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

Lonsdorf, Eric V.; Thogmartin, Wayne E.; Jacobi, Sarah K.; Aagaard, Kevin; Coppen, Jorge; Davis, Amélie Y.; Fox, Timothy; Heglund, Patricia J.; Johnson, Rex R.; Jones, Malcolm T; Kenow, Kevin P.; Lyons, James E.; Luke, Kirsten; Still, Shannon M.; Tavernia, Brian G.

doi:10.1890/14-1947.1

Cited by 3 publications

(27 citation statements)

References 62 publications

(73 reference statements)

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“…We elaborated an energetics-based model of avian migration to more fully realize the stochastic variation in migration induced by daily weather. Our model was able to recreate documented North American avian migration routes (La Sorte, Fink, Hochachka, DeLong, et al, 2014;La Sorte, Fink, Hochachka, Farnsworth, et al, 2014;Lonsdorf et al, 2016) and recover expected rates of survivorship (Lonsdorf et al, 2016) based on nothing more than first-principle arrangements of dabbling duck energetics and behavior. These estimates are commensurate with literature-derived mortality estimates (Davis et al, 2011;Zimmer et al, 2010).…”

Section: Discussionmentioning

confidence: 98%

“…We multiplied the amount of forage provided by each land cover type represented within the node (using food-habit information from the literature; see Lonsdorf et al, 2016) by the proportion of the node classified as each land cover type. We multiplied this value by the proportion of forage available in a node, based on the distance to the nearest roosting site.…”

Section: Landscape Generationmentioning

confidence: 99%

“…For many species these empirical studies elucidated relations between, for example: temperature and metabolism (Hartung, 1967; Klaassen, 1996; Smith & Prince, 1973), body mass and temperature (Baldwin & Kendeigh, 1938; Boos et al, 2007), and flight velocity and duration, and body fat content (Rayner, 1990). Using systems of equations to connect one facet to the next generates a series of expectations about the dynamics of migration (flight velocity, stopover frequency and duration, and survivorship) for a bird of a certain species and specific mass (Aagaard et al, 2018; Lonsdorf et al, 2016). Connecting approaches for predicting environmental effects on migration‐energetic dynamics with approaches evaluating the spatially explicit pattern of energetic‐based migratory movements can reveal how migration is affected by the distribution of forage material on the landscape.…”

Section: Introductionmentioning

confidence: 99%

“…We estimated mean forage availability per site (in 0.1 GJ units) at the outset of the non-breeding period based on an evaluation of land cover using a range of parameter estimates informed by literature(Fredrickson & Reid, 1988;Heitmeyer, 2010; F I G U R E 3 An example of the abundance-weighted center-of-mass for the population on the 50th day of migration, represented by the black dot. The gray shaded areas represent the top 2% most populous nodes on day 50Lonsdorf et al, 2016). Further details of this process are available inLonsdorf et al (2016) and summarized in the File S3; the values used for the forage availability parameter estimates are provided inLonsdorf et al (2016, Appendix C).…”

mentioning

confidence: 99%

“…The gray shaded areas represent the top 2% most populous nodes on day 50Lonsdorf et al, 2016). Further details of this process are available inLonsdorf et al (2016) and summarized in the File S3; the values used for the forage availability parameter estimates are provided inLonsdorf et al (2016, Appendix C).…”

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confidence: 99%

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Effects of weather variation on waterfowl migration: Lessons from a continental‐scale generalizable avian movement and energetics model

Aagaard

Lonsdorf

Thogmartin

2022

Ecology and Evolution

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We developed a continental energetics‐based model of daily mallard (Anas platyrhynchos) movement during the non‐breeding period (September to May) to predict year‐specific migration and overwinter occurrence. The model approximates movements and stopovers as functions of metabolism and weather, in terms of temperature and frozen precipitation (i.e., snow). The model is a Markov process operating at the population level and is parameterized through a review of literature. We applied the model to 62 years of daily weather data for the non‐breeding period. The average proportion of available habitat decreased as weather severity increased, with mortality decreasing as the proportion of available habitat increased. The most commonly used locations during the course of the non‐breeding period were generally consistent across years, with the most inter‐annual variation present in the overwintering area. Our model revealed that the distribution of mallards on the landscape changed more dramatically when the variation in daily available habitat was greater. The main routes for avian migration in North America were predicted by our simulations: the Atlantic, Mississippi, Central, and Pacific flyways. Our model predicted an average of 77.4% survivorship for the non‐breeding period across all years (range = 76.4%–78.4%), with lowest survivorship during autumn (90.5 ± 1.4%), intermediate survivorship in winter (91.8 ± 0.7%), and greatest survivorship in spring (93.6 ± 1.1%). We provide the parameters necessary for exploration within and among other taxa to leverage the generalizability of this migration model to a broader expanse of bird species, and across a range of climate change and land use/land cover change scenarios.

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Section: Discussionmentioning

confidence: 98%

Section: Landscape Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Effects of weather variation on waterfowl migration: Lessons from a continental‐scale generalizable avian movement and energetics model

Aagaard

Lonsdorf

Thogmartin

2022

Ecology and Evolution

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Marginal value analysis reveals shifting importance of migration habitat for waterfowl under a changing climate

Burner,

Golas,

Aagaard

et al. 2023

Ecology and Evolution

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Migratory waterfowl are an important resource for consumptive and non‐consumptive users alike and provide tremendous economic value in North America. These birds rely on a complex matrix of public and private land for forage and roosting during migration and wintering periods, and substantial conservation effort focuses on increasing the amount and quality of target habitat. Yet, the value of habitat is a function not only of a site's resources but also of its geographic position and weather. To quantify this value, we used a continental‐scale energetics‐based model of daily dabbling duck movement to assess the marginal value of lands across the contiguous United States during the non‐breeding period (September to May). We examined effects of eliminating each habitat node (32 × 32 km) in both a particularly cold and a particularly warm winter, asking which nodes had the largest effect on survival. The marginal value of habitat nodes for migrating dabbling ducks was a function of forage and roosting habitat but, more importantly, of geography (especially latitude and region). Irrespective of weather, nodes in the Southeast, central East Coast, and California made the largest positive contributions to survival. Conversely, nodes in the Midwest, Northeast, Florida, and the Pacific Northwest had consistent negative effects. Effects (positive and negative) of more northerly nodes occurred in late fall or early spring when climate was often severe and was most variable. Importance and effects of many nodes varied considerably between a cold and a warm winter. Much of the Midwest and central Great Plains benefited duck survival in a warm winter, and projected future warming may improve the value of lands in these regions, including many National Wildlife Refuges, for migrating dabbling ducks. Our results highlight the geographic variability in habitat value, as well as shifts that may occur in these values due to climate change.

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

Bieri

Sample

Thogmartin³

et al. 2018

Natural Resource Modeling

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Many metrics exist for quantifying the relative value of habitats and pathways used by highly mobile species. Properly selecting and applying such metrics requires substantial background in mathematics and understanding the relevant management arena. To address this multidimensional challenge, we demonstrate and compare three measurements of habitat quality: graph‐, occupancy‐, and demographic‐based metrics. Each metric provides insights into system dynamics, at the expense of increasing amounts and complexity of data and models. Our descriptions and comparisons of diverse habitat‐quality metrics provide means for practitioners to overcome the modeling challenges associated with management or conservation of such highly mobile species. Whereas previous guidance for applying habitat‐quality metrics has been scattered in diversified tracks of literature, we have brought this information together into an approachable format including accessible descriptions and a modeling case study for a typical example that conservation professionals can adapt for their own decision contexts and focal populations. Considerations for Resource Managers Management objectives, proposed actions, data availability and quality, and model assumptions are all relevant considerations when applying and interpreting habitat‐quality metrics. Graph‐based metrics answer questions related to habitat centrality and connectivity, are suitable for populations with any movement pattern, quantify basic spatial and temporal patterns of occupancy and movement, and require the least data. Occupancy‐based metrics answer questions about likelihood of persistence or colonization, are suitable for populations that undergo localized extinctions, quantify spatial and temporal patterns of occupancy and movement, and require a moderate amount of data. Demographic‐based metrics answer questions about relative or absolute population size, are suitable for populations with any movement pattern, quantify demographic processes and population dynamics, and require the most data. More real‐world examples applying occupancy‐based, agent‐based, and continuous‐based metrics to seasonally migratory species are needed to better understand challenges and opportunities for applying these metrics more broadly.

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

Cited by 3 publications

References 62 publications

Effects of weather variation on waterfowl migration: Lessons from a continental‐scale generalizable avian movement and energetics model

Effects of weather variation on waterfowl migration: Lessons from a continental‐scale generalizable avian movement and energetics model

Marginal value analysis reveals shifting importance of migration habitat for waterfowl under a changing climate

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

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