OOS 29-1
Bringing wildlife ecology into focus: Integrating camera traps, remote sensing and citizen science to improve ecological forecasting
Natural resource agencies are tasked with managing wildlife for a diverse group of constituents with interests ranging from hunting to conservation. These agencies rely on population models that are limited by coarse spatiotemporal resolutions that can foster misunderstandings when patterns described at one scale are incompatible with decision-making at another. The challenge lies in developing an open and accessible method for collecting data on wildlife populations at a variety of scales to improve the information used in decision-support models, and to provide the public confidence that the information being used by decision-makers more accurately reflects conditions on the ground. As part of a unique partnership between NASA, the Wisconsin Department of Natural Resources, Zooniverse, and the University of Wisconsin, we are deploying over 2,000 camera traps throughout Wisconsin. These camera traps will be established and maintained by citizen scientists with the specific goal of providing a more accurate picture of wildlife populations. We integrate the data from this large, spatially extensive network of camera traps with measures of landscape pattern and phenology derived from earth observation data. Interpretations of camera-trap data are being accomplished through online crowdsourcing. Our objective is to develop spatially-explicit models of occupancy and abundance for multiple target species including bears, wolves, bobcats and beavers based on camera trap observations and hypothesized environmental drivers of wildlife distribution.
Results/Conclusions
We demonstrate the capacity to engage the public in collection and interpretation of critical monitoring data, then link camera and land cover data to generate preliminary assessments of the relationships between species distributions and landscape variables. We provide a prototype system for crowdsourcing interpretations of camera trap images collected over a broad geographic region. Our data integration and modeling effort has broader applicability to predicting species distributions throughout the northern temperate zone, and, more importantly, generates the framework necessary to integrate citizen science and earth observing data to improve ecological forecasting and resource management by agencies. As such, this work will bring the estimation, prediction and management of wildlife populations into better focus for many years to come.