Reduced gene flow and bottleneck in the threatened giant armadillo (Priodontes maximus): implications for its conservation

The progressive fragmentation and loss of habitats represent the main threats for endangered species, causing genetic consequences that may have potential implications for a population's long-term persistence. Large mammals are the most affected species among vertebrates. The giant armadillo Priodontes maximus is a large South American mammal threatened species, showing nocturnal, solitary and fossorial behavior, occurring at low population densities, and its population dynamics are still poorly known. In this study, we carried out the first assessment of genetic variability and population genetic structure of the species, using a panel of 15 polymorphic microsatellites developed by high-throughput genome sequencing. The spatial Bayesian clustering, Fst and Dest results indicated the presence of two genetic clusters (K = 2) in the study area. These results suggest a reduction in gene flow between individuals inhabiting the Brazilian savanna (Cerrado) and the Pantanal wetlands, with the increased human-driven habitat modifications possibly contributing for this scenario. A bottleneck signal was detected in both populations, and a subpopulation structuring in the Cerrado may also be reflecting consequences of the extensive habitat modifications. Findings from this study provide important and useful information for the future maintenance of genetic diversity and long-term conservation of this flagship species.

Change-point models for identifying behavioral transitions in wild animals

Animal behavior can be difficult, time-consuming, and costly to observe in the field directly. Innovative modeling methods, such as hidden Markov models (HMMs), allow researchers to infer unobserved animal behaviors from movement data, and implementations often assume that transitions between states occur multiple times. However, some behavioral shifts of interest, such as parturition, migration initiation, and juvenile dispersal, may only occur once during an observation period, and HMMs may not be the best approach to identify these changes. We present two change-point models for identifying single transitions in movement behavior: a location-based change-point model and a movement metric-based change-point model. We first conducted a simulation study to determine the ability of these models to detect a behavioral transition given different amounts of data and the degree of behavioral shifts. We then applied our models to two ungulate species in central Pennsylvania that were fitted with global positioning system collars and vaginal implant transmitters to test hypotheses related to parturition behavior. We fit these models in a Bayesian framework and directly compared the ability of each model to describe the parturition behavior across species. Our simulation study demonstrated that successful change point estimation using either model was possible given at least 12 h of post-change observations and 15 min fix interval. However, our models received mixed support among deer and elk in Pennsylvania due to behavioral variation between species and among individuals. Our results demonstrate that when the behavior follows the dynamics proposed by the two models, researchers can identify the timing of a behavioral change. Although we refer to detecting parturition events, our results can be applied to any behavior that results in a single change in time.