Enhancing mammal conservation in multi-functional landscapes using artificial intelligence, joint species distribution modeling and ecological experimentation

Abstract

Poaching and livestock production threaten wildlife and its habitat, requiring strategies to manage human-dominated landscapes to sustain conservation objectives. To better understand the spatiotemporal distribution of wildlife and its response to disturbance factors (i.e., poaching and cattle), I deployed camera traps (CTs) and automated acoustic recording units (ARUs) on cattle ranches in the Colombian Orinoquía region. Data collection resulted in the challenge of processing “Big Data,” comprising a total of 824,883 images and 3,491,528 audio files (25,584 hours of recordings). In Chapter 1, I evaluated artificial intelligence platforms built for processing CT data and developed an open-source GitBook that illustrates the use and evaluation of model performance of each of these platforms. In Chapter 2, I used CT data to detect wildlife and trained machine learning algorithms for detecting cattle and poaching activity from the ARU data. To quantify co-occurrence patterns of poachers, cattle, and wildlife, I analyzed these data using joint species distribution models, finding that co-occurrence patterns between disturbance and wild ungulates were dependent on the data-collection method (i.e., whether CTs or ARUs were used to detect disturbance). Lastly, in Chapter 3, I conducted a cattle exclusion experiment to evaluate the effectiveness of fencing for reducing forest use and habitat degradation by cattle and improving resource availability for wildlife. Collectively, these efforts will guide management in multi-functional landscapes by identifying spatial co-occurrence patterns between wildlife and disturbance factors and by scaling up evidence-based interventions to optimize the use of remaining habitat by wildlife.