Linking Dynamic Habitat Selection with Wading Bird Foraging Distributions across Resource Gradients

RESEARCH ARTICLE Linking Dynamic Habitat Selection with Wading Bird Foraging Distributions across Resource Gradients James M. Beerens1,2*, Erik G. Noonburg1, Dale E. Gawlik1 1 Department of Biological Sciences, Florida Atlantic University, Boca Raton, Florida, United States of America, 2 US Geological Survey, Southeast Ecological Science Center, Fort Lauderdale, Florida, United States of America * Abstract OPEN ACCESS Citation: Beerens JM, Noonburg EG, Gawlik DE (2015) Linking Dynamic Habitat Selection with Wading Bird Foraging Distributions across Resource Gradients. PLoS ONE 10(6): e0128182. doi:10.1371/ journal.pone.0128182 Academic Editor: Chang-Qing Gao, Central South University, CHINA Received: January 30, 2015 Accepted: April 24, 2015 Published: June 24, 2015 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: All relevant data have been uploaded to Figshare: http://figshare.com/ articles/Linking_Dynamic_Habitat_Selection_with_ Wading_Bird_Foraging_Distributions_across_ Resource_Gradients_TFC_Model/1404198 http:// figshare.com/articles/Linking_Dynamic_Habitat_ Selection_with_Wading_Bird_Foraging_ Distributions_across_Resource_Gradients/1404200. Funding: Financial or inkind support was provided by the U.S. This work received support from the Army Corps of Engineers (grant/contract number:W912HZ10-2-0024) and U.S. Geological Survey. The authors appreciate the support of Florida Atlantic University Species distribution models (SDM) link species occurrence with a suite of environmental predictors and provide an estimate of habitat quality when the variable set captures the biological requirements of the species. SDMs are inherently more complex when they include components of a species’ ecology such as conspecific attraction and behavioral flexibility to exploit resources that vary across time and space. Wading birds are highly mobile, demonstrate flexible habitat selection, and respond quickly to changes in habitat quality; thus serving as important indicator species for wetland systems. We developed a spatio-temporal, multi-SDM framework using Great Egret (Ardea alba), White Ibis (Eudocimus albus), and Wood Stork (Mycteria Americana) distributions over a decadal gradient of environmental conditions to predict species-specific abundance across space and locations used on the landscape over time. In models of temporal dynamics, species demonstrated conditional preferences for resources based on resource levels linked to differing temporal scales. Wading bird abundance was highest when prey production from optimal periods of inundation was concentrated in shallow depths. Similar responses were observed in models predicting locations used over time, accounting for spatial autocorrelation. Species clustered in response to differing habitat conditions, indicating that social attraction can co-vary with foraging strategy, water-level changes, and habitat quality. This modeling framework can be applied to evaluate the multi-annual resource pulses occurring in real-time, climate change scenarios, or restorative hydrological regimes by tracking changing seasonal and annual distribution and abundance of high quality foraging patches. Introduction Species distribution models (SDM) link species occurrence with a suite of environmental predictors and have a wide range of applications in wildlife science and management by predicting species distributions across landscapes. They provide a powerful tool for land managers when the variable set captures the biological requirements of the species and can be manipulated to PLOS ONE | DOI:10.1371/journal.pone.0128182 June 24, 2015 1 / 25 Wading Bird Distributions across Resource Gradients and The Everglades Foundation for additional financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. account for various management activities [1]. The resulting suitability metrics are most relevant to population dynamics in anthropogenically disturbed ecosystems when linked with measures of reproductive success and applied to a suite of restoration alternatives [2]. The efficacy of a SDM is impacted by the availability and choice of predictors and scale, the modeling method, and the degree of spatial and temporal extrapolation [3]. Moreover, many issues remain relatively unexplored in the SDM field concerning how a species’ ecology can affect model building and evaluation such as factors of conspecific attraction, phenotypic and behavioral plasticity, and response to environmental gradients. Recent reviews suggest that ecological theory is rarely considered in SDMs [3–5]. Habitat preference, for example, has in large part been assumed to remain unaltered as a function of the variety of habitat types available. However, animals are often confronted with an environment in which the types of habitats available at any given time are constantly changing. For species adapted to these systems, habitat selection has been shown to vary with stronger selection for resources that are relatively rare in the environment [5]. To address dynamic habitat selection, discrete-choice resource selection function (RSF) models were developed to model a series of choices with a discrete set of resources over time [6]. A weakness of this approach is that static resource selection models are still produced and only reflect average preference within the range of habitat conditions encountered during the study period [7]. Therefore, an important consideration must be given to modeling habitat selection over a wide-range of habitat availabilities in order to quantify the functional response in habitat selection [8]. A non-linear response will result when decreased availability of a resource in the landscape results in increased selection for another, which may be expected when, e.g., multiple limiting resources have been depleted by human activities [8–10]. While some authors have attempted to capture changing preference in response to the change in available resources [10–11], these measures did not reflect the changing area of habitat that may be available. Further, studies have commonly examined an animal’s selection of food resources over a spatial gradient of exposure to predators or anthropogenic disturbance [10,12]; however, in pulsed ecosystems foragers may exhibit dynamic responses to variation in food resources that arises from processes operating at multiple temporal scales. For example, in some aquatic ecosystems, an animal’s access to the prey base (i.e., prey availability) can fluctuate under seasonal cycles of drying and flooding (...truncated)