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Modeling the impacts of climate change on Species of Concern (birds) in South Central U.S. based on bioclimatic variables

1 Department of Fish, Wildlife and Conservation Ecology, New Mexico State University, Las Cruces, New Mexico 88003, USA
2 Ecological and Environmental Planning Division, New Mexico Department of Game and Fish, Santa Fe, New Mexico 87507, USA
3 NOAA Geophysical Fluid Dynamics Laboratory, Princeton University Forrestal Campus, Princeton, New Jersey 08540, USA

We used 19 bioclimatic variables, five species distribution modeling (SDM) algorithms, four general circulation models, and two climate scenarios (2050 and 2070) to model nine bird species. Identified as Species of Concern (SOC), we highlighted these birds: Northern/Masked Bobwhite Quail (Colinus virginianus), Scaled Quail (Callipepla squamata), Pinyon Jay (Gymnorhinus cyanocephalus), Juniper Titmouse (Baeolophus ridgwayi), Mexican Spotted Owl (Strix occidentalis lucida), Cassin’s Sparrow (Peucaea cassinii), Lesser Prairie-Chicken (Tympanuchus pallidicinctus), Montezuma Quail (Cyrtonyx montezumae), and White-tailed Ptarmigan (Lagopus leucurus). The Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, Multivariate Adaptive Regression Splines, and an ensemble model were used to identify present day core bioclimatic-envelopes for the species. We then projected future distributions of suitable climatic conditions for the species using data derived from four climate models run according to two greenhouse gas Representative Concentration Pathways (RCPs 2.6 and 8.5). Our models predicted changes in suitable bioclimatic-envelopes for all species for the years 2050 and 2070. Among the nine species of birds, the quails were found to be highly susceptible to climate change and appeared to be of most future conservation concern. The White-tailed Ptarmigan would lose about 62% of its suitable climatic habitat by 2050 and 67% by 2070. Among the species distribution models (SDMs), the Boosted Regression Tree model consistently performed fairly well based on Area Under the Curve (AUC range: 0.89 to 0.97) values. The ensemble models showed improved True Skill Statistics (all TSS values > 0.85) and Kappa Statistics (all K values > 0.80) for all species relative to the individual SDMs.
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