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Comparing generalized and customized spread models for nonnative forest pests.

Emma J Hudgins1, Andrew M Liebhold2,3, Brian Leung1,4

  • 1Biology Department, McGill University, Montreal, Quebec, H3A 1B1, Canada.

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Summary
This summary is machine-generated.

A new semi-generalized model (SDK) significantly improves ecological pest spread predictions by combining general models with species-specific data. This approach offers better accuracy than generalized or customized models alone, aiding rapid invasion response.

Keywords:
exoticmacroecologymultispeciesnonindigenousrisk assessmentsimulationspatially explicit

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Area of Science:

  • Ecology
  • Invasive Species Management
  • Ecological Modeling

Background:

  • General ecological models offer broad applicability but may lack predictive power for specific species.
  • Customized, species-specific models provide higher accuracy but require extensive data.
  • The need exists for models balancing generality and specificity for effective pest spread prediction.

Purpose of the Study:

  • To compare the predictive power of generalized, customized, and semi-generalized ecological spread models.
  • To evaluate if a semi-generalized model can enhance pest spread predictions.
  • To assess the utility of combining general models with species-specific corrections.

Main Methods:

  • Compared a generalized dispersal kernel (GDK) model with customized spread models for three invasive forest pests.
  • Developed semi-generalized dispersal kernel (SDK) models by applying GDK correction factors based on species-specific data.
  • Evaluated model performance by the percentage of spatial variation explained.

Main Results:

  • Customized models were 17% more predictive than the GDK, which explained 57% of spatial variation.
  • The SDK model achieved a mean of 91% of spatial variation explained, surpassing customized models (74%).
  • This study is the first to compare general, customized, and semi-generalized ecological spread models' predictive abilities.

Conclusions:

  • Semi-generalized models offer a significant predictive advantage by integrating general ecological principles with species-specific data.
  • This approach allows for rapid response to invasive species by synthesizing general models with context-specific information.
  • SDK forecasts were generated for 63 US pests, aiding future invasion management.