Disappearance of an ecosystem engineer, the white-lipped peccary (Tayassu pecari), leads to density compensation and ecological release.

Given the rate of biodiversity loss, there is an urgent need to understand community-level responses to extirpation events, with two prevailing hypotheses. On one hand, the loss of an apex predator leads to an increase in primary prey species, triggering a trophic cascade of other changes within the community, while density compensation and ecological release can occur because of reduced competition for resources and absence of direct aggression. White-lipped peccary (Tayassu pecari-WLP), a species that typically co-occurs with collared peccary (Pecari tajacu), undergo major population crashes-often taking 20 to 30-years for populations to recover. Using a temporally replicated camera trapping dataset, in both a pre- and post- WLP crash, we explore how WLP disappearance alters the structure of a Neotropical vertebrate community with findings indicative of density compensation. White-lipped peccary were the most frequently detected terrestrial mammal in the 2006-2007 pre-population crash period but were undetected during the 2019 post-crash survey. Panthera onca (jaguar) camera trap encounter rates declined by 63% following the WLP crash, while collared peccary, puma (Puma concolor), red-brocket deer (Mazama americana) and short-eared dog (Atelocynus microtis) all displayed greater encounter rates (490%, 150%, 280%, and 500% respectively), and increased in rank-abundance. Absence of WLP was correlated with ecological release changes in habitat-use for six species, with the greatest increase in use in the preferred floodplain habitat of the WLP. Surprisingly, community-weighted mean trait distributions (body size, feeding guild and nocturnality) did not change, suggesting functional redundancy in diverse tropical mammal assemblages.

Automated audio recording as a means of surveying tinamous (Tinamidae) in the Peruvian Amazon.

The use of machine learning technologies to process large quantities of remotely collected audio data is a powerful emerging research tool in ecology and conservation.We applied these methods to a field study of tinamou (Tinamidae) biology in Madre de Dios, Peru, a region expected to have high levels of interspecies competition and niche partitioning as a result of high tinamou alpha diversity. We used autonomous recording units to gather environmental audio over a period of several months at lowland rainforest sites in the Los Amigos Conservation Concession and developed a Convolutional Neural Network-based data processing pipeline to detect tinamou vocalizations in the dataset.The classified acoustic event data are comparable to similar metrics derived from an ongoing camera trapping survey at the same site, and it should be possible to combine the two datasets for future explorations of the target species' niche space parameters.Here, we provide an overview of the methodology used in the data collection and processing pipeline, offer general suggestions for processing large amounts of environmental audio data, and demonstrate how data collected in this manner can be used to answer questions about bird biology.