Digital land‐cover data are among the most popular data sources used in ecological research and natural resource management. However, processes for accurate land‐cover classification over large regions are still evolving. We identified inconsistencies in the National Land Cover Dataset 1992, the most current and available representation of land cover for the conterminous United States. We also report means to address these inconsistencies in a bird‐habitat model. We used a Geographic Information System (GIS) to position a regular grid (or lattice) over the upper midwestern United States and summarized the proportion of individual land covers in each cell within the lattice. These proportions were then mapped back onto the lattice, and the resultant lattice was compared to satellite paths, state borders, and regional map classification units. We observed mapping inconsistencies at the borders between mapping regions, states, and Thematic Mapper (TM) mapping paths in the upper midwestern United States, particularly related to grassland‐herbaceous, emergent‐herbaceous wetland, and small‐grain land covers. We attributed these discrepancies to differences in image dates between mapping regions, suboptimal image dates for distinguishing certain land‐cover types, lack of suitable ancillary data for improving discrimination for rare land covers, and possibly differences among image interpreters. To overcome these inconsistencies for the purpose of modeling regional populations of birds, we combined grassland‐herbaceous and pasture‐hay land‐cover classes and excluded the use of emergent‐herbaceous and small‐grain land covers. We recommend that users of digital land‐cover data conduct similar assessments for other regions before using these data for habitat evaluation. Further, caution is advised in using these data in the analysis of regional land‐cover change because it is not likely that future digital land‐cover maps will repeat the same problems, thus resulting in biased estimates of change.
Conserving migratory birds is made especially difficult because of movement among spatially disparate locations across the annual cycle. In light of challenges presented by the scale and ecology of migratory birds, successful conservation requires integrating objectives, management, and monitoring across scales, from local management units to ecoregional and flyway administrative boundaries. We present an integrated approach using a spatially explicit energetic-based mechanistic bird migration model useful to conservation decision-making across disparate scales and locations. This model moves a Mallard-like bird (Anas platyrhynchos), through spring and fall migration as a function of caloric gains and losses across a continental-scale energy landscape. We predicted with this model that fall migration, where birds moved from breeding to wintering habitat, took a mean of 27.5 d of flight with a mean seasonal survivorship of 90.5% (95% Cl = 89.2%, 91.9%), whereas spring migration took a mean of 23.5 d of flight with mean seasonal survivorship of 93.6% (95% CI = 92.5%, 94.7%). Sensitivity analyses suggested that survival during migration was sensitive to flight speed, flight cost, the amount of energy the animal could carry, and the spatial pattern of energy availability, but generally insensitive to total energy availability per se. Nevertheless, continental patterns in the bird-use days occurred principally in relation to wetland cover and agricultural habitat in the fall. Bird-use days were highest in both spring and fall in the Mississippi Alluvial Valley and along the coast and near-shore environments of South Carolina. Spatial sensitivity analyses suggested that locations nearer to migratory endpoints were less important to survivorship; for instance, removing energy from a 1036 km2 stopover site at a time from the Atlantic Flyway suggested coastal areas between New Jersey and North Carolina, including the Chesapeake Bay and the North Carolina piedmont, are essential locations for efficient migration and increasing survivorship during spring migration but not locations in Ontario and Massachusetts. This sort of spatially explicit information may allow decision-makers to prioritize their conservation actions toward locations most influential to migratory success. Thus, this mechanistic model of avian migration provides a decision-analytic medium integrating the potential consequences of local actions to flyway-scale phenomena.
Identification of geographic linkages among breeding, migratory and wintering common loon Gavia immer populations is needed to inform regional and national conservation planning efforts and compensation of loons lost during marine oil spill events. Satellite telemetry and archival geolocator tags were used to determine the migration patterns and wintering locations of breeding adult and young of the year juvenile common loons captured and marked on lakes in Minnesota, Wisconsin and Michigan. Adult loons typically traveled from breeding lakes, often via larger staging lakes, to the Great Lakes (primarily Lake Michigan) and then on to wintering areas. Most radiomarked juvenile common loons utilized natal lakes or local lakes through mid‐November. Subsequently, the first fall migration of juvenile loons was generally initiated later, and more direct and quicker to wintering areas relative to adults. Among adult (n = 103) and juvenile (n = 23) loons that completed fall migration, most wintered in the Gulf of Mexico (GOM), with smaller proportions wintering off the southern Atlantic Coast or impoundments in the southeastern United States. Spring migration of adults to breeding lakes was less prolonged than fall migration, with adult male loons tending to depart wintering areas earlier than adult females. Juvenile common loons migrated during their first spring from wintering sites in the GOM to summer in the Gulf of St Lawrence/Nova Scotia Coastal region. Juvenile mortality was largely linked to parasitic infection and emaciation; spring appeared to be a survival bottleneck among juvenile loons monitored in our study. Our results identify several areas where common loon conservation efforts could be directed to protect key habitats and minimize stressors during the non‐breeding period.
Summary1. Despite the fact that pixels (i.e. picture elements) are the basic sampling units of maps, we are aware of no software package or tool that allows users to model changes that may occur at such fine spatial resolutions over broad geographic extents. 2. Curve Fit is an extension to the application ArcMap that allows users to conduct linear or nonlinear regression analysis on the range of values found within input raster data sets (geo-referenced images), independently for each pixel. 3. Outputs consist of raster surfaces of regression model parameter estimates, standard errors, goodness-of-fit estimates and multimodel inference measures. 4. Curve fit outputs characterize continuous spatial or temporal change across a series of raster data sets.
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