Evaluating range-expansion models for calculating nonnative species' expansion rate
Article first published online: 30 JUN 2014
© 2014 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
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Ecology and Evolution
Volume 4, Issue 14, pages 2812–2822, July 2014
How to Cite
Ecology and Evolution 2014; 4(14):2812–2822
- Issue published online: 21 JUL 2014
- Article first published online: 30 JUN 2014
- Manuscript Accepted: 23 APR 2014
- Manuscript Revised: 26 MAR 2014
- Manuscript Received: 5 NOV 2013
- Swedish Research Council. Grant Number: 215-2006-1869 & VR 2013-3634
- Oscar and Lili Lamm's Foundation
- Biological invasion;
- citizen science;
- citizen-reported data;
- invasive species;
- Metrioptera roeseli ;
- range shift
Species range shifts associated with environmental change or biological invasions are increasingly important study areas. However, quantifying range expansion rates may be heavily influenced by methodology and/or sampling bias. We compared expansion rate estimates of Roesel's bush-cricket (Metrioptera roeselii, Hagenbach 1822), a nonnative species currently expanding its range in south-central Sweden, from range statistic models based on distance measures (mean, median, 95th gamma quantile, marginal mean, maximum, and conditional maximum) and an area-based method (grid occupancy). We used sampling simulations to determine the sensitivity of the different methods to incomplete sampling across the species' range. For periods when we had comprehensive survey data, range expansion estimates clustered into two groups: (1) those calculated from range margin statistics (gamma, marginal mean, maximum, and conditional maximum: ˜3 km/year), and (2) those calculated from the central tendency (mean and median) and the area-based method of grid occupancy (˜1.5 km/year). Range statistic measures differed greatly in their sensitivity to sampling effort; the proportion of sampling required to achieve an estimate within 10% of the true value ranged from 0.17 to 0.9. Grid occupancy and median were most sensitive to sampling effort, and the maximum and gamma quantile the least. If periods with incomplete sampling were included in the range expansion calculations, this generally lowered the estimates (range 16–72%), with exception of the gamma quantile that was slightly higher (6%). Care should be taken when interpreting rate expansion estimates from data sampled from only a fraction of the full distribution. Methods based on the central tendency will give rates approximately half that of methods based on the range margin. The gamma quantile method appears to be the most robust to incomplete sampling bias and should be considered as the method of choice when sampling the entire distribution is not possible.