To effectively restore wildlife habitat, ecological research must be easily translated into practical design criteria. Clear directives from research can support arguments that promote more appropriate restoration strategies. For the federally threatened piping plover (Charadrius melodus), beach stabilization practices often accelerate the degradation of suitable breeding habitat and could be revised to provide more advantageous conditions. Several studies of piping plover habitat selection have been conducted, yet useful and detailed design directives remain undeveloped. In this study, we use classification and regression tree analysis to (1) identify microhabitat characteristics and important variable interactions leading to nest establishment and (2) develop target, trigger, and threshold values for use in effective design and adaptive management of piping plover habitat. We found that nests primarily occur in three distinct habitat conditions defined by percent shell and pebble cover, vegetative cover, and distance to nearest dunes and the high tide line. Nest-site characteristics vary depending on where in the landscape a nest is initiated (backshore, overwash fan, or primary dune). We translate these results into the following pragmatic target design parameters:(1) vegetative cover: less than 10% (backshore), 13% (primary dune); (2) shell/pebble cover: 17-18%; (3) dune height: ≤1.1 m; and (4) dune slope: ≤13%. We also recommend threshold values for adaptive management to maintain habitat that is attractive to plovers. This technique can be applied to many other wildlife habitat restorations. Future studies on niche parameters driving chick survival are necessary to realize the full potential of habitat restoration in increasing overall reproductive success.
Resource limitations often prevent the active management required to maintain habitat quality in protected areas. Because restrictions in access or allowable public activities are the sole conservation measure in these locations, an important question to consider is whether species of conservation concern truly benefit from parcels that are shielded from human disturbance. Here, we assess the conservation benefit of protecting birds from human recreation on over 204 km of sandy beaches by (a) estimating the total area of beach‐nesting bird habitat that has been created by conservation protections; (b) quantifying the change in nesting habitat extent should further conservation protections be implemented; and (c) providing data to inform future protected area expansion. We use a maximum entropy species distribution modeling approach to estimate the extent and quality of suitable habitat for four beach‐nesting bird species of conservation concern under the existing management regime and compare it to scenarios in which the entire study area is either unprotected of fully protected from human disturbance. Managing humans has dramatic conservation returns for least terns and piping plovers, creating extensive nesting habitat that otherwise would not exist. There is considerable scope for conservation gains, potentially tripling the extent of nesting areas. Expanding conservation footprints for American oystercatchers and black skimmers is predicted to enhance the quality of existing nesting areas. The work demonstrates the utility of modeling changes in habitat suitability to inform protected area expansion on ocean beaches and coastal dunes.
Conservation management often requires decision-making without perfect knowledge of the at-risk species or ecosystem. Species distribution models (SDMs) are useful but largely under-utilized due to model uncertainty. We used an ensemble modeling approach of two independently derived SDMs to explicitly address common modeling impediments and directly inform conservation decision-making for piping plovers in a heavily populated mid-Atlantic (USA) coastal zone. We summarized previously published Bayesian network and maximum entropy models to highlight similarities and differences in structure, and we compared the relative importance of predictors used. Despite differences in analytical approach, relative importance of factors driving nest-site selection was consistent. Models demonstrated considerable agreement when comparing a binary (suitable/ unsuitable) measure of suitability. Instances of model consensus (i.e., overlapping areas of predicted piping plover nesting habitat between models) provide a stronger "signal" in model results, reducing uncertainty related to biases or errors associated with either model. We tested model accuracy using a common dataset of plover nests initiated within the focal areas between 2013 and 2015, and we examined congruency in model outputs. Nearly, 90% of all nests occurred in areas predicted suitable by at least one model, and at least 33% of the total nests were predicted in areas suitable by both. Because models predominantly agreed on what drives piping plover nest-site selection, areas predicted suitable by a single model should not be discounted. This case study demonstrates how models can effectively inform conservation planning by explicitly identifying the management objective, presenting robust evidence to allow managers to evaluate outcomes of alternative management decisions, and clearly communicating results that address real-world conservation problems. Our results can
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.