Abstract

AbstractEfficient management and prevention of species invasions requires accurate prediction of where species of concern can arrive and persist. Species distribution models provide one way to identify potentially suitable habitat by developing the relationship between climate variables and species occurrence data. However, these models when applied to freshwater invasions are complicated by two factors. The first is that the range expansions that typically occur as part of the invasion process violate standard species distribution model assumptions of data stationarity. Second, predicting potential range of freshwater aquatic species is complicated by the reliance on terrestrial climate measurements to develop occurrence relationships for species that occur in aquatic environments. To overcome these obstacles, we combined a recently developed algorithm for species distribution modeling—range bagging—with newly available aquatic habitat‐specific information from the North American Great Lakes region to predict suitable habitat for three potential invasive species: golden mussel, killer shrimp, and northern snakehead. Range bagging may more accurately predict relative suitability than other methods because it focuses on the limits of the species environmental tolerances rather than central tendency or “typical” cases. Overlaying the species distribution model output with aquatic habitat‐specific data then allowed for more specific predictions of areas with high suitability. Our results indicate there is suitable habitat for northern snakehead in the Great Lakes, particularly shallow coastal habitats in the lower four Great Lakes where literature suggests they will favor areas of wetland and submerged aquatic vegetation. These coastal areas also offer the highest suitability for golden mussel, but our models suggest they are marginal habitats. Globally, the Great Lakes provide the closest match to the currently invaded range of killer shrimp, but they appear to pose an intermediate risk to the region. Range bagging provided reliable predictions when assessed either by a standard test set or by tests for spatial transferability, with golden mussel being the most difficult to accurately predict. Our approach illustrates the strength of combining multiple sources of data, while reiterating the need for increased measurement of freshwater habitat at high spatial resolutions to improve the ability to predict potential invasive species.

Highlights

  • Aquatic invasive species (AIS) have imposed substantial ecological damage on freshwater ecosystems (Ricciardi and MacIsaac 2000, Cucherousset and Olden 2011), prompting a more proactive, holistic approach to invasive species management (Leung et al 2002, Pagnucco et al 2015)

  • Since the mid-1960s, golden mussel has been unintentionally dispersed across the globe through fouling of shipping vessels and established populations are present in Hong Kong, Taiwan, Japan, Brazil, Paraguay, Uruguay, Bolivia, and Argentina (Ricciardi 1998)

  • Niche centrality was highest in Lake Erie, but never exceeded 0.39, indicating most of the marginal niche models did not include environmental conditions observed in the Great Lakes basin

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Summary

Introduction

Aquatic invasive species (AIS) have imposed substantial ecological damage on freshwater ecosystems (Ricciardi and MacIsaac 2000, Cucherousset and Olden 2011), prompting a more proactive, holistic approach to invasive species management (Leung et al 2002, Pagnucco et al 2015). Species distribution models (SDMs) estimate the statistical relationship between species occurrence and environmental conditions (Elith and Leathwick 2009), and applications of these models have been used to identify suitable habitat outside of the current range (Barve et al 2011), predict range shifts in response to climate change (Austin and Van Niel 2011, VanDerWal et al 2013), and predict the spread of invasive species (Kulhanek et al 2011) These applications involve violation of a key assumption of SDM methods, namely stationarity in species occurrence (Barve et al 2011, Pagel and Schurr 2012, Vaclavık and Meentemeyer 2012). There is still a risk of underestimating the extent of suitable habitat

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