Ecotones shape ground-dwelling mammal and bird diversity along a habitat gradient in the southern coastal dry forests of Vietnam

Understanding biodiversity patterns is essential for ecology and conservation. Globally, conservation efforts often prioritize tropical rainforests due to their high species richness. At the regional scale, the same is true in the Greater Annamites ecoregion of Vietnam and Laos, where conservation efforts have largely focused on broadleaf wet evergreen forest, despite the fact that other habitats remain threatened. One such habitat is the coastal dry forest landscape in southern Vietnam, which has received little conservation focus despite the fact that its forested areas have been severely reduced. Nui Chua National Park in southern Vietnam harbors one of the few remaining sizable areas of dry coastal forest. In this study, we used camera-trap data and a community Royle-Nichols model to explore the community structure of ground-dwelling mammals and birds along a complex habitat gradient in Nui Chua National Park. We first investigated species associations among three habitat types: dry..., , , # Ecotones shape ground-dwelling mammal and bird diversity along a habitat gradient in the southern coastal dry forests of Vietnam

[https://doi.org/10.5061/dryad.69p8cz9cs](https://doi.org/10.5061/dryad.69p8cz9cs)

## Description of the data and file structure

Between 2018 and 2022, we conducted five camera-trap surveys in Nui Chua NP with different study designs and survey efforts. These surveys include four fine-scale surveys where cameras were spaced from 300 m to 600 m in selected areas of the park, and one large-scale survey with camera spacing of approximately 2.5 km across the protected area. Camera-trap photographs were managed and the intermediate outputs for analysis produced using the package *camtrapR* 2.1.1. 0 in R software 4.0.5

To characterize the landscape-scale habitat composition in Nui Chua NP, we ran a random classification forest model in Google Earth Engine using the *smileRandomForest* algorithm. For input variables in the classification, we used 14 bands deriv...,