Using species distribution models to define nesting habitat of the eastern metapopulation of double-crested cormorants

Abstract

When organisms with similar phenotypes have conflicting management and conservation initiatives, approaches are needed to differentiate among subpopulations or discrete groups. For example, the eastern metapopulation of the double-crested cormorant (Phalacrocorax auritus) has a migratory phenotype that is culled because they are viewed as a threat to commercial and natural resources, whereas resident birds are targeted for conservation. Understanding the distinct breeding habitats of resident versus migratory cormorants would aid in identification and management decisions. Here, we use species distribution models (SDM: Maxent) of cormorant nesting habitat to examine the eastern P. auritus metapopulation and the predicted breeding sites of its phenotypes. We then estimate the phenotypic identity of breeding colonies of cormorants where management plans are being developed. We transferred SDMs trained on data from resident bird colonies in Florida and migratory bird colonies in Minnesota to South Carolina in an effort to identify the phenotype of breeding cormorants there based on the local landscape characteristics. Nesting habitat characteristics of cormorant colonies in South Carolina more closely resembled those of the Florida phenotype than those of birds of the Minnesota phenotype. The presence of the resident phenotype in summer suggests that migratory and resident cormorants will co-occur in South Carolina in winter. Thus, there is an opportunity for separate management strategies for the two phenotypes in that state. We found differences in nesting habitat characteristics that could be used to refine management strategies and reduce human conflicts with abundant winter migrants and, at the same time, conserve less common colonies of resident cormorants. The models we use here show potential for advancing the study of geographically overlapping phenotypes with differing conservation and management priorities.

Keywords: conservation; cormorant; metapopulation; nesting habitat; species distribution model; wildlife management.

Figure 1

Conversion of nesting points to polygons. Aerial imagery of island nesting sites reported in Florida where sandy and shell hash substrates (light areas in the photograph) connect P. auritus nesting areas (inset image) in trees. Example polygons drawn around P. auritus colonies. Albers Equal Area Conic projection

Figure 2

Derivation steps for wetland‐related layers used to develop the Maxent model trained on resident cormorants in Florida. Although their base layers used different in geographic extents, the same methods were used to develop wetland‐related layers for the states of Minnesota and South Carolina

Figure 3

Prediction of suitable cormorant nesting habitat in Minnesota. Albers Equal Area Conic projection

Figure 4

Prediction of suitable cormorant nesting habitat in Florida. Albers Equal Area Conic projection

Figure 5

Prediction of suitable nesting habitat in South Carolina. Prediction values of P. auritus based on the parameters that describe the ecological niche of cormorants nesting in (a) Minnesota, (b) Florida, and (c) the MTSS threshold value for Florida altering continuous predicted values to suitable (good) nesting habitat and unsuitable (poor) habitat. Albers Equal Area Conic projection