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Conditional natal dispersal provides a mechanism for populations tracking resource pulses after fire

Cite this dataset

Stillman, Andrew et al. (2021). Conditional natal dispersal provides a mechanism for populations tracking resource pulses after fire [Dataset]. Dryad.


Animals that persist in spatially structured populations face the challenge of tracking the rise and fall of resources across space and time. To combat these challenges, theory predicts that species should use conditional dispersal strategies that allow them to emigrate from patches with declining resources and colonize new resource patches as they appear. We studied natal dispersal movements in the black-backed woodpecker (Picoides arcticus), a species known for its strong association with recent post-fire forests in western North America. We radio-tracked juveniles originating from seven burned areas and tested hypotheses that environmental and individual factors influence dispersal distance and emigration rates – investigating emigration while additionally accounting for imperfect detection with a novel Bayesian model. We found that juveniles were more likely to leave natal areas and disperse longer distances if they were heavier or hatched in older burned areas where resources are increasingly scarce. Juveniles were also more likely to leave their natal burn if they hatched in a nest closer to the fire perimeter. While dispersing across the landscape, black-backed woodpeckers selected for burned forest relative to unburned available habitat. Together, these results strongly support the hypothesis that black-backed woodpecker populations track resource pulses across fire-prone landscapes, with conditional natal dispersal acting as a mechanism for locating and colonizing newly burned areas. Lending empirical support to theoretical predictions, our findings suggest that changes in resource distribution may shape dispersal patterns and, consequently, the distribution and persistence of spatially structured populations. 

Usage notes

This data package includes several components:

(1) Text files of JAGS model code.

(2) .csv files with input data to run models and conduct the analysis.

(3) A README file with definitions of variables.