Share icon
In wildlife management, perception is everything. Natural resource agencies are tasked with managing wildlife for a diverse group of constituents with interests ranging from hunting to conservation. As such, wildlife management is often burdened by disconnects between the public s perception of wildlife abundance and the management goals set forth by agencies. These agencies rely on population models that, although powerful tools for establishing management and harvest targets, are limited by coarse spatiotemporal resolutions that can foster misunderstandings when patterns described or observed 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. Our partner agency, the Wisconsin DNR, has acquired 2,400 camera traps that will be established throughout the state by citizen scientists with the specific goal of providing a more accurate picture of the distribution and abundance of three highly controversial wildlife species: white-tailed deer, black bears, and gray wolves. We propose to integrate data from this large, spatially extensive network of camera traps with measures of landscape pattern and phenology derived from earth observation data. Our objective is to develop spatially-explicit models of occupancy and abundance for deer, bears and wolves based on camera trap observations and hypothesized environmental drivers of wildlife distribution on the landscape. Interpretations of camera-trap data will be accomplished through online crowdsourcing. Our one-year feasibility study will demonstrate our capacity to link camera and land cover data within a geospatially referenced relational database, generate preliminary assessments of the relationships between species distributions and landscape variables, and provide a prototype system for crowdsourcing interpretations of camera trap images collected over a broad geographic region. Our domain of study is Wisconsin since the WDNR is establishing the monitoring network, but the results of our data integration and modeling effort will have broader applicability to predicting species distributions throughout the northern temperate zone, and, more importantly, will generate the framework necessary to use camera trap and remote sensing data to improve management decisions. Our effort will provide data on wildlife distributions needed for monitoring and modeling, but are currently unavailable; from this, the results will be used directly by the management agency to inform their decision support models. The State of Wisconsin has already committed to the camera trap network and personnel necessary to ensure its implementation and continued operation, and we expect considerable public buy-in to the crowdsourcing effort based on the charismatic nature of our target species, and their prominence in the public eye. Through this work, we postulate that our coordinated efforts in linking citizen science, crowdsourcing, and earth observations will provide data required by agencies for better ecological forecasting and alleviate issues related to poor spatiotemporal resolution in existing monitoring data and population models. As such, this work will bring the estimation, prediction and management of wildlife populations into better focus.