Smartphone technologies and Bayesian networks to assess shorebird habitat selection

Abstract

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. 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. Ó

A pragmatic approach for comparing species distribution models to increasing confidence in managing piping plover habitat

“…These areas of overlap, where both models predicted high suitability for piping plover nesting, potentially reduces uncertainty in model predictions. Given that predictive variables considered in both models were based on expert opinion and a review of published literature (e.g., Burger, 1987;Cohen et al, 2008;Flemming, Chiasson, & Austin-Smith, 1992;Maslo et al, 2011;Zeigler et al, 2017), this result may seem intuitive. Of significance, both models exhibited high similarity in identifying drivers of nest-site selection.…”

“…Piping plovers are solitary nesters that typically select flat, open dry sand or pebble beaches with sparse vegetation (Cohen, Wunker, & Fraser, 2008;Maslo et al, 2011;Maslo, Leu, et al, 2016;Zeigler et al, 2017). The Atlantic coast population considered here breeds along~2,000 km of shoreline from northern Canada south to North Carolina, USA.…”

“…Methodology is described in more detail in Zeigler et al (2017. Methodology is described in more detail in Zeigler et al (2017.…”

“…The Atlantic coast population considered here breeds along~2,000 km of shoreline from northern Canada south to North Carolina, USA. Piping plovers are solitary nesters that typically select flat, open dry sand or pebble beaches with sparse vegetation (Cohen, Wunker, & Fraser, 2008;Maslo et al, 2011;Maslo, Leu, et al, 2016;Zeigler et al, 2017). Nesting sites are restricted to areas that are beyond the high-tide line but near low-energy foraging habitats (Loegering & Fraser, 1995;Maslo, Burger, & Handel, 2012).…”

“…The second model evaluated in this study was constructed as a Bayesian network (hereafter, BN) with the program Netica (version 512). Methodology is described in more detail in Zeigler et al (2017. connected (a) four nodes representing discretized continuous variables (distance to ocean, beach width, elevation, and distance to foraging); (b) four nodes representing categorical landcover characteristics (geomorphic setting, substrate type, vegetation type, vegetation density); and (c) an output node for the probability that the specific combination of landscape characteristics represented by the input nodes would be associated with landcover suitable for nesting.…”