Pushing back the baseline: a novel approach to detect long-term changes in terrestrial faunal abundance using historical qualitative descriptions

Duncan McCollin, Richard Preece, Tim H Sparks

Research output: Contribution to JournalArticlepeer-review

Abstract

Studies that examine changes in the populations of flora and fauna often do so against a baseline of relatively recent distribution data. It is much rarer to see evaluations of population change over the longer–term in order to extend the baseline back in time. Here, we use two methods (regression analysis and line of equality) to identify long-term differences in abundance derived from qualitative descriptions, and we test the efficacy of this approach by comparison with contemporary data. We take descriptions of bird population abundance in Cambridgeshire, UK, from the first half of the 19th century and compare these with more recent estimates by converting qualitative descriptions to an ordinal scale. We show, first, that the ordinal scale of abundance corresponds well to quantitative estimates of density and range size based on current data, and, second, that the two methods of comparison revealed both increases and declines in species, some of which were consistent using both approaches but others showed differing responses. We also show that the regional rates of extinction (extirpation) for birds are twice as high as equivalent rates for plants. These data extend analyses of avifaunal change back to a baseline 160-190 years before present, thus bringing a novel perspective on long-term change in populations and categories of conservation concern (e.g., Amber- or Red-lists) based on recent data. Changes in status are discussed in relation to various factors, although perhaps the most pervasive were of anthropogenic origin.
Original languageEnglish
JournalEuropean Journal of Ecology
Volume1
Issue number1
DOIs
Publication statusPublished - 22 Dec 2015

Keywords

  • Birds
  • Cambridgeshire
  • Jenyns
  • historical records
  • land use change
  • long-term
  • population
  • qualitative data

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