Abstract
ABSTRACTUnderstanding patterns of habitat selection across a species’ geographic distribution can be critical for adequately managing populations and planning for habitat loss and related threats. However, studies of habitat selection can be time consuming and expensive over broad spatial scales, and a lack of standardized monitoring targets or methods can impede the generalization of site‐based studies. Our objective was to collaborate with natural resource managers to define available nesting habitat for piping plovers (Charadrius melodus) throughout their U.S. Atlantic coast distribution from Maine to North Carolina, with a goal of providing science that could inform habitat management in response to sea‐level rise. We characterized a data collection and analysis approach as being effective if it provided low‐cost collection of standardized habitat‐selection data across the species’ breeding range within 1–2 nesting seasons and accurate nesting location predictions. In the method developed, >30 managers and conservation practitioners from government agencies and private organizations used a smartphone application, “iPlover,” to collect data on landcover characteristics at piping plover nest locations and random points on 83 beaches and barrier islands in 2014 and 2015. We analyzed these data with a Bayesian network that predicted the probability a specific combination of landcover variables would be associated with a nesting site. Although we focused on a shorebird, our approach can be modified for other taxa. Results showed that the Bayesian network performed well in predicting habitat availability and confirmed predicted habitat preferences across the Atlantic coast breeding range of the piping plover. We used the Bayesian network to map areas with a high probability of containing nesting habitat on the Rockaway Peninsula in New York, USA, as an example application. Our approach facilitated the collation of evidence‐based information on habitat selection from many locations and sources, which can be used in management and decision‐making applications. © 2017 The Authors. Wildlife Society Bulletin published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.
Highlights
Understanding patterns of habitat selection across a species’ geographic distribution can be critical for adequately managing populations and planning for habitat loss and related threats
To help coastal managers plan for threats posed by sea-level rise to piping plovers (Charadrius melodus) and other shorebirds along the U.S Atlantic coast, we investigated the value that can be extracted from knowledge of broadscale piping plover habitat-selection patterns from Maine to North Carolina, USA
We characterized a methodological approach as being effective if it allowed for collection of standardized habitat-selection data across the species’ U.S Atlantic coast breeding range within 1–2 nesting seasons, data assemblage and distribution at a low cost, and development of models that accurately predict nesting locations or “habitat.” The approach we developed integrated existing monitoring efforts by managers and biologists from government agencies and private organizations (Table S1, available online in Supporting Information), who used a smartphone-based tool (“iPlover”) to collect standardized data on nesting habitat selection (Thieler et al 2016)
Summary
Understanding patterns of habitat selection across a species’ geographic distribution can be critical for adequately managing populations and planning for habitat loss and related threats. We characterized a methodological approach as being effective if it allowed for collection of standardized habitat-selection data across the species’ U.S Atlantic coast breeding range within 1–2 nesting seasons, data assemblage and distribution at a low cost, and development of models that accurately predict nesting locations or “habitat.” The approach we developed integrated existing monitoring efforts by managers and biologists from government agencies and private organizations (Table S1, available online in Supporting Information), who used a smartphone-based tool (“iPlover”) to collect standardized data on nesting habitat selection (Thieler et al 2016). We 1) further describe our data collection and analysis approach; 2) test the accuracy of a Bayesian network to predict habitat availability; and 3) provide an example case study that illustrates the potential value to coastal managers by identifying areas with a high probability of containing nesting habitat for piping plovers along a portion of Long Island, New York (Fig. 1)
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