We use a random utility model for birding destination choices based on the reports of Oregon and Washington State members of the Cornell University Laboratory of Ornithology eBird citizen science project. We estimate spatially differentiated welfare effects that birders may experience as a consequence of forecasted changes in land cover and climate. We predict per-trip welfare effects (equivalent variations) expected under a business-as-usual scenario using published forecasts for both land cover and species richness. We find significant county-level heterogeneity across eBirders in predicted average per-trip welfare effects. The results suggest discernible distributional consequences across active birders in different areas.
Citizen science (CS) projects (and some social media) offer selected samples with extensive information about human interactions with the natural world. We independently elicit levels of engagement with the eBird project from (1) members of the eBird CS project and (2) a general population sample. The general-population sample allows an ordered-probit model to explain propensities to engage with eBird, which we transfer to predict selection-correction terms for our independent sample of eBird members. We illustrate, using a question posed only to our eBird member survey sample about the radii of their individual spatial consideration sets for typical one-day birding excursions.Appendix materials can be accessed online at: https://uwpress.wisc.edu/journals/pdfs/LE-98-1-01-Cameron-app.pdf.
Predicting the edges of species distributions is fundamental for species conservation, ecosystem services, and management decisions. In North America, the location of the upstream limit of fish in forested streams receives special attention, because fish-bearing portions of streams have more protections during forest management activities than fishless portions. We present a novel model development and evaluation framework, wherein we compare 26 models to predict upper distribution limits of trout in streams. The models used machine learning, logistic regression, and a sophisticated nested spatial cross-validation routine to evaluate predictive performance while accounting for spatial autocorrelation. The model resulting in the best predictive performance, termed UPstream Regional LiDAR Model for Extent of Trout (UPRLIMET), is a two-stage model that uses a logistic regression algorithm calibrated to observations of Coastal Cutthroat Trout (Oncorhynchus clarkii clarkii) occurrence and variables representing hydro-topographic characteristics of the landscape. We predict trout presence along reaches throughout a stream network, and include a stopping rule to identify a discrete upper limit point above which all stream reaches are classified as fishless. Although there is no simple explanation for the upper distribution limit identified in UPRLIMET, four factors, including upstream channel length above the point of uppermost fish, drainage area, slope, and elevation, had highest importance. Across our study region of western Oregon, we found that more of the fish-bearing network is on private lands than on state, US Bureau of Land Mangement (BLM), or USDA Forest Service (USFS) lands, highlighting the importance of using spatially consistent maps across a region and working across land ownerships. Our research underscores the value of using occurrence data to develop simple, but powerful, prediction tools to capture complex ecological processes that contribute to distribution limits of species.
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.