Making better sense of monitoring data from low density species using a spatially explicit modelling approach

Max Post Van Der Burg, Bartholomew Bly, Tammy VerCauteren, Richard AJ Tyre

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Wildlife managers are limited in the inferences they can draw about low density populations. These limits are imposed by biases in monitoring data not regularly accounted for. 2.We developed a Bayesian hierarchical model to correct biases arising from imperfect detection and spatial autocorrelation. Our analysis incorporated model selection uncertainty by treating model probabilities as parameters to be estimated in the context of model fitting. We fitted our model to count data from a monitoring programme for the mountain plover Charadrius montanus, a low density bird species in Nebraska, USA. 3.Our results demonstrated that previous accounts of the abundance and distribution of plovers in Nebraska were impacted by low detection probabilities (~5-20%). Uncorrected relative abundance estimates showed that the average number of birds per agricultural section increased over time, whereas corrected estimates showed that average abundance was stable. 4.Our method spatially interpolated relative abundance to produce distribution maps. These predictions suggested that birds were selecting some sites more frequently than others based on some habitat feature not explored in our study. Variation in mountain plover abundance appeared more heavily influenced by changes in the number of individuals occupying a few high quality sites, rather than from changes in abundance across many sites. Thus, conservation efforts may not be as efficient when focusing on low to moderate quality sites. 5.Synthesis and applications.Managers who must make decisions based on data-poor systems should adopt rigorous statistical approaches for drawing inferences. Spatial predictions provide information for deciding where to implement management, which is just as important as knowing what kind of management to apply. Our approach provides a step in the direction of making the biological signal in data-poor monitoring programmes more informative for conservation and management.

Original languageEnglish (US)
Pages (from-to)47-55
Number of pages9
JournalJournal of Applied Ecology
Volume48
Issue number1
DOIs
StatePublished - Feb 1 2011

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modeling
relative abundance
bird
mountain
prediction
autocorrelation
population density
monitoring data
habitat
monitoring
detection
programme
distribution
method
analysis
parameter
bird species
decision
wildlife

Keywords

  • Bayesian hierarchical models
  • Detection error
  • Modelling uncertainty
  • Spatial statistics

ASJC Scopus subject areas

  • Ecology

Cite this

Making better sense of monitoring data from low density species using a spatially explicit modelling approach. / Van Der Burg, Max Post; Bly, Bartholomew; VerCauteren, Tammy; Tyre, Richard AJ.

In: Journal of Applied Ecology, Vol. 48, No. 1, 01.02.2011, p. 47-55.

Research output: Contribution to journalArticle

Van Der Burg, Max Post ; Bly, Bartholomew ; VerCauteren, Tammy ; Tyre, Richard AJ. / Making better sense of monitoring data from low density species using a spatially explicit modelling approach. In: Journal of Applied Ecology. 2011 ; Vol. 48, No. 1. pp. 47-55.
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