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A spatially explicit hierarchical model to characterize population viability.

Steven P Campbell1, Erin R Zylstra1, Catherine R Darst2

  • 1School of Natural Resources and the Environment, University of Arizona, Tucson, Arizona, 85721, USA.

Ecological Applications : a Publication of the Ecological Society of America
|September 7, 2018
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Summary
This summary is machine-generated.

This study introduces a new spatial population viability analysis (PVA) model to estimate animal population viability across landscapes. The model reveals spatial variation in demographic rates and extinction risk, crucial for effective conservation of species like the desert tortoise.

Keywords:
Gopherus morafkaiCAR modelSonoran desert tortoisecapture-recapturedemographymulti-state modelpopulation viability analysisrecruitmentspatial autoregressive modelspatial variationsurvival

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Area of Science:

  • Ecology
  • Conservation Biology
  • Population Dynamics

Background:

  • Traditional population viability analyses (PVAs) often overlook spatial variation in demographic processes, limiting their application in conservation.
  • Understanding spatially explicit demographic rates is crucial for predicting population viability across heterogeneous landscapes.
  • The Sonoran desert tortoise (Gopherus morafkai) faces conservation challenges, necessitating spatially informed management strategies.

Purpose of the Study:

  • To develop and apply a novel spatial PVA model that incorporates spatial autocorrelation to estimate demographic rates and viability.
  • To produce spatially explicit estimates of survival, transition, and recruitment rates for the Sonoran desert tortoise.
  • To assess the probability of local extirpation (Pex) across the tortoise's range and identify vulnerable populations.

Main Methods:

  • Utilized a hierarchical, spatial, autoregressive model for capture-recapture data to estimate adult survival (ϕad), juvenile survival (ϕjuv), and juvenile-to-adult transition (ψ).
  • Employed a spatial autoregressive model for recruitment data (R), though data sparsity necessitated a range-wide estimate.
  • Integrated local demographic estimates into stage-structured population models to calculate population growth rate (λ) and forecast local abundance and Pex.

Main Results:

  • Identified modest spatial variation in population growth rate (λ), driven by spatial differences in survival and transition rates.
  • Observed complex spatial patterns in demographic rates, with lower survival and transition, and higher recruitment in the northwestern range.
  • Found that extinction risk (Pex) varied with local abundance, with northwestern, western, and southern populations showing higher vulnerability at lower densities.

Conclusions:

  • The developed spatial PVA model effectively reveals spatial heterogeneity in demographic rates and extinction risk, informing conservation at multiple scales.
  • The findings highlight the vulnerability of Sonoran desert tortoise populations in specific regions, particularly under low abundance scenarios.
  • This approach enhances ecological understanding of landscape-level processes and provides a framework for spatially explicit conservation and management planning.