A generalizable energetics‐based model of avian migration to facilitate continental‐scale waterbird conservation

Abstract

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.

Modeling nonbreeding distributions of shorebirds and waterfowl in response to climate change

“…Distribution models are possibly the best available tool for predicting responses to changes in habitat and environmental conditions (but see Sinclair, White, & Newell, ). To our knowledge, modeling probabilities of occurrence across a season of active migration with empirical data is a relatively unique approach; others have modeled migration directly using mechanistic models (e.g., Lonsdorf et al., ). Projections using the most current climate model data indicate that the probabilities of occurrence of shorebirds and waterfowl in our region of the Great Plains would remain largely unchanged, but that migration could occur earlier (supporting numerous studies) and that suitable climate would shift predominantly westward, supporting Currie and Venne ().…”

Restoring monarch butterfly habitat in the Midwestern US: ‘all hands on deck’

“…We used regression analysis to determine sensitivity of scenario-specific overall gains in milkweed to land cover-specific differences in milkweed. Sensitivity of overall milkweed gains to cover-specific gains was indicated by its standardized regression coefficient [34], calculated from the best fit of a multiple linear regression model,…”

A guide to calculating habitat‐quality metrics to inform conservation of highly mobile species

“…The important step is to represent the spatial arrangement of each member of a community and the interaction among them. This insight would allow waterfowl managers, for instance, to move beyond seeing dabbling ducks as mallard‐like animals (sensu Lonsdorf et al., ) and instead examine the place‐specific community‐level constraints each dabbling duck faces. In addition, a dynamical depiction of the network changing in step with a changing landscape, climate or in the face of competition, coupled with the response of populations to those changes, will provide a more realistic decision context for managers (e.g., Wimberly, ).…”

“…For instance, Lonsdorf et al. () modeled the movement of mallard ( Anas platyrhynchos )‐like birds across North America as a function of caloric gains and losses, and then using marginal value analysis examined the consequences of changing caloric availability in a portion of the species migration via habitat management. They found that locations with decreased caloric availability reduced survival rates during migration.…”