Population assessment without individual identification using camera-traps: a comparison of four methods

A model for the noninvasive, habitat-inclusive estimation of upper limit abundance for synanthropes, exemplified by M. fascicularis

Accurately estimating population sizes for free-ranging animals through noninvasive methods, such as camera trap images, remains particularly limited by small datasets. To overcome this, we developed a flexible model for estimating upper limit populations and exemplified it by studying a group-living synanthrope, the long-tailed macaque (Macaca fascicularis). Habitat preference maps, based on environmental and GPS data, were generated with a maximum entropy model and combined with data obtained from camera traps, line transect distance sampling, and direct sightings to produce an expected number of individuals. The mapping between habitat preference and number of individuals was optimized through a tunable parameter ρ (inquisitiveness) that accounts for repeated observations of individuals. Benchmarking against published data highlights the high accuracy of the model. Overall, this approach combines citizen science with scientific observations and reveals the long-tailed macaque populations to be (up to 80%) smaller than expected. The model's flexibility makes it suitable for many species, providing a scalable, noninvasive tool for wildlife conservation.

Assessing trends in population size of three unmarked species: A comparison of a multi-species N-mixture model and random encounter models

Estimation of changes in abundances and densities is essential for the research, management, and conservation of animal populations. Recently, technological advances have facilitated the surveillance of animal populations through the adoption of passive sensors, such as camera traps (CT). Several methods, including the random encounter model (REM), have been developed for estimating densities of unmarked populations but require additional information. Hierarchical abundance models, such as the N-mixture model (NMM), can estimate abundances without performing additional fieldwork but do not explicitly estimate the area effectively sampled. This obscures the interpretation of its densities and requires its users to focus on relative measures of abundance instead. Hence, the main objective of our study is to evaluate if REM and NMM yield consistent results qualitatively. Therefore, we compare relative trends: (i) between species, (ii) between years and (iii) across years obtained from annual density/abundance estimates of three species (fox, wild boar and red deer) in central Spain monitored by a camera trapping network for five consecutive winter periods. We reveal that NMM and REM provided density estimates in the same order of magnitude for wild boar, but not for foxes and red deer. Assuming a Poisson detection process in the NMM was important to control for inflation of abundance estimates for frequently detected species. Both methods consistently ranked density/abundance across species (between species trend), but did not always agree on relative ranks of yearly estimates within a single population (between years trend), nor on its linear population trends across years (across years trend). Our results suggest that relative trends are generally consistent when the range of variability is large, but can become inconsistent when the range of variability is smaller.

Estimating effective survey duration in camera trap distance sampling surveys

Among other approaches, camera trap distance sampling (CTDS) is used to estimate animal abundance from unmarked populations. It was formulated for videos and observation distances are measured at predetermined 'snapshot moments'. Surveys recording still images with passive infrared motion sensors suffer from frequent periods where animals are not photographed, either because of technical delays before the camera can be triggered again (i.e. 'camera recovery time') or because they remain stationary and do not immediately retrigger the camera following camera recovery time (i.e. 'retrigger delays'). These effects need to be considered when calculating temporal survey effort to avoid downwardly biased abundance estimates. Here, we extend the CTDS model for passive infrared motion sensor recording of single images or short photo series. We propose estimating 'mean time intervals between triggers' as combined mean camera recovery time and mean retrigger delays from the time interval distribution of pairs of consecutive pictures, using a Gamma and Exponential function, respectively. We apply the approach to survey data on red deer, roe deer and wild boar. Mean time intervals between triggers were very similar when estimated empirically and when derived from the model-based approach. Depending on truncation times (i.e. the time interval between consecutive pictures beyond which data are discarded) and species, we estimated mean time intervals between retriggers between 8.28 and 15.05 s. Using a predefined snapshot interval, not accounting for these intervals, would lead to underestimated density by up to 96% due to overestimated temporal survey effort. The proposed approach is applicable to any taxa surveyed with camera traps. As programming of cameras to record still images is often preferred over video recording due to reduced consumption of energy and memory, we expect this approach to find broad application, also for other camera trap methods than CTDS.

A Camera-Trap Survey of Mammals in Thung Yai Naresuan (East) Wildlife Sanctuary in Western Thailand

The Thung Yai Naresuan (East) Wildlife Sanctuary (TYNE), in the core area of the Western Forest Complex of Thailand, harbors a diverse assemblage of wildlife, and the region has become globally significant for mammal conservation. From April 2010 to January 2012, 106 camera traps were set, and, in 1817 trap-nights, registered 1821 independent records of 32 mammal species. Of the 17 IUCN-listed (from Near Threatened to Critically Endangered) mammal species recorded, 5 species listed as endangered or critically endangered included the Asiatic elephant (Elephas maximus), tiger (Panthera tigris), Malayan tapir (Tapirus indicus), dhole (Cuon alpinus), and Sunda pangolin (Manis javanica). The northern red muntjac (Muntiacus vaginalis), large Indian civet (Viverra zibetha), Malayan porcupine (Hystrix brachyuran), and sambar deer (Cervus unicolor) were the most frequently recorded species (10-22 photos/100 trap-nights), representing 62% of all independent records, while the golden jackal (Canis aureus), clouded leopard (Neofelis nebulosa), marbled cat (Pardofelis marmorata), and Sunda pangolin were the least photographed (<0.1/100 trap-nights). Species accumulation curves indicated that the number of camera trap locations needed to record 90% of taxa recorded varied from 26 sites for herbivores to 67 sites for all mammals. TYNE holds a rich community of mammals, but some differences in photo-rates from an adjacent sanctuary and comparisons with other research on local mammals suggest that some species are rare and some are missed because of the limitations of our technique. We also conclude that the management and conservation plan, which involves the exclusion of human activities from some protected areas and strict protection efforts in the sanctuaries, is still suitable for providing key habitats for endangered wildlife populations, and that augmented and regular survey efforts will help in this endeavor.