Foraging flight distances of wintering ducks and geese: a review

Johnson, William P.; Schmidt, Paige M.; Taylor, Dustin

doi:10.5751/ace-00683-090202

Cited by 31 publications

(29 citation statements)

References 75 publications

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“…We assumed that the realized quality (net energy gained) of a forage site declines with increasing distance to the nearest roosting site because of increasing travel costs (Johnson et al. ).

\bar{H} {\overset{F}{true}}_{j} = A_{j} {false\sum}_{c = 1}^{C} K_{c} p_{c j} q_{c j},

where q cj is equal to the proportion of forage available based on distance to the nearest roost site (details of how we determined q cj can be found in Appendix ).…”

Section: Methodsmentioning

confidence: 99%

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

Lonsdorf

Thogmartin

Jacobi

et al. 2016

Ecological Applications

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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.

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“…We assumed that the realized quality (net energy gained) of a forage site declines with increasing distance to the nearest roosting site because of increasing travel costs (Johnson et al. ).

\bar{H} {\overset{F}{true}}_{j} = A_{j} {false\sum}_{c = 1}^{C} K_{c} p_{c j} q_{c j},

where q cj is equal to the proportion of forage available based on distance to the nearest roost site (details of how we determined q cj can be found in Appendix ).…”

Section: Methodsmentioning

confidence: 99%

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

Lonsdorf

Thogmartin

Jacobi

et al. 2016

Ecological Applications

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“…1; Xu et al 2019). Distances between any two nodes were set at >32.5 km, which is the mean of maximum foraging flight distance of geese and ducks (Johnson et al 2014). We defined the strength of nodeto-node connections by the dispersal probability of the study species' direct movement between two nodes (internode dispersal probabilities).…”

Section: Quantify Functional Connectivity Of Migration Networkmentioning

confidence: 99%

Loss of functional connectivity in migration networks induces population decline in migratory birds

Wang

et al. 2019

Ecological Applications

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Migratory birds rely on a habitat network along their migration routes by temporarily occupying stopover sites between breeding and non‐breeding grounds. Removal or degradation of stopover sites in a network might impede movement and thereby reduce migration success and survival. The extent to which the breakdown of migration networks, due to changes in land use, impacts the population sizes of migratory birds is poorly understood. We measured the functional connectivity of migration networks of waterfowl species that migrate over the East Asian‐Australasian Flyway from 1992 to 2015. We analysed the relationship between changes in non‐breeding population sizes and changes in functional connectivity, while taking into account other commonly considered species traits, using a phylogenetic linear mixed model. We found that population sizes significantly declined with a reduction in the functional connectivity of migration networks; no other variables were important. We conclude that the current decrease in functional connectivity, due to habitat loss and degradation in migration networks, can negatively and crucially impact population sizes of migratory birds. Our findings provide new insights into the underlying mechanisms that affect population trends of migratory birds under environmental changes. Establishment of international agreements leading to the creation of systematic conservation networks associated with migratory species’ distributions and stopover sites may safeguard migratory bird populations.

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“…We represent the proportion of forage within a site available to a Mallard as a function of forage location's distance to roosting sites. We assumed that the realized quality (net energy gained) of a forage site declines with increasing distance to the nearest roosting site because of increasing travel costs (Johnson et al 2014).…”

Section: Forage Quantitymentioning

confidence: 99%

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

Lonsdorf¹,

Thogmartin²,

Jacobi³

et al. 2015

Ecological Applications

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Foraging flight distances of wintering ducks and geese: a review

Cited by 31 publications

References 75 publications

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

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

Loss of functional connectivity in migration networks induces population decline in migratory birds

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

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