Climate change is altering the spatial distribution of many species around the world. In response, we need to identify and protect suitable areas for a large proportion of the fauna so that they persist through time. This exercise must also evaluate the ability of existing protected areas to provide safe havens for species in the context of climate change.

The effects of forest degradation, fragmentation, and climate change occur over long time periods, yet relatively few data are available to evaluate the long-term effects of these disturbances on tropical species occurrence. Here, we quantified changes in occupancy of 50 bird species over 17 years on Barro Colorado Island (BCI), Panama, a model system for the long-term effects of habitat fragmentation.

During the last few decades, much attention has focused on how global change is affecting the environment and species distributions. Land-use change is still the major cause of species declines worldwide, but changes in species distributions have been documented even in pristine and protected areas. Here, we document the distribution dynamics of 26 species of frogs and birds within a Caribbean protected area between 2015 and 2019.

The rapidly developing field of ecoacoustics offers methods that can advance multi-taxa animal surveys at policy-relevant extents. While the field is promising, there remain foundational assumptions that need to be tested across different biomes before the methods can be applied widely. Here we test two of these assumptions in the Amazon: 1) acoustic indices can be used to predict soundscapes of different habitat types, and 2) acoustic indices are related to vegetation structure.

Automated acoustic recorders can collect long-term soundscape data containing species-specific signals in remote environments. Deep learning methods have gained recent attention for automating the process of species identification in soundscape recordings. We present an end-to-end pipeline for training a convolutional neural network (CNN) for multispecies multi-label classification of soundscape recordings, starting from raw, unlabeled audio.

In this study, we evaluated deep convolutional neural networks for classifying the calls of 24 birds and amphibian species detected in ambient field recordings. In classifying a test set of manually validated positive and negative template based detections, our proposed model achieves 97.7% sensitivity (true positive rate), 96.4% specificity (true negative rate) and 99.5% Area Under a Curve (AUC). This multi-label multi-species classification methodology and its framework can be easily adopted by other acoustic classification problems. Please email us at contact@rfcx.org for a copy of this article.