The rapid improvement of camera traps in recent decades has revolutionized biodiversity monitoring. Despite clear applications in conservation science, camera traps have seldom been used to model the abundance of unmarked animal populations. We sought to summarize the challenges facing abundance estimation of unmarked animals, compile an overview of existing analytical frameworks, and provide guidance for practitioners seeking a suitable method. When a camera records multiple detections of an unmarked animal, one cannot determine whether the images represent multiple mobile individuals or a single individual repeatedly entering the camera viewshed. Furthermore, animal movement obfuscates a clear definition of the sampling area and, as a result, the area to which an abundance estimate corresponds. Recognizing these challenges, we identified 6 analytical approaches and reviewed 927 camera‐trap studies published from 2014 to 2019 to assess the use and prevalence of each method. Only about 5% of the studies used any of the abundance‐estimation methods we identified. Most of these studies estimated local abundance or covariate relationships rather than predicting abundance or density over broader areas. Next, for each analytical approach, we compiled the data requirements, assumptions, advantages, and disadvantages to help practitioners navigate the landscape of abundance estimation methods. When seeking an appropriate method, practitioners should evaluate the life history of the focal taxa, carefully define the area of the sampling frame, and consider what types of data collection are possible. The challenge of estimating abundance of unmarked animal populations persists; although multiple methods exist, no one method is optimal for camera‐trap data under all circumstances. As analytical frameworks continue to evolve and abundance estimation of unmarked animals becomes increasingly common, camera traps will become even more important for informing conservation decision‐making.
Human disturbance may fundamentally alter the way that species interact, a prospect that remains poorly understood. We investigated whether anthropogenic landscape modification increases or decreases co-occurrence—a prerequisite for species interactions—within wildlife communities. Using 4 y of data from >2,000 camera traps across a human disturbance gradient in Wisconsin, USA, we considered 74 species pairs (classifying pairs as low, medium, or high antagonism to account for different interaction types) and used the time between successive detections of pairs as a measure of their co-occurrence probability and to define co-occurrence networks. Pairs averaged 6.1 [95% CI: 5.3, 6.8] d between detections in low-disturbance landscapes (e.g., national forests) but 4.1 [3.5, 4.7] d between detections in high-disturbance landscapes, such as those dominated by urbanization or intensive agriculture. Co-occurrence networks showed higher connectance (i.e., a larger proportion of the possible co-occurrences) and greater proportions of low-antagonism pairs in disturbed landscapes. Human-mediated increases in species abundance (possibly via resource subsidies) appeared more important than behavioral mechanisms (e.g., changes in daily activity timing) in driving these patterns of compressed co-occurrence in disturbed landscapes. The spatiotemporal compression of species co-occurrences in disturbed landscapes likely strengthens interactions like competition, predation, and infection unless species can avoid each other at fine spatiotemporal scales. Regardless, human-mediated increases in co-occurrence with—and hence increased exposure to—predators or competitors might elevate stress levels in individual animals, with possible cascading effects across populations, communities, and ecosystems.
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