WGCPO Forested Wetland Decision Support Model

FOR non-AGOL ACCOUNT HOLDERS, DOWNLOAD THIS GEOSPATIAL DATA HERE: https://gis-fws.opendata.arcgis.com/search?tags=lmvjv This Decision Support Model is intended to highlight those areas where conservation efforts (i.e., protection or management) directed at existing forested wetlands will have the greatest potential for positive impact on landbird populations. We began with Minimum Viable Populations (MVP) based on the variability around simulated population trajectories from Breeding Bird Survey data. A sustainable population was defined as a population large enough to have >95% chance of remaining above 25 individuals over a 50-year interval. For each species, we reviewed the literature for (1) breeding area (i.e., territory) requirements per pair; and (2) natal dispersal distances. For some species we could not find information on natal dispersal distance in the literature; therefore, we used an allometric equation to estimate dispersal distance. We converted area requirements per pair to area requirements per MVP. We used metrics of area requirements and natal dispersal distances to calculate carrying capacity and available suitable habitat on the landscape. For these species, we used woody wetland identified in the 2011 National Land Cover Database (NLCD; land cover class = 90) as the base layer to quantify the amount of potentially “suitable habitat” on the landscape. Additionally, we used the National Hydrography Dataset - High Resolution to define primary order streams. We combined these data with the National Hydrography Dataset Plus to define secondary and tertiary order along with primary order streams from NHD - HR. We identified habitat patches through a clumping process and refined these data by removing those clumps of habitat that did not have enough suitable habitat in close enough proximity to support a minimum viable population, with habitat suitability and proximity as defined below for each species. The amount of suitable habitat was determined by neighborhood analysis and was not restricted to contiguous patches, but patches needed to be within natal dispersal distance to allow for post-fledgling movements among patches. To further characterize the landscape for all of our umbrella species, we defined a circular area that was within the natal dispersal distance of each species (Table 5). This area was assumed to be available for 14 natal dispersal. We designated this circular area an “avicentric” landscape. These landscapes were species specific, based on presumed natal dispersal distance: Equation 1. Avicentric Landscape (ha) = [π * (Dispersal Distance [m]) 2 ]/10000. The percent of suitable habitat required within each avicentric landscape to support the species MVP was determined by: Equation 2. Area requirement for MVP (ha) ÷ Avicentric Landscape (ha) If the species-specific avicentric landscape harbored at least the required percentage of suitable habitat then the landscape was characterized as suitable for the species. Alternatively, if the avicentric landscape harbored less than the required percentage of habitat, those landscapes were deem incapable of supporting a MVP of the species. Based on the landscape characterization from the area requirements analysis within avicentric landscapes, we developed a base layer of habitat presumed to be supportive of minimum viable population of each species. These maps identified areas with sufficient potential breeding habitat within natal dispersal proximities capable of supporting minimum viable populations of each species. Additional habitat factors were applied to these potential habitat layers to ultimately develop the decision support models. We applied habitat-specific parameters to each species in order to prioritize potential breeding habitat using a habitat suitability modeling approach.This approach closely examines the relationships of important features in the landscape to umbrella species, although Red-shouldered Hawk was not included in the assessment. Algorithmic relationships for species suitability with particular habitat features were applied using geospatial data that were either easily produced or readily available. The habitat features used in our models included: • Distance to Water • Percent Forest in the landscape • Flood Tolerance and Flood Preference • Bald-cypress - Tupelo Floodplain Forest