Modeling community occupancy from line transect data: a case study with large mammals in post‐war Angola

Abstract

Human disturbance can have a profound effect on the occurrence and distribution of wildlife. Such disturbance often extends into protected areas (PAs), particularly in countries that have undergone civil strife and lack the institutional capacity to effectively mitigate anthropogenic threats. We demonstrate the first application of a multi‐species hierarchical occupancy model to spatially correlated detections from vehicle‐based spoor transects, estimating species richness and species‐specific drivers of occurrence of a large mammal community comprising five large carnivores [cheetah (Acinonyx jubatus), leopard (Panthera pardus), lion (Panthera leo), spotted hyena (Crocuta crocuta), and wild dog (Lycaon pictus)] and six large herbivores [buffalo (Syncerus caffer), eland (Taurotragus oryx), elephant (Loxodonta africana), giraffe (Giraffa giraffa), roan (Hippotragus equinus), and sable (Hippotragus niger)] in Luengue‐Luiana and Mavinga National Parks, Angola. This area is the largest contiguous national park complex within a single African country and part of the largest transfrontier conservation area in the world, and is still recovering from the effects of civil war. In this post‐war landscape, the most substantive drivers of community‐level occupancy were anthropogenic, with occupancy associated with lower frequency of human sign, proximity to adjacent national park, and distance away from human settlement. In contrast, ecological variables (precipitation, vegetation cover, seasonal water availability) had less explanatory value. Our results highlight the deleterious effects of human incursion into PAs on the richness and distribution of large mammal species, underscoring the need for intensive mitigation of anthropogenic threats (e.g. poaching, bushmeat hunting) to maintain species of high conservation value in areas impacted by war.

The African Elephant (<i>Loxodonta africana</i>): Mini-Review of an Endangered Species

“…The references are in accordance with the ones suggested in the articles tools, except the references of 6 articles (Bastille-Rousseau et al (2019) [5], [6], [90], Petracca et al (2019) [2], Schiffmann et al (2019) [106] and Watson et al (2019) [110]), that are in accordance with the date of their first publication. Thus, the dates of the articles mentioned here are between July 2015 and December 2019.…”

Forest cover mediates large and medium-sized mammal occurrence in a critical link of the Mesoamerican Biological Corridor

“…Contrary to our predictions, human presence and EVI were positively and negatively associated with overall species habitat use, respectively, though both effects were weak. While human presence is often used as a proxy for disturbance and has found to be negatively associated with habitat use at the community level [ 48 ], in our case the presence of humans may have a different interpretation. For example, people in our study area (e.g.…”

“…We used a multi-level (or hierarchical) community occupancy model in a Bayesian framework to calculate jaguar and puma habitat use probability and species richness [ 46 – 48 , 50 ]. Unlike traditional occupancy models, the Bayesian framework can account for unobserved heterogeneity across species, space or time through random effects [ 15 ].…”

“…Habitat use probability for each species at each site was estimated as: where ξ il ∼ Normal( μ ξ , τ ξ ) is the random intercept for each species i at each block l (CVC JCU, Corridor Blocks 1 and 2, and TC JCU), μ ξ is the community-level or hyperparameter mean for the intercept on habitat use, and τ ξ is its precision. The random intercept was used to account for potential spatial and temporal heterogeneity in habitat use due to differences by species and survey block [ 47 , 48 , 50 ]. In addition, α i are estimated beta coefficients on habitat use for species i , where α i ∼ Normal( μ α, τ α); μ α is the community-level or hyperparameter mean for each beta coefficient, and τ α is its precision.…”

“…Modern occupancy models account for imperfect detection by incorporating detection probability [ 43 , 44 ], producing less biased estimates compared to naive occupancy estimation [ 45 ]. The extension of occupancy models to a community or multi-species framework [ 46 – 48 ] allows the estimation of species and community-level occupancy by drawing species-level estimates from community-level hyperparameters [ 45 , 47 – 50 ], which can improve estimates for rare or elusive species. For these reasons, a multiple species approach to assess biodiversity, evaluate the effects of impacts or management actions, and evaluate connectivity has proven to be a useful alternative to the aforementioned methods [ 16 , 46 – 48 , 50 ].…”