Accuracy assessment of predictive models of grassland bird abundances in the Prairie Hardwood Transition Bird Conservation Region

Les Daniel Murray, Christine A. Ribic, Wayne E. Thogmartin, Melinda G. Knutson

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

We tested statistical models developed to predict abundances of grassland birds in the Prairie Hardwood Transition Bird Conservation Region of the upper midwestern United States. Roadside surveys were used to estimate relative abundances of grassland birds in 800 ha areas in the Iowa, Minnesota, and Wisconsin portions of the region in 2003-2005. We then compared observed abundances with predicted abundances from spatial hierarchical models for seven species. Spearman's rho statistic for rank correlations suggested that observed abundances were positively correlated with predicted abundances for all species (rs = 0.21-0.60) except the Henslow's Sparrow (Ammodramus henslowii; rs = 0.01). Observed abundances also were positively correlated with percent grassland in an area, and rank correlation values were similar to those obtained from the predictive models. Model accuracy was positively related to species' abundance and niche breadth. Our accuracy assessment suggested that the spatial hierarchical models would have limited use in guiding management at a regional scale; a measure of habitat quantity performed equally as well as the models at predicting observed abundances. Future efforts to model grassland bird abundances would be improved by more accurate information on the distribution of grasslands in the region, more detailed information on grassland composition and structure, and a better understanding of the biological significance of environmental variables for grassland bird populations.

Original languageEnglish (US)
Pages (from-to)747-755
Number of pages9
JournalCondor
Volume110
Issue number4
DOIs
StatePublished - Dec 1 2008

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Animal Science and Zoology

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