Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape.

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.

Measuring the intensity of conflicts in conservation.

Conflicts between the interests of biodiversity conservation and other human activities pose a major threat to natural ecosystems and human well-being, yet few methods exist to quantify their intensity and model their dynamics. We develop a categorization of conflict intensity based on the curve of conflict, a model originally used to track the escalation and deescalation of armed conflicts. Our categorization assigns six intensity levels reflecting the discourse and actions of stakeholders involved in a given conflict, from coexistence or collaboration to physical violence. Using a range of case studies, we demonstrate the value of our approach in quantifying conflict trends, estimating transition probabilities between conflict stages, and modeling conflict intensity as a function of relevant covariates. By taking an evidence-based approach to quantifying stakeholder behavior, the proposed framework allows for a better understanding of the drivers of conservation conflict development across a diverse range of socioecological scenarios.