Using population monitoring programs to detect changes in mammalian communities

Abstract

The global biodiversity crisis is escalating rapidly, with nearly a million species facing extinction risk in the near future. Monitoring of Essential Biodiversity Variables (EBV; key variables measured to detect changes in biodiversity) over time is important for biodiversity conservation, which in turn necessitates baseline estimates of EBVs. Using camera-trap data for six years (2013–2018) and a Bayesian multispecies occupancy model, we estimated species richness and species-specific occupancy of a mammalian community in Pakke Tiger Reserve, India. We detected 28 species of mammals in our camera traps. The model-based estimate of species richness during the study period was 41 (95 % CRI: 39–42) species, with no evidence of any substantial temporal variation. We found that carnivore and herbivore species richness was higher in greener habitats and that omnivore species richness was higher at lower-elevation sites. Annual average species occupancy probability (at the scale of 4-km2 sampling units) did not vary substantially between years but was lowest in 2014 (0.54, 95 % CRI: 0.20–0.84) and highest in 2013 (0.59, 95 % CRI: 0.27–0.87). Mean species-specific occupancy averaged across the years varied substantially among species, ranging from a low of 0.03 (95 % CRI: 0.00–0.08) for the masked palm civet Paguma larvata to a high of 0.94 (95 % CRI: 0.87–0.98) for the Asian elephant Elephas maximus. Mean occupancy probability was highest for herbivores followed by omnivores and carnivores. Species-specific detection probability (for a 30–60-day survey period) was not influenced by survey effort but increased with body mass for carnivores and decreased with body mass for herbivores and omnivores. We propose a framework for efficient monitoring of species and community-level EBV, utilizing data collected during the monitoring of high-profile species of conservation concern, such as tigers Panthera tigris in India.