Camera traps have quickly transformed the way in which many ecologists study the distribution of wildlife species, their activity patterns and interactions among members of the same ecological community. Although they provide a cost‐effective method for monitoring multiple species over large spatial and temporal scales, the time required to process the data can limit the efficiency of camera‐trap surveys. Thus, there has been considerable attention given to the use of artificial intelligence (AI), specifically deep learning, to help process camera‐trap data. Using deep learning for these applications involves training algorithms, such as convolutional neural networks (CNNs), to use particular features in the camera‐trap images to automatically detect objects (e.g. animals, humans, vehicles) and to classify species. To help overcome the technical challenges associated with training CNNs, several research communities have recently developed platforms that incorporate deep learning in easy‐to‐use interfaces. We review key characteristics of four AI platforms—Conservation AI, MegaDetector, MLWIC2: Machine Learning for Wildlife Image Classification and Wildlife Insights—and two auxiliary platforms—Camelot and Timelapse—that incorporate AI output for processing camera‐trap data. We compare their software and programming requirements, AI features, data management tools and output format. We also provide R code and data from our own work to demonstrate how users can evaluate model performance. We found that species classifications from Conservation AI, MLWIC2 and Wildlife Insights generally had low to moderate recall. Yet, the precision for some species and higher taxonomic groups was high, and MegaDetector and MLWIC2 had high precision and recall when classifying images as either ‘blank’ or ‘animal’. These results suggest that most users will need to review AI predictions, but that AI platforms can improve efficiency of camera‐trap‐data processing by allowing users to filter their dataset into subsets (e.g. of certain taxonomic groups or blanks) that can be verified using bulk actions. By reviewing features of popular AI‐powered platforms and sharing an open‐source GitBook that illustrates how to manage AI output to evaluate model performance, we hope to facilitate ecologists' use of AI to process camera‐trap data.
Future climate projections of warming, drying, and increased weather variability indicate that conventional agricultural and production practices within the Northern Great Plains (NGP) will become less sustainable, both ecologically and economically. As a result, the livelihoods of people that rely on these lands will be adversely impacted. This is especially true for Native American communities, who were relegated to reservations where the land is often vast but marginal and non-tribal operators have an outsized role in food production. In addition, NGP lands are expected to warm and dry disproportionately relative to the rest of the United States. It is therefore critical to identify models of sustainable land management that can improve ecological function and socio-economic outcomes for NGP communities, all while increasing resilience to a rapidly changing climate. Efforts led by Native American Nations to restore North American Plains bison (Bison bison bison) to tribal lands can bring desired socio-ecological benefits to underserved communities while improving their capacity to influence the health of their lands, their people, and their livelihoods. Ecological sustainability will depend on the restoration of bison herds and bison’s ability to serve as ecosystem engineers of North America’s Plains. The historically broad distribution of bison suggests they can adapt to a variety of conditions, making them resilient to a wide range of management systems and climates. Here we review bison’s ecological, cultural, and economic value using four case studies from tribal communities within the NGP. We discuss the potential contributions of bison to food sovereignty, sustainable economies, and conservation of a working landscape with limited protections and significant risk of conversion. The ecological role of bison within this setting has potential due to cultural acceptance and the vast availability of suitable lands; however, it is critical to address tribal needs for funding support, enhanced community capacity, and solving complex landownership for these goals to be achieved.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.