Evaluating Ecological Inferences from Camera Trap Data: A Comparative Analysis of Two Sampling Designs

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This study examines how camera trap sampling design influences activity estimates of roe deer and wild boar in Belgium’s National Park Hoge Kempen. We compared two three-year designs: a systematic-random (SYS) and a stratified-random (STRAT) design, differing in camera trap (CT) number, deployment duration, and number of sampled locations. While activity levels were largely consistent across designs, diel activity patterns varied significantly, especially among years. This suggests that the use of different sampling designs in LTEM is not the main driver of differences in activity estimations. Hence, LTEM may be facilitated by adapting CT sampling designs to incorporate multiple study designs in one LTEM project. Activity patterns Long-term ecological monitoring Camera traps Sampling design Wildlife monitoring Figures Figure 1 1. Introduction Long-term ecological monitoring (LTEM) is essential for understanding natural processes that occur over extended timescales, such as climate fluctuations and biodiversity changes (GEO Secretariat, 2023 ; Gitzen et al., 2012 ; IPCC, 2023 ; Jetz et al., 2019 ; Jones & Driscoll, 2022 ; Kuebbing et al., 2018 ). Traditional short-term studies often overlook these long-term processes. Despite its value, LTEM is demotivated by funding constraints, lack of clear objectives, long waits for results and non-hypothesis driven experimental design (Burton et al., 2015 ; Gitzen et al., 2012 ; Hamel et al., 2013 ; Lindenmayer & Likens, 2010 ; Vucetich et al., 2020 ). Wildlife camera traps (CTs) have revolutionised wildlife monitoring by enabling cost-efficient, autonomous, non-invasive long-term data collection of wildlife in their natural habitats. They facilitate research on population dynamics (Zhao et al., 2021 ), abundance (Gilbert et al., 2021 ; Twining et al., 2024 ), space use (Bollen et al., 2024 ; Zuleger et al., 2023 ), behaviour (Amir et al., 2022 ; Frey et al., 2017 ; Wevers et al., 2020 ), and health status (Barroso et al., 2023 ; Bollen et al., 2021 ). Although widely used, few CT studies are integrated into LTEM frameworks (Harmsen et al., 2017 ; Swanson et al., 2015 ; Twining et al., 2024 ; Zuleger et al., 2023 ), and guidelines for studying diel activity patterns remain underdeveloped compared to population and occupancy studies (Frey et al., 2017 ; Kays et al., 2020 ; Rovero & Zimmermann, 2016 ; Vazquez et al., 2019 ). To estimate wildlife activity accurately, at least 100 detections are advised (Lashley et al., 2018 ). In terms of the sampling design, it is assumed that the CTs are placed randomly with respect to the diel patterns of movement (Rowcliffe et al., 2014 ). Especially for studying ungulates, this includes random camera trap placement to provide a realistic representation of their activity (Tanwar et al., 2021 ). However, apart from this advice, there are no detailed guidelines for other aspects of the sampling design in activity studies. Since habitats are not uniformly distributed throughout the landscape, animal movement (and therefore activity) is expected to be highly heterogeneous depending on the species’ ecology. Hence, other aspects of sampling designs, besides random CT placement, may influence wildlife activity estimations as well. The number of camera traps generally determines the sample size; a large number of CTs allows a large sample size, which results in more precise estimations (Kays et al., 2020 ). Additionally, a large sample size facilitates sampling large study sites, or in-depth sampling in small-scale studies. Nevertheless, using many CTs brings a heavy initial financial cost, which may demotivate researchers. Frequent maintenance required to refresh battery packs combined with the necessity of processing and peer-checking the images (even with ongoing advancements in AI tools (Duggan et al., 2021 ; Kissling et al., 2024 )), renders using a large quantity of CTs labour-intensive. Furthermore, choice of deployment length represents the trade-off between increasing the number of locations sampled and increasing certainty in local estimates. Sampling more locations is generally more interesting for detection rates and increases the covered spatial range (Si et al., 2014 ). However, deploying cameras for a longer time will increase the probability of observing all present species, and improve precision of local ecological inferences (Kays et al., 2021 ). To our knowledge, neither the number of CTs nor deployment length have been evaluated in respect to wildlife activity, contrary to other parameters such as abundance or occupancy estimation (Kays et al., 2020 , 2021 ; Si et al., 2014 ). This study monitored roe deer ( Capreolus capreolus ) and wild boar ( Sus scrofa ) in the National Park Hoge Kempen (Belgium) over six years using two distinct CT sampling designs, each deployed for three years. These differed in CT number, deployment duration, and site selection. We analysed diel activity levels (proportion of time active per day) and activity patterns (diel distribution of observations). In order to determine the interchangeability for LTEM, we tested for differences in the outcomes of the two sampling designs. 2. Methods & Materials Two consecutive CT surveys (May 2017–2020, May 2020–2023) were used to study medium-sized mammals in the National Park Hoge Kempen, a human-dominated protected area in Belgium. Reconyx Hyperfire HC600 cameras were mounted 50 cm high, facing north (Wevers et al., 2020 ). Upon motion trigger, 10 consecutive images were taken without delay. No bait/lure was used and opportunistic sampling of trails was avoided. The first survey employed a systematic-random sampling (SYS) design (Wevers et al., 2020 ), dividing the area into 40 compartments (1.5 km² each) overlaid with a 300 x 300 m grid. Monthly, one randomly selected grid cell centroid per compartment was selected for CT placement, resulting in 1,013 locations in total. The second survey used a stratified-random sampling (STRAT) design. Sixty sampling locations were selected taking into account the proportional abundances of habitats and labour intensity. These were divided into two subsets of 30 locations, which were sampled on an alternating basis (Supplementary table 1 ). Images were processed in Agouti ( agouti.eu ), grouped into events, and classified (Wevers et al., 2020 ). Roe deer and wild boar activity were analysed using the R-package ‘activity’ (Rowcliffe et al., 2014 ) and daily variation in daylength was corrected for using double anchoring (Nouvellet et al., 2012 ). Activity levels were compared using Wald tests, overlap in activity patterns using both Watson-Wheeler-tests and a randomisation test based on overlap indices (Ridout & Linkie, 2009 ). The Watson-Wheeler-test evaluates differences in either variance or means of two samples, whereas the randomisation test determines statistical significance by comparing the observed overlap indices to a randomized distribution (Ridout & Linkie, 2009 ). Both tests were applied, as the Watson-Wheeler-test is commonly used in wildlife activity studies but has a tendency to detect significance more readily. Activity was compared between sampling designs seasonally (following the astronomical calendar), and between all sampling years (within sampling designs). A significance level of 0.05 was used, and the Bonferroni correction adjusted for multiple comparisons (Dunn, 1961 ). Analyses were conducted in R (R Core Team, 2023 ) via Rstudio (RStudio team, 2024 ). 3. Results The SYS design resulted in 38,730 trap days across 1,013 sites, yielding 15,820 roe deer and 3,916 wild boar detections. The STRAT design resulted in 25,305 trap days at 60 sites, recording 8,234 roe deer and 2,459 wild boar detections. Estimated activity patterns differed between the sampling designs, whereas activity levels were similar across sampling designs for both species and every seasonal classification (Table 1 ). The only significant difference in activity level between SYS and STRAT design was for year-round wild boar data (Table 1 ). However, activity patterns differed significantly across most comparisons using both statistical tests. Only the summer activity pattern of wild boar was not significant using the randomisation test, and wild boar activity pattern in autumn was not significant using both statistical tests (Table 1 ). When comparing activity levels across all sampling years, roe deer exhibited considerable inter-annual variation (Fig. 1 A), whereas wild boar activity levels remained relatively consistent throughout (Fig. 1 B). For activity patterns, most pairwise comparisons differed significantly for both species. More specifically, roe deer activity distribution was similar in sampling years 4 and 5, and 5 and 6, following the Watson-Wheeler test (Fig. 1 C), while the randomisation test identified significant differences in roe deer activity patterns for all possible pairs of sampling years, except between years 5 and 6 (Fig. 1 E). For wild boar activity patterns, the Watson-Wheeler test indicated similarities when comparing year 1 with years 4 or 5, and when comparing years 5 and 6 (Fig. 1 D). However, the randomisation test revealed only similar activity patterns when comparing sampling year 1 with years 4 and 5 (Fig. 1 F). 4. Discussion Within the context using data collected with different sampling designs but as part of the same LTEM, we investigated the differences in estimated wildlife activity based on inferences made from two different sampling designs in the same natural area. One sampling design (SYS) was specifically designed to study the drivers influencing the distribution and activity of medium-sized mammals, while the other one (STRAT) was tailored towards long-term monitoring of medium-sized mammals in the national park. They differed in number of CTs used, number of locations sampled and deployment length. We found differences in inferred activity patterns between both sampling designs and within each sampling design. For wild boar both sampling designs estimated similar levels of activity, especially when all sampling years were compared pairwise. However, the activity patterns were remarkably different between both set-ups. Roe deer activity levels and activity patterns both differed notably between and within the two sampling designs. In particular, we found considerable inter-annual variation in the estimation of activity. To our knowledge, this has not yet been shown in a LTEM study (Buchholz et al., 2021 ). Other estimates have also shown similar temporal variation in long-term ecological monitoring (Barlow et al., 2009 ; Harmsen et al., 2017 ; Krebs et al., 2023 ; Lincoln et al., 2020 ). Since both between and within differences occur, it is unlikely that the observed differences can be attributed to the change of sampling design. Potential ecological differences, such as environmental variation or behavioural changes, may have contributed to the observed patterns (Podgórski et al., 2013 ; Stache et al., 2013 ). Future studies should verify whether this is also the case for other ecological estimators, such as species richness and population density, and ascertain which assumptions and corrections would be necessary to preserve the integrity of the long-term monitoring data. In brief, our investigation on the consistency of wildlife activity estimates across two CT sampling designs supports the idea that data derived from projects with different sampling designs can be combined for LTEM. Table 1 Comparison of activity estimates derived from two different sampling designs used in National Park Hoge Kempen. Significant values are shown in bold . Species Timeframe SYS design (activity estimate ± SE) STRAT design (activity estimate ± SE) Wald test (p-value) Watson-Wheeler test (p-value) Randomisation test (p-value) Roe deer Total 0.6020 ± 0.0124 0.6251 ± 0.0159 0.2522 < 0.0001 < 0.0001 Spring 0.5622 ± 0.0171 0.5655 ± 0.0273 0.9198 < 0.0001 < 0.0001 Summer 0.5729 ± 0.0171 0.6009 ± 0.0255 0.3618 < 0.0001 < 0.0001 Autumn 0.5517 ± 0.0202 0.5331 ± 0.0269 0.5802 0.0028 < 0.0001 Winter 0.4938 ± 0.0193 0.4807 ± 0.0195 0.6323 < 0.0001 < 0.0001 Wild boar Total 0.4539 ± 0.0107 0.5024 ± 0.0172 0.0160 < 0.0001 < 0.0001 Spring 0.3962 ± 0.0185 0.4018 ± 0.0185 0.8287 0.0151 0.1061 Summer 0.4151 ± 0.0157 0.4126 ± 0.0281 0.9386 < 0.0001 < 0.0001 Autumn 0.4770 ± 0.0209 0.5324 ± 0.0250 0.0886 0.0725 0.0891 Winter 0.4583 ± 0.0200 0.4811 ± 0.0359 0.5807 < 0.0001 < 0.0001 Declarations Author Contributions Statement S.I. drafted and wrote the main manuscript, as well as analysed the data and prepared figures and/or tables. M.B. performed the experiments, and supervised. J.C. and N.B. conceived and designed the experiments, performed the experiments, and supervised. All authors reviewed the manuscript. Acknowledgements This work makes use of data and/or infrastructure provided by INBO and UHasselt, and funded by Research Foundation Flanders (FWO) as part of the Belgian contribution to LifeWatch. S.I. is a PhD fellow funded by a BOF mandate at Hasselt University. We thank Wim Kuypers and Imke Tomsin for review and feedback on the manuscript, and for helping with the experimental implementation. We are grateful to ANB, Regionaal Landschap Kempen & Maasland, the municipalities of As, Dilsen-Stokkem, Maasmechelen, Lanaken and Zutendaal, the tourist offices of NPHK, hunters and residents to allow us to place camera traps on their properties. Further, we thank all students and volunteers that aided in the field or processed and annotated pictures. Ethical Approval This declaration is not applicable as data was gathered using camera traps, which are considered to be a non-invasive method for animal studies. Funding S.I. is a PhD-fellow funded by a BOF-mandate at Hasselt University (BOF23OWB23). The data and/or infrastructure were provided by INBO and UHasselt, and funded by Research Foundation Flanders (FWO) as part of the Belgian contribution to Lifewatch. Availability of data and materials Camera trap observation data and the R-script processing, analysing, and visualizing the data can be provided by the authors upon request. References Amir, Z., Sovie, A., & Luskin, M. S. (2022). Inferring predator–prey interactions from camera traps: A Bayesian co‐abundance modeling approach. Ecology and Evolution , 12 (12). https://doi.org/10.1002/ece3.9627 Barlow, A. C. D., McDougal, C., Smith, J. L. D., Gurung, B., Bhatta, S. R., Kumal, S., Mahato, B., & Tamang, D. B. (2009). 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Supplementary Files Supplementarytable1.docx Cite Share Download PDF Status: Published Journal Publication published 24 Oct, 2025 Read the published version in European Journal of Wildlife Research → Version 1 posted Editorial decision: Revision requested 23 May, 2025 Reviews received at journal 29 Apr, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviewers invited by journal 29 Mar, 2025 Editor assigned by journal 14 Mar, 2025 Submission checks completed at journal 14 Mar, 2025 First submitted to journal 07 Mar, 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. 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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-6177782","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":439903201,"identity":"ec4b5cd7-5d43-4410-9739-22040867d23d","order_by":0,"name":"Siebe Indestege","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABMElEQVRIiWNgGAWjYDACZgY2ICnBwHAAwpcDsYHAggHKwKuFsQHIMoaqlJDAqYUBrIUBriWxgZAW+XbmYw8+MFjk8R1vf/7gwy+b9A23mx9//FEhUccg3XwAmxaDw2zphjMYJIolz5wxbJzZl5a74c4xM2meM0BbZI4lYNXCzANUwCCRuOFGDmMzb8/h3A03EsyYGdtADssxwOqwZv5v0n9AWu4/f9j8t+d/usGN9M8ff/4Dacn/gNUzh3nYpBnAtjAYNjP8OJBgcCPHQIK3AWwLVh1Av5gb9hhIJM48k2M4s7ch2XDmjZwyaZ5jEpJtMsewO6z/8LMHPyrqEvuOH3/w4ccfO3m+G+mbP/6oseHnl25+gNUaiF1QmrENSZANt3pk8Ic4ZaNgFIyCUTCyAADuAWTfz3gXMwAAAABJRU5ErkJggg==","orcid":"","institution":"Hasselt University","correspondingAuthor":true,"prefix":"","firstName":"Siebe","middleName":"","lastName":"Indestege","suffix":""},{"id":439903202,"identity":"5c520cdd-e96e-4e72-afc0-2c2fc0df3d24","order_by":1,"name":"Martijn Bollen","email":"","orcid":"","institution":"Hasselt University","correspondingAuthor":false,"prefix":"","firstName":"Martijn","middleName":"","lastName":"Bollen","suffix":""},{"id":439903203,"identity":"169f8ab4-c63b-485b-9f7d-1b3a014e8c55","order_by":2,"name":"Jim Casaer","email":"","orcid":"","institution":"Research Institute for Nature and Forest","correspondingAuthor":false,"prefix":"","firstName":"Jim","middleName":"","lastName":"Casaer","suffix":""},{"id":439903204,"identity":"b80aa811-ef4c-4bcd-a73b-8de0849638d4","order_by":3,"name":"Natalie Beenaerts","email":"","orcid":"","institution":"Hasselt University","correspondingAuthor":false,"prefix":"","firstName":"Natalie","middleName":"","lastName":"Beenaerts","suffix":""}],"badges":[],"createdAt":"2025-03-07 11:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6177782/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6177782/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10344-025-02013-3","type":"published","date":"2025-10-24T16:17:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80848462,"identity":"d26acfbf-2521-4d1b-a3a2-39aae3e988fa","added_by":"auto","created_at":"2025-04-17 18:00:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":312224,"visible":true,"origin":"","legend":"\u003cp\u003eP-values of pairwise comparison tests of year-round activity between sampling years. (a) Activity levels of roe deer (\u003cem\u003eCapreolus capreolus\u003c/em\u003e), assessed using the Wald test. (b) Activity levels of wild boar (\u003cem\u003eSus scrofa\u003c/em\u003e), assessed using the Wald test. (c) Activity patterns of roe deer, assessed using the Watson-Wheeler test. (d) Activity patterns of wild boar, assessed using the Watson-Wheeler test. (e) Activity patterns of roe deer, assessed using the randomisation test. (f) Activity patterns for wild boar, assessed using the randomisation test.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6177782/v1/178f371523c43542bfda76bc.png"},{"id":94490135,"identity":"968d69d0-38aa-4d63-a83c-de84335c0aa3","added_by":"auto","created_at":"2025-10-27 17:07:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":763017,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6177782/v1/1a4548ea-e1f5-419c-9f14-86cd6be7ff78.pdf"},{"id":80848466,"identity":"108fbf8d-eec0-409a-a522-4a6ed12c7815","added_by":"auto","created_at":"2025-04-17 18:00:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13146,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6177782/v1/1bea787694039ed7e557c2f4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating Ecological Inferences from Camera Trap Data: A Comparative Analysis of Two Sampling Designs","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLong-term ecological monitoring (LTEM) is essential for understanding natural processes that occur over extended timescales, such as climate fluctuations and biodiversity changes (GEO Secretariat, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gitzen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; IPCC, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jetz et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jones \u0026amp; Driscoll, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kuebbing et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Traditional short-term studies often overlook these long-term processes. Despite its value, LTEM is demotivated by funding constraints, lack of clear objectives, long waits for results and non-hypothesis driven experimental design (Burton et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gitzen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hamel et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lindenmayer \u0026amp; Likens, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Vucetich et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWildlife camera traps (CTs) have revolutionised wildlife monitoring by enabling cost-efficient, autonomous, non-invasive long-term data collection of wildlife in their natural habitats. They facilitate research on population dynamics (Zhao et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), abundance (Gilbert et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Twining et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), space use (Bollen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zuleger et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), behaviour (Amir et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Frey et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wevers et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and health status (Barroso et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bollen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although widely used, few CT studies are integrated into LTEM frameworks (Harmsen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Swanson et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Twining et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zuleger et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and guidelines for studying diel activity patterns remain underdeveloped compared to population and occupancy studies (Frey et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kays et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rovero \u0026amp; Zimmermann, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vazquez et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo estimate wildlife activity accurately, at least 100 detections are advised (Lashley et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In terms of the sampling design, it is assumed that the CTs are placed randomly with respect to the diel patterns of movement (Rowcliffe et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Especially for studying ungulates, this includes random camera trap placement to provide a realistic representation of their activity (Tanwar et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, apart from this advice, there are no detailed guidelines for other aspects of the sampling design in activity studies.\u003c/p\u003e \u003cp\u003eSince habitats are not uniformly distributed throughout the landscape, animal movement (and therefore activity) is expected to be highly heterogeneous depending on the species\u0026rsquo; ecology. Hence, other aspects of sampling designs, besides random CT placement, may influence wildlife activity estimations as well. The number of camera traps generally determines the sample size; a large number of CTs allows a large sample size, which results in more precise estimations (Kays et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, a large sample size facilitates sampling large study sites, or in-depth sampling in small-scale studies. Nevertheless, using many CTs brings a heavy initial financial cost, which may demotivate researchers. Frequent maintenance required to refresh battery packs combined with the necessity of processing and peer-checking the images (even with ongoing advancements in AI tools (Duggan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kissling et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)), renders using a large quantity of CTs labour-intensive.\u003c/p\u003e \u003cp\u003eFurthermore, choice of deployment length represents the trade-off between increasing the number of locations sampled and increasing certainty in local estimates. Sampling more locations is generally more interesting for detection rates and increases the covered spatial range (Si et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, deploying cameras for a longer time will increase the probability of observing all present species, and improve precision of local ecological inferences (Kays et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To our knowledge, neither the number of CTs nor deployment length have been evaluated in respect to wildlife activity, contrary to other parameters such as abundance or occupancy estimation (Kays et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Si et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study monitored roe deer (\u003cem\u003eCapreolus capreolus\u003c/em\u003e) and wild boar (\u003cem\u003eSus scrofa\u003c/em\u003e) in the National Park Hoge Kempen (Belgium) over six years using two distinct CT sampling designs, each deployed for three years. These differed in CT number, deployment duration, and site selection. We analysed diel activity levels (proportion of time active per day) and activity patterns (diel distribution of observations). In order to determine the interchangeability for LTEM, we tested for differences in the outcomes of the two sampling designs.\u003c/p\u003e"},{"header":"2. Methods \u0026 Materials","content":"\u003cp\u003eTwo consecutive CT surveys (May 2017\u0026ndash;2020, May 2020\u0026ndash;2023) were used to study medium-sized mammals in the National Park Hoge Kempen, a human-dominated protected area in Belgium. Reconyx Hyperfire HC600 cameras were mounted 50 cm high, facing north (Wevers et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Upon motion trigger, 10 consecutive images were taken without delay. No bait/lure was used and opportunistic sampling of trails was avoided.\u003c/p\u003e \u003cp\u003eThe first survey employed a systematic-random sampling (SYS) design (Wevers et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), dividing the area into 40 compartments (1.5 km\u0026sup2; each) overlaid with a 300 x 300 m grid. Monthly, one randomly selected grid cell centroid per compartment was selected for CT placement, resulting in 1,013 locations in total. The second survey used a stratified-random sampling (STRAT) design. Sixty sampling locations were selected taking into account the proportional abundances of habitats and labour intensity. These were divided into two subsets of 30 locations, which were sampled on an alternating basis (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImages were processed in Agouti (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eagouti.eu\u003c/span\u003e), grouped into events, and classified (Wevers et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Roe deer and wild boar activity were analysed using the R-package \u0026lsquo;activity\u0026rsquo; (Rowcliffe et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and daily variation in daylength was corrected for using double anchoring (Nouvellet et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Activity levels were compared using Wald tests, overlap in activity patterns using both Watson-Wheeler-tests and a randomisation test based on overlap indices (Ridout \u0026amp; Linkie, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The Watson-Wheeler-test evaluates differences in either variance or means of two samples, whereas the randomisation test determines statistical significance by comparing the observed overlap indices to a randomized distribution (Ridout \u0026amp; Linkie, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Both tests were applied, as the Watson-Wheeler-test is commonly used in wildlife activity studies but has a tendency to detect significance more readily. Activity was compared between sampling designs seasonally (following the astronomical calendar), and between all sampling years (within sampling designs). A significance level of 0.05 was used, and the Bonferroni correction adjusted for multiple comparisons (Dunn, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1961\u003c/span\u003e). Analyses were conducted in R (R Core Team, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) via Rstudio (RStudio team, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe SYS design resulted in 38,730 trap days across 1,013 sites, yielding 15,820 roe deer and 3,916 wild boar detections. The STRAT design resulted in 25,305 trap days at 60 sites, recording 8,234 roe deer and 2,459 wild boar detections. Estimated activity patterns differed between the sampling designs, whereas activity levels were similar across sampling designs for both species and every seasonal classification (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The only significant difference in activity level between SYS and STRAT design was for year-round wild boar data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, activity patterns differed significantly across most comparisons using both statistical tests. Only the summer activity pattern of wild boar was not significant using the randomisation test, and wild boar activity pattern in autumn was not significant using both statistical tests (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen comparing activity levels across all sampling years, roe deer exhibited considerable inter-annual variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), whereas wild boar activity levels remained relatively consistent throughout (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). For activity patterns, most pairwise comparisons differed significantly for both species. More specifically, roe deer activity distribution was similar in sampling years 4 and 5, and 5 and 6, following the Watson-Wheeler test (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), while the randomisation test identified significant differences in roe deer activity patterns for all possible pairs of sampling years, except between years 5 and 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). For wild boar activity patterns, the Watson-Wheeler test indicated similarities when comparing year 1 with years 4 or 5, and when comparing years 5 and 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). However, the randomisation test revealed only similar activity patterns when comparing sampling year 1 with years 4 and 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWithin the context using data collected with different sampling designs but as part of the same LTEM, we investigated the differences in estimated wildlife activity based on inferences made from two different sampling designs in the same natural area. One sampling design (SYS) was specifically designed to study the drivers influencing the distribution and activity of medium-sized mammals, while the other one (STRAT) was tailored towards long-term monitoring of medium-sized mammals in the national park. They differed in number of CTs used, number of locations sampled and deployment length. We found differences in inferred activity patterns between both sampling designs and within each sampling design.\u003c/p\u003e \u003cp\u003eFor wild boar both sampling designs estimated similar levels of activity, especially when all sampling years were compared pairwise. However, the activity patterns were remarkably different between both set-ups. Roe deer activity levels and activity patterns both differed notably between and within the two sampling designs. In particular, we found considerable inter-annual variation in the estimation of activity. To our knowledge, this has not yet been shown in a LTEM study (Buchholz et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Other estimates have also shown similar temporal variation in long-term ecological monitoring (Barlow et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Harmsen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Krebs et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lincoln et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince both between and within differences occur, it is unlikely that the observed differences can be attributed to the change of sampling design. Potential ecological differences, such as environmental variation or behavioural changes, may have contributed to the observed patterns (Podg\u0026oacute;rski et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Stache et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Future studies should verify whether this is also the case for other ecological estimators, such as species richness and population density, and ascertain which assumptions and corrections would be necessary to preserve the integrity of the long-term monitoring data. In brief, our investigation on the consistency of wildlife activity estimates across two CT sampling designs supports the idea that data derived from projects with different sampling designs can be combined for LTEM.\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 activity estimates derived from two different sampling designs used in National Park Hoge Kempen. Significant values are shown in \u003cb\u003ebold\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTimeframe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSYS design (activity estimate\u0026thinsp;\u0026plusmn;\u0026thinsp;SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTRAT design (activity estimate\u0026thinsp;\u0026plusmn;\u0026thinsp;SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWald test (p-value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWatson-Wheeler test (p-value)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRandomisation test (p-value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eRoe deer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.6020\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.6251\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" 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\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.4583\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.4811\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\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":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.I. drafted and wrote the main manuscript, as well as analysed the data and prepared figures and/or tables. \u0026nbsp;M.B. performed the experiments, and supervised. J.C. and N.B. conceived and designed the experiments, performed the experiments, and supervised. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work makes use of data and/or infrastructure provided by INBO and UHasselt, and funded by Research Foundation Flanders (FWO) as part of the Belgian contribution to LifeWatch. S.I. is a PhD fellow funded by a BOF mandate at Hasselt University. We thank Wim Kuypers and Imke Tomsin for review and feedback on the manuscript, and for helping with the experimental implementation. We are grateful to ANB, Regionaal Landschap Kempen \u0026amp; Maasland, the municipalities of As, Dilsen-Stokkem, Maasmechelen, Lanaken and Zutendaal, the tourist offices of NPHK, hunters and residents to allow us to place camera traps on their properties. Further, we thank all students and volunteers that aided in the field or processed and annotated pictures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis declaration is not applicable as data was gathered using camera traps, which are considered to be a non-invasive method for animal studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.I. is a PhD-fellow funded by a BOF-mandate at Hasselt University (BOF23OWB23). The data and/or infrastructure were provided by INBO and UHasselt, and funded by Research Foundation Flanders (FWO) as part of the Belgian contribution to Lifewatch.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCamera trap observation data and the R-script processing, analysing, and visualizing the data can be provided by the authors upon request.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmir, Z., Sovie, A., \u0026amp; Luskin, M. S. (2022). Inferring predator\u0026ndash;prey interactions from camera traps: A Bayesian co‐abundance modeling approach. \u003cem\u003eEcology and Evolution\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(12). https://doi.org/10.1002/ece3.9627\u003c/li\u003e\n\u003cli\u003eBarlow, A. C. D., McDougal, C., Smith, J. L. D., Gurung, B., Bhatta, S. R., Kumal, S., Mahato, B., \u0026amp; Tamang, D. B. (2009). Temporal Variation in Tiger (Panthera tigris) Populations and its Implications for Monitoring. \u003cem\u003eJournal of Mammalogy\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e(2), 472\u0026ndash;478. https://doi.org/10.1644/07-MAMM-A-415.1\u003c/li\u003e\n\u003cli\u003eBarroso, P., Relimpio, D., Zearra, J. A., Cer\u0026oacute;n, J. J., Palencia, P., Cardoso, B., Ferreras, E., Escobar, M., C\u0026aacute;ceres, G., L\u0026oacute;pez-Olvera, J. R., \u0026amp; Gort\u0026aacute;zar, C. (2023). Using integrated wildlife monitoring to prevent future pandemics through one health approach. \u003cem\u003eOne Health\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, 100479. https://doi.org/10.1016/j.onehlt.2022.100479\u003c/li\u003e\n\u003cli\u003eBollen, M., Casaer, J., Neyens, T., \u0026amp; Beenaerts, N. (2024). When and where? 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Long-term monitoring of mammal communities in the Peneda-Ger\u0026ecirc;s National Park using camera-trap data. \u003cem\u003eBiodiversity Data Journal\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, e99588. https://doi.org/10.3897/BDJ.11.e99588\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-wildlife-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejwr","sideBox":"Learn more about [European Journal of Wildlife Research](http://link.springer.com/journal/10344)","snPcode":"10344","submissionUrl":"https://submission.nature.com/new-submission/10344/3","title":"European Journal of Wildlife Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Activity patterns, Long-term ecological monitoring, Camera traps, Sampling design, Wildlife monitoring","lastPublishedDoi":"10.21203/rs.3.rs-6177782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6177782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEffective long-term ecological monitoring (LTEM) is critical for monitoring wildlife activity, yet the consequences of the study design choices on its results are not well established. This study examines how camera trap sampling design influences activity estimates of roe deer and wild boar in Belgium\u0026rsquo;s National Park Hoge Kempen. We compared two three-year designs: a systematic-random (SYS) and a stratified-random (STRAT) design, differing in camera trap (CT) number, deployment duration, and number of sampled locations. While activity levels were largely consistent across designs, diel activity patterns varied significantly, especially among years. This suggests that the use of different sampling designs in LTEM is not the main driver of differences in activity estimations. Hence, LTEM may be facilitated by adapting CT sampling designs to incorporate multiple study designs in one LTEM project.\u003c/p\u003e","manuscriptTitle":"Evaluating Ecological Inferences from Camera Trap Data: A Comparative Analysis of Two Sampling Designs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 18:00:29","doi":"10.21203/rs.3.rs-6177782/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-23T07:51:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-29T11:18:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98920972510589983478340344925625406045","date":"2025-03-31T06:49:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-29T06:27:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-14T07:39:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-14T07:39:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Wildlife Research","date":"2025-03-07T11:07:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-wildlife-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejwr","sideBox":"Learn more about [European Journal of Wildlife Research](http://link.springer.com/journal/10344)","snPcode":"10344","submissionUrl":"https://submission.nature.com/new-submission/10344/3","title":"European Journal of Wildlife Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3c141a64-1561-4fe9-9e2f-a0b72f3e5f79","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:28:20+00:00","versionOfRecord":{"articleIdentity":"rs-6177782","link":"https://doi.org/10.1007/s10344-025-02013-3","journal":{"identity":"european-journal-of-wildlife-research","isVorOnly":false,"title":"European Journal of Wildlife Research"},"publishedOn":"2025-10-24 16:17:12","publishedOnDateReadable":"October 24th, 2025"},"versionCreatedAt":"2025-04-17 18:00:29","video":"","vorDoi":"10.1007/s10344-025-02013-3","vorDoiUrl":"https://doi.org/10.1007/s10344-025-02013-3","workflowStages":[]},"version":"v1","identity":"rs-6177782","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6177782","identity":"rs-6177782","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}