DOI: 10.5061/dryad.fttdz091v
title: French Guianan mammal and bird population densities with spatial-capture recapture, line transect distance sampling, and 'unmarked' density models

description: Estimating population densities of large wildlife in forests has
 traditionally relied on either spatial capture-recapture (SCR) for animals
 that are individually marked, or line transect distance sampling (LTDS)
 for species observable by researchers. Recent advances have now introduced
 more general camera trap models that are applicable to a large range of
 terrestrial medium to large-sized species. However, validation of these
 unmarked density (UD) models remains scarce due to the lack of available
 density estimates that can serve as references. We tested the accuracy and
 precision of three UD models (the random encounter model, camera trap
 distance sampling, and the time-to-event model) against independent SCR
 estimates for ocelots and jaguars, and against LTDS estimates for eight
 unmarked species varying in abundance and ecology. We found that UD model
 estimates for ocelots were relatively accurate in matching the estimates
 from SCR, albeit with less precision. Additionally, UD model estimates
 were similar to LTDS estimates for seven out of eight studied species.
 Estimates for jaguar were, however, not similar between UD models and SCR,
 implying that UD models are not useful for rare species. Overall, our
 findings show that UD models are promising tools for monitoring abundant
 to relatively rare unmarked species in forests, although SCR remains
 preferred for marked species. To ultimately aid conservationists in making
 informed conservation decisions, efforts should be directed towards
 enhancing the precision of these models as well as their ease of access
 for non-technical practitioners.
description: In 2022-2023, we conducted three surveys at a single tropical
 forest site in French Guiana: 1) a camera trap survey for unmarked density
 (UD) models for all terrestrial mammals and birds; 2) a camera trap
 spatial capture-recapture (SCR) survey for ocelot and jaguar; and 3) a
 line transect distance sampling (LTDS) survey for all large-bodied mammals
 and birds that can be observed by researchers walking on transects. The
 study site is a continuous block of coastal plain swamp forest contained
 within the boundaries of the Centre Spatial Guyanais (CSG) in French
 Guiana (RichardHansen et al., 2006; Petit et al., 2018).
 <strong>Camera trap survey for unmarked density
 modelling<br></strong>For the unmarked density models, we
 installed CTs at 150 deployment locations by rotating 50 camera traps
 (model BTC-8A) three times after a sampling period of approximately one
 month. CTs were installed on a systematic grid with 0.5 km interspacing at
 predetermined points to avoid selecting locations with biased encounter
 rates. We aimed to install cameras within 10 m of predesignated points and
 randomly pointed the device either north or south to avoid sun glare.
 Points were re-designated within 30 m if no suitable tree was present
 (e.g. due to water or a tree fall gap). Cameras were installed on tree
 trunks at a height of 0.3 m and configured to make 8 pictures per trigger
 with 1 s delay (the fastest option) at 8 MP resolution. The UD models
 require that the angle and radius to animals in the viewshed are known, so
 we calibrated our cameramodel following the camera trap tracking protocol
 (https://github.com/MarcusRowcliffe/CTtracking) and made approximately 20
 pictures with a marked pole at various viewshed positions at each
 deployment location.  <strong>Camera trap survey
 for SCR modelling<br></strong>For both ocelot and jaguar SCR,
 we deployed a second camera trap grid in 2022-2023 to estimate jaguar
 density through SCR. This survey essentially replicated the design of
 Petit et al. (2018), who previously estimated jaguar density in the study
 area by installing 70 paired CTs across the study area to observe both
 flanks of jaguars. Unlike the UD survey, devices were installed on trails
 to maximize detections, and with approximately 3 km interspacing between
 devices. Across both the UD survey and the SCR survey, which were deployed
 at the same time and location, we recaptured and identified enough
 individual ocelots to estimate densities for this species with SCR as
 well. The last camera trap survey used in this study was conducted by
 AUTHOR OF THIS PAPER, ANONYMIZED FOR PEER REVIEW in 2014-2015 to estimate
 tapir density through SCR, but it was inconclusive due to too few
 detections. This survey deployed 66 paired CTs with approximately 750 m
 interspacing. While inconclusive for its original purpose, it did yield
 many detections of other species, which we used as supplementary data for
 the activity level parameter required for the UD models (Supplementary
 Information).  <strong>Line transect distance
 sampling survey<br></strong>We conducted an LTDS survey at the
 study site in December 2022 to compare density estimates from UD models to
 those derived from LTDS surveys. Trained observers walked a total of 179
 km at a pace of ~1 km/h on 4 pre-cut trails, between 0700 and 1100 and
 1430 and 1800, following standard line transect protocols (Denis et al.,
 2017). Perpendicular distances between observed animals and the transect
 were recorded to the nearest meter with a laser range finder. Observers
 noted group sizes for group-living animals as well as whether they were
 likely to have observed all members of the group or not; only ‘confident’
 group size observations were used to calculate mean group size for these
 species. <strong>Data
 processing</strong> <br>We identified ocelots and jaguars
 based on pelage patterns and constructed capture histories for individual
 animals. We fit these to a spatially explicit capture-recapture model with
 the SCR package for R 4.3.1 (Efford and Efford, 2023). Spatial
 capture-recapture (SCR) models estimate population densities by
 calculating the locations of animal activity centers (i.e. the midpoint of
 their home range) in a landscape (Borchers and Efford, 2008). The input
 for SCR models are recaptures of individual animals at known locations. It
 includes a state model describing the locations of the animal activity
 centers as well as an observation model describing the detection
 probability of animals at sensors (e.g. camera traps) as a function of the
 distance to their activity center. We fit hybrid mixture models with
 sex-specific capture probabilities (𝑔0) and ranging parameters (𝜎) and
 compared AICs to select the best model. The probability of detecting
 animals as a function of the distance to their home range center was
 modeled with a half-normal detection function. With the
 line transect survey data, we estimated densities with the Distance
 package for R 4.3.1 (Miller et al., 2019) for all terrestrial taxa that
 were observedmore than once. These were: red-rumped agouti (Dasyprocta
 leporina), red acouchi (Myoprocta acouchy), South American coati (Nasua
 nasua), collared peccary (Pecari tajacu), black curassow (Crax alector),
 grey-winged trumpeter (Psophia crepitans), brocket deer (Mazama americana
 and Passalites nemorivagus) and tinamous (Tinamus major and Crypturellus
 variegatus/cinereus). Brocket deer and tinamous were each grouped when
 comparing UD models to LTDS models as these taxa were often difficult to
 identify to the species level during the LTDS survey. For the detection
 function, we supplemented the perpendicular distances observed on the
 transects with data from other transects in French Guiana (Denis et al.,
 2017). We then righttruncated the outer 10% of the detections and fit both
 half-normal and uniform distributions with and without adjustments, using
 the AIC criterion to select the best model. Encounters of multiple animals
 were modeled as if for a single individual only, and afterward we
 multiplied this ‘group density’ with the estimated mean group size, based
 on confident counts only.  <strong>Estimation of
 the snapshot interval<br></strong>CTDS is based on the
 assumption that animals in front of the cameras are continuously captured
 on photographs, which requires estimation of the ‘snapshot rate’ of a
 continuously triggering camera. The average time in between photographs is
 the snapshot interval 𝑡 from (Howe et al., 2017). While 𝑡 can simply be
 calculated from the camera specifications given the manufacturer, these
 have often been found to be inaccurate (Corlatti et al., 2020). Most
 studies have therefore estimated an ‘empirical 𝑡’ by continually
 triggering a camera. Kühl et al. (2023) however found that in reality,
 such empirical 𝑡 values are still biased because 1) animals may not
 trigger cameras similarly to humans, and 2) animals are not continuously
 moving in the viewshed and therefore do not trigger the motion sensor
 instantly. In this study, we therefore estimate species-specific values
 for 𝑡 as simply the mean time interval in seconds between consecutive
 camera triggers of individual animals. Because individual animals cannot
 be identified, it is however difficult to determine whether individuals
 have actually remained in the viewshed in between triggers. A threshold 𝑇
 is therefore determined, giving the time between triggers beyond which it
 is deemed more likely that the animal has left the viewshed before the
 second trigger than that it has remained in sight without triggering the
 device. A formal approach for estimating 𝑇 is not available yet (Kühl et
 al., 2023), and we here chose 𝑇 =20 by visually inspecting the photo data
 and making a subjective judgement on whether animals were more likely to
 have remained in place (e.g. animals sitting in the same place in both
 sequences) or to have left the viewshed in between sequences (e.g. animals
 moving near or towards the edge of the zone).
 <strong>Estimation of the effective detection
 zone<br></strong>The angles and radii between animals in the
 camera viewshed and the camera itself were derived following the CT
 tracking protocol (https://github.com/MarcusRowcliffe/CTtracking). We
 calculated 𝑟 and 𝜃 for all species by fitting hazard, half-normal, and
 uniform detection functions with and without polynomial, cosine, and
 hermite adjustments to the angles and radii of the positions at which
 animals were first detected by the camera. We only used the first photo of
 each detection for these functions, since the last seven pictures are
 always taken independently of the camera sensor response. We selected the
 best performing detection models with the AIC criterion.
 <strong>Estimation of day range<br></strong>To
 estimate day range 𝑣, we estimated species-specific movement speeds for
 sequences of photographs separated by less than 20 seconds as the
 cumulative displacement between animal positions divided by the time
 passed between pictures, and we averaged speeds to produce one value per
 independent detection. We discarded speeds above 1.5 m/s, which were
 deemed unrealistically high, since running animals were virtually never
 observed. To account for the fact that cameras are more likely to
 encounter fast-moving animals, we modeled speed with log-normal, gamma, or
 Weibull distributions following Rowcliffe et al. (2016), selecting the
 best performing model for each species with the AICc criterion. Because
 animals are not active at all times of the day, the movement speed
 observed by the CTs needs to be corrected for the time that the animals
 are actually active and are moving at those speeds. We therefore estimated
 species-specific activity levels (i.e. the proportion of time an animal
 populations is available for detection) by fitting a circular kernel model
 to the radian time data with the activity package for R (Rowcliffe et al.,
 2014; Rowcliffe, 2016). To ensure independence between observations, we
 only used observations separated by at least one hour for activity level.
 This approach assumes that all animals in the population are active at the
 time of peak activity for the species. Previous research has recommended a
 minimum of 100 independent detections to estimate activity levels (Lashley
 et al., 2018), which was not reached for curassow, ocelot and jaguar. We
 therefore supplemented the time-ofday detection records for all species
 with encounters and timestamps from all CT surveys combined. Including
 these data, we achieved 199, 232 and 96 independent encounters for
 curassow, jaguar and ocelot respectively.
description: # Validating density estimation methods for unmarked wildlife with camera
 traps
 [https://doi.org/10.5061/dryad.fttdz091v](https://doi.org/10.5061/dryad.fttdz091v) ## Description of the data and file structure The scripts for the unmarked density models are provided as follows: * o   **image-processing-unmarked-2024.R**: this script takes as input the annotated image database (which we made in the TimeLapse2 sofware) as a.csv and processes the images. The camera model is calibrated following the CTtracking protocol and various unmarked parameters are estimated such as encounter rate, activity level, and animal movement speed. o   **unmarked-models-2024:** this script takes the output from the former script and uses the parameters to estimate the effective detection zones and eventually the densities with CTDS, the TTE model, and the REM. The data that are required as input for the first script are provided; the input for the second script is the output from the first script. For the first script, we provide: * The **"dataTable_2025.csv"** file with all the annotated data. 'NA' is used for values that are not applicable either to animal pictures or to non-animal pictures. The columns are as follows: * Id, image_id, deploymentID: two identifiers of the image and one for the camera trap deployment. * Empty: whether the photo does not contain a person/animal. * Behavior: for animals, the depicted behavior (no animal = NA). * bottom* *stick and top stick: in meters, the bottom and top measurements on the calibration stick that were used for calibration. * start_date: date of installation of the camera * end_date: date of recovery of the camera * adj* *end date: final date of the data used (some later data was excluded to remain in a single season) * effort: total number of observation days * number of objects coords, bottom stick XY t, top stick XY t: these are the coordinate measurements as outputted by the TimeLapse2 software. number of objects coords indicate animal coordinates, bottom and top stick coordinates indicate the two measurements of the calibration stick. * Calibration: whether the photo is used for calibration or not. * first_detection: whether the photo concerns a first instance of an animal in the viewshed or not. * group_size: the number of animals associated with this picture. * ImageWidth / ImageHeight: photo size, in pixels * The **calibrated-cameras.rData** file with the calibrated cameras following Marcus Rowcliffe's CTtracking protocol * The **CTtracking.R** file developed by Marcus Rowcliffe, from his github. At some point in time this'll become an actual package. * **calModel_8A_2024.rData**, which is an R object containing the calibrated camera model. Not required if using the calibrated-cameras.rData file * **activity-data-previous-surveys-2025.csv** - additional encounter data from the survey site to help estimate activity levels. The rows are species detections with their timestamps. ## Sharing/Access information The CTtracking protocol and associated scripts can be found on Marcus Rowcliffe's github: [https://github.com/MarcusRowcliffe/CTtracking](https://github.com/MarcusRowcliffe/CTtracking)