Using passive acoustic monitoring to investigate the occurrence of invasive Asian house geckos (Hemidacylus frenatus) on an Oceanic Island

Using passive acoustic monitoring to investigate the occurrence of invasive Asian house geckos (Hemidacylus frenatus) on an Oceanic Island | 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 Using passive acoustic monitoring to investigate the occurrence of invasive Asian house geckos (Hemidacylus frenatus) on an Oceanic Island Jacopo I Bartholomew, Lin Schwarzkopf, Slade Allen-Ankins This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6876411/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Establishing the distributions of invasive species is critical, particularly on island ecosystems that support endemic populations highly susceptible to ecological disruption. However, evaluations of methods capable of efficiently detecting invasive species in remote or inaccessible areas at high temporal and spatial resolutions remain limited. Passive acoustic monitoring (PAM) has proven effective for monitoring a variety of invasive vertebrates, yet its application to monitor reptiles has been largely unexplored. Here we aim to assess the utility of PAM to detect the invasive Asian house gecko ( Hemidactylus frenatus ) and identify environmental variables associated with its occurrence on Christmas Island (Indian Ocean). We searched 29,640 hours of audio recordings collected from May to June 2023 by a network of 81 acoustic recorders deployed across Christmas Island using a semi-automated acoustic detection method to identify the gecko’s ‘multiple chirp’ call. Asian house geckos were detected by 66 recorders, in both disturbed and natural habitats, and were recorded during 61.6% of survey nights at occupied sites. Survey-level detection rates varied markedly across sites, potentially reflecting local abundance, suggesting that future research could enable abundance estimation from acoustic detection rates. A single-season occupancy model revealed areas at higher elevation with taller canopies were associated with gecko absence. Notably, these areas also support the highest abundance of a larger, endemic gecko species, suggesting that competitive exclusion may limit the Asian house gecko’s distribution on Christmas Island. Our findings demonstrate that PAM is an effective, scalable method for detecting an invasive soniferous reptile, and can enhance invasive species monitoring and risk assessment on islands. Invasive species Bayesian occupancy modelling Passive Acoustic Monitoring BirdNET embeddings Christmas Island Hemidactylus frenatus Figures Figure 1 Figure 2 Figure 3 Introduction Island-endemics are disproportionally threatened compared to other terrestrial vertebrates (Tershy et al. 2015 ), and invasive species are recognized as the primary driver of their decline (Tershy et al. 2015 ; Bellard et al. 2016 ; Spatz et al. 2017 ; Dueñas et al. 2021 ). Human-inhabited islands tend to experience high propagule pressure, leading to frequent alien species establishment (Simberloff 2009 ; Russell et al. 2017 ). Further, species restricted to a single island or island archipelago typically have small population sizes, limited distributions, and behavioural naivety to novel predators and competitors, amplifying their susceptibility to the impacts of invaders (Fernández-Palacios et al. 2021 ). While prevention of novel introductions is crucial to conserve island communities (Holden et al., 2016 ), non-native and invasive species already exist on most islands with vulnerable endemics and require control (Spatz et al. 2017 ). A key prerequisite to manage invasive species is to establish their state of invasion, which requires knowledge of their distribution (Stohlgren and Schnase 2006 ). Traditionally, manual surveying techniques are used for species detection and monitoring, but they can be costly, time-consuming, and usually require trained observers. Manual surveys are especially challenging on islands, which might be hard to reach, or difficult to stay on for extended periods (Agius 2023 making these data difficult to obtain, especially for cryptic invaders. Thus, novel monitoring methods capable of collecting reliable data efficiently are necessary to establish invasive species distributions in remote areas. Passive acoustic monitoring (PAM) is an emerging method to detect and monitor soniferous organisms (Blumstein et al. 2011 ; Sugai et al. 2019 ). PAM involves recording environmental audio using autonomous recording units (ARUs) and subsequently identifying target sounds and vocalizations in the recorded audio. Recent advances in machine learning algorithms to automate the sound recognition process (e.g., Kahl et al. 2021 ), and the arrival of accessible, low-cost acoustic recorders and data storage systems have enabled PAM to become an efficient method to monitor several vertebrate groups (Darras et al. 2019 ; Gibb et al. 2019 ). This extends to invasive terrestrial vertebrates (Juanes 2018 ; Ribeiro et al. 2022 ), in which early applications of PAM to establish the occurrence of invasive amphibians (Bota et al. 2024 ; Leung et al. 2025 ), mammals (McEwen et al. 2024 ), and birds (Wood et al. 2024 ) have been successful. However, its efficacy to detect and monitor soniferous reptiles has not been established (McKnight et al. 2015 ; Hoefer et al. 2024 ). Many reptiles are encountered infrequently during standard active search surveys, making informed survey selection imperative for efficient reptilian monitoring. Methods such as artificial refugia replication (Michael et al. 2012 ) and passive trapping techniques have been developed and demonstrated to improve reptilian encounter rates (Hoefer et al., 2024 ), while remote sensing approaches remain largely unassessed and rarely applied (but see McKnight et al. 2015 ; Richardson et al. 2017 ; Nordstrom et al. 2022 ; Dubos et al. 2023 ). Some geckos, a reptilian taxon with many successful invasives (Kraus 2009 ), use acoustic signalling for social communication (Marcellini 1977 ), potentially making PAM a viable monitoring tool. Introduced geckos have high establishment success rates (Bomford et al. 2009 ) and sometimes competitively displace resident geckos in urban and natural settings, but their impacts are understudied and underestimated (Perella and Behm 2020 ). One of the most successful gekkonid invaders is the Asian house gecko ( Hemidactylus frenatus ) (Bomford et al. 2009 ), a moderately sized generalist predator probably native to Southeast Asia. This species thrives in human developments and disturbed habitats, which facilitate its human-mediated dispersal, resulting in a now pantropical non-native distribution (Weterings and Vetter 2018 ). Following its introduction, the Asian house gecko has been implicated in the decline of numerous native geckos on various islands (Case et al. 1994 ; Cole et al. 2005 ; Dame and Petren 2006 ). However, Asian house geckos have shown inconsistent invasion success into natural habitats across their introduced range, prompting interest concerning their potential to invade seemingly resistant native communities (Newbery and Jones 2007 ; McKay et al. 2009 ; Hoskin 2011 ; Vanderduys and Kutt 2012 ; Barnett et al. 2017 ) and the mechanisms which shape their establishment success (Petren and Case 1998 ; Zozaya et al. 2015 ; Garner et al. 2020 ). Asian house geckos produce a loud and distinctive ‘multiple chirp’ call enabling their detection through active listening surveys (Barnett et al. 2017 ) and through manual inspection of recorded audio (Hopkins et al. 2021 ). As such, large-scale efforts to detect this gecko may be substantially improved through the application of PAM. Additionally, as PAM can yield both detections and non-detections, the implementation of more robust predictive modelling frameworks, like occupancy models (MacKenzie et al. 2002 ), are available to identify habitat features associated with invasion resistance and to infer probabilistic cause of the gecko’s exclusion (Nichols and Cooch 2025 ). Thus, PAM may provide a useful monitoring tool capable of assessing the risk of further spread and providing support to hypothesized mechanisms shaping the gecko’s occurrence. Christmas Island is a moderately-sized external Australian territory (135 km²) located in the north-east Indian Ocean (10°25` S, 105°42` E). Once home to five native lizard species, including two geckos, Christmas Island has undergone a series of reptilian extirpations and extinctions (Smith et al. 2012 ; Emery et al. 2021 ). Today, the Christmas Island giant gecko ( Cyrtodactylus sadlieri ) is the only native lizard that remains, while the Lister’s gecko ( Lepidodactylus listeri) and the Christmas Island blue-tailed skink ( Cryptoblepharus egeriae ) persist only in captivity (Andrew et al. 2018 ; Emery et al. 2021 ). Following the decline of native reptiles, introduced Asian house geckos expanded from anthropogenically disturbed areas into natural habitats (Cogger and Sadlier 1998 ; Smith et al. 2012 ). This range shift has raised concerns regarding its ecological impact, including potential competition with any future reintroduced native reptiles, and its role as a vector of disease (Rose 2017; Emery et al. 2025 ). Therefore, describing the distribution of the Asian house gecko on Christmas Island and establishing efficient methods for ongoing monitoring are critical. In this study, we begin by evaluating PAM as a method to detect Asian house geckos using an established acoustic recorder network on Christmas Island. Next, we use the resulting detection data to test broad hypotheses pertaining to the site characteristics associated with the gecko’s occurrence. These hypotheses are: (1) Asian house gecko occurrence is primarily driven by proximity to anthropogenic disturbance, (2) Asian house gecko occurrence is primarily driven by habitat characteristics, and (3) Asian house gecko occurrence is associated with both anthropogenic disturbance and habitat characteristics. Methods Study Site Christmas Island is characterized by limestone coastal cliffs, marginal inland cliffs, and terraces that surround a large central plateau that rises to 361 m (Andrews 1900 ). Five broad native habitat types occur, each closely linked with soil depth (Claussen 2005 , Fig. 1 ). The deep soils of the central plateau support a closed-canopy evergreen forest, where canopy heights reach up to 40 m. In shallower soils surrounding this forest, a closed-canopy semi-deciduous forest occurs, distinguished by a lower canopy of 15–30 m. On the island’s margins, where steep slopes and terraces bear very shallow soils, semi-deciduous scrub dominates, featuring a dense understory and a canopy height of 10–15 m. Lastly, coastal herbland occupies exposed areas between the scrub and coastal cliffs where harsh conditions limit the growth of most flora. The island has an established road network that connects the main settlement located in the northeast to several phosphate mines, trails, and isolated developments (Fig. 1 ). The various anthropogenic disturbances and diverse native habitats of Christmas Island combined with the expansion of Asian house geckos into natural habitats make it an ideal system in which to investigate environmental factors linked with the invasive gecko’s occurrence pattern. Sampling design The ARU network used in this study was established as part of the ‘Managing and monitoring resilience in Australia’s national parks’ project under the Resilient Landscapes Hub of the National Environment and Science Program (NESP) to test the efficacy of a network of ARUs to monitor the island’s threatened forest birds and flying fox. Although not originally intended to detect invasive geckos, the network design did not require modification for this purpose. ARU deployment locations were positioned no closer together than 350m (mean = 803.04 m, sd = 200.55 m) and were selected to represent environmental variation on the island while maintaining site accessibility via placement in proximity to roads or tracks. At each site, one Long Term Bioacoustic Recorder (BAR-LT™, Frontier Labs) was secured on a tree 2 m above the ground and set to record 24 hours a day at a sampling rate of 44.1 kHz in .wav file format. On the 5th of May 2023, eighty-one ARUs began recording and continued until their batteries were depleted, SD cards were full, or a malfunction occurred. After collection, acoustic recordings were archived and managed using Ecosounds (QUT Ecoacoustics, https://www.ecosounds.org ). Acoustic Analysis To standardise varying ARU recording durations, we used a survey period from May 5th to June 4th, providing up to 31 days of continuous recording at each site. A survey event was defined as occurring between 18:00 and 06:00 h as Asian house geckos are nocturnal with calling activity peaks at sunset and 30 minutes before sunrise (Hopkins et al. 2021 ). We searched for gecko vocalizations in the acoustic recordings using BirdNET v2.4 embeddings, a novel, semi-automated method for rapid species search and detection in audio data (Kahl et al. 2021 ; Allen-Ankins et al. 2025 ). This approach separates the recorded audio into non-overlapping, 3-second segments and measures each segment’s similarity to a selected audio segment of a target sound based on the embedding’s representation of those sounds from the BirdNET model. Similarity is quantified by measuring the Euclidean distance between the embeddings of the target vocalisation and the embeddings of the audio segments selected for search, where a lower distance indicates greater similarity and a greater chance of being an instance of the target vocalisation. A ‘multiple chirp’ call was sourced from a previous laboratory study on the gecko’s acoustic behaviour to serve as our reference audio segment (Hopkins et al. 2021 ). From each survey, the most similar audio segment to our reference segment was selected for manual inspection and used to determine if a gecko was detected on a survey night. All manual annotation was done using the software Kaleidoscope Lite (Wildlife Acoustics, https://www.wildlifeacoustics.com ). This approach allowed us to efficiently construct a detection/non-detection matrix under the assumption that the absence of a gecko vocalization in the selected audio segment denoted a non-detection on the respective survey night. Occupancy Analysis To investigate the distribution of Asian house geckos on Christmas Island, and to test competing hypotheses about their occurrence patterns, we analysed gecko detection/non-detection data using a single-season occupancy model in a Bayesian framework with the ubms package (Kellner et al. 2022 ; Kellner 2024 ) in R v4.3.3 (R Core Team, 2023 ). A basic assumption of occupancy models is that site occupancy status does not change during the survey period (i.e., sites remain ‘closed’) (MacKenzie et al. 2002 ). We acknowledge that, for mobile geckos, a 31-day survey period likely violates this assumption; however, it is important to use all available data to inform the current distribution of the Asian house gecko as it is an invasive species. In this context, we redefine ‘site occupancy’ as ‘site use’ to more accurately reflect the process being modelled (Mackenzie and Royle 2005 ). Occupancy models incorporate temporal and spatial variation in detection and occupancy probabilities through the inclusion of observation-level and site-level variables. Observation-level variables We considered three variables to model variation in Asian house gecko detectability arising from differences in vocal activity based on evidence from previous studies. Specifically, minimum daily temperature (Marcellini 1974 ) and daily rainfall (Marcellini 1971 ; Cole et al. 2005 ), and moon fraction (Lardner et al. 2015 ; Nordberg and Schwarzkopf 2022 ) were included. Meteorological data were sourced from the Australian Bureau of Meteorology’s Christmas Island weather station (station 200790), located in the north-east section of the island, and moon fraction was sourced using the suncalc package in R (Thieurmel et al. 2019 ). In addition, site was included as a random effect to account for any unmeasured variation in gecko detection probability across sites. Site-level variables We used six spatial variables to develop candidate models aligned with our hypotheses of Asian house gecko site use patterns. Distance to nearest building, distance to nearest main road, and distance to nearest disturbance (composed of infrastructure, mining, and secondary vegetation growth) were included as predictors to test if site use is primarily driven by proximity to anthropogenic disturbance (Fig. 1 ). In addition, we included canopy height, elevation, and habitat type (Fig. 1 ) as predictors to test if habitat characteristics are the primary driver of site use. Initially, habitat type contained six factors (Evergreen Forest n = 34, Semi-deciduous Forest n = 12, Semi-deciduous Scrub n = 6, Coastal fringe vegetation n = 1, Perennial Wetland Forest n = 1, and Disturbed Areas n = 27). However, due to under-sampling, we merged coastal fringe vegetation and perennial wetland forest with semi-deciduous scrub, as these habitats were closely associated. To ensure our measure of canopy height accurately reflected biological processes of interest, a buffer area for mean canopy height aggregation was determined by estimating its scale-of-effect, defined as the biologically relevant area particular to a spatial variable (Jackson and Fahrig 2015 ). To do this, we aggregated mean canopy height at different dimensions centred around the ARU using QGIS v3.34.0 (QGIS Development Team 2023) and retained the area that corresponded with the greatest model predictive performance. Univariate occupancy models were created for each aggregation scale (50 m, 100 m, 150 m, and 200 m radius buffers) in combination with the most parsimonious detection model (see model construction and selection section), and the 100 m buffer size was retained. Finally, we combined the best supported models from hypotheses 1 and 2 to test if both the proximity of anthropogenic disturbance and habitat characteristics best describe the gecko’s site use patterns. Model Construction and Selection Prior to model construction, we assessed multicollinearity among site-level and observation-level variables using the usdm package (Naimi et al. 2014 ). All variables were retained as no variance inflation factors scored above 3 (Knock 2015 ). Candidate models were run with default priors using four MCMC chains, each with 20,000 iterations and a burn-in of 10,000. Chain convergence and mixture were evaluated by visually inspecting trace plots, and we ensured all parameters had effective sample sizes greater than 400, and Gelman–Rubin diagnostic values below 1.01 (Vats and Knudson 2021 ). Candidate detection and occupancy models were constructed by initially including all relevant variables, then iteratively removing the least informative variable until none remained. Model fit for each candidate model was assessed using k -fold cross-validation with 10 folds, and the most parsimonious model was identified as the one with the highest expected log pointwise predictive density (elpd) value (Yates et al. 2022 ). The model selection process began by selecting the most parsimonious detection model while holding occupancy at its intercept. Then the most parsimonious occupancy models for hypotheses 1 and 2 were selected and combined to form a third model representing hypothesis 3. The hypothesis with the highest elpd value was considered most supported, and goodness-of-fit was assessed with the MacKenzie–Bailey chi-square test to ensure the model adequately described the observed data (MacKenzie and Bailey 2004 ). Results We searched 29,640 hours of acoustic data collected over 2,470 survey nights across 81 sites for the Asian house gecko ‘multiple chirp’ call using a semi-automated detection method. The audio recorders failed to record on 41 of 2,511 survey nights, with an average failure rate of 0.506 surveys per recorder and a maximum of 4 survey failures over the 31-day survey period. Gecko calls were detected in 1,239 surveys at 66 sites (naïve detection probability = 0.616) and went undetected at 15 sites. Asian house gecko detection rates varied considerably among sites. For example, geckos were detected on every survey night at 14 sites, whereas at another 14 sites, detections occurred on only 1 to 5 survey nights (Fig. 2 a). The top performing detection model included site as a random effect (σ = 3.01 ± 0.37, 95% CI = 2.39 to 3.82) and daily minimum temperature as a negative effect (β = -0.33 ± 0.07, 95% CI = -0.47 to -0.19, Fig. 2 b). Daily rainfall and moon luminosity did not improve the model’s predictive performance (Online Resource 1). Asian house gecko site use was best explained by our most parsimonious habitat characteristics model (Table 1 , Online Resource 1) which performed well in the MacKenzie–Bailey chi-square test (posterior predictive p = 0.646). This model indicates the probability of Asian house gecko site use decreased significantly with both canopy height (β = − 1.62 ± 0.51, 95% CI: − 2.71 to − 0.72, Fig. 3 c) and elevation (β = − 1.37 ± 0.64, 95% CI: − 2.77 to − 0.28, Fig. 3 b). Upon the addition of predictors from the most parsimonious disturbance model for hypothesis 3 (Online Resource 1), model predictive performance decreased slightly (ΔELPD = -2.86; SE_DIFF = 1.14, Table 1 ), while predictive performance of the disturbance model, which included only distance to main roads and distance to buildings, was far worse (ΔELPD = -10.60) and had a similar predictive accuracy as the null occupancy model (Table 1 ). Although habitat type was not a supported predictor in our top model, it was well distinguished by variation in elevation and canopy height. Closed canopy evergreen forest consistently occurred at higher elevations and had a taller canopy and was the only habitat type aligned with areas of low predicted Asian house gecko site use (Figs. 1 and 3 a). Table 1 Comparison of the most parsimonious candidate occupancy models for each hypothesis based on predictive performance, measured as the expected log predictive density (ELPD), for the Asian house gecko’s ( Hemidactylus frenatus ) site use patterns on Christmas Island. Reported are values are model ELPD, the difference in ELPD from the most supported model (Δ ELPD) and the standard error of that difference (ΔSE). Site as a random effect (rSite) and daily minimum temperature C (minTemp) are included in all detection models. Canopy height (CH), elevation (elev), distance to disturbances (dist_disturb) and distance to buildings (dist_build) are used as predictors to construct candidate occupancy models. Hypothesis Model formula ELPD Δ ELPD SE of ΔELPD Habitat Characteristics p(rSite + min temp) ψ(CH + elev) -809.61 0.00 0.00 Combined Model p(rSite + min temp) ψ(CH + elev + dist disturb + dist build) -812.47 -2.86 1.14 Anthropogenic Disturbance p(rSite + min temp) ψ(dist disturb + dist build) -820.21 -10.60 4.49 Null Occupancy p(rSite + min temp) ψ(.) -821.05 -11.44 4.44 Discussion We found that PAM was an effective survey method for Asian house geckos. By targeting the gecko’s ‘multiple chirp’ call, we detected this invasive reptile in 61.6% of survey nights at occupied sites (i.e., where geckos were detected at least once). To our knowledge, this is the first demonstration of PAM as an effective method to detect and describe the distribution of Asian house geckos and, more broadly, reptiles in a natural setting. PAM has been successfully applied to investigate the acoustic characteristics and activity patterns of geckos (Yu et al. 2011 ; Hopkins et al. 2021 ), but not as a tool for species detection. Previous attempts to detect soniferous reptiles using PAM reported few or no detections largely because target species called infrequently, called at low volumes, or were absent (McKnight et al. 2015 ; Staniewicz 2020 ; Hoefer et al. 2024 ). Besides this, PAM’s potential in reptilian systems has been largely overlooked (Hoefer et al. 2023 ). While most reptiles are silent or only vocalize during copulatory or agonistic interactions, several geckos and crocodilians use acoustic signalling for communication, a behaviour which likely is under-described in this group (Vergne et al. 2009 ; Jono and Inui 2012 ; Lin et al. 2024). Thus, the potential for using PAM as an alternative to resource-intensive active reptile surveys exists and should be explored further. Acoustic surveys indicated that Asian house geckos were widely distributed across Christmas Island, occupying both native and disturbed habitats, but were notably absent from many sites on the island’s central plateau (Fig. 3 a). This distribution pattern aligns with the latest findings of island-wide observer-based surveys (Smith et al., 2012 ), which employed methods such as spotlighting and active listening for gecko calls. The consistency between these independent survey techniques suggests that PAM-derived non-detections likely represent true absences rather than false negatives. Since the introduction of Asian house geckos on Christmas Island (~ ca. 1930s), they remained confined to disturbed areas until at least 1979 (Cogger 1983). Later, a survey in 1998 documented their expansion into undisturbed habitats (Cogger and Sadlier 1998 ), which was further observed in surveys from 2004 to 2011 (James 2008 ; Smith et al. 2012 ). Our findings suggest this expansion has stalled, with many sites on the central plateau remaining unoccupied, but continued monitoring is essential to determine whether the invasive gecko remains limited or continues its range expansion. Using an occupancy modelling framework, we show that Asian house gecko site use on Christmas Island was best explained by habitat characteristics rather than disturbance regimes, or a combination of both. Specifically, gecko presence was negatively associated with high elevation and tall canopy, features closely associated with the island’s closed canopy evergreen forest (Fig. 2 ). On Amamioshima Island, Japan, Asian house geckos were similarly absent from primary broad-leaf evergreen forest, but abundant in urban environments and scrubby vegetation (Kurita 2013 ). In Australia’s Northern Territory, the opposite was observed, where dense canopy forest harboured established populations that were rare in eucalypt woodland (McKay et al. 2009 ). Characteristics of natural habitats, such as increased structural complexity and a dispersed food resource distribution have been linked with a decrease in the gecko’s locomotive performance (Cole et al. 2005 ; Garner et al. 2020 ) and food consumption rate (Petren and Case 1996 ; Petren and Case 1998 ), respectively. However, these disadvantages may only reduce fitness enough to exclude the gecko when combined with interspecific competition. The Christmas Island giant gecko ( Cyrtodactylus sadlieri ) is a large, endemic gecko that occurs across the island but reaches peak densities in the plateau’s evergreen forest (Cogger et al. 1983 ). This suggests that closed-canopy evergreen forests of the central plateau may have remained largely unoccupied by the invader, due to elevated competition with Christmas Island giant geckos. The exclusion of Asian house geckos from natural habitats, potentially due to competitive displacement from larger geckos, is observed in other systems (Newbery et al. 2007; Garner et al. 2020 ). Further, a study on the gecko’s dispersal and establishment patterns on islands in the Indian Ocean found that despite a similar propagule pressure across islands in the Seychelles, Asian house geckos remained absent for much longer on those with native reptiles, compared to those without (Rocha et al. 2022 ). Acoustic surveys can eliminate many of the confounding variables that influence Asian house gecko detectability in observer-based surveys, such as searcher and listener proficiencies, density of visual barriers, and headlamp type and brightness (Lardner et al. 2015 ). As a result, data collected using acoustic sensors can provide more biologically meaningful estimates of the effect of environmental variables on detectability. Our detection model suggests that Asian house geckos were more acoustically detectable on cooler nights (Fig. 2 b), which contrasts with previous studies reporting increased acoustic activity at higher temperatures (Marcellini 1974 ; Hopkins et al. 2021 ). However, our ability to estimate temperature effects on calling activity was constrained by the limited temperature variation that occurred over the duration of this study, and the coarse spatial resolution of available temperature data. Asian house gecko detection rates varied considerably among sites. This was reflected in the substantial improvement in our model’s predictive performance when site was included as a random effect on detection probability, after accounting for observation-level variables influencing activity (i.e., minimum temperature, Online Resource 1). Several factors may contribute to site variation in detection rate, including (1) differences in site-level calling activity, (2) differences in acoustic recognizer performance, and (3) variation in ARU effective detection area. We suggest that variation in detection rates was primarily driven by variation in calling activity, which likely reflected differences in abundance, and the presence of transient individuals moving in and out of the detection space. Direct observations have shown Asian house gecko calling activity is positively correlated with abundance (Frenkel 2006 ), affecting detection probability when acoustic recognizers have imperfect recall. Moreover, these geckos display a high tendency for exploratory behaviours in laboratory experiments (Nordberg et al. 2021 ) suggesting they may move in and out of sites, causing site occupancy status to change during the survey period. Acoustically similar sounds and background noise can reduce recognizer performance, especially when training data are limited (Pérez-Granados 2023 ). However, no recurring, interfering sounds were identified during manual annotation, and while few sites were exposed to ambient ocean noise, this did not appear to impact recognizer performance. Similarly, while variation in detection space can influence detectability (Rappaport et al. 2020 ), it is unlikely this alone explains the magnitude of variation in detectability observed. Our recorders were sufficiently far apart (~ 350 m) that a single gecko would probably be unlikely to travel between them in one night. Further research into the species’ movement and calling behaviour could inform our ability to infer relative abundance from acoustic data, an insight that would enhance the utility of PAM for long-term monitoring of this species. If attempted, however, it is important to note the ‘multiple chirp’ call is only uttered by adult males (Hopkins et al. 2021 ), and calls uttered by females should be included to avoid biases. The occupancy analysis was limited by somewhat outdated and potentially imprecise observation-level and site-level variables available for the occupancy analysis. Site-level variables were sourced from surveys conducted in 2011, although these still appeared to broadly reflect the island’s current characteristics based on site visits when recorders were set up, and recent satellite imagery. Additionally, minimum temperature and daily rainfall were gathered from a single weather station and applied uniformly across all sites. In this case, inferences were made with caution, and we recommend further investigation to confirm the observed weather variable associations in this study. Our findings support PAM as an effective tool for monitoring an invasive gecko and we demonstrate its ability to generate data suitable for hypothesis testing and predictive distribution modelling. Using a semi-automated call detection method, we eliminated false positive detections, and although no concurrent validation method was used, comparisons with previous surveys suggest that false negative detections were minimal. PAM is underutilized to monitor reptiles and invasive species. This study further demonstrates its effective application, and we find it to be a promising tool that could greatly enhance conservation and invasive species management on islands. Declarations Author Contributions All authors conceived the study. Jacopo Bartholomew performed the audio validation, statistical analysis, and visualisations with advice from Lin Schwarzkopf and Slade Allen-Ankins. Slade Allen-Ankins designed the audio analysis methodology. Jacopo Bartholomew wrote the draft manuscript and finalized the manuscript, with editorial input from Lin Schwarzkopf and Slade Allen-Ankins. Funding The project was supported with funding from the Australian Government under the National Environmental Science Program’s Resilient Landscapes Hub and Parks Australia. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Acknowledgements Project management and collection of audio recordings was undertaken by Parks Australia Science Team with support from the Christmas Island National Park team. We thank Jess Williams, Lil Blake, Nick Macgregor, Margarita Goumas, and Brendan Tiernan at Parks Australia. We also thank Cecile Espigole and Paul Roe for their assistance in the field, Sheryn Brodie for uploading all audio files to Ecosounds and for field assistance, and Matthew Quin for support with the occupancy analysis. Field work was conducted under James Cook University Animal Ethics Approval number A2907. References Agius K (2023) Island settings and their influence on geographical research methods. Geog Res 61(1):117–126. https://doi.org/10.1111/1745-5871.12571 Allen-Ankins S, Hoefer S, Bartholomew J, Brodie S, Schwarzkopf L (2025) The use of BirdNET embeddings as a fast solution to find novel sound classes in audio recordings. Front Ecol Evol 12:1409407. https://doi.org/10.3389/fevo.2024.1409407 Andrew P, Cogger HG, Driscoll D, Flakus S, Harlow P, Maple D, Misso M, Caitlin P, Retallick K, Rose K, Tiernan B, West J, Woinarski JCZ (2018) Somewhar saved: a captive breeding programme for two endemic Christmas Island lizard species, now extinct in the wild. Oryx 52(1):171–174. https://doi.org/10.1017/S0030605316001071 Andrews CW (1900) A Monograph of Christmas Island (Indian Ocean): Physical Features and Geology. British Museum (Natural History), London, England Barnett LK, Phillips BL, Hoskin CJ (2017) Going feral: Time and propagule pressure determine range expansion of Asian house geckos into natural environments. Austral Ecol 42(2):165–175. https://doi.org/10.1111/aec.12416 Bellard C, Cassey P, Blackburn TM (2016) Alien species as a driver of recent extinctions. Biol Lett 12(2):20150623. https://doi.org/10.1098/rsbl.2015.0623 Blumstein DT, Mennill DJ, Clemins P, Girod L, Yao K, Patricelli G, Deppe JL, Krakauer AH, Clark C, Cortopassi KA (2011) Acoustic monitoring in terrestrial environments using microphone arrays: applications, technological considerations and prospectus. J Appl Ecol 48(3):758–767. https://doi.org/10.1111/j.1365-2664.2011.01993.x Bomford M, Kraus F, Barry SC, Lawrence E (2009) Predicting establishment success for alien reptiles and amphibians: a role for climate matching. Biol Invasions 11(3):713–724. https://doi.org/10.1007/s10530-008-9285-3 Bota G, Manzano-Rubio R, Fanlo H, Franch N, Brotons L, Villero D, Devisscher S, Pavesi A, Cavaletti E, Pérez-Granados C (2024) Passive acoustic monitoring and automated detection of the American bullfrog. Biol Invasions 26(4):1269–1279. https://doi.org/10.1007/s10530-023-03244-8 Case TJ, Bolger DT, Petren K (1994) Invasions and Competitive Displacement among House Geckos in the Tropical Pacific. Ecol 75(2):464–477. https://doi.org/10.2307/1939550 Claussen J (2005) Native Plants of Christmas Island. CSIRO Publishing Cogger HG, Sadlier R, Cameron E (1983) The terrestrial reptiles of Australia's island territories. Australian National Parks and Wildlife Service, Canberra Cogger HG, Sadlier R (1998) The terrestrial reptiles of Christmas Island: a reappraisal of their status. Australian Museum, Sydney Cole NC, Jones CG, Harris S (2005) The need for enemy-free space: The impact of an invasive gecko on island endemics. Biol Conserv 125(4):467–474. https://doi.org/10.1016/j.biocon.2005.04.017 Dame EA, Petren K (2006) Behavioural mechanisms of invasion and displacement in Pacific island geckos ( Hemidactylus ). Anim Behav 71(5):1165–1173. https://doi.org/10.1016/j.anbehav.2005.10.009 Darras K, Batáry P, Furnas BJ, Grass I, Mulyani YA, Tscharntke T (2019) Autonomous sound recording outperforms human observation for sampling birds: a systematic map and user guide. Ecol Appl 29(6):e01954. https://doi.org/10.1002/eap.1954 Dubos N, Porcel X, Roesch MA, Claudin J, Pinel R, Probst JM, Deso G (2023) A bird's-eye view: Evaluating drone imagery for the detection and monitoring of endangered and invasive day geckos. Biotropica 55(5):911–919. https://doi.org/10.1111/btp.13240 Dueñas MA, Hemming DJ, Roberts A, Diaz-Soltero H (2021) The threat of invasive species to IUCN-listed critically endangered species: A systematic review. Glob Ecol Conserv 26:e01476. https://doi.org/10.1016/j.gecco.2021.e01476 Emery JP, Hollanders M, Valentine L, Tiernan B, Retallick K, Cogger H, Woinarski J, Mitchell N (2025) Evaluation of trial reintroductions of two extinct in the wild reptile species on Christmas Island. Anim Conserv 28(1):19–32. https://doi.org/10.1111/acv.12940 Emery JP, Mitchell NJ, Cogger H, Agius J, Andrew P, Arnall S, Detto T, Driscoll DA, Flakus S, Green P (2021) The lost lizards of Christmas Island: A retrospective assessment of factors driving the collapse of a native reptile community. Conserv Sci Pract 3(2):e358. https://doi.org/10.1111/csp2.358 Fernández-Palacios JM, Kreft H, Irl SDH, Norder S, Ah-Peng C, Borges PA, Burns KC, de Nascimento L, Meyer JY, Montes E, Drake DR (2021) Scientists' warning - The outstanding biodiversity of islands is in peril. Glob Ecol Conserv 31:e01847. https://doi.org/10.1016/j.gecco.2021.e01847 Frenkel C (2006) Hemidactylus frenatus (Squamata: Gekkonidae): call frequency, movement and condition of tail in Costa Rica. Rev Biol Trop 54(4):1125–1130 Garner AM, Pamfilie AM, Hamad EJ, Kindig R, Taylor JT, Unsworth CK, Niewiarowski PH (2020) Home-field advantage: native gecko exhibits improved exertion capacity and locomotor ability in structurally complex environments relative to its invasive counterpart. Front Zool 17:23. https://doi.org/10.1186/s12983-020-00368-8 Gibb R, Browning E, Glover-Kapfer P, Jones KE (2019) Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods Ecol Evol 10(2):169–185. https://doi.org/10.1111/2041-210X.13101 Hoefer S, McKnight DT, Allen-Ankins S, Nordberg EJ, Schwarzkopf L (2023) Passive acoustic monitoring in terrestrial vertebrates: a review. Bioacoustics 32(5):506–531. https://doi.org/10.1080/09524622.2023.2209052 Hoefer S, McKnight DT, Allen-Ankins S, Nordberg EJ, Schwarzkopf L (2024) Diverse Methods for Diverse Systems: A Large-Scale Comparison of Reptile Sampling Methods. Herpetol 80(1):40–50. https://doi.org/10.1655/Herpetologica-D-23-00022.1 Holden MH, Nyrop JP, Ellner SP (2016) The economic benefit of time-varying surveillance effort for invasive species management. J Appl Ecol 53(3):712–721. https://doi.org/10.1111/1365-2664.12617 Hopkins JM, Higgie M, Hoskin CJ (2021) Calling behaviour in the invasive Asian house gecko ( Hemidactylus frenatus ) and implications for early detection. Wildl Res 48(2):152–162. https://doi.org/10.1071/wr20003 Hoskin CJ (2011) The invasion and potential impact of the Asian House Gecko ( Hemidactylus frenatus ) in Australia. Austral Ecol 36(3):240–251. https://doi.org/10.1111/j.1442-9993.2010.02143.x Jackson HB, Fahrig L (2015) Are ecologists conducting research at the optimal scale? Glob Ecol Biogeogr 24(1):52–63. https://doi.org/10.1111/geb.12233 James DJ, Retallick K (2008) Christmas Island biodiversity monitoring program: December 2003 to April 2007. Report to the Department of Finance and Deregulation from the Director of National Parks, Christmas Island Jono T, Inui Y (2012) Secret calls from under the eaves: acoustic behavior of the Japanese house gecko, Gekko japonicus . Copeia 2012(1):145–149. https://doi.org/10.1643/CE-10-169 Juanes F (2018) Visual and acoustic sensors for early detection of biological invasions: Current uses and future potential. J Nat Conserv 42:7–11. https://doi.org/10.1016/j.jnc.2018.01.003 Kahl S, Wood CM, Eibl M, Klinck H (2021) BirdNET: A deep learning solution for avian diversity monitoring. Ecol Inf 61:101236. https://doi.org/10.1016/j.ecoinf.2021.101236 Kellner KF, Fowler NL, Petroelje TR, Kautz TM, Beyer DE Jr, Belant JL (2022) ubms: an R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods Ecol Evol 13(3):577–584. https://doi.org/10.1111/2041-210X.13777 Kellner K (2024) ubms: Bayesian Models for Data from Unmarked Animals using ‘Stan’. R package version 1.2.7 Knock N (2015) Common Method Bias in PLS-SEM: A full collinearity assessment approach. IJeC 11(4):1–10. https://doi.org/10.4018/ijec.2015100101 Kraus F (2009) Alien reptiles and amphibians: a scientific compendium and analysis. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8946-6 Kurita T (2013) Current Status of the Introduced Common House Gecko, Hemidactylus frenatus (Squamata: Gekkonidae), on Amamioshima Island of the Ryukyu Archipelago, Japan. Curr Herpetol 32(1):50–60. https://doi.org/10.5358/hsj.32.50 Lardner B, Rodda GH, Adams AAY, Savidge JA, Reed RN (2015) Detection rates of geckos in visual surveys: turning confounding variables into useful knowledge. J Herpetol 49(4):522–532. https://doi.org/10.1670/14-048 Leung FKW, Schwarzkopf L, Allen-Ankins S (2025) Advancing invasive species monitoring: A free tool for detecting invasive cane toads using continental-scale data. Ecol Inf 89:103172. https://doi.org/10.1016/j.ecoinf.2025.103172 MacKenzie DI, Bailey LL (2004) Assessing the fit of site-occupancy models. J Agric Biol Environ Stat 9(3):300–318. https://doi.org/10.1198/108571104x3361 MacKenzie DI, Nichols JD, Lachman GB, Droege S, Andrew Royle J, Langtimm CA (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecol 83(8):2248–2255. https://doi.org/10.1890/0012-9658( 2002)083[2248:ESORWD]2.0.CO;2 Mackenzie DI, Royle JA (2005) Designing occupancy studies: general advice and allocating survey effort. J Appl Ecol 42(6):1105–1114. https://doi.org/10.1111/j.1365-2664.2005.01098.x Marcellini DL (1977) Acoustic and visual display behavior of gekkonid lizards. Am Zool 17(1):251–260. https://doi.org/10.1093/icb/17.1.251 Marcellini DL (1971) Activity Patterns of the Gecko Hemidactylus frenatus . Copeia 1971(4):631–635. https://doi.org/10.2307/1442631 Marcellini DL (1974) Acoustic Behaviour of the Gekkonid Lizard Hemidactylus frenatus. Herpetol 30(1):44–52 McEwen B, Bainbridge-Smith A, Gutschmidt S, Green R, Atlas J (2024) An invasive species model and dataset for bioacoustic monitoring of common brushtail possum. N Z J Ecol 48(1):1–6. https://doi.org/10.20417/nzjecol.48.3566 McKay JL, Griffiths AD, Crase B (2009) Distribution and habitat use by Hemidactylus frenatus Dumeril and Bibron (Gekkonidae) in the Northern Territory, Australia. Beagle 25:111–116. https://doi.org/10.5962/p.287454 McKnight DT, Harmon JR, McKnight JL, Ligon DB (2015) Taxanomic Biases of Seven Methods Used to Survey a Diverse Herpetofaunal Community. Herpetol Conserv Biol 10(2):666–678 Michael DR, Cunningham RB, Donnelly CF, Lindenmayer DB (2012) Comparative use of active searches and artificial refuges to survey reptiles in temperate eucalypt woodlands. Wildl Res 39(2):149–162. https://doi.org/10.1071/wr11118 Naimi B, Hamm NAS, Groen TA, Skidmore AK, Toxopeus AG (2014) Where is positional uncertainty a problem for species distribution modelling? Ecography 37(2):191–203. https://doi.org/10.1111/j.1600-0587.2013.00205.x Newbery B, Jones DN (2007) Presence of Asian House Gecko Hemidactylus frenatus across an urban gradient in Brisbane: influence of habitat and potential for impact on native gecko species. In: Lunmey D, Eby P, Hutchings, Burgin S (eds) Pest or Guest: The Zoology of Overabundance. Royal Zoological Society of New South Wales, Mosman, pp 59–65. http://dx.doi.org/10.7882/FS.2007.009 Nichols JD, Cooch EG (2025) Predictive models are indeed useful for causal inference. Ecol 106:e4517. https://doi.org/10.1002/ecy.4517 Nordberg EJ, Denny R, Schwarzkopf L (2021) Testing measures of boldness and exploratory activity in native versus invasive species: geckos as a model system. Anim Behav 177:215–222. https://doi.org/10.1016/j.anbehav.2021.05.013 Nordberg EJ, Schwarzkopf L (2022) Afraid of the Dark? The Influence of Natural and Artificial Light at Night on the Behavioral Activity of a Nocturnal Gecko. Front Ecol Evol 10:821335. https://doi.org/10.3389/fevo.2022.821335 Nordstrom B, Mitchell N, Byrne M, Jarman S (2022) A review of applications of environmental DNA for reptile conservation and management. Ecol Evol 12(6):e8995. https://doi.org/10.1002/ece3.8995 Perella CD, Behm JE (2020) Understanding the spread and impact of exotic geckos in the greater Caribbean region. Biodivers Conserv 29(4):1109–1134. https://doi.org/10.1007/s10531-020-01939-1 Pérez-Granados C (2023) BirdNET: applications, performance, pitfalls and future opportunities. Ibis 165(3):1068–1075. https://doi.org/10.1111/ibi.13193 Petren K, Case TJ (1996) An experimental demonstration of exploitation competition in an ongoing invasion. Ecol 77(1):118–132. https://doi.org/10.2307/2265661 Petren K, Case TJ (1998) Habitat structure determines competition intensity and invasion success in gecko lizards. Proc Natl Acad Sci U S A 95(20):11739–11744. https://doi.org/10.1073/pnas.95.20.11739 R Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria Rappaport DI, Royle JA, Morton DC (2020) Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes. Ecol Indic 113:106172. https://doi.org/10.1016/j.ecolind.2020.106172 Ribeiro JW Jr, Harmon K, Leite GA, de Melo TN, LeBien J, Campos-Cerqueira M (2022) Passive Acoustic Monitoring as a Tool to Investigate the Spatial Distribution of Invasive Alien Species. Remote Sens 14(18):4565. https://doi.org/10.3390/rs14184565 Richardson E, Nimmo DG, Avitabile S, Tworkowski L, Watson SJ, Welbourne D, Leonard SWJ (2017) Camera traps and pitfalls: an evaluation of two methods for surveying reptiles in a semiarid ecosystem. Wildl Res 44(8):637–647. https://doi.org/10.1071/wr16048 Rocha S, Trinks A, Harris DJ, Larson G, Cheke AS (2022) The Global and Western Indian Ocean Dispersal of House Geckos From Asia Using Historical and Mitochondrial DNA Perspectives. Front Ecol Evol 10:791762. https://doi.org/10.3389/fevo.2022.791762 Rödder D, Solé M, Böhme W (2008) Predicting the potential distributions of two alien invasive Housegeckos (Gekkonidae: Hemidactylus frenatus , Hemidactylus mabouia ). North-west J Zool 4(2):236–246 Rose K, Aguis J, Hall J, Thompson P, Eden JS, Srivastava M, Tiernan B, Jenkins C, Phalen D (2017) Emergent multisystemic Enterococcus infection threatens endangered Christmas Island reptile populations. PLoS ONE 12(7):e0181240. https://doi.org/10.1371/journal.pone.0181240 Russell JC, Meyer JY, Holmes ND, Pagad S (2017) Invasive alien species on islands: impacts, distribution, interactions and management. Environ Conserv 44(4):359–370. https://doi.org/10.1017/S0376892917000297 Simberloff D (2009) The role of propagule pressure in biological invasions. Annu Rev Ecol Evol Syst 40(1):81–102. https://doi.org/10.1146/annurev.ecolsys.110308.120304 Smith MJ, Cogger H, Tiernan B, Maple D, Boland C, Napier F, Detto T, Smith P (2012) An oceanic island reptile community under threat: the decline of reptiles on Christmas Island, Indian Ocean. Herpetol Conserv Biol 7(2):206–218 Spatz DR, Zilliacus KM, Holmes ND, Butchart SHM, Genovesi P, Ceballos G, Tershy BR, Croll DA (2017) Globally threatened vertebrates on islands with invasive species. Sci Adv 3(10):e1603080. https://doi.org/10.1126/sciadv.1603080 Staniewicz AM (2020) Acoustic Communication of Rare and Threatened Crocodilians and Its Use for Population Monitoring. Dissertation., University of Bristol Stohlgren TJ, Schnase JL (2006) Risk analysis for biological hazards: what we need to know about invasive species. Risk Anal 26(1):163–173. https://doi.org/10.1111/j.1539-6924.2006.00707.x Sugai LSM, Silva TSF, Ribeiro JW Jr, Llusia D (2019) Terrestrial passive acoustic monitoring: review and perspectives. Biosci 69(1):15–25. https://doi.org/10.1093/biosci/biy147 Tershy BR, Shen KW, Newton KM, Holmes ND, Croll DA (2015) The importance of islands for the protection of biological and linguistic diversity. Biosci 65(6):592–597. https://doi.org/10.1093/biosci/biv031 Thieurmel B, Elmarhraoui A, Thieurmel MB (2019) Package ‘suncalc’. R package version 0.5.1 Vanderduys EP, Kutt AS (2012) Is the Asian house gecko, Hemidactylus frenatus , really a threat to Australia's biodiversity? Aust J Zool 60(6):361–367. https://doi.org/10.1071/zo12077 Vats D, Knudson C (2021) Revisiting the Gelman-Rubin Diagnostic. Stat Sci 36(4):518–529. https://doi.org/10.1214/20-sts812 Vergne AL, Pritz MB, Mathevon N (2009) Acoustic communication in crocodilians: from behaviour to brain Weterings R, Vetter KC (2018) Invasive house geckos ( hemidactylus spp.): their current, potential and future distribution. Curr Zool 64(5):559–573. https://doi.org/10.1093/cz/zox052 Wood CM, Günther F, Rex A, Hofstadter DF, Reers H, Kahl S, Peery MZ, Klinck H (2024) Real-time acoustic monitoring facilitates the proactive management of biological invasions. Biol Invasions 26:3989–3996. https://doi.org/10.1007/s10530-024-03426-y Yates L, Aandahl Z, Richards SA, Brook BW (2022) Cross validation for model selection: A primer with examples from ecology. Ecol Monogr 93(1):e1557. https://doi.org/10.1002/ecm.1557 Yu X, Peng Y, Aowphol A, Ding L, Brauth S, Tang YZ (2011) Geographic variation in the advertisement calls of Gekko gecko in relation to variations in morphological features: implications for regional population differentiation. Ethol Ecol Evol 23(3):211–228. https://doi.org/10.1080/03949370.2011.566581 Zozaya SM, Alford RA, Schwarzkopf L (2015) Invasive house geckos are more willing to use artificial lights than are native geckos. Austral Ecol 40(8):982–987. https://doi.org/10.1111/aec.12287 Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 17 Jul, 2025 Editor invited by journal 19 Jun, 2025 Editor assigned by journal 12 Jun, 2025 First submitted to journal 11 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6876411","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486837160,"identity":"14aa5b1a-0544-4ba5-b611-565d2a97d63a","order_by":0,"name":"Jacopo I Bartholomew","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0009-2661-547X","institution":"James Cook University - Townsville City Campus: James Cook University","correspondingAuthor":true,"prefix":"","firstName":"Jacopo","middleName":"I","lastName":"Bartholomew","suffix":""},{"id":486837161,"identity":"379c0336-c2d6-448a-aaec-4674d9ac90cf","order_by":1,"name":"Lin Schwarzkopf","email":"","orcid":"","institution":"James Cook University - Townsville City Campus: James Cook University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Schwarzkopf","suffix":""},{"id":486837162,"identity":"0433fc25-2ffe-44ee-9855-2c6a77b8a133","order_by":2,"name":"Slade Allen-Ankins","email":"","orcid":"https://orcid.org/0000-0002-7902-0455","institution":"James Cook University - Townsville City Campus: James Cook University","correspondingAuthor":false,"prefix":"","firstName":"Slade","middleName":"","lastName":"Allen-Ankins","suffix":""}],"badges":[],"createdAt":"2025-06-12 04:35:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6876411/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6876411/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87324144,"identity":"44abb49d-6b4a-49f2-9ae9-a3dc8adf7ca6","added_by":"auto","created_at":"2025-07-22 17:10:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":784338,"visible":true,"origin":"","legend":"\u003cp\u003eChristmas Island showing acoustic recorder deployment sites, vegetation types, and infrastructure. Solid white lines denote main roads, and dotted white lines denote tracks. Vegetation types include closed-canopy evergreen forest, semi-deciduous scrub, semi-deciduous forest, perennial wetland forest, coastal herbland, and disturbed areas.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6876411/v1/62c52f7fb7c583a24305cd4a.png"},{"id":87324141,"identity":"8dfb2f6d-75ca-4f26-ab48-4dfa957cc80a","added_by":"auto","created_at":"2025-07-22 17:10:55","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101592,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHemidactylus frenatus\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003edetection summary. \u003cstrong\u003e(A) \u003c/strong\u003eDistribution of the proportion of surveys with detections across sites.\u003cstrong\u003e (B)\u003c/strong\u003e Effect of daily minimum temperature on detection probability (solid line) with 95% credible intervals (dotted lines).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6876411/v1/8080b17fcacdb0032f67b7d5.jpeg"},{"id":87324146,"identity":"1cee9739-3dca-41cc-baf2-4a7eed84dfd0","added_by":"auto","created_at":"2025-07-22 17:10:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":335868,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial predictors of \u003cem\u003eHemidactylus frenatus\u003c/em\u003e site use on Christmas Island. \u003cstrong\u003e(A) \u003c/strong\u003ePredicted probability of site use using median posterior beta estimates from the most parsimonious occupancy model, which included the effect of canopy height and elevation.\u003cstrong\u003e(B) \u003c/strong\u003ePredicted effect of elevation on \u003cem\u003eH. frenatus\u003c/em\u003e site use. \u003cstrong\u003e(C) \u003c/strong\u003ePredicted effect of canopy height on site use. Dotted lines represent 95% credible intervals.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6876411/v1/a4d1dfab3154840f28556f1d.png"},{"id":87325382,"identity":"c4b20712-ecf3-42ea-b64c-c102f9020c0f","added_by":"auto","created_at":"2025-07-22 17:26:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1672323,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6876411/v1/5e7f8132-cb3f-460a-99d2-9f1afae48f1f.pdf"},{"id":87324142,"identity":"fb1195f5-8263-448f-9ef5-301de5d75255","added_by":"auto","created_at":"2025-07-22 17:10:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16193,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6876411/v1/458d81f0595e7ad3a88fc12d.docx"}],"financialInterests":"","formattedTitle":"Using passive acoustic monitoring to investigate the occurrence of invasive Asian house geckos (Hemidacylus frenatus) on an Oceanic Island","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIsland-endemics are disproportionally threatened compared to other terrestrial vertebrates (Tershy et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and invasive species are recognized as the primary driver of their decline (Tershy et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bellard et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Spatz et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Due\u0026ntilde;as et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Human-inhabited islands tend to experience high propagule pressure, leading to frequent alien species establishment (Simberloff \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Russell et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Further, species restricted to a single island or island archipelago typically have small population sizes, limited distributions, and behavioural naivety to novel predators and competitors, amplifying their susceptibility to the impacts of invaders (Fern\u0026aacute;ndez-Palacios et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While prevention of novel introductions is crucial to conserve island communities (Holden et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), non-native and invasive species already exist on most islands with vulnerable endemics and require control (Spatz et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA key prerequisite to manage invasive species is to establish their state of invasion, which requires knowledge of their distribution (Stohlgren and Schnase \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Traditionally, manual surveying techniques are used for species detection and monitoring, but they can be costly, time-consuming, and usually require trained observers. Manual surveys are especially challenging on islands, which might be hard to reach, or difficult to stay on for extended periods (Agius \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e making these data difficult to obtain, especially for cryptic invaders. Thus, novel monitoring methods capable of collecting reliable data efficiently are necessary to establish invasive species distributions in remote areas.\u003c/p\u003e\u003cp\u003ePassive acoustic monitoring (PAM) is an emerging method to detect and monitor soniferous organisms (Blumstein et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sugai et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). PAM involves recording environmental audio using autonomous recording units (ARUs) and subsequently identifying target sounds and vocalizations in the recorded audio. Recent advances in machine learning algorithms to automate the sound recognition process (e.g., Kahl et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the arrival of accessible, low-cost acoustic recorders and data storage systems have enabled PAM to become an efficient method to monitor several vertebrate groups (Darras et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gibb et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This extends to invasive terrestrial vertebrates (Juanes \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ribeiro et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), in which early applications of PAM to establish the occurrence of invasive amphibians (Bota et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Leung et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), mammals (McEwen et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and birds (Wood et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have been successful. However, its efficacy to detect and monitor soniferous reptiles has not been established (McKnight et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hoefer et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMany reptiles are encountered infrequently during standard active search surveys, making informed survey selection imperative for efficient reptilian monitoring. Methods such as artificial refugia replication (Michael et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and passive trapping techniques have been developed and demonstrated to improve reptilian encounter rates (Hoefer et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while remote sensing approaches remain largely unassessed and rarely applied (but see McKnight et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Richardson et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nordstrom et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dubos et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Some geckos, a reptilian taxon with many successful invasives (Kraus \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), use acoustic signalling for social communication (Marcellini \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1977\u003c/span\u003e), potentially making PAM a viable monitoring tool. Introduced geckos have high establishment success rates (Bomford et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and sometimes competitively displace resident geckos in urban and natural settings, but their impacts are understudied and underestimated (Perella and Behm \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). One of the most successful gekkonid invaders is the Asian house gecko (\u003cem\u003eHemidactylus frenatus\u003c/em\u003e) (Bomford et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), a moderately sized generalist predator probably native to Southeast Asia. This species thrives in human developments and disturbed habitats, which facilitate its human-mediated dispersal, resulting in a now pantropical non-native distribution (Weterings and Vetter \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Following its introduction, the Asian house gecko has been implicated in the decline of numerous native geckos on various islands (Case et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Cole et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Dame and Petren \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, Asian house geckos have shown inconsistent invasion success into natural habitats across their introduced range, prompting interest concerning their potential to invade seemingly resistant native communities (Newbery and Jones \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; McKay et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hoskin \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Vanderduys and Kutt \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Barnett et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the mechanisms which shape their establishment success (Petren and Case \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Zozaya et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Garner et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAsian house geckos produce a loud and distinctive \u0026lsquo;multiple chirp\u0026rsquo; call enabling their detection through active listening surveys (Barnett et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and through manual inspection of recorded audio (Hopkins et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As such, large-scale efforts to detect this gecko may be substantially improved through the application of PAM. Additionally, as PAM can yield both detections and non-detections, the implementation of more robust predictive modelling frameworks, like occupancy models (MacKenzie et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), are available to identify habitat features associated with invasion resistance and to infer probabilistic cause of the gecko\u0026rsquo;s exclusion (Nichols and Cooch \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Thus, PAM may provide a useful monitoring tool capable of assessing the risk of further spread and providing support to hypothesized mechanisms shaping the gecko\u0026rsquo;s occurrence.\u003c/p\u003e\u003cp\u003eChristmas Island is a moderately-sized external Australian territory (135 km\u0026sup2;) located in the north-east Indian Ocean (10\u0026deg;25` S, 105\u0026deg;42` E). Once home to five native lizard species, including two geckos, Christmas Island has undergone a series of reptilian extirpations and extinctions (Smith et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Emery et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Today, the Christmas Island giant gecko (\u003cem\u003eCyrtodactylus sadlieri\u003c/em\u003e) is the only native lizard that remains, while the Lister\u0026rsquo;s gecko (\u003cem\u003eLepidodactylus\u003c/em\u003e listeri) and the Christmas Island blue-tailed skink (\u003cem\u003eCryptoblepharus egeriae\u003c/em\u003e) persist only in captivity (Andrew et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Emery et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Following the decline of native reptiles, introduced Asian house geckos expanded from anthropogenically disturbed areas into natural habitats (Cogger and Sadlier \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Smith et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This range shift has raised concerns regarding its ecological impact, including potential competition with any future reintroduced native reptiles, and its role as a vector of disease (Rose 2017; Emery et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, describing the distribution of the Asian house gecko on Christmas Island and establishing efficient methods for ongoing monitoring are critical.\u003c/p\u003e\u003cp\u003eIn this study, we begin by evaluating PAM as a method to detect Asian house geckos using an established acoustic recorder network on Christmas Island. Next, we use the resulting detection data to test broad hypotheses pertaining to the site characteristics associated with the gecko\u0026rsquo;s occurrence. These hypotheses are: (1) Asian house gecko occurrence is primarily driven by proximity to anthropogenic disturbance, (2) Asian house gecko occurrence is primarily driven by habitat characteristics, and (3) Asian house gecko occurrence is associated with both anthropogenic disturbance and habitat characteristics.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Site\u003c/b\u003e\u003c/p\u003e\u003cp\u003eChristmas Island is characterized by limestone coastal cliffs, marginal inland cliffs, and terraces that surround a large central plateau that rises to 361 m (Andrews \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1900\u003c/span\u003e). Five broad native habitat types occur, each closely linked with soil depth (Claussen \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The deep soils of the central plateau support a closed-canopy evergreen forest, where canopy heights reach up to 40 m. In shallower soils surrounding this forest, a closed-canopy semi-deciduous forest occurs, distinguished by a lower canopy of 15\u0026ndash;30 m. On the island\u0026rsquo;s margins, where steep slopes and terraces bear very shallow soils, semi-deciduous scrub dominates, featuring a dense understory and a canopy height of 10\u0026ndash;15 m. Lastly, coastal herbland occupies exposed areas between the scrub and coastal cliffs where harsh conditions limit the growth of most flora. The island has an established road network that connects the main settlement located in the northeast to several phosphate mines, trails, and isolated developments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The various anthropogenic disturbances and diverse native habitats of Christmas Island combined with the expansion of Asian house geckos into natural habitats make it an ideal system in which to investigate environmental factors linked with the invasive gecko\u0026rsquo;s occurrence pattern.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSampling design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe ARU network used in this study was established as part of the \u0026lsquo;Managing and monitoring resilience in Australia\u0026rsquo;s national parks\u0026rsquo; project under the Resilient Landscapes Hub of the National Environment and Science Program (NESP) to test the efficacy of a network of ARUs to monitor the island\u0026rsquo;s threatened forest birds and flying fox. Although not originally intended to detect invasive geckos, the network design did not require modification for this purpose.\u003c/p\u003e\u003cp\u003eARU deployment locations were positioned no closer together than 350m (mean\u0026thinsp;=\u0026thinsp;803.04 m, sd\u0026thinsp;=\u0026thinsp;200.55 m) and were selected to represent environmental variation on the island while maintaining site accessibility \u003cem\u003evia\u003c/em\u003e placement in proximity to roads or tracks. At each site, one Long Term Bioacoustic Recorder (BAR-LT\u0026trade;, Frontier Labs) was secured on a tree 2 m above the ground and set to record 24 hours a day at a sampling rate of 44.1 kHz in .wav file format. On the 5th of May 2023, eighty-one ARUs began recording and continued until their batteries were depleted, SD cards were full, or a malfunction occurred. After collection, acoustic recordings were archived and managed using Ecosounds (QUT Ecoacoustics, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ecosounds.org\u003c/span\u003e\u003cspan address=\"https://www.ecosounds.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAcoustic Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo standardise varying ARU recording durations, we used a survey period from May 5th to June 4th, providing up to 31 days of continuous recording at each site. A survey event was defined as occurring between 18:00 and 06:00 h as Asian house geckos are nocturnal with calling activity peaks at sunset and 30 minutes before sunrise (Hopkins et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We searched for gecko vocalizations in the acoustic recordings using BirdNET v2.4 embeddings, a novel, semi-automated method for rapid species search and detection in audio data (Kahl et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Allen-Ankins et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This approach separates the recorded audio into non-overlapping, 3-second segments and measures each segment\u0026rsquo;s similarity to a selected audio segment of a target sound based on the embedding\u0026rsquo;s representation of those sounds from the BirdNET model. Similarity is quantified by measuring the Euclidean distance between the embeddings of the target vocalisation and the embeddings of the audio segments selected for search, where a lower distance indicates greater similarity and a greater chance of being an instance of the target vocalisation. A \u0026lsquo;multiple chirp\u0026rsquo; call was sourced from a previous laboratory study on the gecko\u0026rsquo;s acoustic behaviour to serve as our reference audio segment (Hopkins et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). From each survey, the most similar audio segment to our reference segment was selected for manual inspection and used to determine if a gecko was detected on a survey night. All manual annotation was done using the software Kaleidoscope Lite (Wildlife Acoustics, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wildlifeacoustics.com\u003c/span\u003e\u003cspan address=\"https://www.wildlifeacoustics.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This approach allowed us to efficiently construct a detection/non-detection matrix under the assumption that the absence of a gecko vocalization in the selected audio segment denoted a non-detection on the respective survey night.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOccupancy Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo investigate the distribution of Asian house geckos on Christmas Island, and to test competing hypotheses about their occurrence patterns, we analysed gecko detection/non-detection data using a single-season occupancy model in a Bayesian framework with the \u003cem\u003eubms\u003c/em\u003e package (Kellner et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kellner \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in R v4.3.3 (R Core Team, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A basic assumption of occupancy models is that site occupancy status does not change during the survey period (i.e., sites remain \u0026lsquo;closed\u0026rsquo;) (MacKenzie et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). We acknowledge that, for mobile geckos, a 31-day survey period likely violates this assumption; however, it is important to use all available data to inform the current distribution of the Asian house gecko as it is an invasive species. In this context, we redefine \u0026lsquo;site occupancy\u0026rsquo; as \u0026lsquo;site use\u0026rsquo; to more accurately reflect the process being modelled (Mackenzie and Royle \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Occupancy models incorporate temporal and spatial variation in detection and occupancy probabilities through the inclusion of observation-level and site-level variables.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObservation-level variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe considered three variables to model variation in Asian house gecko detectability arising from differences in vocal activity based on evidence from previous studies. Specifically, minimum daily temperature (Marcellini \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) and daily rainfall (Marcellini \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1971\u003c/span\u003e; Cole et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), and moon fraction (Lardner et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Nordberg and Schwarzkopf \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were included. Meteorological data were sourced from the Australian Bureau of Meteorology\u0026rsquo;s Christmas Island weather station (station 200790), located in the north-east section of the island, and moon fraction was sourced using the \u003cem\u003esuncalc\u003c/em\u003e package in R (Thieurmel et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, site was included as a random effect to account for any unmeasured variation in gecko detection probability across sites.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSite-level variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used six spatial variables to develop candidate models aligned with our hypotheses of Asian house gecko site use patterns. Distance to nearest building, distance to nearest main road, and distance to nearest disturbance (composed of infrastructure, mining, and secondary vegetation growth) were included as predictors to test if site use is primarily driven by proximity to anthropogenic disturbance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, we included canopy height, elevation, and habitat type (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) as predictors to test if habitat characteristics are the primary driver of site use. Initially, habitat type contained six factors (Evergreen Forest n\u0026thinsp;=\u0026thinsp;34, Semi-deciduous Forest n\u0026thinsp;=\u0026thinsp;12, Semi-deciduous Scrub n\u0026thinsp;=\u0026thinsp;6, Coastal fringe vegetation n\u0026thinsp;=\u0026thinsp;1, Perennial Wetland Forest n\u0026thinsp;=\u0026thinsp;1, and Disturbed Areas n\u0026thinsp;=\u0026thinsp;27). However, due to under-sampling, we merged coastal fringe vegetation and perennial wetland forest with semi-deciduous scrub, as these habitats were closely associated. To ensure our measure of canopy height accurately reflected biological processes of interest, a buffer area for mean canopy height aggregation was determined by estimating its scale-of-effect, defined as the biologically relevant area particular to a spatial variable (Jackson and Fahrig \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To do this, we aggregated mean canopy height at different dimensions centred around the ARU using QGIS v3.34.0 (QGIS Development Team 2023) and retained the area that corresponded with the greatest model predictive performance. Univariate occupancy models were created for each aggregation scale (50 m, 100 m, 150 m, and 200 m radius buffers) in combination with the most parsimonious detection model (see model construction and selection section), and the 100 m buffer size was retained. Finally, we combined the best supported models from hypotheses 1 and 2 to test if both the proximity of anthropogenic disturbance and habitat characteristics best describe the gecko\u0026rsquo;s site use patterns.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel Construction and Selection\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrior to model construction, we assessed multicollinearity among site-level and observation-level variables using the \u003cem\u003eusdm\u003c/em\u003e package (Naimi et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). All variables were retained as no variance inflation factors scored above 3 (Knock \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Candidate models were run with default priors using four MCMC chains, each with 20,000 iterations and a burn-in of 10,000. Chain convergence and mixture were evaluated by visually inspecting trace plots, and we ensured all parameters had effective sample sizes greater than 400, and Gelman\u0026ndash;Rubin diagnostic values below 1.01 (Vats and Knudson \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCandidate detection and occupancy models were constructed by initially including all relevant variables, then iteratively removing the least informative variable until none remained. Model fit for each candidate model was assessed using \u003cem\u003ek\u003c/em\u003e-fold cross-validation with 10 folds, and the most parsimonious model was identified as the one with the highest expected log pointwise predictive density (elpd) value (Yates et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The model selection process began by selecting the most parsimonious detection model while holding occupancy at its intercept. Then the most parsimonious occupancy models for hypotheses 1 and 2 were selected and combined to form a third model representing hypothesis 3. The hypothesis with the highest elpd value was considered most supported, and goodness-of-fit was assessed with the MacKenzie\u0026ndash;Bailey chi-square test to ensure the model adequately described the observed data (MacKenzie and Bailey \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe searched 29,640 hours of acoustic data collected over 2,470 survey nights across 81 sites for the Asian house gecko \u0026lsquo;multiple chirp\u0026rsquo; call using a semi-automated detection method. The audio recorders failed to record on 41 of 2,511 survey nights, with an average failure rate of 0.506 surveys per recorder and a maximum of 4 survey failures over the 31-day survey period. Gecko calls were detected in 1,239 surveys at 66 sites (na\u0026iuml;ve detection probability\u0026thinsp;=\u0026thinsp;0.616) and went undetected at 15 sites. Asian house gecko detection rates varied considerably among sites. For example, geckos were detected on every survey night at 14 sites, whereas at another 14 sites, detections occurred on only 1 to 5 survey nights (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe top performing detection model included site as a random effect (σ\u0026thinsp;=\u0026thinsp;3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37, 95% CI\u0026thinsp;=\u0026thinsp;2.39 to 3.82) and daily minimum temperature as a negative effect (β = -0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07, 95% CI = -0.47 to -0.19, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Daily rainfall and moon luminosity did not improve the model\u0026rsquo;s predictive performance (Online Resource 1). Asian house gecko site use was best explained by our most parsimonious habitat characteristics model (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Online Resource 1) which performed well in the MacKenzie\u0026ndash;Bailey chi-square test (posterior predictive p\u0026thinsp;=\u0026thinsp;0.646). This model indicates the probability of Asian house gecko site use decreased significantly with both canopy height (β = \u0026minus;\u0026thinsp;1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51, 95% CI: \u0026minus;\u0026thinsp;2.71 to \u0026minus;\u0026thinsp;0.72, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) and elevation (β = \u0026minus;\u0026thinsp;1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64, 95% CI: \u0026minus;\u0026thinsp;2.77 to \u0026minus;\u0026thinsp;0.28, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Upon the addition of predictors from the most parsimonious disturbance model for hypothesis 3 (Online Resource 1), model predictive performance decreased slightly (ΔELPD = -2.86; SE_DIFF\u0026thinsp;=\u0026thinsp;1.14, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), while predictive performance of the disturbance model, which included only distance to main roads and distance to buildings, was far worse (ΔELPD = -10.60) and had a similar predictive accuracy as the null occupancy model (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although habitat type was not a supported predictor in our top model, it was well distinguished by variation in elevation and canopy height. Closed canopy evergreen forest consistently occurred at higher elevations and had a taller canopy and was the only habitat type aligned with areas of low predicted Asian house gecko site use (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\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\u003eComparison of the most parsimonious candidate occupancy models for each hypothesis based on predictive performance, measured as the expected log predictive density (ELPD), for the Asian house gecko\u0026rsquo;s (\u003cem\u003eHemidactylus frenatus\u003c/em\u003e) site use patterns on Christmas Island. Reported are values are model ELPD, the difference in ELPD from the most supported model (Δ ELPD) and the standard error of that difference (ΔSE). Site as a random effect (rSite) and daily minimum temperature C (minTemp) are included in all detection models. Canopy height (CH), elevation (elev), distance to disturbances (dist_disturb) and distance to buildings (dist_build) are used as predictors to construct candidate occupancy models.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003eHypothesis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel formula\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eELPD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔ ELPD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSE of ΔELPD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHabitat Characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep(rSite\u0026thinsp;+\u0026thinsp;min temp)\u003c/p\u003e\u003cp\u003eψ(CH\u0026thinsp;+\u0026thinsp;elev)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-809.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep(rSite\u0026thinsp;+\u0026thinsp;min temp)\u003c/p\u003e\u003cp\u003eψ(CH\u0026thinsp;+\u0026thinsp;elev\u0026thinsp;+\u0026thinsp;dist disturb\u0026thinsp;+\u0026thinsp;dist build)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-812.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnthropogenic Disturbance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep(rSite\u0026thinsp;+\u0026thinsp;min temp)\u003c/p\u003e\u003cp\u003eψ(dist disturb + dist build)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-820.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-10.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNull\u003c/p\u003e\u003cp\u003eOccupancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep(rSite\u0026thinsp;+\u0026thinsp;min temp)\u003c/p\u003e\u003cp\u003eψ(.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-821.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-11.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.44\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\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe found that PAM was an effective survey method for Asian house geckos. By targeting the gecko\u0026rsquo;s \u0026lsquo;multiple chirp\u0026rsquo; call, we detected this invasive reptile in 61.6% of survey nights at occupied sites (i.e., where geckos were detected at least once). To our knowledge, this is the first demonstration of PAM as an effective method to detect and describe the distribution of Asian house geckos and, more broadly, reptiles in a natural setting. PAM has been successfully applied to investigate the acoustic characteristics and activity patterns of geckos (Yu et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hopkins et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), but not as a tool for species detection. Previous attempts to detect soniferous reptiles using PAM reported few or no detections largely because target species called infrequently, called at low volumes, or were absent (McKnight et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Staniewicz \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hoefer et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Besides this, PAM\u0026rsquo;s potential in reptilian systems has been largely overlooked (Hoefer et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While most reptiles are silent or only vocalize during copulatory or agonistic interactions, several geckos and crocodilians use acoustic signalling for communication, a behaviour which likely is under-described in this group (Vergne et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jono and Inui \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lin et al. 2024). Thus, the potential for using PAM as an alternative to resource-intensive active reptile surveys exists and should be explored further.\u003c/p\u003e\u003cp\u003eAcoustic surveys indicated that Asian house geckos were widely distributed across Christmas Island, occupying both native and disturbed habitats, but were notably absent from many sites on the island\u0026rsquo;s central plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). This distribution pattern aligns with the latest findings of island-wide observer-based surveys (Smith et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which employed methods such as spotlighting and active listening for gecko calls. The consistency between these independent survey techniques suggests that PAM-derived non-detections likely represent true absences rather than false negatives. Since the introduction of Asian house geckos on Christmas Island (~\u0026thinsp;ca. 1930s), they remained confined to disturbed areas until at least 1979 (Cogger 1983). Later, a survey in 1998 documented their expansion into undisturbed habitats (Cogger and Sadlier \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), which was further observed in surveys from 2004 to 2011 (James \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Smith et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Our findings suggest this expansion has stalled, with many sites on the central plateau remaining unoccupied, but continued monitoring is essential to determine whether the invasive gecko remains limited or continues its range expansion.\u003c/p\u003e\u003cp\u003eUsing an occupancy modelling framework, we show that Asian house gecko site use on Christmas Island was best explained by habitat characteristics rather than disturbance regimes, or a combination of both. Specifically, gecko presence was negatively associated with high elevation and tall canopy, features closely associated with the island\u0026rsquo;s closed canopy evergreen forest (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). On Amamioshima Island, Japan, Asian house geckos were similarly absent from primary broad-leaf evergreen forest, but abundant in urban environments and scrubby vegetation (Kurita \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In Australia\u0026rsquo;s Northern Territory, the opposite was observed, where dense canopy forest harboured established populations that were rare in eucalypt woodland (McKay et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Characteristics of natural habitats, such as increased structural complexity and a dispersed food resource distribution have been linked with a decrease in the gecko\u0026rsquo;s locomotive performance (Cole et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Garner et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and food consumption rate (Petren and Case \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Petren and Case \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), respectively. However, these disadvantages may only reduce fitness enough to exclude the gecko when combined with interspecific competition. The Christmas Island giant gecko (\u003cem\u003eCyrtodactylus sadlieri\u003c/em\u003e) is a large, endemic gecko that occurs across the island but reaches peak densities in the plateau\u0026rsquo;s evergreen forest (Cogger et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). This suggests that closed-canopy evergreen forests of the central plateau may have remained largely unoccupied by the invader, due to elevated competition with Christmas Island giant geckos. The exclusion of Asian house geckos from natural habitats, potentially due to competitive displacement from larger geckos, is observed in other systems (Newbery et al. 2007; Garner et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Further, a study on the gecko\u0026rsquo;s dispersal and establishment patterns on islands in the Indian Ocean found that despite a similar propagule pressure across islands in the Seychelles, Asian house geckos remained absent for much longer on those with native reptiles, compared to those without (Rocha et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAcoustic surveys can eliminate many of the confounding variables that influence Asian house gecko detectability in observer-based surveys, such as searcher and listener proficiencies, density of visual barriers, and headlamp type and brightness (Lardner et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As a result, data collected using acoustic sensors can provide more biologically meaningful estimates of the effect of environmental variables on detectability. Our detection model suggests that Asian house geckos were more acoustically detectable on cooler nights (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), which contrasts with previous studies reporting increased acoustic activity at higher temperatures (Marcellini \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Hopkins et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, our ability to estimate temperature effects on calling activity was constrained by the limited temperature variation that occurred over the duration of this study, and the coarse spatial resolution of available temperature data.\u003c/p\u003e\u003cp\u003eAsian house gecko detection rates varied considerably among sites. This was reflected in the substantial improvement in our model\u0026rsquo;s predictive performance when site was included as a random effect on detection probability, after accounting for observation-level variables influencing activity (i.e., minimum temperature, Online Resource 1). Several factors may contribute to site variation in detection rate, including (1) differences in site-level calling activity, (2) differences in acoustic recognizer performance, and (3) variation in ARU effective detection area. We suggest that variation in detection rates was primarily driven by variation in calling activity, which likely reflected differences in abundance, and the presence of transient individuals moving in and out of the detection space. Direct observations have shown Asian house gecko calling activity is positively correlated with abundance (Frenkel \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), affecting detection probability when acoustic recognizers have imperfect recall. Moreover, these geckos display a high tendency for exploratory behaviours in laboratory experiments (Nordberg et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) suggesting they may move in and out of sites, causing site occupancy status to change during the survey period. Acoustically similar sounds and background noise can reduce recognizer performance, especially when training data are limited (P\u0026eacute;rez-Granados \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, no recurring, interfering sounds were identified during manual annotation, and while few sites were exposed to ambient ocean noise, this did not appear to impact recognizer performance. Similarly, while variation in detection space can influence detectability (Rappaport et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), it is unlikely this alone explains the magnitude of variation in detectability observed. Our recorders were sufficiently far apart (~\u0026thinsp;350 m) that a single gecko would probably be unlikely to travel between them in one night. Further research into the species\u0026rsquo; movement and calling behaviour could inform our ability to infer relative abundance from acoustic data, an insight that would enhance the utility of PAM for long-term monitoring of this species. If attempted, however, it is important to note the \u0026lsquo;multiple chirp\u0026rsquo; call is only uttered by adult males (Hopkins et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and calls uttered by females should be included to avoid biases.\u003c/p\u003e\u003cp\u003eThe occupancy analysis was limited by somewhat outdated and potentially imprecise observation-level and site-level variables available for the occupancy analysis. Site-level variables were sourced from surveys conducted in 2011, although these still appeared to broadly reflect the island\u0026rsquo;s current characteristics based on site visits when recorders were set up, and recent satellite imagery. Additionally, minimum temperature and daily rainfall were gathered from a single weather station and applied uniformly across all sites. In this case, inferences were made with caution, and we recommend further investigation to confirm the observed weather variable associations in this study.\u003c/p\u003e\u003cp\u003eOur findings support PAM as an effective tool for monitoring an invasive gecko and we demonstrate its ability to generate data suitable for hypothesis testing and predictive distribution modelling. Using a semi-automated call detection method, we eliminated false positive detections, and although no concurrent validation method was used, comparisons with previous surveys suggest that false negative detections were minimal. PAM is underutilized to monitor reptiles and invasive species. This study further demonstrates its effective application, and we find it to be a promising tool that could greatly enhance conservation and invasive species management on islands.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors conceived the study. Jacopo Bartholomew performed the audio validation, statistical analysis, and visualisations with advice from Lin Schwarzkopf and Slade Allen-Ankins. Slade Allen-Ankins designed the audio analysis methodology. Jacopo Bartholomew wrote the draft manuscript and finalized the manuscript, with editorial input from Lin Schwarzkopf and Slade Allen-Ankins.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project was supported with funding from the Australian Government under the National Environmental Science Program\u0026rsquo;s Resilient Landscapes Hub and Parks Australia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProject management and collection of audio recordings was undertaken by Parks Australia Science Team with support from the Christmas Island National Park team. We thank Jess Williams, Lil Blake, Nick Macgregor, Margarita Goumas, and Brendan Tiernan at Parks Australia. We also thank Cecile Espigole and Paul Roe for their assistance in the field, Sheryn Brodie for uploading all audio files to Ecosounds and for field assistance, and Matthew Quin for support with the occupancy analysis. Field work was conducted under James Cook University Animal Ethics Approval number A2907.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgius K (2023) Island settings and their influence on geographical research methods. Geog Res 61(1):117\u0026ndash;126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1745-5871.12571\u003c/span\u003e\u003cspan address=\"10.1111/1745-5871.12571\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllen-Ankins S, Hoefer S, Bartholomew J, Brodie S, Schwarzkopf L (2025) The use of BirdNET embeddings as a fast solution to find novel sound classes in audio recordings. Front Ecol Evol 12:1409407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fevo.2024.1409407\u003c/span\u003e\u003cspan address=\"10.3389/fevo.2024.1409407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndrew P, Cogger HG, Driscoll D, Flakus S, Harlow P, Maple D, Misso M, Caitlin P, Retallick K, Rose K, Tiernan B, West J, Woinarski JCZ (2018) Somewhar saved: a captive breeding programme for two endemic Christmas Island lizard species, now extinct in the wild. Oryx 52(1):171\u0026ndash;174. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0030605316001071\u003c/span\u003e\u003cspan address=\"10.1017/S0030605316001071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndrews CW (1900) A Monograph of Christmas Island (Indian Ocean): Physical Features and Geology. British Museum (Natural History), London, England\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarnett LK, Phillips BL, Hoskin CJ (2017) Going feral: Time and propagule pressure determine range expansion of Asian house geckos into natural environments. Austral Ecol 42(2):165\u0026ndash;175. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/aec.12416\u003c/span\u003e\u003cspan address=\"10.1111/aec.12416\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBellard C, Cassey P, Blackburn TM (2016) Alien species as a driver of recent extinctions. Biol Lett 12(2):20150623. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rsbl.2015.0623\u003c/span\u003e\u003cspan address=\"10.1098/rsbl.2015.0623\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlumstein DT, Mennill DJ, Clemins P, Girod L, Yao K, Patricelli G, Deppe JL, Krakauer AH, Clark C, Cortopassi KA (2011) Acoustic monitoring in terrestrial environments using microphone arrays: applications, technological considerations and prospectus. J Appl Ecol 48(3):758\u0026ndash;767. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2664.2011.01993.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2664.2011.01993.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBomford M, Kraus F, Barry SC, Lawrence E (2009) Predicting establishment success for alien reptiles and amphibians: a role for climate matching. Biol Invasions 11(3):713\u0026ndash;724. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10530-008-9285-3\u003c/span\u003e\u003cspan address=\"10.1007/s10530-008-9285-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBota G, Manzano-Rubio R, Fanlo H, Franch N, Brotons L, Villero D, Devisscher S, Pavesi A, Cavaletti E, P\u0026eacute;rez-Granados C (2024) Passive acoustic monitoring and automated detection of the American bullfrog. Biol Invasions 26(4):1269\u0026ndash;1279. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10530-023-03244-8\u003c/span\u003e\u003cspan address=\"10.1007/s10530-023-03244-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCase TJ, Bolger DT, Petren K (1994) Invasions and Competitive Displacement among House Geckos in the Tropical Pacific. Ecol 75(2):464\u0026ndash;477. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1939550\u003c/span\u003e\u003cspan address=\"10.2307/1939550\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClaussen J (2005) Native Plants of Christmas Island. CSIRO Publishing\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCogger HG, Sadlier R, Cameron E (1983) The terrestrial reptiles of Australia's island territories. Australian National Parks and Wildlife Service, Canberra\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCogger HG, Sadlier R (1998) The terrestrial reptiles of Christmas Island: a reappraisal of their status. Australian Museum, Sydney\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCole NC, Jones CG, Harris S (2005) The need for enemy-free space: The impact of an invasive gecko on island endemics. Biol Conserv 125(4):467\u0026ndash;474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocon.2005.04.017\u003c/span\u003e\u003cspan address=\"10.1016/j.biocon.2005.04.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDame EA, Petren K (2006) Behavioural mechanisms of invasion and displacement in Pacific island geckos (\u003cem\u003eHemidactylus\u003c/em\u003e). Anim Behav 71(5):1165\u0026ndash;1173. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.anbehav.2005.10.009\u003c/span\u003e\u003cspan address=\"10.1016/j.anbehav.2005.10.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDarras K, Bat\u0026aacute;ry P, Furnas BJ, Grass I, Mulyani YA, Tscharntke T (2019) Autonomous sound recording outperforms human observation for sampling birds: a systematic map and user guide. Ecol Appl 29(6):e01954. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/eap.1954\u003c/span\u003e\u003cspan address=\"10.1002/eap.1954\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDubos N, Porcel X, Roesch MA, Claudin J, Pinel R, Probst JM, Deso G (2023) A bird's-eye view: Evaluating drone imagery for the detection and monitoring of endangered and invasive day geckos. Biotropica 55(5):911\u0026ndash;919. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/btp.13240\u003c/span\u003e\u003cspan address=\"10.1111/btp.13240\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDue\u0026ntilde;as MA, Hemming DJ, Roberts A, Diaz-Soltero H (2021) The threat of invasive species to IUCN-listed critically endangered species: A systematic review. Glob Ecol Conserv 26:e01476. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gecco.2021.e01476\u003c/span\u003e\u003cspan address=\"10.1016/j.gecco.2021.e01476\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEmery JP, Hollanders M, Valentine L, Tiernan B, Retallick K, Cogger H, Woinarski J, Mitchell N (2025) Evaluation of trial reintroductions of two extinct in the wild reptile species on Christmas Island. Anim Conserv 28(1):19\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/acv.12940\u003c/span\u003e\u003cspan address=\"10.1111/acv.12940\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEmery JP, Mitchell NJ, Cogger H, Agius J, Andrew P, Arnall S, Detto T, Driscoll DA, Flakus S, Green P (2021) The lost lizards of Christmas Island: A retrospective assessment of factors driving the collapse of a native reptile community. Conserv Sci Pract 3(2):e358. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/csp2.358\u003c/span\u003e\u003cspan address=\"10.1111/csp2.358\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-Palacios JM, Kreft H, Irl SDH, Norder S, Ah-Peng C, Borges PA, Burns KC, de Nascimento L, Meyer JY, Montes E, Drake DR (2021) Scientists' warning - The outstanding biodiversity of islands is in peril. Glob Ecol Conserv 31:e01847. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gecco.2021.e01847\u003c/span\u003e\u003cspan address=\"10.1016/j.gecco.2021.e01847\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrenkel C (2006) \u003cem\u003eHemidactylus frenatus\u003c/em\u003e (Squamata: Gekkonidae): call frequency, movement and condition of tail in Costa Rica. Rev Biol Trop 54(4):1125\u0026ndash;1130\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarner AM, Pamfilie AM, Hamad EJ, Kindig R, Taylor JT, Unsworth CK, Niewiarowski PH (2020) Home-field advantage: native gecko exhibits improved exertion capacity and locomotor ability in structurally complex environments relative to its invasive counterpart. Front Zool 17:23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12983-020-00368-8\u003c/span\u003e\u003cspan address=\"10.1186/s12983-020-00368-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGibb R, Browning E, Glover-Kapfer P, Jones KE (2019) Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods Ecol Evol 10(2):169\u0026ndash;185. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/2041-210X.13101\u003c/span\u003e\u003cspan address=\"10.1111/2041-210X.13101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoefer S, McKnight DT, Allen-Ankins S, Nordberg EJ, Schwarzkopf L (2023) Passive acoustic monitoring in terrestrial vertebrates: a review. Bioacoustics 32(5):506\u0026ndash;531. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09524622.2023.2209052\u003c/span\u003e\u003cspan address=\"10.1080/09524622.2023.2209052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoefer S, McKnight DT, Allen-Ankins S, Nordberg EJ, Schwarzkopf L (2024) Diverse Methods for Diverse Systems: A Large-Scale Comparison of Reptile Sampling Methods. Herpetol 80(1):40\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1655/Herpetologica-D-23-00022.1\u003c/span\u003e\u003cspan address=\"10.1655/Herpetologica-D-23-00022.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolden MH, Nyrop JP, Ellner SP (2016) The economic benefit of time-varying surveillance effort for invasive species management. J Appl Ecol 53(3):712\u0026ndash;721. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365-2664.12617\u003c/span\u003e\u003cspan address=\"10.1111/1365-2664.12617\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHopkins JM, Higgie M, Hoskin CJ (2021) Calling behaviour in the invasive Asian house gecko (\u003cem\u003eHemidactylus frenatus\u003c/em\u003e) and implications for early detection. Wildl Res 48(2):152\u0026ndash;162. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/wr20003\u003c/span\u003e\u003cspan address=\"10.1071/wr20003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoskin CJ (2011) The invasion and potential impact of the Asian House Gecko (\u003cem\u003eHemidactylus frenatus\u003c/em\u003e) in Australia. Austral Ecol 36(3):240\u0026ndash;251. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1442-9993.2010.02143.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1442-9993.2010.02143.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJackson HB, Fahrig L (2015) Are ecologists conducting research at the optimal scale? Glob Ecol Biogeogr 24(1):52\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/geb.12233\u003c/span\u003e\u003cspan address=\"10.1111/geb.12233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJames DJ, Retallick K (2008) Christmas Island biodiversity monitoring program: December 2003 to April 2007. Report to the Department of Finance and Deregulation from the Director of National Parks, Christmas Island\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJono T, Inui Y (2012) Secret calls from under the eaves: acoustic behavior of the Japanese house gecko, \u003cem\u003eGekko japonicus\u003c/em\u003e. Copeia 2012(1):145\u0026ndash;149. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1643/CE-10-169\u003c/span\u003e\u003cspan address=\"10.1643/CE-10-169\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJuanes F (2018) Visual and acoustic sensors for early detection of biological invasions: Current uses and future potential. J Nat Conserv 42:7\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jnc.2018.01.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jnc.2018.01.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKahl S, Wood CM, Eibl M, Klinck H (2021) BirdNET: A deep learning solution for avian diversity monitoring. Ecol Inf 61:101236. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoinf.2021.101236\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoinf.2021.101236\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKellner KF, Fowler NL, Petroelje TR, Kautz TM, Beyer DE Jr, Belant JL (2022) ubms: an R package for fitting hierarchical occupancy and N-mixture abundance models in a Bayesian framework. Methods Ecol Evol 13(3):577\u0026ndash;584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/2041-210X.13777\u003c/span\u003e\u003cspan address=\"10.1111/2041-210X.13777\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKellner K (2024) ubms: Bayesian Models for Data from Unmarked Animals using \u0026lsquo;Stan\u0026rsquo;. R package version 1.2.7\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnock N (2015) Common Method Bias in PLS-SEM: A full collinearity assessment approach. IJeC 11(4):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4018/ijec.2015100101\u003c/span\u003e\u003cspan address=\"10.4018/ijec.2015100101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKraus F (2009) Alien reptiles and amphibians: a scientific compendium and analysis. Springer, Dordrecht. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-4020-8946-6\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4020-8946-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKurita T (2013) Current Status of the Introduced Common House Gecko, \u003cem\u003eHemidactylus frenatus\u003c/em\u003e (Squamata: Gekkonidae), on Amamioshima Island of the Ryukyu Archipelago, Japan. Curr Herpetol 32(1):50\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5358/hsj.32.50\u003c/span\u003e\u003cspan address=\"10.5358/hsj.32.50\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLardner B, Rodda GH, Adams AAY, Savidge JA, Reed RN (2015) Detection rates of geckos in visual surveys: turning confounding variables into useful knowledge. J Herpetol 49(4):522\u0026ndash;532. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1670/14-048\u003c/span\u003e\u003cspan address=\"10.1670/14-048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeung FKW, Schwarzkopf L, Allen-Ankins S (2025) Advancing invasive species monitoring: A free tool for detecting invasive cane toads using continental-scale data. Ecol Inf 89:103172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecoinf.2025.103172\u003c/span\u003e\u003cspan address=\"10.1016/j.ecoinf.2025.103172\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMacKenzie DI, Bailey LL (2004) Assessing the fit of site-occupancy models. J Agric Biol Environ Stat 9(3):300\u0026ndash;318. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1198/108571104x3361\u003c/span\u003e\u003cspan address=\"10.1198/108571104x3361\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMacKenzie DI, Nichols JD, Lachman GB, Droege S, Andrew Royle J, Langtimm CA (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecol 83(8):2248\u0026ndash;2255. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/0012-9658(\u003c/span\u003e\u003cspan address=\"10.1890/0012-9658(\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e2002)083[2248:ESORWD]2.0.CO;2\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMackenzie DI, Royle JA (2005) Designing occupancy studies: general advice and allocating survey effort. J Appl Ecol 42(6):1105\u0026ndash;1114. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2664.2005.01098.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2664.2005.01098.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarcellini DL (1977) Acoustic and visual display behavior of gekkonid lizards. Am Zool 17(1):251\u0026ndash;260. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/icb/17.1.251\u003c/span\u003e\u003cspan address=\"10.1093/icb/17.1.251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarcellini DL (1971) Activity Patterns of the Gecko \u003cem\u003eHemidactylus frenatus\u003c/em\u003e. Copeia 1971(4):631\u0026ndash;635. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1442631\u003c/span\u003e\u003cspan address=\"10.2307/1442631\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarcellini DL (1974) Acoustic Behaviour of the Gekkonid Lizard Hemidactylus frenatus. Herpetol 30(1):44\u0026ndash;52\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcEwen B, Bainbridge-Smith A, Gutschmidt S, Green R, Atlas J (2024) An invasive species model and dataset for bioacoustic monitoring of common brushtail possum. N Z J Ecol 48(1):1\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.20417/nzjecol.48.3566\u003c/span\u003e\u003cspan address=\"10.20417/nzjecol.48.3566\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcKay JL, Griffiths AD, Crase B (2009) Distribution and habitat use by \u003cem\u003eHemidactylus frenatus\u003c/em\u003e Dumeril and Bibron (Gekkonidae) in the Northern Territory, Australia. Beagle 25:111\u0026ndash;116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5962/p.287454\u003c/span\u003e\u003cspan address=\"10.5962/p.287454\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcKnight DT, Harmon JR, McKnight JL, Ligon DB (2015) Taxanomic Biases of Seven Methods Used to Survey a Diverse Herpetofaunal Community. Herpetol Conserv Biol 10(2):666\u0026ndash;678\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMichael DR, Cunningham RB, Donnelly CF, Lindenmayer DB (2012) Comparative use of active searches and artificial refuges to survey reptiles in temperate eucalypt woodlands. Wildl Res 39(2):149\u0026ndash;162. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/wr11118\u003c/span\u003e\u003cspan address=\"10.1071/wr11118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNaimi B, Hamm NAS, Groen TA, Skidmore AK, Toxopeus AG (2014) Where is positional uncertainty a problem for species distribution modelling? Ecography 37(2):191\u0026ndash;203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1600-0587.2013.00205.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1600-0587.2013.00205.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNewbery B, Jones DN (2007) Presence of Asian House Gecko \u003cem\u003eHemidactylus frenatus\u003c/em\u003e across an urban gradient in Brisbane: influence of habitat and potential for impact on native gecko species. In: Lunmey D, Eby P, Hutchings, Burgin S (eds) Pest or Guest: The Zoology of Overabundance. Royal Zoological Society of New South Wales, Mosman, pp 59\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.7882/FS.2007.009\u003c/span\u003e\u003cspan address=\"10.7882/FS.2007.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNichols JD, Cooch EG (2025) Predictive models are indeed useful for causal inference. Ecol 106:e4517. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ecy.4517\u003c/span\u003e\u003cspan address=\"10.1002/ecy.4517\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNordberg EJ, Denny R, Schwarzkopf L (2021) Testing measures of boldness and exploratory activity in native versus invasive species: geckos as a model system. Anim Behav 177:215\u0026ndash;222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.anbehav.2021.05.013\u003c/span\u003e\u003cspan address=\"10.1016/j.anbehav.2021.05.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNordberg EJ, Schwarzkopf L (2022) Afraid of the Dark? The Influence of Natural and Artificial Light at Night on the Behavioral Activity of a Nocturnal Gecko. Front Ecol Evol 10:821335. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fevo.2022.821335\u003c/span\u003e\u003cspan address=\"10.3389/fevo.2022.821335\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNordstrom B, Mitchell N, Byrne M, Jarman S (2022) A review of applications of environmental DNA for reptile conservation and management. Ecol Evol 12(6):e8995. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.8995\u003c/span\u003e\u003cspan address=\"10.1002/ece3.8995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePerella CD, Behm JE (2020) Understanding the spread and impact of exotic geckos in the greater Caribbean region. Biodivers Conserv 29(4):1109\u0026ndash;1134. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10531-020-01939-1\u003c/span\u003e\u003cspan address=\"10.1007/s10531-020-01939-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eP\u0026eacute;rez-Granados C (2023) BirdNET: applications, performance, pitfalls and future opportunities. Ibis 165(3):1068\u0026ndash;1075. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ibi.13193\u003c/span\u003e\u003cspan address=\"10.1111/ibi.13193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePetren K, Case TJ (1996) An experimental demonstration of exploitation competition in an ongoing invasion. Ecol 77(1):118\u0026ndash;132. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2265661\u003c/span\u003e\u003cspan address=\"10.2307/2265661\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePetren K, Case TJ (1998) Habitat structure determines competition intensity and invasion success in gecko lizards. Proc Natl Acad Sci U S A 95(20):11739\u0026ndash;11744. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.95.20.11739\u003c/span\u003e\u003cspan address=\"10.1073/pnas.95.20.11739\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR Core Team (2023) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRappaport DI, Royle JA, Morton DC (2020) Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes. Ecol Indic 113:106172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2020.106172\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2020.106172\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRibeiro JW Jr, Harmon K, Leite GA, de Melo TN, LeBien J, Campos-Cerqueira M (2022) Passive Acoustic Monitoring as a Tool to Investigate the Spatial Distribution of Invasive Alien Species. Remote Sens 14(18):4565. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs14184565\u003c/span\u003e\u003cspan address=\"10.3390/rs14184565\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRichardson E, Nimmo DG, Avitabile S, Tworkowski L, Watson SJ, Welbourne D, Leonard SWJ (2017) Camera traps and pitfalls: an evaluation of two methods for surveying reptiles in a semiarid ecosystem. Wildl Res 44(8):637\u0026ndash;647. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/wr16048\u003c/span\u003e\u003cspan address=\"10.1071/wr16048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRocha S, Trinks A, Harris DJ, Larson G, Cheke AS (2022) The Global and Western Indian Ocean Dispersal of House Geckos From Asia Using Historical and Mitochondrial DNA Perspectives. Front Ecol Evol 10:791762. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fevo.2022.791762\u003c/span\u003e\u003cspan address=\"10.3389/fevo.2022.791762\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR\u0026ouml;dder D, Sol\u0026eacute; M, B\u0026ouml;hme W (2008) Predicting the potential distributions of two alien invasive Housegeckos (Gekkonidae: \u003cem\u003eHemidactylus frenatus\u003c/em\u003e, \u003cem\u003eHemidactylus mabouia\u003c/em\u003e). North-west J Zool 4(2):236\u0026ndash;246\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRose K, Aguis J, Hall J, Thompson P, Eden JS, Srivastava M, Tiernan B, Jenkins C, Phalen D (2017) Emergent multisystemic Enterococcus infection threatens endangered Christmas Island reptile populations. PLoS ONE 12(7):e0181240. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0181240\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0181240\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRussell JC, Meyer JY, Holmes ND, Pagad S (2017) Invasive alien species on islands: impacts, distribution, interactions and management. Environ Conserv 44(4):359\u0026ndash;370. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S0376892917000297\u003c/span\u003e\u003cspan address=\"10.1017/S0376892917000297\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimberloff D (2009) The role of propagule pressure in biological invasions. Annu Rev Ecol Evol Syst 40(1):81\u0026ndash;102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev.ecolsys.110308.120304\u003c/span\u003e\u003cspan address=\"10.1146/annurev.ecolsys.110308.120304\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith MJ, Cogger H, Tiernan B, Maple D, Boland C, Napier F, Detto T, Smith P (2012) An oceanic island reptile community under threat: the decline of reptiles on Christmas Island, Indian Ocean. Herpetol Conserv Biol 7(2):206\u0026ndash;218\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpatz DR, Zilliacus KM, Holmes ND, Butchart SHM, Genovesi P, Ceballos G, Tershy BR, Croll DA (2017) Globally threatened vertebrates on islands with invasive species. Sci Adv 3(10):e1603080. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/sciadv.1603080\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.1603080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStaniewicz AM (2020) Acoustic Communication of Rare and Threatened Crocodilians and Its Use for Population Monitoring. Dissertation., University of Bristol\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStohlgren TJ, Schnase JL (2006) Risk analysis for biological hazards: what we need to know about invasive species. Risk Anal 26(1):163\u0026ndash;173. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1539-6924.2006.00707.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1539-6924.2006.00707.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSugai LSM, Silva TSF, Ribeiro JW Jr, Llusia D (2019) Terrestrial passive acoustic monitoring: review and perspectives. Biosci 69(1):15\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/biosci/biy147\u003c/span\u003e\u003cspan address=\"10.1093/biosci/biy147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTershy BR, Shen KW, Newton KM, Holmes ND, Croll DA (2015) The importance of islands for the protection of biological and linguistic diversity. Biosci 65(6):592\u0026ndash;597. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/biosci/biv031\u003c/span\u003e\u003cspan address=\"10.1093/biosci/biv031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThieurmel B, Elmarhraoui A, Thieurmel MB (2019) Package \u0026lsquo;suncalc\u0026rsquo;. R package version 0.5.1\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVanderduys EP, Kutt AS (2012) Is the Asian house gecko, \u003cem\u003eHemidactylus frenatus\u003c/em\u003e, really a threat to Australia's biodiversity? Aust J Zool 60(6):361\u0026ndash;367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/zo12077\u003c/span\u003e\u003cspan address=\"10.1071/zo12077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVats D, Knudson C (2021) Revisiting the Gelman-Rubin Diagnostic. Stat Sci 36(4):518\u0026ndash;529. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1214/20-sts812\u003c/span\u003e\u003cspan address=\"10.1214/20-sts812\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVergne AL, Pritz MB, Mathevon N (2009) Acoustic communication in crocodilians: from behaviour to brain\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeterings R, Vetter KC (2018) Invasive house geckos (\u003cem\u003ehemidactylus\u003c/em\u003e spp.): their current, potential and future distribution. Curr Zool 64(5):559\u0026ndash;573. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cz/zox052\u003c/span\u003e\u003cspan address=\"10.1093/cz/zox052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWood CM, G\u0026uuml;nther F, Rex A, Hofstadter DF, Reers H, Kahl S, Peery MZ, Klinck H (2024) Real-time acoustic monitoring facilitates the proactive management of biological invasions. Biol Invasions 26:3989\u0026ndash;3996. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10530-024-03426-y\u003c/span\u003e\u003cspan address=\"10.1007/s10530-024-03426-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYates L, Aandahl Z, Richards SA, Brook BW (2022) Cross validation for model selection: A primer with examples from ecology. Ecol Monogr 93(1):e1557. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ecm.1557\u003c/span\u003e\u003cspan address=\"10.1002/ecm.1557\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu X, Peng Y, Aowphol A, Ding L, Brauth S, Tang YZ (2011) Geographic variation in the advertisement calls of Gekko gecko in relation to variations in morphological features: implications for regional population differentiation. Ethol Ecol Evol 23(3):211\u0026ndash;228. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/03949370.2011.566581\u003c/span\u003e\u003cspan address=\"10.1080/03949370.2011.566581\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZozaya SM, Alford RA, Schwarzkopf L (2015) Invasive house geckos are more willing to use artificial lights than are native geckos. Austral Ecol 40(8):982\u0026ndash;987. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/aec.12287\u003c/span\u003e\u003cspan address=\"10.1111/aec.12287\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biological-invasions","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binv","sideBox":"Learn more about [Biological Invasions](https://www.springer.com/journal/10530)","snPcode":"10530","submissionUrl":"https://submission.nature.com/new-submission/10530/3","title":"Biological Invasions","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Invasive species, Bayesian occupancy modelling, Passive Acoustic Monitoring, BirdNET embeddings, Christmas Island, Hemidactylus frenatus","lastPublishedDoi":"10.21203/rs.3.rs-6876411/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6876411/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEstablishing the distributions of invasive species is critical, particularly on island ecosystems that support endemic populations highly susceptible to ecological disruption. However, evaluations of methods capable of efficiently detecting invasive species in remote or inaccessible areas at high temporal and spatial resolutions remain limited. Passive acoustic monitoring (PAM) has proven effective for monitoring a variety of invasive vertebrates, yet its application to monitor reptiles has been largely unexplored. Here we aim to assess the utility of PAM to detect the invasive Asian house gecko (\u003cem\u003eHemidactylus frenatus\u003c/em\u003e) and identify environmental variables associated with its occurrence on Christmas Island (Indian Ocean). We searched 29,640 hours of audio recordings collected from May to June 2023 by a network of 81 acoustic recorders deployed across Christmas Island using a semi-automated acoustic detection method to identify the gecko\u0026rsquo;s \u0026lsquo;multiple chirp\u0026rsquo; call. Asian house geckos were detected by 66 recorders, in both disturbed and natural habitats, and were recorded during 61.6% of survey nights at occupied sites. Survey-level detection rates varied markedly across sites, potentially reflecting local abundance, suggesting that future research could enable abundance estimation from acoustic detection rates. A single-season occupancy model revealed areas at higher elevation with taller canopies were associated with gecko absence. Notably, these areas also support the highest abundance of a larger, endemic gecko species, suggesting that competitive exclusion may limit the Asian house gecko\u0026rsquo;s distribution on Christmas Island. Our findings demonstrate that PAM is an effective, scalable method for detecting an invasive soniferous reptile, and can enhance invasive species monitoring and risk assessment on islands.\u003c/p\u003e","manuscriptTitle":"Using passive acoustic monitoring to investigate the occurrence of invasive Asian house geckos (Hemidacylus frenatus) on an Oceanic Island","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 17:10:51","doi":"10.21203/rs.3.rs-6876411/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-07-17T10:46:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Biological Invasions","date":"2025-06-19T20:07:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-12T05:21:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biological Invasions","date":"2025-06-12T00:35:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biological-invasions","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binv","sideBox":"Learn more about [Biological Invasions](https://www.springer.com/journal/10530)","snPcode":"10530","submissionUrl":"https://submission.nature.com/new-submission/10530/3","title":"Biological Invasions","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d1a5ab00-3a70-49d5-a339-35ac3eb63b3f","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T16:56:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 17:10:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6876411","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6876411","identity":"rs-6876411","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}