecology and capture rates along an urban gradient in southeast Queensland. As the first study to employ radio tracking of platypuses in Queensland, our research extends the spatial range of current knowledge on the species' ecology in a region where data has been historically limited, yet urbanisation pressures continue to intensify. Urban driven changes to natural resources essential for platypus survival appear to influence their distribution and habitat use across multiple scales. We find that platypuses in urban streams exhibit substantially larger home ranges compared to rural areas, with cross study analysis demonstrating home ranges in urban environments approximately 2.36 times larger than in rural habitats. Results from environmental DNA surveys reveal shifts in platypus prey communities alongside reductions in woody riparian vegetation as waterways transition from rural to urban settings. Changes in macroinvertebrate SIGNAL scores across our study gradient indicate factors influencing water quality and consequently the persistence of sensitive macroinvertebrate species. The significant positive correlation between SIGNAL scores and elevation indicates that sample sites at higher elevations, characteristic of rural environments, maintain higher water quality. These insights, in conjunction with changes in macroinvertebrate richness, suggest that underlying chemical and physical factors influence water quality and consequently platypus food sources (Azrina et al. 2006 ), with cascading effects on platypus spatial requirements and population viability. Platypus distribution extends across Australia's east coast (Bino et al. 2019 ), yet other than Greater Melbourne where substantial research has been conducted (Ahrens et al. 2025 ; Coleman et al. 2022 ; Furlan et al. 2013 ; Lugg et al. 2018 ; Martin et al. 2014 ; Serena & Pettigrove 2005 ; Serena et al. 1998 ; Serena & Williams 2012 ; Serena & Williams 2021 ; Serena et al. 2014 ), relatively few studies have focused on platypus ecology within urban environments. This represents a substantial knowledge gap regarding habitat use and responses to urban impacts outside Victoria. Past research has documented platypus presence in disturbed catchments of Brisbane, Queensland (Brunt et al. 2018 ; Brunt et al. 2021 ; Brunt 2023 ), the Eden Region, New South Wales (Lunney et al. 1998 ), and Richmond catchment, New South Wales (Rohweder & Baverstock 2014 ). Current knowledge on platypuses in the Brisbane area remains sparse (Brunt et al. 2018 ). Prior to the first eDNA survey conducted in 2016 by the Wildlife Preservation Society of Queensland, there was a recorded 20 year gap in platypus monitoring in southeast Queensland (Brunt 2023 ). This substantial monitoring gap subjected platypuses in southeast Queensland to unknown rates of population decline from unmanaged threats (Brunt et al. 2021 ). A study on platypus distribution in the Greater Brisbane region between 1990 and 2019 demonstrated clear evidence of this decline (Brunt et al. 2021 ), with comparison of historical observation data and current eDNA sampling revealing possible local extinction of platypuses in five Brisbane waterways, raising urgent concerns regarding current population status of platypuses in Queensland. Our cross study analysis revealed that home ranges in urban environments were approximately 2.36 times larger than those in rural habitats, with the model explaining 57.5% of variance in home range size. This finding aligns with theoretical predictions that resource scarcity drives spatial expansion in foraging mammals (Grant 2004 ). In resource limited environments, platypuses must forage over greater distances, increasing spatial separation between key resources such as burrows and foraging grounds (Crane et al. 2022). The need to cover larger areas, combined with fewer shelter options, drives the observed expansion in home range as individuals adapt to meet survival requirements (Casula et al. 2019 ; Wolff 1985 ). Urban waterways typically experience degraded habitat quality through mechanisms consistent with urban stream syndrome, including altered flow regimes, increased pollutant loads, channelisation and loss of riparian vegetation (Walsh et al. 2005 ; Vietz et al. 2016 ). These environmental changes reduce prey availability and shelter sites, forcing platypuses to expand their foraging territories to acquire sufficient resources. Our findings align with observations from Melbourne's urban streams, where platypuses similarly exhibit expanded spatial requirements in response to habitat degradation (Serena et al. 1998 ; Martin et al. 2014 ). In this study, tracking undertaken from July to August observed an average home range of 1,718 m ± 678 SD, based on observations from four male and two female adult platypuses. Our observed average home range falls within the reported extent of other studies when accounting for seasonal and sex specific variation. Boulton et al. (2022) conducted tracking from March to June, observing a smaller average home range of 919 m ± 373 SD from three female adult platypuses in Melbourne's urban streams. Serena et al. ( 1998 ) conducted tracking from June to October, observing a larger home range of 3,725 m ± 865 SD from three male and one female adult platypuses in Melbourne. Due to the seasonality of platypus home ranges associated with breeding (Hawke et al. 2021b ; Griffiths et al. 2014 ), radiotracking studies conducted outside of the breeding season with higher proportions of females would be expected to yield smaller average home ranges. Male platypuses exhibit increased mobility during the breeding season from August to October, associated with mate searching behaviour and territorial competition (Griffiths et al. 2014 ), while females show expanded ranges during lactation from November to February due to heightened energetic demands (Serena 1994 ). The higher proportion of females in Boulton et al. (2022)'s study, along with tracking duration occurring before the breeding season, provides reasoning for the smaller average home range compared with both Serena et al. ( 1998 ) and this study. This comparison demonstrates that the average home range observed in our Queensland study falls within expected ranges when accounting for seasonality and sexual selection patterns established in Victorian populations, reinforcing that our findings are consistent with established ecological patterns across the species' distribution. Our synthesis of rural radiotracking studies (Crane et al. 2022; Gardner & Serena 1995 ; Gust & Handasyde 1995 ; Serena 1994 ) revealed an average platypus home range length of 1,489 m ± 1,288 SD, while urban radiotracking studies demonstrated an average of 2,121 m ± 1,304 SD. This difference supports the hypothesis that urban platypus home ranges expand as a result of platypuses travelling further distances to locate essential resources, likely reflecting resource degradation in urban streams. The consistency of this pattern across geographically distant populations in both Victoria and Queensland suggests a generalised response to urbanisation rather than region specific effects. This trend raises urgent concerns regarding the capacity of urban streams to support viable platypus populations into the future, particularly as urbanisation pressures continue to intensify across Australia's east coast. The sensitivity of platypuses to urbanisation is indicated by observed declines and local extinctions in urban streams across Australia (Hawke et al. 2020 ; Brunt et al. 2021 ). Sufficient natural resources are essential to maintain stable populations, as they reduce competition intensity among individuals for survival (Pekkonen et al. 2013 ). Limiting resources intensify intra and interspecific competition, driving population declines through multiple mechanisms (Casula et al. 2019 ; Whisson et al. 2016 ). Increased competition strains individual health through elevated stress hormone levels, reduces reproductive success through decreased body condition and delayed sexual maturity, and ultimately decreases population density through elevated mortality and emigration rates (Wiegert & Owen 1971 ). Our capture rate analysis revealed that net type emerged as the dominant predictor of CPUE, with fyke nets capturing platypuses at approximately 72% lower rates than mesh nets. This substantial methodological effect has critical implications for comparative studies and long term monitoring programmes. The performance difference likely reflects behavioural responses to trap configuration and deployment location, with mesh nets potentially more effective in deeper pool habitats where both females in our study were captured. Fyke nets, traditionally deployed in shallower runs and riffles, may be less effective at capturing platypuses that preferentially use deep pool refugia, particularly in urban streams where pool habitats provide critical thermal refuge and predator avoidance opportunities (Grant & Temple Smith 1998; Serena & Williams 2012 ). This finding underscores the necessity for standardised trapping protocols when comparing platypus abundance across sites or evaluating temporal trends. Employing different net types may inadvertently introduced substantial bias into abundance estimates, potentially confounding habitat or temporal effects with methodological artefacts. Future monitoring efforts should explicitly account for net type effects or standardise methodology to enable robust comparisons across time and space. While we observed a 40% reduction in CPUE within urban environments compared to rural habitats, this difference did not reach statistical significance. This result warrants cautious interpretation given the substantial sample size imbalance between rural (n = 50) and urban (n = 13) sites in our cross-study dataset, which constrained statistical power to detect habitat effects. The non-significant trend suggests potential density reductions in urban areas, consistent with previous observations from Melbourne's urban catchments (Hawke et al. 2020 ; Coleman et al. 2022 ), yet our analysis cannot definitively establish whether urbanisation influences platypus abundance independent of capture methodology. The challenge of detecting abundance effects is compounded by the naturally low detection probability of platypuses even in optimal habitats (Lugg et al. 2018 ), requiring substantial sampling effort to achieve adequate statistical power. Platypus presence in an environment is driven primarily by instream habitat features that provide essential resources for foraging, shelter and reproduction (Brunt 2023 ). The relationship between woody riparian vegetation and platypus spatial ecology observed in our environmental surveys corroborates research highlighting platypus reliance on intact riparian vegetation for multiple functions, including bank stability for burrowing, overhead cover for predator avoidance, terrestrial invertebrate subsidies, and maintenance of stable instream temperatures (Brunt 2023 ; Hawke et al. 2020 ; Serena & Pettigrove 2005 ). The positive correlation between vegetation cover and macroinvertebrate SIGNAL scores in our study reinforces the mechanistic link between habitat quality and resource availability. Riparian vegetation influences stream conditions through multiple pathways, including temperature regulation via shading, nutrient cycling through leaf litter inputs, bank stabilisation that maintains pool habitat structure, and provision of woody debris that creates hydraulic complexity (Walsh et al. 2005 ). These interconnected processes generate habitat conditions that support diverse and abundant macroinvertebrate communities, which form the primary prey base for platypuses. Urban streams characteristically exhibit reduced riparian vegetation cover due to clearing for infrastructure, altered hydrology that precludes vegetation establishment, and ongoing disturbance from maintenance activities (Walsh et al. 2005 ; Coleman et al. 2022 ). This degradation cascade from vegetation loss through reduced prey availability to expanded platypus home ranges illustrates the cumulative impacts of urbanisation on aquatic ecosystems. Several limitations constrain interpretation of our findings and highlight priorities for future research. Macroinvertebrate eDNA surveys detected only presence or absence, providing no indication of abundance or biomass. As an important food resource for platypuses, understanding how benthic macroinvertebrate abundance and size distributions change throughout urban landscapes remains paramount to platypus conservation. Platypus energetic requirements depend not only on prey species richness but critically on prey density and size, with larger bodied invertebrates providing disproportionate energetic returns (McLachlan Troup et al. 2010). Future research should pair eDNA surveys with quantitative macroinvertebrate sampling using kick nets or Surber samplers (Turak et al. 2004 ) to provide comprehensive understanding of food availability across urban gradients. The limited sample size of captured platypuses (n = 6) in our tracking study, while sufficient for initial spatial analyses, restricts our capacity to detect subtle patterns in movement behaviour and habitat selection across the urban gradient. Larger sample sizes would enable more nuanced analyses of individual variation in space use strategies and how personality or condition dependent factors mediate responses to urbanisation. The cross study CPUE analysis, though incorporating 63 observations from published studies and monitoring programmes, suffered from substantial sample size imbalance between habitat types, limiting our capacity to detect habitat effects with adequate statistical power. This imbalance likely reflects historical sampling bias toward rural sites in platypus research and emphasises the critical need for expanded monitoring efforts in urban waterways (Coleman et al. 2022 ). Additionally, temporal variation in platypus detection rates associated with breeding seasonality introduces potential confounding effects when comparing studies conducted across different months (Hawke et al. 2021b ; Serena & Williams 2012 ), though we attempted to mitigate this by restricting analyses to adult individuals and accounting for tracking duration. The substantial energetic demands of reproduction, particularly for lactating females, alter activity patterns and habitat use in ways that influence capture probability (Griffiths et al. 2014 ). Future cross study syntheses should explicitly model seasonal effects on detection probability to improve comparability across studies conducted at different times of year. The substantial reliance platypuses have on benthic macroinvertebrates for diet and consolidated banks with overhanging riparian vegetation for burrowing and shelter demonstrates the species' vulnerability to habitat degradation. Our findings indicate that maintaining and restoring riparian vegetation corridors, protecting water quality through improved stormwater management, and preserving pool habitats represent critical conservation priorities for platypus populations persisting in urbanising landscapes. The expanded home ranges observed in urban environments suggest that individual platypuses require access to longer stream reaches to meet resource requirements, highlighting the importance of maintaining longitudinal connectivity in urban waterways. Barriers such as weirs, culverts and gross pollutant traps that fragment stream networks may disproportionately impact urban platypus populations already operating at expanded spatial scales (Furlan et al. 2013 ; Kolomyjec et al. 2013 ). Future conservation planning should prioritise protecting sufficient habitat extent to accommodate these enlarged spatial requirements while simultaneously addressing the underlying mechanisms of resource degradation that necessitate such spatial expansion. The Melbourne experience demonstrates that long term, systematic monitoring combined with adaptive management can successfully support platypus populations in urban landscapes (Coleman et al. 2022 ). The Melbourne Urban Platypus Program, operating since the 1990s, has generated invaluable insights into urban platypus ecology while simultaneously informing waterway management decisions across the region (Melbourne Water 2020 ). Similar programmes in southeast Queensland could provide the baseline data necessary to detect population trends, evaluate management interventions, and guide evidence based conservation policy. Our study represents an important first step in developing this knowledge base for Queensland populations, yet sustained effort over multiple years will be required to adequately characterise population dynamics and responses to management actions. Urban waterway restoration efforts should focus on recreating the structural and functional characteristics of intact riparian ecosystems rather than simply revegetating stream banks. This includes reestablishing appropriate vegetation density and diversity to provide shade and organic matter inputs, creating hydraulic complexity through installation of large woody debris, protecting and enhancing pool habitats that provide critical refuge during extreme conditions, and managing flow regimes to maintain natural patterns of inundation and drying (Walsh et al. 2005 ; Vietz et al. 2016 ). The challenge lies in implementing these restoration activities within the constraints of urban landscapes where competing demands for flood control, public access and infrastructure maintenance must be balanced against ecological objectives. Innovative solutions such as water sensitive urban design that manages stormwater at source, setback requirements that protect riparian zones from development impacts, and community engagement programmes that build support for waterway protection offer promising pathways forward (Coleman et al. 2022 ). Climate change represents an additional stressor that will interact with urbanisation to further challenge platypus populations. Projected changes in rainfall patterns, with more intense rainfall events interspersed with prolonged dry periods, will alter flow regimes and potentially exacerbate urban stream syndrome effects (Bino et al. 2020 ). Increased water temperatures may exceed thermal tolerance thresholds for platypuses, particularly in urban streams where reduced riparian shading and altered flow regimes already elevate baseline temperatures (Klamt et al. 2011 ). Managing for climate resilience will require maintaining and restoring the ecological processes that buffer against environmental variability, including intact riparian vegetation that moderates temperature extremes, deep pool habitats that provide thermal refuge, and sufficient habitat extent to enable behavioural thermoregulation through movement across thermal gradients. Our research demonstrates that platypuses persist in urban environments of southeast Queensland, yet exhibit clear signatures of environmental stress through expanded spatial requirements. The consistency of these patterns with observations from Melbourne's urban streams suggests generalisable responses to urbanisation that transcend regional differences. However, persistence should not be equated with thriving populations. The expanded home ranges, potential density reductions, and degraded prey communities we observed indicate that urban platypus populations operate under resource limitation that may compromise long term viability. Effective conservation will require sustained commitment to protecting and restoring urban waterway ecosystems, informed by continued research that elucidates mechanisms linking habitat quality to population outcomes. As Australia's human population continues to concentrate in coastal urban centres, the challenge of maintaining viable platypus populations in modified landscapes will intensify, demanding innovative management approaches that integrate ecological understanding with urban planning and design. Declarations Author Contribution G.B. secured funding, conceptualised the study design, and provided primary supervision. G.B. and A.Y. undertook all field work. All authors conducted data analysis and wrote the manuscript. All authors reviewed the manuscript. Acknowledgements This project was funded by the Brisbane City Council. We thank the Moggill Creek Catchment Group for their enthusiasm and support. We thank Rhys Coleman from Melbourne Water for providing platypus CPUE values across Greater Melbourne. We would like to extend my thanks to all the volunteers who assisted in tracking and trapping platypuses and Dr Tamielle Brunt for her expertise, guidance and support. Data availability statement All data supporting the findings of this study are available within the paper and its Supplementary Information. Conflicts of interest The authors declare no conflicts of interest. Declaration of Funding This research was funded by Brisbane City Council Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work the authors used Claude AI in order to improve grammar and clarity of the writing. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. References Aarts, G., MacKenzie, M., McConnell, B., Fedak, M. & Matthiopoulos, J. (2008). Estimating space-use and habitat preference from wildlife telemetry data. Ecography 31, 140-160. doi: 10.1111/j.2007.0906-7590.05236.x. Ahrens, C. W., Griffiths, J., Danger, A., Coleman, R., van Rooyen, A., Furlan, E., & Weeks, A. R. (2025). Genetic diversity and structure lag the effects of contemporary environmental changes in a platypus meta-population. 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1","display":"","copyAsset":false,"role":"figure","size":136734,"visible":true,"origin":"","legend":"\u003cp\u003eThe location of environmental DNA survey sites and land use types on rural upstream Moggill Creek (Moggill Creek US), urban downstream Moggill Creek (Moggill Creek DS), and Gold Creek.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8168416/v1/b514e9fddb6a6976921b804e.jpg"},{"id":97451441,"identity":"9ef47540-2b3b-4a99-b706-cc90cf9e44f5","added_by":"auto","created_at":"2025-12-04 13:43:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150976,"visible":true,"origin":"","legend":"\u003cp\u003ea. The location of trapping sites detailing the type of net used and platypus captures on rural upstream Moggill Creek, urban downstream Moggill Creek, and Gold Creek. Triangles represent fyke nets and squares represent mesh nets. Green points represent platypus capture, and red points represent no platypus capture.\u003c/p\u003e\n\u003cp\u003eb. The detected locations for each of the six radio-tracked platypuses (F – female, M – male)\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8168416/v1/c1fcf42865ee7dfca82a7e18.jpg"},{"id":97668053,"identity":"7f0af139-58f1-41dd-87e8-8105eac6b3ed","added_by":"auto","created_at":"2025-12-08 09:24:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57198,"visible":true,"origin":"","legend":"\u003cp\u003eNon-metric Multidimensional Scaling (NMDS) plots of macroinvertebrate community composition across creek sites. Points represent sampling sites, coloured by creek, with 68% confidence ellipses showing clustering by creek with (a) environmental vectors indicating significant predictors: Band 1 vegetation cover, woody vegetation, elevation, urban residential, rural residential, recreational, and conserved land areas. Arrow length and direction reflect the strength and influence of each environmental predictor on macroinvertebrate community composition, with significant variables marked by asterisks (*). (b) NMDS ordination shows macroinvertebrate orders as vectors, with arrow length and direction representing the influence of each order on community structure.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8168416/v1/6c9294f33f43749e8b63bcd1.jpg"},{"id":97668989,"identity":"b7ec581c-fac0-4f91-bb25-195573a06f91","added_by":"auto","created_at":"2025-12-08 09:26:54","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43429,"visible":true,"origin":"","legend":"\u003cp\u003ea-d. Predicted effects of the model predictors on home range size (meters) from reviewed literature, based on the fitted Generalized Linear Model (GLM) with a Gamma distribution and log link. The plot shows the relationship between the predicted home range size and each predictor variable ((a) Tracking duration, (b) Habitat type, (c) Sex, and (d) Latitude) while holding other variables constant. Shaded areas represent 95% confidence intervals around the predicted values.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8168416/v1/6c47620d799770616415a194.jpg"},{"id":97451440,"identity":"da7a1acb-54a0-4e4d-b498-0ab7380e94b6","added_by":"auto","created_at":"2025-12-04 13:43:38","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":31868,"visible":true,"origin":"","legend":"\u003cp\u003ea-c. Predicted effects of the model predictors on CPUE from reviewed literature, based on the fitted Generalized Linear Model (GLM) with a Gamma distribution and log link. The plot shows the relationship between CPUE and each predictor variable ((a) Habitat type, (b) Latitude and (c) Net type) while holding other variables constant. Shaded areas represent 95% confidence intervals around the predicted values.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8168416/v1/943c73b2058b2be558d01c92.jpg"},{"id":100642941,"identity":"dff03f9f-0d9e-477a-bac3-9ae99bc11c94","added_by":"auto","created_at":"2026-01-20 03:46:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1404884,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8168416/v1/c68c2612-2f93-4764-b565-f751eef08f79.pdf"},{"id":97451437,"identity":"237bc675-4a75-4df3-8ae6-9cd46eced7b3","added_by":"auto","created_at":"2025-12-04 13:43:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":313505,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-8168416/v1/c9fa9303fef8ea45bff13e5e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Resource Limitation Shapes Platypus Spatial Ecology in Urban Streams","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe human population is increasing rapidly, expected to reach 8.5\u0026nbsp;billion by 2030 (United Nations 2022). Rising urbanisation rates have resulted in expansion of urban areas to accommodate growing populations (G\u0026uuml;neralp et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This has come at the expense of natural environments, impacting remaining green-spaces and the species that inhabit them (Faulkner \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In urban environments, riparian zones and adjacent waterways are often encroached by infrastructure as they provide a wide array of ecosystem services (Paul \u0026amp; Meyer \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Consequently, these environments undergo structural simplification and further alteration, predisposing them to an array of environmental pressures (McKinney \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eObserved effects that urbanisation has on waterways includes rapid and extreme responses to rainfall, alterations to channel morphology, higher concentrations of effluents and anthropogenic pollution, increased runoff from infrastructure, increase in dominance of tolerant species, and a reduction of biotic richness (Beavan et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Paul \u0026amp; Meyer \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Sabha et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This continual degradation of urban waterways has been referred to as \u0026lsquo;urban stream syndrome\u0026rsquo;, the almost universal response of waterways to the urbanisation of surrounding catchments (Vietz et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The complexity in which these pressures interact with the environment deems their consideration and understanding a critical aspect to conserving freshwater ecosystems (Palmer \u0026amp; Ruhi \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Australia, freshwater ecosystems are a precious resource for humans, flora and fauna. On a world scale, the aquatic biodiversity of Australia is highly significant and is particularly renowned for supporting a high degree of endemic species (Dunn \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). With freshwater ecosystems experiencing far greater declines in biodiversity than terrestrial ecosystems, it is paramount that the requirements of species inhabiting waterways are investigated and understood for the implementation of effective conservation management (Dudgeon et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCurrent methods of determining species presence in waterways range from observational surveys to DNA metabarcoding. DNA that can be extracted from environmental samples is termed environmental DNA (eDNA) and is used to monitor populations that both occupy the water and the environment surrounding (Rees et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This method of determining species presence involves the detection of mitochondrial DNA and as such allows the detection of target species without trapping or physically observing them (Hebert \u0026amp; Gregory \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This can be especially beneficial when targeting a species that is more difficult to locate or catch (Rees et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnlike eDNA sampling, live trapping of species allows for additional information to be collected from individuals along with measures of species richness, abundance, and composition (De Bondi et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The close analysis of individual subjects granted by live trapping, provides the means to investigate population dynamics dependent on individuals and their characteristics (Croose et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Live trapping provides the opportunity for subsequent monitoring of individuals to further understand their requirements (Nichols \u0026amp; Pollock \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). Radio-tagging and tracking individuals means monitoring fine-scale movement of individuals through their environment is attainable, providing crucial data for evidence-based conservation initiatives (Paxton et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Observing how wildlife interact with their surrounding environment can be a key indicator of where threats and environmental pressures may be present (Cooke \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Coupling tracking and species presence data with remotely sensed data allows for investigation into how environmental variation impacts species on a larger scale (Neumann et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These methods of monitoring species and the environment are crucial aspects of evidence-based conservation and allow for effective conservation initiatives to be implemented to conserve iconic species of interest (Aarts et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOne of Australia\u0026rsquo;s most iconic freshwater species, which inhabits urban waterways, is the platypus (\u003cem\u003eOrnithorhynchus anatinus\u003c/em\u003e). As the only remaining living representative of the Ornithorhynchidae family, platypuses are one of the most phylogenetically and evolutionary distinct species in the world (Bino et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Grant \u0026amp; Temple-Smith \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Platypuses represent one of five remaining species of egg-laying mammals, belonging to the order Monotremata (Bino et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Dependent on water for feeding, the distribution of platypuses is restricted to permanent water sources in eastern Australia. Platypuses will inhabit ephemeral streams in proximity to permanent pools, acting as refuges during periods of drought (Grant \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The irregular mosaic of Australia\u0026rsquo;s water bodies results in the discontinuity of platypus distributions across river catchments (Grant \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The distribution of platypuses encompasses most of Eastern Australia, including King Island and Tasmania, typically abundant to the east of the Great Dividing Range (Grant \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Additionally, platypuses can be found on Kangaroo Island (South Australia) due to the introduction of a small population (Bino et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOccurring naturally in freshwater lakes, rivers, and streams (Gongora et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), platypuses spend most of their life in the water (Grant \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). With home ranges varying between 0.5 and 15 km of linear river habitats, males tend to have larger home ranges than females, particularly as juveniles and in the lead up to the breeding season (Bino et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Water depths between 1\u0026ndash;5 m allow platypuses to forage efficiently while also avoiding predation (Bethge et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Bino et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Platypuses have foraging preference for coarser bottom substrate providing fixed habitats for the aquatic invertebrates on which they feed (Grant \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The burrows of platypuses are generally found in the banks of waterways that are consolidated by the roots of vegetation and have an undercut to protect burrow entrances (Serena et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Movement outside of water is usually limited for platypuses due to the risks of predation (Bino et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), overheating (Grant \u0026amp; Dawson \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) and associated excessive energy expenditure (Fish et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Platypuses have been observed using logs and other debris for shelter instead of burrows, indicating behavioural flexibility (Serena \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe primary aim of this project is to assess the prevalence and fine-scale habitat use of platypuses along a gradient of development in Moggill Creek, Queensland (QLD). Through eDNA surveys, live-trapping, radio-tracking, analyses of remotely sensed data, and review of current literature, we pursued identifying the environmental predictors influencing platypuses and other freshwater species along a gradient of development. We hypothesised that the gradient of urban development would be a key driver for the presence of riparian vegetation and preferred food sources of platypuses, consequently influencing the presence and home-ranges of platypuses within the study area. Beyond these specific aims, this study also seeks to engage with broader ecological theory regarding the effects of urbanisation on aquatic biota. By considering the tenets of \u0026lsquo;urban stream syndrome\u0026rsquo;, this study investigates their hypothesised effect on key environmental factors, such as food availability, vegetation cover and biotic richness (Vietz et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), influencing urban platypus population viability. eDNA surveys were conducted to assess distribution and food availability throughout the study site and live trapping of platypuses was undertaken to estimate abundance across the sites. This study is the first to radio-track platypuses in QLD, addressing major gaps in knowledge surrounding their ecology in this region and contributing to the current body of knowledge on the effects of urbanisation on aquatic species.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy Area\u003c/p\u003e\u003cp\u003eThis study was undertaken on the first 16 km of Moggill Creek from its convergence with the Brisbane River and the first 10 km of Gold Creek from its convergence with Moggill Creek (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Originating in Brisbane Forest Park, Moggill Creek flows roughly 15 km southeast before meeting with the Brisbane River. The main tributaries of Moggill Creek are Gap Creek, Wonga Creek, and Gold Creek, forming the Moggill Creek catchment. Moggill Creek and its associated tributaries flow through varying landscapes and are exposed to several methods of land use and environmental conditions (He et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The upper limits of the Moggill Creek catchment undergo a steep change in elevation as the creeks flow towards the Brisbane River. This results in high velocity runoff following rain events, causing major disruption to riparian vegetation and freshwater biodiversity (Vietz et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, as the catchment approaches the Brisbane River, land use types change from agricultural and rural to urbanised areas of high recreational activity (He et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The movement of these waterways through changing landscapes, suggest that there are varying environmental conditions throughout the Moggill Creek catchment impacting the ecological communities present (Pellissier et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For the purposes of this study, we analyse data for three creek sections, dividing Moggill Creek into upstream (rural) and downstream (urban) sections associated with changes in land use, as well as Gold Creek (rural), (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTrapping and Tagging\u003c/p\u003e\u003cp\u003eLive trapping of platypuses was conducted along Moggill Creek and Gold Creek for six consecutive nights from 29/07/2024\u0026ndash;4/08/2024. Five nights of trapping were undertaken on Moggill Creek sites and one on Gold Creek. At sites along Moggill Creek, either unweighted mesh nets (80 mm multifilament nets 25m x 2m), fyke nets (30-mm knotless 20-ply nylon, 1m x 5m wings, and 0.8m x 5m wings) or a combination of both net types were used. At the Gold Creek site only fyke nets were used (Hawke et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOn nights where both mesh and fyke nets were used, two pairs of fyke nets, facing opposite directions (upstream/downstream), were set at either end of a pool which had an unweighted mesh net set in it. If the pool at the trapping site was over 50m long, two 25m mesh nets were tied together, otherwise a single 25m x 2m mesh net was used. On the night where only fyke nets were used, four pairs of fyke nets were set at four different sites along the creek (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eNet set up commenced at 16:00 each day using a small 6-ft punt to set unweighted mesh nets along the pool, parallel to the bank. Fyke nets were set by hand in suitable shallow sections of the creek. Both fyke and mesh nets were anchored with either star pickets or tied to vegetation. The wings of fyke nets were additionally secured along the creek bed with rocks to eliminate gaps underneath the wing. Unweighted mesh nets were consistently monitored using a spotlight and any animal captures were removed immediately. Fyke nets were thoroughly checked every three hours (19:00, 22:00, 01:00, 04:00) and any animal caught was removed. Mesh nets remained set until 00:00 and fyke nets remained set until 04:00.\u003c/p\u003e\u003cp\u003eTrapping success rate was calculated using a Catch Per Unit Effort (CPUE) metric to represent the number of platypuses caught per net effort. A 50m unweighted mesh net was defined as one unit of net effort and a pair of two fyke nets was defined as one unit of net effort. CPUE was calculated for the overall study, mesh nets and fyke nets.\u003c/p\u003e\u003cp\u003eFollowing capture, platypuses were placed in pillowcases and moved to a quiet location until they were processed. For processing, platypuses were placed in a portable isolation chamber where isoflurane (Pharmachem 2\u0026ndash;5%) in oxygen (2-3L/min) was administered to anaesthetise them (Chinnadurai et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fiorello et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Body temperature, heart rate, and oxygen levels of platypuses was continuously monitored using a Darvall H100N (Bino et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The tail volume index (TVI) of platypuses were determined through physical examination of their tail fat content and given a score from 1\u0026ndash;5. Platypuses were weighed, sexed, measured (tail, body, bill), and aged based on the morphology of spurs (Williams et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Each platypus was scanned for the presence of a microchip, and if absent, a Passive Integrated Transponder (PIT) (Trovan) was inserted between the shoulder blades for subsequent identification (Grant \u0026amp; Whittington \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Additional samples were taken from each platypus (mouth-cheek swabs, tissue biopsy, bloods, fur sample) for related studies. Once processed, each platypus was taken off anaesthetic and placed in a quiet place to recover.\u003c/p\u003e\u003cp\u003eRadio Tracking\u003c/p\u003e\u003cp\u003eAll platypuses were fitted with radio transmitters (ATS F1540, battery life approximately 70 days). A small (2x2cm) patch of fur was shaved from the dorsal side of each platypus, anterior to the tail and fast-setting non-toxic superglue was used to secure the radio transmitter to the shaved area (Casper \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Platypuses were released back to their respective capture location once fully recovered. Lotek receivers and antennas were used to track radio tagged platypuses throughout the study site. Tracking was undertaken during the day to determine potential burrow sites and the monitor the movement of platypuses through altered and natural environments. Between the 30th of July 2024 and 30th of August 2024, a total of 25 days of tracking were conducted.\u003c/p\u003e\u003cp\u003eTracking was undertaken in two sessions, one in the rural upstream Moggill Creek site and the other in the urban downstream Moggill Creek site. The two sessions of day tracking were split into AM and PM sessions and each day the rural and urban sites would alternate between AM and PM surveys. AM tracking sessions were undertaken between 09:00 and 12:00 and PM tracking sessions were undertaken between 13:00 and 16:00. At the determined point of strongest signal for each frequency, coordinates were taken and stored with a GPS (Gamin GPSMap 65S) along with the date, time, and any notes on the location. Platypus locations were mapped using \u0026lsquo;sf\u0026rsquo; (Pebesma \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and \u0026lsquo;mapview\u0026rsquo; (Appelhans et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) packages using R statistical software (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eEnvironmental DNA survey\u003c/p\u003e\u003cp\u003eEnvironmental DNA (eDNA) surveys were undertaken along the 26 km study site at 2\u0026ndash;3 km intervals (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 14 sites, 10 along Moggill Creek and 4 along Gold Creek, were identified. At each site, two samples, two meters apart were collected using Wilderlab eDNA mini kits (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wilderlab.co.nz\u003c/span\u003e\u003cspan address=\"https://www.wilderlab.co.nz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For each sample 50 mL of water was drawn into a 60 mL sterile syringe from just below the water\u0026rsquo;s surface and then passed through a filter. This process was repeated until the filter became clogged or had passed 1 L of water. The filter was capped and injected with a DNA/RNA Shield preservative buffer. Each filter was then put back into its respective bag and sealed to avoid contamination. New sampling equipment was used for each sample and care was taken to avoid contamination between sites. Samples were sent to Wilderlab for multispecies sample sequencing and analysis using the basic freshwater eDNA package. Wilderlab extracted DNA from the samples, PCR-amplifying the extracts using fusion-tag mitochondrial and nuclear rRNA assays for detecting the primer sequences of invertebrates and vertebrates. Fusion tag primers used were Illumina TruSeq\u0026trade; sequencing primer bind site and Illumina P5 and P7 adapter sequences, respectively. Output sequences were de-multiplexed and filtered to produce a series of exact amplicon sequence variants (ASVs), which were then assigned to the lowest possible taxonomic rank (Wilkinson \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEnvironmental DNA analysis\u003c/p\u003e\u003cp\u003eWe filtered eDNA macroinvertebrate data to retain phyla: Annelida, Arthropoda, Cnidaria, Gastrotricha, Mollusca, Nemertea, Nematoda, and Platyhelminthes, known to contribute to the diet of platypuses (Hawke et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Data was then summarised at the order level and classified as presence or absence for each sample. The total number of orders was then summarised for each sample.\u003c/p\u003e\u003cp\u003eIn addition, a modified Stream Invertebrate Grade Number \u0026ndash; Average Level (SIGNAL) score was calculated at the family scale (Chessman \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The SIGNAL score is a metric used to evaluate the strength and importance of ecological indicators in assessing environmental health and ecosystem quality. It assigns scores to various macroinvertebrate taxa based on their sensitivity to pollution, with higher scores indicating greater sensitivity and, thus, better environmental conditions. Normally an abundance weighting is used to adjust the score, but given eDNA data provided presence/absence information, an equal abundance weighting was given to each detected family. We used the \u0026lsquo;biomonitoR\u0026rsquo; package in R (Laini et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to calculate the SIGNAL score. We then calculate a dissimilarity matrix based on the Bray-Curtis method using the \u0026lsquo;vegdist\u0026rsquo; function from the \u0026lsquo;vegan\u0026rsquo; package (Oksanen et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo examine if environmental predictors influenced macroinvertebrate community composition between creeks, we used the \u0026lsquo;adonis2\u0026rsquo; function from the \u0026lsquo;vegan\u0026rsquo; package (Oksanen et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to perform a Permutational Multivariate Analysis of Variance (PERMANOVA). The adjusted R\u003csup\u003e2\u003c/sup\u003e value, F-value and p-value were extracted from the results of each predictor to assess their contributions.\u003c/p\u003e\u003cp\u003eNon-metric Multidimensional Scaling (NMDS) was then conducted on the macroinvertebrate data using the \u0026lsquo;metaMDS\u0026rsquo; function from the \u0026lsquo;vegan\u0026rsquo; package (Oksanen et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with the jaccard distance metric and 1000 permutations, to explore patterns in the community structure. To assess correlations between environmental variables and NMDS axes, we used the \u0026lsquo;envfit\u0026rsquo; function, testing significance with tested 999 permutations. NMDS sample coordinates were extracted and converted into a data frame. Creek NMDS scores were plotted against the NMDS axes, along with 68% confidence ellipses and the environmental vectors from the envfit results. Additionally, the NMDS scores for macroinvertebrate orders were plotted with 68% confidence ellipses and vectors representing macroinvertebrate orders. 68% confidence ellipses were used to represent one standard deviation either side of the mean.\u003c/p\u003e\u003cp\u003eThe \u0026lsquo;simper\u0026rsquo; function from the \u0026lsquo;vegan\u0026rsquo; package (Oksanen et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) was used to perform a similarity percentage (SIMPER) analysis of the response data (community composition) and predictor data (creek), running 10,000 permutations to assess the significance of the results. Three specific comparisons (Gold x Moggill upstream, Gold x Moggill downstream, Moggill upstream x Moggill downstream) were defined to determine which invertebrate orders were driving the dissimilarity in community composition between creeks.\u003c/p\u003e\u003cp\u003eA Generalised Linear Model (GLM) was created by fitting a Poisson regression model to predict the effect of the environmental predictors on the number of invertebrate orders. The \u0026lsquo;dredge\u0026rsquo; function from the \u0026lsquo;MuMIn\u0026rsquo; package (Bartoń \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) was used to generate models of all possible predictor combinations. A model averaging approach was applied to the generated models to select those with an Akaike Information Criterion (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}\\)\u003c/span\u003e\u003c/span\u003eAIC) of less than 2 for averaging. Models with cumulative \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}\\)\u003c/span\u003e\u003c/span\u003eAIC weights of 0.95 or less were used to create a set of 95% confidence models. These models were averaged to generate the predicted number of invertebrate orders and their standard errors.\u003c/p\u003e\u003cp\u003eA fitted GLM containing all the predictor variables was created to visualise the relationship between SIGNAL and the predictors. Model selection was performed to generate all possible models and rank them based on their \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}\\)\u003c/span\u003e\u003c/span\u003eAIC value. A model averaging approach was applied to the generated models to select those with an \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}\\)\u003c/span\u003e\u003c/span\u003eAIC of less than 2 for averaging. The 95% confidence models were averaged further using the \u0026lsquo;model.avg\u0026rsquo; function to produce the final model. Using the \u0026lsquo;geom_point\u0026rsquo; function, the observed data was plotted against Elevation. A Gamma GLM of the effect of Elevation on predicted SIGNAL was added to the plot using the \u0026lsquo;geom_smooth\u0026rsquo; function. We used the mvabund package (Wang et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to model multiple response variables of GLMs. The response data was transformed into a multivariate format using the \u0026lsquo;mvabund\u0026rsquo; function. The most influential predictors were visualised in coefficient plots by using the \u0026lsquo;coefplot.manyglm\u0026rsquo; function. A final reduced statistical summary of the model, including the coefficients for the key predictors, was created to help visualise the effect of the predictors on invertebrate orders.\u003c/p\u003e\u003cp\u003eHabitat metrics\u003c/p\u003e\u003cp\u003eRemotely sensed data was collated across our study site to explore possible drivers of observed eDNA results. Elevation data was sourced from the Queensland LiDAR Data - Brisbane 2019 Project, collected through airborne laser scanning (ALS) using fixed-wing aircraft in 2019, and covers the greater Brisbane area. Processed data was used to generate a Digital Elevation Model (DEM) with a 1-meter grid. Data was accessed from the Elevation and Depth Foundation Spatial Data (ELVIS) portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://elevation.fsdf.org.au/\u003c/span\u003e\u003cspan address=\"https://elevation.fsdf.org.au/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Woody vegetation extent data from the 2021\u0026ndash;22 Statewide Landcover and Trees Study (SLATS) based Sentinel-2 satellite imagery was used to assess vegetation clearing and regrowth (Queensland Government 2024). We used the seasonal fractional cover which provided the proportion of bare, green and non-green cover, created from a time series of Sentinel-2 imagery at medium resolution of 10 m. Using an image from July 2024, we focused on Band 1 which provides the estimated fractions of bare ground cover (Joint Remote Sensing Research Program \u0026amp; Department of Environment and Science (2017\u0026ndash;2023) 2022). We collated land use data from the Catchment Scale Land Use of Australia \u0026ndash; Update December 2023 (CLUM) dataset, a national compilation of land use information developed by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) as part of the Australian Collaborative Land Use and Management Program (ACLUMP). This dataset integrates vector-based land use data, satellite imagery, and field-collected information, producing a 50-meter resolution raster showing the dominant land use type per area. Land use was classified according to the Australian Land Use and Management (ALUM) Classification Version 8, facilitating standardized analysis across regions and timeframes (2008\u0026ndash;2023). Using the CLUM 2023 dataset, land uses were grouped into categories reflecting key areas of interest: conservation (1.1.7 for preserved natural spaces), urban residential (5.4.1), rural residential (5.4.3), and recreational land use, which includes 5.5.2 (public services) and 5.5.3 (recreation and culture). For each site, the average land use area per category was calculated, representing the predominant land types and intensity of use per classification. For each of the four datasets, the total value within 1 km buffer surrounding each of the 14 eDNA survey sites was calculated using the \u0026lsquo;st_buffer\u0026rsquo; function in the \u0026lsquo;sf\u0026rsquo; package in R.\u003c/p\u003e\u003cp\u003eReview of platypus home ranges\u003c/p\u003e\u003cp\u003eA review of the literature on platypus radio tracking was conducted for comparative analyses with the findings of this study. Google Scholar, Environment Complete, and University of New South Wales Library databases were searched using the terms: \u0026lsquo;platypus\u0026rsquo; OR \u0026lsquo;\u003cem\u003eOrnithorhynchus anatinus\u003c/em\u003e\u0026rsquo;, \u0026lsquo;radio-tracking\u0026rsquo; OR \u0026lsquo;radiotelemetry\u0026rsquo;, \u0026lsquo;home range\u0026rsquo; OR \u0026lsquo;movement\u0026rsquo; OR \u0026lsquo;activity\u0026rsquo;. These searches yielded six published articles (Boulton et al. 2022; Crane et al. 2022, Gardner \u0026amp; Serena \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Gust \u0026amp; Handasyde \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Serena \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Serena et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1998\u003c/span\u003e,). Data on locations, habitat type, overall home ranges, tracking duration, age class, and sex were recorded for each study (Appendix 1).\u003c/p\u003e\u003cp\u003eLand use types for each study were obtained from the Land Use of Australia Web Map (Australian Bureau of Agricultural and Resource Economics and Sciences \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and used to determine habitat type. Studies in urban intensive use areas were classified as urban-dominated and studies in nature-conserved, rural residential, grazing native vegetation use areas, were classified as rural-dominated. Four studies were classified as undertaken in rural-dominated environments (Crane et al. 2022; Gardner \u0026amp; Serena \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Gust \u0026amp; Handasyde \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Serena \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) and two in urban-dominated environments (Boulton et al. 2022; Serena et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). As juvenile platypuses can move significantly large distances during dispersal periods (Bino et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), only data from adult platypuses was used for comparison with the findings of this study. Tracking data from Crane et al. (2022) and Boulten et al. (2022) was imported into Google Earth Pro software (version 7.3.6) to calculate the extent of tracked home ranges for adult platypuses. We performed a Generalized Linear Model (GLM) analysis using the Gamma distribution with a log link function, appropriate for continuous, positively skewed data. The dependent variable considered in the model was home range size, and the explanatory variables included latitude, sex, duration of tracking (days), and rural\\urban classification.\u003c/p\u003e\u003cp\u003eReview of platypus density\u003c/p\u003e\u003cp\u003eA literature review was undertaken to collate information relating to platypus abundance, measured as catch-per-unit-effort (CPUE) from previous studies. The same databases (Google Scholar, Environment Complete, and University of New South Wales Library) were used with search terms such as: \u0026lsquo;platypus\u0026rsquo; OR \u0026lsquo;\u003cem\u003eOrnithorhynchus anatinus\u003c/em\u003e\u0026rsquo;, \u0026lsquo;density\u0026rsquo; OR \u0026lsquo;abundance\u0026rsquo; OR \u0026lsquo;capture\u0026rsquo; OR \u0026lsquo;CPUE\u0026rsquo;. Data from relevant studies were extracted, including study locations, sampling effort, habitat type, and reported CPUE values (Appendix 2). Where raw CPUE data was available, it was standardised to captures per net night to ensure comparability. A net night was defined for fyke nets as a single pair of fyke nets set and for mesh nets, as 50m length set. CPUE was then standardised for net hour, assuming 12 hours for fykes and 6 hours for mesh nets. Where raw CPUE data was unavailable, CPUE was calculated using the number of platypuses caught. CPUE calculations for major catchments across Greater Melbourne were provided by Melbourne Water (Coleman Pers. Comm.), using the most recent fyke netting data (2013\u0026ndash;2024, 162 sites) from the Melbourne Urban Platypus Program (Melbourne Water (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Coleman et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)). Other unpublished data was sourced from one PhD thesis (Brunt, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and data from the Platypus Conservation Initiative (Bino Pers. Comm.). A study conducted on Dingo Creek was excluded from the review of platypus CPUE due to recent droughts and fires, suggested to significantly reduce platypus densities (Bino et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We applied a Generalized Linear Model (GLM) using the Tweedie distribution with a log link function, suitable for modelling continuous data. The dependent variable considered in the model was CPUE, and the explanatory variables included latitude, net type (fyke\\net), and rural\\urban classification.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTrapping and tracking\u003c/p\u003e\u003cp\u003eOver six nights of trapping (14 fyke pairs and four mesh nets deployments), six platypuses were captured (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All platypuses were adults, four males and two females. Of the four adult males, three were captured using fyke nets and one was captured using mesh nets. Both adult females were captured using mesh nets in deep pools (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). CPUE across all six trapping nights was 0.36, 0.25 for all 14 fyke pairs, and 0.5 for the four mesh deployments. In Gold Creek no platypuses were captured, while the overall CPUE in upstream Moggill Creek was 0.38 (0.5 fyke and 0.25 mesh) and the overall CPUE in downstream Moggill Creek was 0.44 (0.13 fyke and 0.75 mesh; Appendix 2).\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\u003eThe sex, age, weight (kg), total body length, TVI, tracking days, detections, and range (metres) of Platypuses trapped and tagged in Moggill Creek and Gold Creek, QLD.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeight [kg]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal body length [mm]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTail volume index\u003c/p\u003e\u003cp\u003e[TVI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDuration tracked [days]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNumber of detections\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRange [m]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1,480\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e625\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2,420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2,140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1,400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2,240\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe mean weight of platypuses varied between females (1.08 kg\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 SD), and males (1.62 kg\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 SD). The mean total body length of platypuses also varied between females (444.5 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;33.23 SD) and males (526.75 mm\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6 SD). Both females had a TVI of 3 and male TVI scores ranged between 1 and 3, indicating overall good fat storages (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRadio tags remained attached to platypuses for the entirety of the tracking durations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Platypuses were detected within the rural upstream Moggill Creek (F1, M2, M3) and urban downstream Moggill Creek (F2, M1, M4) sites during the 25 days of tracking (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The number of detections varied between platypuses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and between females (19.0 average\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 SD) and males (13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 SD). The mean number of detections in the rural upstream Moggill Creek was 13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5 SD and the mean number of detections in the urban downstream Moggill Creek was 16.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 SD.\u003c/p\u003e\u003cp\u003eHome range varied between individual platypuses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean home range length for female platypuses was 1052.5 m\u0026thinsp;\u0026plusmn;\u0026thinsp;604.6 SD and mean home range for male platypuses was 2050.0 m\u0026thinsp;\u0026plusmn;\u0026thinsp;448.5 SD. Mean platypus home range length in the urban downstream Moggill Creek was 1761.6 m\u0026thinsp;\u0026plusmn;\u0026thinsp;988.5 and mean platypus home range length in the rural upstream Moggill Creek was 1673.3 m\u0026thinsp;\u0026plusmn;\u0026thinsp;406.1 SD.\u003c/p\u003e\u003cp\u003eEnvironmental DNA\u003c/p\u003e\u003cp\u003eFrom the 14 eDNA sites sampled (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e), 13 were positive for platypus DNA. Twenty-five out of 28 samples taken detected presence of platypus DNA. Both samples taken at the most downstream site on Moggill Creek were negative for platypus DNA. PERMANOVA model indicated that environmental predictors contributed significantly to macroinvertebrate community composition (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Creek had the largest effect (Adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.33, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), followed by elevation (Adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.16, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), area of recreation (Adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.14, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), proportion of bare ground (Adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), area of woody vegetation (Adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), area of urban residential (Adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), area of rural residential (Adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and area of conserved habitat (Adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003ePERMANOVA results for macroinvertebrate prey species variance across sites\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdjusted_R2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreek\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecreation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBand1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWoody\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConserved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNMDS analysis indicated significant differences in the community composition of macroinvertebrate orders between upstream rural and downstream urban sites on Moggill Creek (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Elevation was the strongest predictor of community composition (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.42, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), followed by area of urban residential (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.36, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), area of rural residential (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.37, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), area of recreation (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.41, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), area of Band1 vegetation (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.34, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and area of woody vegetation (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.23, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) also had significant influence on community structure. Conserved areas showed a weaker, non-significant influence on macroinvertebrate community structure (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.21, P\u0026thinsp;=\u0026thinsp;0.09).\u003c/p\u003e\u003cp\u003eDifferences in the community composition were driven by key orders (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The SIMPER analysis demonstrated the contribution of macroinvertebrate orders to the dissimilarity between creeks (Appendix 3). The order with the largest average contribution (AC) to variance between Gold Creek and downstream Moggill Creek was Amphipoda (crustaceans) (AC\u0026thinsp;=\u0026thinsp;0.03, P\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.05), followed by Spongilida (freshwater sponges) (AC\u0026thinsp;=\u0026thinsp;0.08, P\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Together these two orders contributed to 14.2% of the cumulative differences between the sites. The order with the largest average contribution to variance between Gold Creek and upstream Moggill Creek was Diplostraca (water fleas) (AC\u0026thinsp;=\u0026thinsp;0.04, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by Lumbriculida (worms) (AC\u0026thinsp;=\u0026thinsp;0.03, P\u0026thinsp;=\u0026thinsp;0.025), with these two orders contributing to 14.7% of the cumulative differences between the sites. The order with the largest average contribution to variance between upstream Moggill Creek and downstream Moggill Creek was Amphipoda (crustaceans) (AC\u0026thinsp;=\u0026thinsp;0.05, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by Diplostraca (water fleas) (AC\u0026thinsp;=\u0026thinsp;0.04, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Together these two orders contributed to 17.2% of the cumulative differences.\u003c/p\u003e\u003cp\u003eThe number of potential prey orders detected across all sites varied from 5 to 15 (10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8 SD) but no significant associations were found between evaluated environmental predictors and the number of prey orders across sites (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Appendix 4). SIGNAL score varied considerably more across sites, ranging from 4 to 56 (23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8 SD) and was correlated with prey orders (Spearman r\u0026thinsp;=\u0026thinsp;0.79, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Model averaging identified SIGNAL score was negatively associated with elevation (P\u0026thinsp;=\u0026thinsp;0.001), Band 1 vegetation cover (P\u0026thinsp;=\u0026thinsp;0.003), and Woody vegetation cover (P\u0026thinsp;=\u0026thinsp;0.003), while positively associated with recreational area (P\u0026thinsp;=\u0026thinsp;0.026) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Appendix 5). Multivariate GLM analysis did not identify any significant association between assessed predictors and the prevalence of any particular prey order (Appendix 6).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel averaging results for environmental predictor influence on number of macroinvertebrate prey orders\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBand1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.588\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWoody\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.603\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConserved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.616\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.556\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.545\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eConditional averaged model coefficients for predicting SIGNAL based on environmental factors\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBand1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecreation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWoody\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConserved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRange and density in urban and rural environments\u003c/p\u003e\u003cp\u003ePlatypus home range sizes from six studies across the species' distribution ranged from 330 m to 7300 m (2,009 m\u0026thinsp;\u0026plusmn;\u0026thinsp;1,698 SD) (Appendix 1). We found significant associations between range sizes and several predictor variables (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The estimated baseline home range for a female in a rural habitat at Sydney's latitude (34\u0026deg;S) with zero tracking duration was 570 m (95% CI: 8 to 51,303, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Tracking duration exerted a small but significant positive effect on home range size, with each additional day of tracking associated with a 1% increase in range (95% CI: 1.00 to 1.01, P\u0026thinsp;=\u0026thinsp;0.028, Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Habitat type emerged as a strong predictor. Home ranges in urban environments were approximately 2.36 times larger than in rural habitats (95% CI: 1.58 to 3.63, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Sex also significantly influenced home range, with males maintaining ranges 1.63 times larger than females (95% CI: 1.19 to 2.23, P\u0026thinsp;=\u0026thinsp;0.002, Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Latitude showed a negative effect on home range size, reducing it by a factor of 0.95 per degree increase (95% CI: 0.89 to 1.01, P\u0026thinsp;=\u0026thinsp;0.101, Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), though this relationship did not reach statistical significance. The model explained 57.5% of variance in home range size, with habitat type and sex representing the most influential predictors.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGeneralized Linear Model (GLM) results for home range size (top) and CPUE (bottom). Considered predictors for home range size included tracking duration, habitat type (reference: non-urban), latitude, and sex (reference: female). Considered predictors for CPUE included habitat type (reference: non-urban), latitude, and net type (reference: fyke).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHome Range\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.86\u0026ndash;975.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89\u0026ndash;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex [M]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.19\u0026ndash;2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u0026ndash;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHabitat [Urban]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.58\u0026ndash;3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e Nagelkerke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e0.575\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eCPUE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u0026ndash;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.054\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHabitat [U]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.24\u0026ndash;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enet type [Fyke]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16\u0026ndash;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLatitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.94\u0026ndash;1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.802\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e Nagelkerke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e0.511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAnalysis of CPUE revealed that net type was a highly significant predictor, with fyke nets capturing platypuses at approximately 73% lower rates than mesh nets (Estimate\u0026thinsp;=\u0026thinsp;0.27, 95% CI: 0.16 to 0.47, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). We observed a 44% reduction in CPUE within urban environments compared to rural habitats (Estimate\u0026thinsp;=\u0026thinsp;0.56, 95% CI: 0.24 to 1.29, P\u0026thinsp;=\u0026thinsp;0.179, Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), though this difference did not reach statistical significance. This result warrants cautious interpretation given the substantial sample size imbalance between rural (n\u0026thinsp;=\u0026thinsp;52) and urban (n\u0026thinsp;=\u0026thinsp;11) sites, which likely constrained statistical power to detect habitat effects. Latitude exerted negligible influence on CPUE (Estimate\u0026thinsp;=\u0026thinsp;0.99, 95% CI: 0.94 to 1.05, P\u0026thinsp;=\u0026thinsp;0.802, Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The model explained 51.1% of variance in CPUE, indicating that net type accounts for a major proportion of variation in capture rates across study sites.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides critical insight into platypus spatial ecology and capture rates along an urban gradient in southeast Queensland. As the first study to employ radio tracking of platypuses in Queensland, our research extends the spatial range of current knowledge on the species' ecology in a region where data has been historically limited, yet urbanisation pressures continue to intensify. Urban driven changes to natural resources essential for platypus survival appear to influence their distribution and habitat use across multiple scales. We find that platypuses in urban streams exhibit substantially larger home ranges compared to rural areas, with cross study analysis demonstrating home ranges in urban environments approximately 2.36 times larger than in rural habitats. Results from environmental DNA surveys reveal shifts in platypus prey communities alongside reductions in woody riparian vegetation as waterways transition from rural to urban settings. Changes in macroinvertebrate SIGNAL scores across our study gradient indicate factors influencing water quality and consequently the persistence of sensitive macroinvertebrate species. The significant positive correlation between SIGNAL scores and elevation indicates that sample sites at higher elevations, characteristic of rural environments, maintain higher water quality. These insights, in conjunction with changes in macroinvertebrate richness, suggest that underlying chemical and physical factors influence water quality and consequently platypus food sources (Azrina et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), with cascading effects on platypus spatial requirements and population viability.\u003c/p\u003e\u003cp\u003ePlatypus distribution extends across Australia's east coast (Bino et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), yet other than Greater Melbourne where substantial research has been conducted (Ahrens et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Coleman et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Furlan et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lugg et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Martin et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Serena \u0026amp; Pettigrove \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Serena et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Serena \u0026amp; Williams \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Serena \u0026amp; Williams \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Serena et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), relatively few studies have focused on platypus ecology within urban environments. This represents a substantial knowledge gap regarding habitat use and responses to urban impacts outside Victoria. Past research has documented platypus presence in disturbed catchments of Brisbane, Queensland (Brunt et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Brunt et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Brunt \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the Eden Region, New South Wales (Lunney et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and Richmond catchment, New South Wales (Rohweder \u0026amp; Baverstock \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Current knowledge on platypuses in the Brisbane area remains sparse (Brunt et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Prior to the first eDNA survey conducted in 2016 by the Wildlife Preservation Society of Queensland, there was a recorded 20 year gap in platypus monitoring in southeast Queensland (Brunt \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This substantial monitoring gap subjected platypuses in southeast Queensland to unknown rates of population decline from unmanaged threats (Brunt et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A study on platypus distribution in the Greater Brisbane region between 1990 and 2019 demonstrated clear evidence of this decline (Brunt et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with comparison of historical observation data and current eDNA sampling revealing possible local extinction of platypuses in five Brisbane waterways, raising urgent concerns regarding current population status of platypuses in Queensland.\u003c/p\u003e\u003cp\u003eOur cross study analysis revealed that home ranges in urban environments were approximately 2.36 times larger than those in rural habitats, with the model explaining 57.5% of variance in home range size. This finding aligns with theoretical predictions that resource scarcity drives spatial expansion in foraging mammals (Grant \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In resource limited environments, platypuses must forage over greater distances, increasing spatial separation between key resources such as burrows and foraging grounds (Crane et al. 2022). The need to cover larger areas, combined with fewer shelter options, drives the observed expansion in home range as individuals adapt to meet survival requirements (Casula et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wolff \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Urban waterways typically experience degraded habitat quality through mechanisms consistent with urban stream syndrome, including altered flow regimes, increased pollutant loads, channelisation and loss of riparian vegetation (Walsh et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Vietz et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These environmental changes reduce prey availability and shelter sites, forcing platypuses to expand their foraging territories to acquire sufficient resources. Our findings align with observations from Melbourne's urban streams, where platypuses similarly exhibit expanded spatial requirements in response to habitat degradation (Serena et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Martin et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, tracking undertaken from July to August observed an average home range of 1,718 m\u0026thinsp;\u0026plusmn;\u0026thinsp;678 SD, based on observations from four male and two female adult platypuses. Our observed average home range falls within the reported extent of other studies when accounting for seasonal and sex specific variation. Boulton et al. (2022) conducted tracking from March to June, observing a smaller average home range of 919 m\u0026thinsp;\u0026plusmn;\u0026thinsp;373 SD from three female adult platypuses in Melbourne's urban streams. Serena et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) conducted tracking from June to October, observing a larger home range of 3,725 m\u0026thinsp;\u0026plusmn;\u0026thinsp;865 SD from three male and one female adult platypuses in Melbourne. Due to the seasonality of platypus home ranges associated with breeding (Hawke et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Griffiths et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), radiotracking studies conducted outside of the breeding season with higher proportions of females would be expected to yield smaller average home ranges. Male platypuses exhibit increased mobility during the breeding season from August to October, associated with mate searching behaviour and territorial competition (Griffiths et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), while females show expanded ranges during lactation from November to February due to heightened energetic demands (Serena \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). The higher proportion of females in Boulton et al. (2022)'s study, along with tracking duration occurring before the breeding season, provides reasoning for the smaller average home range compared with both Serena et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and this study. This comparison demonstrates that the average home range observed in our Queensland study falls within expected ranges when accounting for seasonality and sexual selection patterns established in Victorian populations, reinforcing that our findings are consistent with established ecological patterns across the species' distribution.\u003c/p\u003e\u003cp\u003eOur synthesis of rural radiotracking studies (Crane et al. 2022; Gardner \u0026amp; Serena \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Gust \u0026amp; Handasyde \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Serena \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) revealed an average platypus home range length of 1,489 m\u0026thinsp;\u0026plusmn;\u0026thinsp;1,288 SD, while urban radiotracking studies demonstrated an average of 2,121 m\u0026thinsp;\u0026plusmn;\u0026thinsp;1,304 SD. This difference supports the hypothesis that urban platypus home ranges expand as a result of platypuses travelling further distances to locate essential resources, likely reflecting resource degradation in urban streams. The consistency of this pattern across geographically distant populations in both Victoria and Queensland suggests a generalised response to urbanisation rather than region specific effects. This trend raises urgent concerns regarding the capacity of urban streams to support viable platypus populations into the future, particularly as urbanisation pressures continue to intensify across Australia's east coast. The sensitivity of platypuses to urbanisation is indicated by observed declines and local extinctions in urban streams across Australia (Hawke et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Brunt et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Sufficient natural resources are essential to maintain stable populations, as they reduce competition intensity among individuals for survival (Pekkonen et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Limiting resources intensify intra and interspecific competition, driving population declines through multiple mechanisms (Casula et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Whisson et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Increased competition strains individual health through elevated stress hormone levels, reduces reproductive success through decreased body condition and delayed sexual maturity, and ultimately decreases population density through elevated mortality and emigration rates (Wiegert \u0026amp; Owen \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e1971\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur capture rate analysis revealed that net type emerged as the dominant predictor of CPUE, with fyke nets capturing platypuses at approximately 72% lower rates than mesh nets. This substantial methodological effect has critical implications for comparative studies and long term monitoring programmes. The performance difference likely reflects behavioural responses to trap configuration and deployment location, with mesh nets potentially more effective in deeper pool habitats where both females in our study were captured. Fyke nets, traditionally deployed in shallower runs and riffles, may be less effective at capturing platypuses that preferentially use deep pool refugia, particularly in urban streams where pool habitats provide critical thermal refuge and predator avoidance opportunities (Grant \u0026amp; Temple Smith 1998; Serena \u0026amp; Williams \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This finding underscores the necessity for standardised trapping protocols when comparing platypus abundance across sites or evaluating temporal trends. Employing different net types may inadvertently introduced substantial bias into abundance estimates, potentially confounding habitat or temporal effects with methodological artefacts. Future monitoring efforts should explicitly account for net type effects or standardise methodology to enable robust comparisons across time and space. While we observed a 40% reduction in CPUE within urban environments compared to rural habitats, this difference did not reach statistical significance. This result warrants cautious interpretation given the substantial sample size imbalance between rural (n\u0026thinsp;=\u0026thinsp;50) and urban (n\u0026thinsp;=\u0026thinsp;13) sites in our cross-study dataset, which constrained statistical power to detect habitat effects. The non-significant trend suggests potential density reductions in urban areas, consistent with previous observations from Melbourne's urban catchments (Hawke et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Coleman et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), yet our analysis cannot definitively establish whether urbanisation influences platypus abundance independent of capture methodology. The challenge of detecting abundance effects is compounded by the naturally low detection probability of platypuses even in optimal habitats (Lugg et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), requiring substantial sampling effort to achieve adequate statistical power.\u003c/p\u003e\u003cp\u003ePlatypus presence in an environment is driven primarily by instream habitat features that provide essential resources for foraging, shelter and reproduction (Brunt \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The relationship between woody riparian vegetation and platypus spatial ecology observed in our environmental surveys corroborates research highlighting platypus reliance on intact riparian vegetation for multiple functions, including bank stability for burrowing, overhead cover for predator avoidance, terrestrial invertebrate subsidies, and maintenance of stable instream temperatures (Brunt \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hawke et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Serena \u0026amp; Pettigrove \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The positive correlation between vegetation cover and macroinvertebrate SIGNAL scores in our study reinforces the mechanistic link between habitat quality and resource availability. Riparian vegetation influences stream conditions through multiple pathways, including temperature regulation via shading, nutrient cycling through leaf litter inputs, bank stabilisation that maintains pool habitat structure, and provision of woody debris that creates hydraulic complexity (Walsh et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). These interconnected processes generate habitat conditions that support diverse and abundant macroinvertebrate communities, which form the primary prey base for platypuses. Urban streams characteristically exhibit reduced riparian vegetation cover due to clearing for infrastructure, altered hydrology that precludes vegetation establishment, and ongoing disturbance from maintenance activities (Walsh et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Coleman et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This degradation cascade from vegetation loss through reduced prey availability to expanded platypus home ranges illustrates the cumulative impacts of urbanisation on aquatic ecosystems.\u003c/p\u003e\u003cp\u003eSeveral limitations constrain interpretation of our findings and highlight priorities for future research. Macroinvertebrate eDNA surveys detected only presence or absence, providing no indication of abundance or biomass. As an important food resource for platypuses, understanding how benthic macroinvertebrate abundance and size distributions change throughout urban landscapes remains paramount to platypus conservation. Platypus energetic requirements depend not only on prey species richness but critically on prey density and size, with larger bodied invertebrates providing disproportionate energetic returns (McLachlan Troup et al. 2010). Future research should pair eDNA surveys with quantitative macroinvertebrate sampling using kick nets or Surber samplers (Turak et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) to provide comprehensive understanding of food availability across urban gradients. The limited sample size of captured platypuses (n\u0026thinsp;=\u0026thinsp;6) in our tracking study, while sufficient for initial spatial analyses, restricts our capacity to detect subtle patterns in movement behaviour and habitat selection across the urban gradient. Larger sample sizes would enable more nuanced analyses of individual variation in space use strategies and how personality or condition dependent factors mediate responses to urbanisation.\u003c/p\u003e\u003cp\u003eThe cross study CPUE analysis, though incorporating 63 observations from published studies and monitoring programmes, suffered from substantial sample size imbalance between habitat types, limiting our capacity to detect habitat effects with adequate statistical power. This imbalance likely reflects historical sampling bias toward rural sites in platypus research and emphasises the critical need for expanded monitoring efforts in urban waterways (Coleman et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, temporal variation in platypus detection rates associated with breeding seasonality introduces potential confounding effects when comparing studies conducted across different months (Hawke et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Serena \u0026amp; Williams \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), though we attempted to mitigate this by restricting analyses to adult individuals and accounting for tracking duration. The substantial energetic demands of reproduction, particularly for lactating females, alter activity patterns and habitat use in ways that influence capture probability (Griffiths et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Future cross study syntheses should explicitly model seasonal effects on detection probability to improve comparability across studies conducted at different times of year.\u003c/p\u003e\u003cp\u003eThe substantial reliance platypuses have on benthic macroinvertebrates for diet and consolidated banks with overhanging riparian vegetation for burrowing and shelter demonstrates the species' vulnerability to habitat degradation. Our findings indicate that maintaining and restoring riparian vegetation corridors, protecting water quality through improved stormwater management, and preserving pool habitats represent critical conservation priorities for platypus populations persisting in urbanising landscapes. The expanded home ranges observed in urban environments suggest that individual platypuses require access to longer stream reaches to meet resource requirements, highlighting the importance of maintaining longitudinal connectivity in urban waterways. Barriers such as weirs, culverts and gross pollutant traps that fragment stream networks may disproportionately impact urban platypus populations already operating at expanded spatial scales (Furlan et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kolomyjec et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Future conservation planning should prioritise protecting sufficient habitat extent to accommodate these enlarged spatial requirements while simultaneously addressing the underlying mechanisms of resource degradation that necessitate such spatial expansion.\u003c/p\u003e\u003cp\u003eThe Melbourne experience demonstrates that long term, systematic monitoring combined with adaptive management can successfully support platypus populations in urban landscapes (Coleman et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Melbourne Urban Platypus Program, operating since the 1990s, has generated invaluable insights into urban platypus ecology while simultaneously informing waterway management decisions across the region (Melbourne Water \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similar programmes in southeast Queensland could provide the baseline data necessary to detect population trends, evaluate management interventions, and guide evidence based conservation policy. Our study represents an important first step in developing this knowledge base for Queensland populations, yet sustained effort over multiple years will be required to adequately characterise population dynamics and responses to management actions.\u003c/p\u003e\u003cp\u003eUrban waterway restoration efforts should focus on recreating the structural and functional characteristics of intact riparian ecosystems rather than simply revegetating stream banks. This includes reestablishing appropriate vegetation density and diversity to provide shade and organic matter inputs, creating hydraulic complexity through installation of large woody debris, protecting and enhancing pool habitats that provide critical refuge during extreme conditions, and managing flow regimes to maintain natural patterns of inundation and drying (Walsh et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Vietz et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The challenge lies in implementing these restoration activities within the constraints of urban landscapes where competing demands for flood control, public access and infrastructure maintenance must be balanced against ecological objectives. Innovative solutions such as water sensitive urban design that manages stormwater at source, setback requirements that protect riparian zones from development impacts, and community engagement programmes that build support for waterway protection offer promising pathways forward (Coleman et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eClimate change represents an additional stressor that will interact with urbanisation to further challenge platypus populations. Projected changes in rainfall patterns, with more intense rainfall events interspersed with prolonged dry periods, will alter flow regimes and potentially exacerbate urban stream syndrome effects (Bino et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Increased water temperatures may exceed thermal tolerance thresholds for platypuses, particularly in urban streams where reduced riparian shading and altered flow regimes already elevate baseline temperatures (Klamt et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Managing for climate resilience will require maintaining and restoring the ecological processes that buffer against environmental variability, including intact riparian vegetation that moderates temperature extremes, deep pool habitats that provide thermal refuge, and sufficient habitat extent to enable behavioural thermoregulation through movement across thermal gradients.\u003c/p\u003e\u003cp\u003eOur research demonstrates that platypuses persist in urban environments of southeast Queensland, yet exhibit clear signatures of environmental stress through expanded spatial requirements. The consistency of these patterns with observations from Melbourne's urban streams suggests generalisable responses to urbanisation that transcend regional differences. However, persistence should not be equated with thriving populations. The expanded home ranges, potential density reductions, and degraded prey communities we observed indicate that urban platypus populations operate under resource limitation that may compromise long term viability. Effective conservation will require sustained commitment to protecting and restoring urban waterway ecosystems, informed by continued research that elucidates mechanisms linking habitat quality to population outcomes. As Australia's human population continues to concentrate in coastal urban centres, the challenge of maintaining viable platypus populations in modified landscapes will intensify, demanding innovative management approaches that integrate ecological understanding with urban planning and design.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eG.B. secured funding, conceptualised the study design, and provided primary supervision. G.B. and A.Y. undertook all field work. All authors conducted data analysis and wrote the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by the Brisbane City Council. We thank the Moggill Creek Catchment Group for their enthusiasm and support. We thank Rhys Coleman from Melbourne Water for providing platypus CPUE values across Greater Melbourne. We would like to extend my thanks to all the volunteers who assisted in tracking and trapping platypuses and Dr Tamielle Brunt for her expertise, guidance and support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest \u0026nbsp; \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Funding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Brisbane City Council\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the authors used Claude AI in order to improve grammar and clarity of the writing. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAarts, G., MacKenzie, M., McConnell, B., Fedak, M. \u0026amp; Matthiopoulos, J. (2008). Estimating space-use and habitat preference from wildlife telemetry data. \u003cem\u003eEcography\u003c/em\u003e \u003cstrong\u003e31,\u003c/strong\u003e 140-160. doi: 10.1111/j.2007.0906-7590.05236.x.\u003c/li\u003e\n\u003cli\u003eAhrens, C. W., Griffiths, J., Danger, A., Coleman, R., van Rooyen, A., Furlan, E., \u0026amp; Weeks, A. R. (2025). Genetic diversity and structure lag the effects of contemporary environmental changes in a platypus meta-population. Heredity, 134(7), 427\u0026ndash;438. https://doi.org/10.1038/s41437-025-00774-w\u003c/li\u003e\n\u003cli\u003eAhrens, C.W., Griffiths, J., Danger, A., Coleman, R., Van Rooyen, A., Furlan, E., Weeks, A.R. (2025). 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Health assessment for urban rivers based on the pressure, state and response framework\u0026mdash;a case study of the Shiwuli river. \u003cem\u003eEcological Indicators\u003c/em\u003e\u003cstrong\u003e99\u003c/strong\u003e, 324-331. doi: 10.1016/j.ecolind.2018.12.023.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Habitat selection, Macroinvertebrate, Ornithorhynchus anatinus, Resource availability ","lastPublishedDoi":"10.21203/rs.3.rs-8168416/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8168416/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs urbanisation accelerates, freshwater ecosystems face growing threats, particularly for species reliant on riparian zones, like the platypus (Ornithorhynchus anatinus). This study examines platypus presence, distribution and habitat use along Moggill Creek in Queensland across an urban-rural gradient. Using environmental DNA (eDNA) sampling, live trapping and radio tracking, we assessed urban development influences on platypus home range and habitat preferences. Over six nights of trapping, we captured six adult platypuses (four males, two females). Cross study analysis of radiotracking data revealed that platypuses in urban environments maintain home ranges approximately 2.36 times larger than those in rural habitats (95% CI: 1.58 to 3.63, P\u0026lt;0.001), with the model explaining 57.5% of variance. Net type emerged as the dominant predictor of capture rates, with fyke nets capturing platypuses at 72% lower rates than mesh nets (P\u0026lt;0.001), representing a critical methodological consideration for comparative studies. We confirmed platypus DNA at 13 of 14 sites through eDNA sampling, with notable absence at the most downstream urban site suggesting potential habitat limitations. Analysis of macroinvertebrate communities revealed significant differences between urban and rural sites, driven by environmental factors including elevation and riparian vegetation, which correlated with higher biodiversity and water quality in rural areas. These findings underscore platypus capacity to persist in urban environments whilst revealing ecological costs, including substantially expanded home ranges likely driven by resource limitation. This research, the first to radio track platypuses in Queensland, emphasises the urgent need for conservation strategies targeting urban waterways to maintain habitat quality and support platypus populations amidst accelerating urbanisation pressures.\u003c/p\u003e","manuscriptTitle":"Resource Limitation Shapes Platypus Spatial Ecology in Urban Streams","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-04 13:43:33","doi":"10.21203/rs.3.rs-8168416/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":"89da8bf8-d3c5-4eff-844e-ccb9cae31c20","owner":[],"postedDate":"December 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T03:44:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-04 13:43:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8168416","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8168416","identity":"rs-8168416","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}