1. Understanding how diverse assemblages of scavengers can coexist on shared ecological resources is a fundamental challenge in community ecology. However, current approaches typically focus on behaviour at carcass provisioning sites, missing how important differences in movement behaviour and foraging strategies can facilitate sympatric species coexistence. Such information is particularly important for vultures - obligate scavengers representing the most endangered avian foraging guild. Their loss from ecosystems can trigger trophic cascades, mesopredator release, and disease outbreaks. 2. We use a combination of morphometric measurements and movement data from wild King (Sarcoramphus papa) and Greater yellow-headed (Cathartes melambrotus) vultures, coupled with carcass visitation data from animal carcass provisioning experiments to characterise scavenger community structure and strategies in the Peruvian Amazon. 3. King vulture body mass and tarsi length were larger (43% and 45% respectively), and tail shorter (22%) than the Greater-yellow headed vulture. King vultures also had substantially larger home ranges (~500%), flew higher than Greater yellow headed vultures (695m vs. 360 m), and despite flying similar distances each day, were active later in the day. At carcasses, Greater-yellow headed vultures typically arrived first, but were rapidly outnumbered by both King and Black vultures (Coragyps atratus). 4. We find that the movement behaviour of obligate apex scavengers in the western Amazon is linked to their ability to coexist - the Greater -yellow headed vultures, a smaller stature ‘scouting’ species adapted to fly low and forage early, arrive first, but are ultimately displaced by larger-bodied king vultures at large ephemeral carrion resources. Expansion of future GPS tracking initiatives should facilitate the exploration of direct facultative interactions from animal movement data and give further insight into how diverse communities assemble and interact.

1. The use of machine learning technologies to process large quantities of remotely-collected audio data is a powerful emerging research tool in ecology and conservation. 2. We applied these methods to a field study of tinamou (Tinamidae) biology in Madre de Dios, Peru, a region expected to have high levels of interspecies competition and niche partitioning as a result of high tinamou alpha diversity. We used autonomous recording units to gather environmental audio over a period of several months at lowland rainforest sites in the Los Amigos Conservation Concession and developed a Convolutional Neural Network-based data processing pipeline to detect tinamou vocalizations in the dataset. 3. The classified acoustic event data are comparable to similar metrics derived from an ongoing camera trapping survey at the same site, and it should be possible to combine the two datasets for future explorations of the target species’ niche space parameters. 4. Here we provide an overview of the methodology used in the data collection and processing pipeline, offer general suggestions for processing large amounts of environmental audio data, and demonstrate how data collected in this manner can be used to answer questions about bird biology.