Incorporating citizen science data in spatially explicit integrated population models

Abstract

Information about population abundance, distribution, and demographic rates is critical for understanding a species’ ecology and for effective conservation and management. To collect data over large spatial and temporal extents for such inferences, especially for species with low densities or wide distributions, citizen science can be an efficient approach. Integrated models have also emerged as an important methodology to estimate population parameters by combining multiple types of data, including citizen science data. We developed a spatially explicit integrated model that combines opportunistically collected presence–absence (PA) data, commonly collected in citizen science efforts, with systematically collected spatial capture–recapture (SCR) data, which are often limited to small spatial and temporal extents. We conducted single and multi‐season simulations with parameters informed by North American black bear (Ursus americanus) populations, to evaluate the influence of varying amounts of opportunistic PA data collected at larger spatial and temporal extents on the estimation of population‐level parameters. Integrating opportunistic PA data increased the precision and accuracy of posterior estimates of abundance, and survival and recruitment rates. In some cases, adding PA locations improved abundance estimates more than increasing PA detection probability. Posterior estimates were as precise and unbiased as when higher quality, but sparse, SCR data were available. We also applied the integrated model to SCR and citizen science PA data collected on black bears in New York, with results consistent with our simulations. Our findings indicate that citizen science in integrated models can be a cost‐efficient way to improve estimates of population parameters and increase the spatiotemporal extent of inference. Continued developments with integrated models and citizen science data will offer additional ways to improve our understanding of population structure and demographics.

A generalized observation confirmation model to account for false positive error in species detection-nondetection data

“…We simulated 100 replicate datasets to demonstrate proof of concept. Sampling within which the base model is unbiased (Ramsey et al 2015), we expect that it is more likely to see usage as a component within integrated models (Sun et al 2019), and we suggest that it may be more fruitful to explore sensitivity to false positives within this class of model. Table S3.…”

Multi‐species occupancy models: review, roadmap, and recommendations

“…With technological and statistical advances, and a growth in citizen science initiatives, distribution studies are resulting in increasingly large datasets that need to be managed (Hampton et al 2013, Hines et al 2015, Sun et al 2019). There has also been an associated increase in software to manage such projects (Krishnappa and Turner 2014, Bubnicki et al 2016, Niedballa et al 2016, Ramachandran and Devarajan 2018, Thomson et al 2018, Young et al 2018).…”

An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research

“…Improving the quality of spatially explicit health and environmental data through systematic collection of high-resolution data and public participation GIS approaches such as “crowdsourcing” or “citizen science data” is increasingly popular in both public and environmental health monitoring efforts ( 213 – 215 ). Additionally, the use of existing databases as passive surveillance systems and improving systematic data collection are suggested as ways to generate spatially explicit animal health databases ( 203 ).…”