Diversity and Distributions · 2019 · 27 citations · 73 references
Life SciencesEcologyZoologySpatial EcologyWildlife BiologyWildlife ManagementBird ConservationEcological ModelingSpatial Distribution AnalysisWildlife ConservationWildlife EcologyWildlife-habitat RelationshipsCitizen Science Abundance DataHabitat PrioritizationOccupancy-abundance ModelingSpatial Abundance EstimationSpatially Explicit Occupancy ModelsSpatio-temporal Abundance ModelsSpecies Distribution ForecastingWaterfowl Conservation
Abstract Aim Estimating the distribution and abundance of wildlife is an essential task in species conservation, wildlife management and habitat prioritization. Although a host of methods and tools have been proposed to accomplish this undertaking, several challenges remain in accurately forecasting occurrence and abundance for highly mobile species. Exhibiting extensive geographic ranges with seasonally varying local occupancy, migratory ducks are exemplar highly mobile species and are foci for waterfowl conservation and management globally. With the goal of informing species conservation and management, our aim was to leverage citizen science data to estimate occurrence and relative abundance for ten dabbling duck species across the continental United States. Location Conterminous United States. Methods We applied spatially and temporally explicit Bayesian hierarchical modelling to jointly estimate season‐specific occurrence and relative abundance for ten dabbling duck species in the Genus Anas . Our conditionally dependent model design enabled relative abundance estimates to be informed by occurrence probability while accounting for cumulative spatial and temporal errors across shared model components. Results Outcomes suggest that although dabbling duck distributions show little inter‐annual variability at the continental scale, local relative abundances may differ year to year. Commensurate with being highly mobile migratory species, estimates indicate considerable intra‐annual variation with occurrence probability, local abundance and habitat preferences differing by season and species. Main conclusions Our approach offers a powerful and flexible framework for quantifying intra‐/inter‐annual duck occurrence and relative abundance while accounting for spatial, temporal and data collection biases. We believe that model produced maps can be applied to inform waterfowl conservation and management throughout the continental United States.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10