The recent spreading of African swine fever (ASF) over the Eurasian continent has been acknowledged as a serious economic threat for the pork industry. Consequently, an extensive body of research focuses on the epidemiology and control of ASF. Nevertheless, little information is available on the combined effect of ASF and ASF-related control measures on wild boar (Sus scrofa) population abundances. This is crucial information given the role of the remaining wild boar that act as an important reservoir of the disease. Given the high potential of camera traps as a non-invasive method for ungulate trend estimation, we assess the effectiveness of ASF control measures using a camera trap network. In this study, we focus on a major ASF outbreak in 2018-2020 in the South of Belgium. This outbreak elicited a strong management response, both in terms of fencing off a large infected zone as well as an intensive culling regime. We apply a Bayesian multi-season site-occupancy model to wild boar detection-nondetection data. Our results show that (1) occupancy rates at the onset of our monitoring period reflect the ASF infection status; (2) ASF-induced mortality and culling efforts jointly lead to decreased occupancy over time; and (3) the estimated mean total extinction rate ranges between 22.44% and 91.35%, depending on the ASF infection status. Together, these results confirm the effectiveness of ASF-control measures implemented in Wallonia (Belgium), which has regained its disease-free status in December 2020, as well as the usefulness of a camera trap network to monitor these effects.

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 field work. Hierarchical abundance models, such as the N-mixture model (NMM), can estimate densities 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. We compare relative trends in density/ abundance for three species (wild boar, red deer, and fox) based on the REM and NMM. The NMM applied here is adapted to overcome two issues potentially leading to poor abundance estimates: (i) we specify a joint observation model, based on a beta distribution, for all species within a community to strengthen the inference on infrequently detected species, and (ii) we model species-specific counts as a Poisson process, relaxing the assumption that each individual can only be detected once per survey. 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 density estimates for frequently detected species. Both methods correctly identified species ranking of abundance/density but did not always agree on relative ranks of yearly estimates within a single population, nor on its linear population trends. Our results suggest that relative population trends are better preserved between NMM and REM compared to absolute densities. Thus practitioners working with counts-only data should resort to relative abundances, rather than absolute densities.

The need for knowledge about abundance to guide conservation and management strategies in combination with low detectability of many species has led to a widespread use in ecology and management of a range of hierarchical models (HMs) for abundance. These models also appear like good candidates for inference about local abundance in nature reserves studied by camera traps. However, the best choice among these models is unclear, particularly how they perform in the face of several complicating features of realistic populations that include: (i) movements relative to sites, (ii) multiple detections of unmarked individuals within a single survey, and (iii) low probabilities of detection. We conducted a simulation-based comparison of three HMs (Royle-Nichols, binomial Nmixture and Poisson N-mixture model) in the context of small populations of elusive animals in a single study area, where animals cannot be distinguished individually and hence double counting occurs. We generated count data by simulating camera traps monitoring individuals moving according to a Gaussian random walk. Under the simulated scenarios none of the three HMs yielded accurate abundance estimates. Moreover, the performance of each HM depended on the interpretation of abundance. By pooling abundance estimates for trend estimation, each models performance markedly improves. Overall, the Royle-Nichols and Poisson N-mixture models outperform a binomial N-mixture model. This emphasizes the importance of choosing the appropriate HM for the data problem.

Knowledge on animal abundances is essential in ecology, but is complicated by low detectability of many species. This has led to a widespread use of hierarchical models (HMs) for species abundance, which are also commonly applied in the context of nature areas studied by camera traps. However, the best choice among these models is unclear, particularly based on how they perform in the face of complicating features of realistic populations, including: movements relative to sites, multiple detections of unmarked individuals within a single survey, and low detectability. We conducted a simulation-based comparison of three HMs (Royle-Nichols, binomial N-mixture and Poisson N-mixture model) by generating groups of individuals moving according to a bivariate Ornstein-Uhlenbeck process, and monitored by camera traps. Under a range of simulated scenarios, none of the HMs consistently yielded accurate abundances. Yet, the Poisson N-mixture model performed well when animals did move across sites, despite accidental double counting of individuals. Absolute abundances were better captured by Royle-Nichols and Poisson N-mixture models, while a binomial N-mixture model better estimated the actual number of individuals that used a site. Focusing on relative trends in abundance improved the performance of all HMs, and were captured with similar accuracy across these models.