Clustering community science data to infer songbird migratory connectivity in the Western Hemisphere

Vincent, Jaimie G.; Schuster, Richard; Wilson, Scott; Fink, Daniel; Bennett, Joseph

doi:10.1002/ecs2.4011

Cited by 6 publications

(5 citation statements)

References 59 publications

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“…Existing networks of monitoring sites, such as those coordinated by the MAPS program, provide an under‐utilized but potentially efficient framework for deploying and recovering tags or obtaining biological samples and providing demographic data for such analyses. The value of these direct measures of movement and demography can be further enhanced by taking advantage of other large‐scale observational data sets, such as eBird (Sullivan et al, 2009 ), which can help to refine understanding of the timing and extent of seasonal movements (Fournier et al, 2017 ; Vincent et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Demographic responses to climate‐driven variation in habitat quality across the annual cycle of a migratory bird species

Saracco

Cormier

Humple

et al. 2022

Ecology and Evolution

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The demography and dynamics of migratory bird populations depend on patterns of movement and habitat quality across the annual cycle. We leveraged archival GPStagging data, climate data, remote-sensed vegetation data, and bird-banding data to better understand the dynamics of black-headed grosbeak (Pheucticus melanocephalus) populations in two breeding regions, the coast and Central Valley of California (Coastal California) and the Sierra Nevada mountain range (Sierra Nevada), over 28 years . Drought conditions across the annual cycle and rainfall timing on the molting grounds influenced seasonal habitat characteristics, including vegetation greenness and phenology (maturity dates). We developed a novel integrated population model with population state informed by adult capture data, recruitment rates informed by age-specific capture data and climate covariates, and survival rates informed by adult capture-mark-recapture data and climate covariates. Population size was relatively variable among years for Coastal California, where numbers of recruits and survivors were positively correlated, and years of population increase were largely driven by recruitment. In the Sierra Nevada, population size was more consistent and showed stronger evidence of population regulation (numbers of recruits and survivors negatively correlated). Neither region showed evidence of longterm population trend. We found only weak support for most climate-demographic rate relationships. However, recruitment rates for the Coastal California region were higher when rainfall was relatively early on the molting grounds and when wintering grounds were relatively cool and wet. We suggest that our approach of integrating movement, climate, and demographic data within a novel modeling framework can provide a useful method for better understanding the dynamics of broadly distributed migratory species.

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Section: Discussionmentioning

confidence: 99%

Demographic responses to climate‐driven variation in habitat quality across the annual cycle of a migratory bird species

Saracco

Cormier

Humple

et al. 2022

Ecology and Evolution

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“…These models are designed to use population-level data to estimate population-level traits (e.g. migratory connectivity), and to predict movement between spatial cells across time (Fuentes et al, 2023;Meehan et al, 2022;Vincent et al, 2022). Although useful for answering targeted questions, these models are currently limited in the breadth of questions they can address.…”

Section: Introductionmentioning

confidence: 99%

“…Although useful for answering targeted questions, these models are currently limited in the breadth of questions they can address. Critically, previous such attempts to simulate migration do not account for individual variation in behaviour, instead either circumventing the representation of individual behaviour all together (Meehan et al, 2022), or deriving estimates of individual movement from the modelled probabilistic flow of whole populations between spatial cells (Fuentes et al, 2023;Vincent et al, 2022). Without the biologically realistic representation of how individuals move across the landscape, including biological constraints (e.g.…”

Section: Introductionmentioning

confidence: 99%

Individual‐based models of avian migration for estimating behavioural traits and predicting ecological interactions

Tonelli,

Zelin,

Dearborn

et al. 2023

Methods Ecol Evol

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Rapid advances in the field of movement ecology have led to increasing insight into both the population‐level abundance patterns and individual‐level behaviour of migratory species. Despite this progress, research questions that require scaling individual‐level understanding of the behaviour of migrating organisms to the population level remain difficult to investigate. To bridge this gap, we introduce a generalizable framework for training full‐annual cycle individual‐based models of migratory movements by combining information from tracking studies and species occurrence records. Focusing on migratory birds, we call this method: Models of Individual Movement of Avian Species (MIMAS). We implement MIMAS to design individual‐based models of avian migration that are trained using previously published weekly occurrence maps and fit via Approximate Bayesian Computation. MIMAS models leverage individual‐ and population‐level information to faithfully represent continental‐scale migration patterns. Models can be trained successfully for species even when little existing individual‐level data is available for parameterization by relying on population‐level information. In contrast to existing mathematical models of migration, MIMAS explicitly represents and estimates behavioural attributes of migrants. MIMAS can additionally be used to simulate movement over consecutive migration seasons, and models can be easily updated or validated as new empirical data on migratory behaviours becomes available. MIMAS can be applied to a variety of research questions that require representing individual movement at large scales. We demonstrate three applied uses for MIMAS: estimating population‐specific migratory phenology, predicting the spatial patterns and magnitude of ectoparasite dispersal by migrants, and simulating the spread of a pathogen across the annual cycle of a migrant species. Currently, MIMAS can easily be used to build models for hundreds of migratory landbird species but can also be adapted in the future to build models of other types of migratory animals.

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“…One approach used deterministic models based on the concept of global energy efficiency, in which simulated birds are distributed to optimize both resource acquisition and energy expenditure (Somveille et al, 2021). Another approach used clustering methods along with an assumption of parallel migration to investigate connectivity (Vincent et al, 2022). All these approaches provide valuable but limited lenses to understand migration because they analyze specific aspects of migration and do not model full trajectories that individual birds may take.…”

Section: Introductionmentioning

confidence: 99%

BirdFlow: Learning Seasonal Bird Movements from eBird Data

Fuentes

Doren

Fink

et al. 2022

Preprint

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1. Large-scale monitoring of seasonal animal movement is integral to science, conservation, and outreach. However, gathering representative movement data across entire species ranges is frequently intractable. Citizen science databases collect millions of animal observations through- out the year, but it is challenging to infer individual movement behavior solely from observational data. 2. We present BirdFlow, a probabilistic modeling framework that draws on citizen science data from the eBird database to model the population flows of migratory birds. 3. We apply the model to 11 species of North American birds, using GPS and satellite tracking data to tune and evaluate model performance. We show that BirdFlow models can accurately infer individual seasonal movement behavior directly from eBird relative abundance estimates. Supplementing the model with a sample of tracking data from wild birds improves performance. 4. Researchers can extract a number of behavioral inferences from model results, including migration routes, timing, connectivty, and forecasts. The BirdFlow framework has the potential to advance migration ecology research, boost insights gained from direct tracking studies, and serve a number of applied functions in conservation, disease surveillance, aviation, and public outreach.

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Clustering community science data to infer songbird migratory connectivity in the Western Hemisphere

Cited by 6 publications

References 59 publications

Demographic responses to climate‐driven variation in habitat quality across the annual cycle of a migratory bird species

Demographic responses to climate‐driven variation in habitat quality across the annual cycle of a migratory bird species

Individual‐based models of avian migration for estimating behavioural traits and predicting ecological interactions

BirdFlow: Learning Seasonal Bird Movements from eBird Data

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