Data integration reveals dynamic and systematic patterns of breeding habitat use by a threatened shorebird.

Autor: Ellis KS; U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA. kellis@usgs.gov., Anteau MJ; U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA., MacDonald GJ; U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA., Swift RJ; U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA., Ring MM; U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA., Toy DL; U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA., Sherfy MH; U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA., Post van der Burg M; U.S. Geological Survey, Northern Prairie Wildlife Research Center, 8711 37th St SE, Jamestown, ND, 58401, USA.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Apr 13; Vol. 13 (1), pp. 6087. Date of Electronic Publication: 2023 Apr 13.
DOI: 10.1038/s41598-023-32886-w
Abstrakt: Incorporating species distributions into conservation planning has traditionally involved long-term representations of habitat use where temporal variation is averaged to reveal habitats that are most suitable across time. Advances in remote sensing and analytical tools have allowed for the integration of dynamic processes into species distribution modeling. Our objective was to develop a spatiotemporal model of breeding habitat use for a federally threatened shorebird (piping plover, Charadrius melodus). Piping plovers are an ideal candidate species for dynamic habitat models because they depend on habitat created and maintained by variable hydrological processes and disturbance. We integrated a 20-year (2000-2019) nesting dataset with volunteer-collected sightings (eBird) using point process modeling. Our analysis incorporated spatiotemporal autocorrelation, differential observation processes within data streams, and dynamic environmental covariates. We evaluated the transferability of this model in space and time and the contribution of the eBird dataset. eBird data provided more complete spatial coverage in our study system than nest monitoring data. Patterns of observed breeding density depended on both dynamic (e.g., surface water levels) and long-term (e.g., proximity to permanent wetland basins) environmental processes. Our study provides a framework for quantifying dynamic spatiotemporal patterns of breeding density. This assessment can be iteratively updated with additional data to improve conservation and management efforts, because reducing temporal variability to average patterns of use may cause a loss in precision for such actions.
(© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)
Databáze: MEDLINE
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