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Keywords:

  • atlas data;
  • birds;
  • climate change;
  • detection probability;
  • range margin shifts;
  • survey effort

Abstract

Global climate warming is predicted to lead to global and regional changes in the distribution of organisms. One influential approach to test this prediction using temporally repeated mapping surveys of organisms was suggested in a seminal paper by Thomas & Lennon (1999, Nature). The Thomas & Lennon approach corrects observed changes in the range margin for changes in the range size, and thus potentially controls for other broad-scale environmental changes between surveys, however the approach does not necessarily account for potential biases in sampling effort. To verify whether the issue of variation in sampling effort affects empirical estimates of shifts in range margin, we reanalyzed all three published studies exploring range margin changes of breeding birds in Great Britain (GB), Finland, and New York State (NY). Accounting for changes in survey effort on range margins lowered the estimated shift for breeding birds in New York, but the shift remained statistically significant. For Great Britain and Finland, for which no direct estimate of survey effort is available, we used species richness (a strong correlate of survey effort in New York) as a proxy and found that in both cases the estimated shift in range margin was significantly reduced and became nonsignificant. To understand how robust the approach is to sampling biases, we use a simulation model to show that the Thomas & Lennon approach is, under certain conditions, sensitive to changes in detection probability (probability to detect true occupancy) which in turn may be affected by changes in surveying effort between surveys. We thus found evidence that temporal changes in the distribution of breeding birds based on repeated mapping surveys may be inflated by changes in survey effort along range boundaries. We discuss possible approaches to deal with this issue in the analysis and design of national or regional surveys.