DOI: 10.5061/dryad.gtht76j0c
title: Data from: Inferring camera trap detection zones for rare species using species- and camera-specific traits: A meta-level analysis

description: Camera trapping is a vital tool for wildlife monitoring. Accurately
 estimating a camera’s detection zone, the area where animals are detected,
 is essential, particularly for calculating population densities of
 unmarked species. However, obtaining enough detection events to estimate
 detection zones accurately remains difficult, particularly for rare
 species. Given that detection zones are influenced by species- and
 camera-specific traits, it may be possible to infer detection zones from
 these traits when data are scarce. We conducted a meta-analysis to assess
 how the number of detection events, species traits, and site-specific
 variables influence the estimation of the effective camera trap detection
 distance and angle. We reviewed published studies on detection zones,
 performed a power analysis to estimate the sample sizes required for
 accurate and precise estimates, and used mixed-effects models to test
 whether detection zones can be predicted from biological and technical
 traits. Our results show that approximately 50 detection events are needed
 to achieve error rates below 10%. The mixed-effects models explained 81%
 and 85% of the variation in effective detection distance and angle,
 respectively. Key predictors of detection distance included body mass,
 right-truncation distance, and camera brand, while angle was predicted by
 camera brand and installation height. Importantly, we demonstrate that
 combining model-based predictions with limited empirical data (fewer than
 25 detections) can reduce estimation error to below 15% for rare species.
 This study highlights that detection zones can be predicted not only
 within, but also across, studies using shared traits, and that the
 right-truncation distance is a useful metric to account for
 habitat-specific visibility. These findings enhance the utility of
 detection zones in ecological studies and support better study design,
 especially for rare or understudied species.
description: <em>Literature search</em> We
 searched for published articles reporting effective camera trap detection
 zones and the selected covariates on Web of Science using the following
 query: (camera trap* OR trail camera*) AND (effective radius OR detection
 zone OR detection distance OR Random Encounter Model OR Camera Trap
 Distance Sampling OR time-to-event model OR space-to-event model). The
 three density models for unmarked wildlife were included in the query as
 these always involve detection zone estimation to some degree. We excluded
 detection zones that were based on either human trials or manufacturer
 specifications, and detection zones that were estimated across multiple
 camera trap brands. We excluded two articles that already modeled
 detection zone parameters as a function of a covariate, but did not
 provide the raw data to do so independently. In total, 24 studies with
 data from 17 countries met our criteria (Table 1). Additionally, we
 included our own data set of 20 species from a study site in French
 Guiana, the collection of which is described in Wiegers et al.
 (2024). Choice of
 covariates We focused on six potential predictors that
 were consistently reported by papers or are accessible from the
 literature: 1) animal body mass; 2) animal phylogeny; 3) camera trap
 installation height; 4) camera trap brand; 5) the maximum observed angle
 per site (max 𝜃), and the maximum considered distance measurement (i.e.
 the right-truncation distance, hereafter max r) per site, as proxies for
 local vegetation density or ‘visibility’; and 6) the time, in seconds,
 between consecutive positional measurements to animals in the viewshed,
 also known as the ‘snapshot interval’ 𝑇 . The latter was included to
 account for differences arising from some studies only measuring distance
 to animals as they enter the camera viewshed (high 𝑇), versus studies that
 took measurements at many pictures per sequence at regular intervals (low
 𝑇 ). The first approach is more common when using detection zones in the
 Random Encounter Model (Rowcliffe et al., 2008), and the latter for Camera
 Trap Distance Sampling (Howe et al., 2017), both of which are common
 models for estimating the abundance of unmarked populations. We did not
 include animal movement speed because such estimates are rarely reported.
 We note that movement speed is unlikely to be a useful detection zone
 predictor because it is known to vary markedly between sites, and even
 within species (Palencia et al., 2023). Site-specific speed data would
 therefore be required, which is likely unavailable for species for which
 detections are already limited. Additionally, we were not able to include
 animal skin surface temperature, because this parameter is virtually
 unknown for most if not all wildlife species. While internal body
 temperature measurements can often be found in literature, we note that
 these are not good proxies for emitted heat as observed by camera trap
 sensors (Welbourne et al., 2016). Finally, we did not include measures of
 camera trap sensitivity, because only few articles reported
 these. Body mass estimates were acquired from the
 studies themselves when reported, and when not, from Jones et al. (2009),
 Tobias et al. (2022), Richard-Hansen et al. (1999), and Phillipps (2016).
 Camera trap installation heights and brands were either taken from the
 articles directly or by contacting the corresponding authors. Taxonomic
 ranks for the species were taken from the Wildlife Insights database
 (Ahumada et al., 2020). For most studies, max r (range 3 - 26 m) and max 𝜃
 could be computed from the data or taken from the manuscript. For two
 studies that did not provide detection data but used marked poles to
 calibrate the distance to the camera, we approximated max r as the
 distance to the farthest used marker. For two unspecified max 𝜃 values, we
 took those as reported by the manufacturer or from other studies using the
 same model. Finally, we assigned studies that took one measurement for
 each ‘independent contact’, i.e. the first picture of an animal per photo
 sequence, we assigned a value of 𝑇 =1500 seconds, which was the otherwise
 highest reported value. All analyses were performed in
 R v. 4.3.1 (R Core Team, 2021). We estimated the EDD and EDA using the
 Distance package (Miller et al., 2019) by fitting detection functions with
 up to 2 adjustments (Rowcliffe et al., 2011) for all species for which
 data were available. For these species, we first excluded detections of
 animals reacting to the camera, and based on a preliminary power analysis,
 further only considered detection zones with at least 50 trigger events.
 For the EDD, we left-truncated the data when the detection probability
 near the camera was lower than expected and right-truncated it when the
 detection probability became negligible (Buckland et al., 2015). For the
 EDA, we discarded the farthest 5% of measurements, as these are more
 likely to be influenced by distance rather than angle (Rowcliffe et al.,
 2011). We excluded detection zones with very poor fits to a detection
 function (n = 1 for EDD, n = 0 for EDA). EDD and EDA values from studies
 that did not provide data (n = 5 for EDD, n = 4 for EDA) were simply taken
 as stated in their respective articles. In total, the two final datasets
 included 167 EDDs from 24 studies, and 104 EDAs from 16 studies.
 To investigate the required sample size for accurate detection
 zone estimation, we estimated EDDs and EDAs with a similar approach for
 subsets of the complete dataset for all species with at least 200
 detections (n = 75 for EDD, n = 35 for EDA). To this end, we created
 measurement subsets of varying sample sizes for each species-site
 combination, ranging from 10 to 200 through sampling with replacement. For
 each subset, we then estimated both the EDD and EDA by fitting hazard rate
 and half-normal functions. (Rowcliffe et al., 2011). For this power
 analysis, we did not fit adjustments to reduce computing time. This
 process was repeated 100 times for each species and subset sample size
 combination. Then, to investigate how the estimation error relates to
 sample size, we computed the mean relative error (MRE) between the EDD and
 EDA estimates of the subsets [𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒^𝑠𝑢𝑏^−𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒^𝑓𝑢𝑙𝑙^] and the
 estimates calculated from the full datasets as
 [𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒^𝑠𝑢𝑏^−𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒^𝑓𝑢𝑙𝑙^]
 / 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒<sup>𝑓𝑢𝑙𝑙</sup>.  
 Mixed-effects
 models We created two linear mixed-effects models with
 the nlme package (Pinheiro et al., 2007) to determine the relationship
 between the selected covariates and the detection zone parameters. For
 both the EDD and EDA, we constructed a full model with the logarithm of
 body mass, camera trap brand, camera trap height, species group, the
 logarithm of the snapshot interval, and either max r or max 𝜃 as fixed
 effects, and study site as a random effect. We considered the following
 species groups which were grouped based on similar morphology and/or
 lifestyles: 1) ungulates (odd- and even-toed ungulates), 2)
 carnivores/insectivores (carnivorans, opossums, pangolins, armadillos, and
 the aardvark); 3) small mammals (rodents, hares, and hedgehogs); 4)
 primates; and 5) birds. We used the approach from Nakagawa and Schielzeth
 (2013), calculating the conditional and marginal R<sup>2</sup>
 to demonstrate the variation explained by respectively all effects
 (𝑅<sub>𝑐</sub><sup>2</sup>) and the fixed effects
 (𝑅<sub>𝑚</sub><sup>2</sup> ). Then, to investigate
 the predictive power of the covariates, we assessed the performance of
 these models at predicting the EDD and EDA for three common scenarios that
 practitioners interested in detection zones may be faced with:
 estimating the detection zone for a species at a
 study site, where detection zones for more common species are available,
 but not for the target species; estimating the
 detection zone for a species at a study site where detection zones for
 more common species are available, but where the practitioner also has a
 limited amount of measurements for the target species;
 estimating the detection zone for any species at a novel study
 site for which no detection zone measurements are available at
 all. To test the within-sample predictive
 performance of the EDD and EDA models (scenario 1), we conducted
 leave-one-out cross-validation (LOOCV), in which we iteratively trained
 the model with the data for all detection zones except for one, and then
 predicted the EDD/EDA for the removed entry. We assessed model performance
 of the two models and reported the MRE value and the
 R<sup>2</sup> between the observed and predicted detection
 zone estimates. We then considered scenario 2, in which practitioners have
 not met the required number of measurements for accurate detection zone
 estimation, but they did collect a limited dataset ranging from 1 to 49
 measurements. For this case, we investigated whether prediction accuracy
 can be improved by combining the model output from scenario 1 with an
 estimate produced by fitting detection functions to the limited data set.
 To this end, we used the output from the fitted detection functions that
 were already computed for the power analysis, for which we subsampled the
 data for each species-and-site specific detection zone 100 times for
 sample sizes 𝑛 = 10, 20, 30, … 200. Each detection zone produced by such a
 subsample was first combined with the predicted estimate provided by the
 scenario 1 model by taking the mean of both. We then computed the relative
 error between this joint estimate and the ‘true’ estimate that was
 produced by fitting a detection function to the complete dataset. Then,
 across all 100 subsamples per species and per site, we took the mean
 relative error of this joint estimate at each value for 𝑛 to show how, on
 average, its accuracy improves by increasing 𝑛. Finally, instead of simply
 taking the mean of the scenario 1 prediction and the estimate produced by
 fitting detection functions to the subsample, we also tested the effect of
 assigning different relative weights to either estimate on predictive
 performance. We therefore calculated the relative error per 𝑛 for eleven
 different weight distributions, in which we incrementally reduced the
 weight assigned to one estimate from 100% to 0% in steps of 10%. Finally,
 for scenario 3 we also conducted leave-group-out cross-validation (LGOCV),
 in which we iteratively trained the model on the detection zones from all
 studies but one. The predictive performance was then tested on the
 detection zones from the study left out to evaluate how the models fare
 when no site-specific trigger events are available at all. For the LGOCV,
 we only considered the brands Cuddeback, Browning, Bushnell, and Reconyx,
 since only these were used by more than one study. Similarly, we excluded
 primates for the EDA LGOCV due to this taxon being only present in one
 study that reported EDAs.
description: # Data from: Inferring camera trap detection zones for rare species using
 species- and camera-specific traits: A meta-level analysis Dataset DOI:
 [10.5061/dryad.gtht76j0c](10.5061/dryad.gtht76j0c) ## Description of the
 data and file structure ##### Predicting detection zones for your own
 dataset We provide code to predict detection zones for your own dataset on
 Github:
 [https://github.com/jnwiegers/predict_detection_zones](https://github.com/jnwiegers/) ### Files and variables These are the data used to compute effective detection distances (EDDs) and angles (EDAs) for camera trap data: * datasets_2025.zip contains formatted versions of all datasets used. Required for 'power' and 'analyses' scripts. Each dataset is a collection of distance/angular measurements to animals in camera trap viewsheds. Please not that these datasets could not be deposited on Dryad, which requires a CC0 license. Instead, they are available from the corresponding author upon request: * Datasets originally released under **CC BY 4.0** are included in this archive and we can share these. * Datasets originally released under e.g. **CC BY-NC-ND 4.0** are *not* included, as redistribution of modified versions is not permitted under that license. Interested researchers should contact the original authors directly for access to those datasets. - **Variables**: * **Species**: species identifier * **distance**: radial measurement to animal * **ref**: reference identifier * **angle**: if present, the angular measurement. Detection zones with distance\ measurements only have 'NA' in this column. Own data/temporary outputs: * r_trunc.csv: all radial truncation distances used for each detection zone * **Variables:** * **ID**: detection zone identifier (species+site combination) * **left**: left-truncation distance used * **right:** right-truncation distance used * **ref**: reference identifier * metadata_X.csv: metadata for the datasets and for the literature-only EDAs/EDDs * **Variables:** * **ID**: detection zone identifier (species+site combination) * **T**: snapshot interval, in seconds * **reference**: reference identifier * **site**: study site identifier. Can be the same as reference if only one site was used. * **Scientific_name**: binomial name of species * **ct_brand**: camera trap brand * **ct_type**: camera trap model, if specified * **Country**: country of study * **ct_height**: height of camera trap installation in meters * **body_mass**: body mass in kilograms * **start_year**: the first year of data collection * full_output_X.rData: temporary outputs of the computed EDDs/EDAs, explained in scripts * LOOCV_data 2025.csv: temporary outputs of the computed EDDs/EDAs, explained in scripts * Wildlife Insights taxonomy database: [https://www.wildlifeinsights.org/get-started/taxonomy](https://www.wildlifeinsights.org/get-started/taxonomy) ## Code/software These are the scripts used to compute EDD and EDA estimates. Both the EDD and EDA have three scripts: 'power', 'compute', and 'analyses'. * 'power' scripts subsample the data and compute EDD/EDA values for subsets with hazard (EDD) or halfnormal (EDA) functions * 'compute' scripts compute the EDD/EDA with hazard/halfnormal/uniform functions and select the best one per detection zone * 'analyses' scripts train predictive models for the EDD/EDA and test their performance. External scripts used: * Marcus Rowcliffe's distancedf.R: [https://github.com/MarcusRowcliffe/distanceDF](https://github.com/MarcusRowcliffe/distanceDF) ## Access information Camera trap detection data were derived from the following sources: Bessone, M., Kühl, H. S., Hohmann, G., Herbinger, I., N’Goran, K. P., Asanzi, P., Da Costa, P. B., Dérozier, V., Fotsing, E. D. B., Beka, B. I., Iyomi, M. D., Iyatshi, I. B., Kafando, P., Kambere, M. A., Moundzoho, D. B., Wanzalire, M. L. K., & Fruth, B. (2020). 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