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Comparing generalized and customized spread models for nonnative forest pests
Author(s) -
Hudgins Emma J.,
Liebhold Andrew M.,
Leung Brian
Publication year - 2020
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1002/eap.1988
Subject(s) - predictive power , context (archaeology) , biological dispersal , ecology , generality , lymantria dispar , biology , predictive modelling , computer science , machine learning , population , paleontology , philosophy , lepidoptera genitalia , demography , epistemology , sociology , psychotherapist , psychology
While generality is often desirable in ecology, customized models for individual species are thought to be more predictive by accounting for context specificity. However, fully customized models require more information for focal species. We focus on pest spread and ask: How much does predictive power differ between generalized and customized models? Further, we examine whether an intermediate “semi‐generalized” model, combining elements of a general model with species‐specific modifications, could yield predictive advantages. We compared predictive power of a generalized model applied to all forest pest species (the generalized dispersal kernel or GDK ) to customized spread models for three invasive forest pests (beech bark disease [ Cryptococcus fagisuga ], gypsy moth [ Lymantria dispar ], and hemlock woolly adelgid [ Adelges tsugae ]), for which time‐series data exist. We generated semi‐generalized dispersal kernel models ( SDK ) through GDK correction factors based on additional species‐specific information. We found that customized models were more predictive than the GDK by an average of 17% for the three species examined, although the GDK still had strong predictive ability (57% spatial variation explained). However, by combining the GDK with simple corrections into the SDK model, we attained a mean of 91% of the spatial variation explained, compared to 74% for the customized models. This is, to our knowledge, the first comparison of general and species‐specific ecological spread models’ predictive abilities. Our strong predictive results suggest that general models can be effectively synthesized with context‐specific information for single species to respond quickly to invasions. We provided SDK forecasts to 2030 for all 63 United States pests in our data set.