Evaluating range-expansion models for calculating nonnative species' expansion rate.

Sonja Preuss,Åsa Berggren,Anna Cassel‐Lundhagen,Matthew Low

doi:10.1002/ece3.1106

Abstract

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.

Highlights

Understanding the factors determining distributions of species in equilibrium with environmental conditions is central to ecology (Andrewartha and Birch 1954; Brown et al 1996), focus has more recently turned to organisms undergoing range shifts associated with climate change (Parmesan and Yohe 2003; Brooker et al 2007) and the filling of empty ecological niches during biological invasions (Elith et al 2010; Vaclavık and Meentemeyer 2012)
There were two distinct groups of range expansion estimates, with distance-based methods calculated at the range margin all giving very similar results a 2014 The Authors
Estimates of range expansion rates using the change in a range statistic measure over time are a function of two key modeling components: calculation of the yearly range statistic from the distribution data, and the fitting of a model to quantify the temporal trend across years

Summary

Introduction

Understanding the factors determining distributions of species in equilibrium with environmental conditions is central to ecology (Andrewartha and Birch 1954; Brown et al 1996), focus has more recently turned to organisms undergoing range shifts associated with climate change (Parmesan and Yohe 2003; Brooker et al 2007) and the filling of empty ecological niches during biological invasions (Elith et al 2010; Vaclavık and Meentemeyer 2012). There are many ways to calculate species’ range expansions or shifts; some of these methods are complex and require detailed ecological life-history information (e.g., Van den Bosch et al 1990; Lensink 1997; Hill et al 2001). Because detailed ecological knowledge for many species is missing, less complex methods based on species presence data are often used to assess distributional changes.

Objectives

Methods

Results

Conclusion