Using citizen science monitoring data in species distribution models to inform isotopic assignment of migratory connectivity in wetland birds

Abstract

Stable isotopes have been used to estimate migratory connectivity in many species. Estimates are often greatly improved when coupled with species distribution models (SDMs), which temper estimates in relation to occurrence. SDMs can be constructed using point locality data from a variety of sources including extensive monitoring data typically collected by citizen scientists. However, one potential issue with SDM is that these data often have sampling bias. To avoid this potential bias, we created SDMs based on marsh bird monitoring program data collected by citizen scientists and other participants following protocols specifically designed to maximize detections of species of interest at locations representative of larger areas of inference. We then used the SDMs to refine isotopic assignments of breeding areas of autumn‐migrating and wintering sora Porzana carolina, Virginia rail Rallus limicola, and yellow rail Coturnicops noveboracensis based on feathers collected from individuals caught at various locations in the United States from Minnesota south to Louisiana and South Carolina. Sora were assigned to an area that included much of the western U.S. and prairie Canada, covering parts of the Pacific, Central, and Mississippi waterfowl Flyways. Yellow rails were assigned to a broad area along Hudson and James Bay in northern Manitoba and Ontario, as well as smaller parts of Québec, Minnesota, Wisconsin, and Michigan, including parts of the Mississippi and Atlantic Flyways. Virginia rails were from several discrete areas, including parts of Colorado, New Mexico, the central valley of California, and southern Saskatchewan and Manitoba in the Pacific and Central Flyways. Our study demonstrates extensive data from organized citizen science monitoring programs are especially useful for improving isotopic assignments of migratory connectivity in birds, which can ultimately lead to better informed management decisions and conservation actions.

Species‐Habitat Relationships and Priority Areas for Marsh‐Breeding Birds in Ontario

“…Expansion of the Joint Ventures' wetland conservation work into these priority marshes will be an effective step towards conservation of all birds because it will help slow or maybe even reverse declining trends in distributions and populations of marsh‐breeding bird species in the region. Each of these species, however, spends a considerable portion of the year south of the study area, so ensuring successful conservation will also require assessing and dealing with threats and stressors throughout their full annual life cycle (Fournier et al 2017). The conservation needs of other priority wetland bird species in the region, including swallows (Falconer et al 2016) and shorebirds (Ross et al 2012), may not be entirely represented in the expanded priority areas.…”

Community science validates climate suitability projections from ecological niche modeling

“…While there are several examples of incorporating volunteer‐collected occurrence data (e.g., eBird, BBS, CBC) within correlative ENMs to expand the scale at which projections are made (Langham et al 2015, Coxen et al 2017, Fournier et al 2017), our study leverages the power of a novel community science program to produce completely independent, real‐world observations for ENM validation. In addition, incorporation of a spatially structured random effect within our modeling framework enabled us to obtain stratum‐specific occupancy estimates useful for species management.…”

“…Community science (also called citizen science), i.e., research conducted through collaborative efforts between scientists and volunteers, is currently undergoing a period of rapid growth across several disciplines (Cooper et al 2014). Such programs engage volunteers at regional, national, or global scales, and often produce large data sets spanning broad spatial distributions (Fournier et al 2017). The extensive spatial coverage and sample sizes of community science data provide the scale and scope needed for evaluating ENM predictive accuracy with independent observations.…”

“…However, while numerous studies have incorporated community science data into ENMs (Langham et al 2015, Bradsworth et al 2017, Coxen et al 2017, Fournier et al 2017, Wang et al 2018, Wilsey et al 2019), no study has yet leveraged the spatial coverage of such data sets to provide a validation of ENM projections across species’ ranges. This gap is partly due to the minimal standardization of many community science protocols, which limits the ability to correct for variation in effort, observer ability, or imperfect detection (La Sorte et al 2018).…”

Special issue: state and progress in avian species distribution models

“…Five additional studies apply SDMs in innovative ways to assist avian conservation. Fournier et al (2017) couple SDMs with stable isotope profiles to improve predictions of migratory connectivity in three North American rail species. Fourcade et al (2017) use SDMs to compare the value of umbrella species based on niche overlap metrics from local bird communities.…”