Filters: partyWithName: Catherine S Jarnevich (X)
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We developed habitat suitability models for invasive plant species selected by Department of Interior land management agencies. We applied the modeling workflow developed in Young et al. 2020 to species not included in the original case studies. Our methodology balanced trade-offs between developing highly customized models for a few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions (Engelstad et al. 2022 Table S1: https://doi.org/10.1371/journal.pone.0263056) and relied on human input based on natural history knowledge to further narrow the variable set for each species before...
Ecosystems are changing worldwide and critical decisions that affect ecosystem health and sustainability are being made every day. As ecologists, we have a responsibility to ensure that these decisions are made with access to the best available science. However, to bring this idea into practice, ecology needs to make a substantial leap forward towards becoming a more predictive science. Furthermore, even for basic, conceptual questions there is a lot to be gained by addressing problems from a forecasting perspective, with more frequent data-model comparisons helping to highlight misunderstandings and reframe long-standing questions. Ecological forecasting is occurring across a wide range of ecological sub-disciplines,...
Siberian elm spatial data containing 9 rasters. Each of the rasters represent the following: 1) X1st_random - ensemble of 5 models with random background data and 1st percentile threshold 2) X10th_random - ensemble of 5 models with random background data and 10th percentile threshold 3) MaxSS_random - ensemble of 5 models with random background data and MaxSS threshold 4) X1st_Salix_1st - ensemble of 5 models with random background data and 1st percentile threshold 5) X10th_Salix - ensemble of 5 models with random background data and 10th percentile threshold 6) MaxSS_Salix - ensemble of 5 models with random background data and MaxSS threshold 7) X1st_combined - ensemble of 10 models with random and Salix background...
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