A benchmark for computational analysis of animal behavior, using animal-borne tags.

Background: Animal-borne sensors ('bio-loggers') can record a suite of kinematic and environmental data, which are used to elucidate animal ecophysiology and improve conservation efforts. Machine learning techniques are used for interpreting the large amounts of data recorded by bio-loggers, but there exists no common framework for comparing the different machine learning techniques in this domain. This makes it difficult to, for example, identify patterns in what works well for machine learning-based analysis of bio-logger data.

A Reexamination of the Herpetofauna of Oaxaca, Mexico: Composition Update, Physiographic Distribution, and Conservation Commentary.

A substantial number of changes to the composition of the herpetofauna of the Mexican state of Oaxaca, including taxonomic additions and deletions, have occurred in the five years since our original assessment of this region. These changes now establish a herpetofauna of 480 species for the state. A number of taxonomic and nomenclatural changes involving the Oaxacan herpetofauna also are discussed.

Effects of human-made resource hotspots on seasonal spatial strategies by a desert pitviper.

Habitat heterogeneity and local resource distribution play key roles in animal search patterns. Optimal strategies are often considered for foraging organisms, but many of the same predictions are applicable to mate searching. We quantified movement and space use by a pitviper to test whether Native Habitats (NH) and human-made Resource Hotspots (RH) facilitate alternative seasonal spatial strategies as a result of critical resources, including potential mating partners, being widely dispersed in NH and clustered in RH.