BioLake: A first assessment of lake temperature-derived bioclimatic predictors for aquatic invasive species

被引:1
|
作者
Burner, Ryan C. [1 ]
Daniel, Wesley M. [2 ]
Engelstad, Peder S. [3 ]
Churchill, Christopher J. [1 ]
Erickson, Richard A. [1 ]
机构
[1] US Geol Survey, Upper Midwest Environm Sci Ctr, La Crosse, WI 54601 USA
[2] US Geol Survey, Wetland & Aquat Res Ctr, Gainesville, FL USA
[3] Colorado State Univ, Nat Resource Ecol Lab, Ft Collins, CO USA
来源
ECOSPHERE | 2023年 / 14卷 / 07期
关键词
aquatic invasive species (AIS); BioLake; CHELSA BioClim; climatch; climate matching; ERA5-Land climate reanalysis; fundamental niche; horizon scanning; invasion risk assessment; lake temperature; species distribution models (SDM); ESTABLISHMENT SUCCESS; ECOLOGICAL IMPACTS; CLIMATE; BIODIVERSITY; RISK; POLICY; PLANTS;
D O I
10.1002/ecs2.4616
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Aquatic invasive species (AIS) present major ecological and economic challenges globally, endangering ecosystems and human livelihoods. Managers and policy makers thus need tools to predict invasion risk and prioritize species and areas of concern, and they often use native range climate matching to determine whether a species could persist in a new location. However, climate matching for AIS often relies on air temperature rather than water temperature due to a lack of global water temperature data layers, and predictive power of models is seldom evaluated. We developed 12 global lake (water) temperature-derived "BioLake" bioclimatic layers for distribution modeling of aquatic species and compared "climatch" climate matching predictions (from climatchR package) from BioLake with those based on BioClim temperature layers and with a null model. We did this for 73 established AIS in the United States, training the models on their ranges outside of the United States and Canada. Models using either set of climate layers outperformed the null expectation by a similar (but modest) amount on average, but some species were occasionally found in locations with low climatch scores. Mean US climatch scores were higher for most species when using air temperature. Including additional climate layers in models reduced mean climatch scores, indicating that commonly used climatch score thresholds are not absolute but can be context specific and may require calibration based upon climate data used. Although finer resolution global lake temperature data would likely improve predictions, our BioLake layers provide a starting point for aquatic species distribution modeling. Climate matching was most effective for some species that originated at low latitudes or had small ranges. Climatch scores remain useful but limited for predicting AIS risk, perhaps because current ranges seldom fully reflect climatic tolerances (fundamental niches). Managers could consider climate matching as one of a suite of tools that can be used in AIS prioritization.
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页数:15
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