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Accuracy of climate-based forecasts of pathogen spread

View Article: PubMed Central - PubMed

ABSTRACT

Species distribution models (SDMs) are a tool for predicting the eventual geographical range of an emerging pathogen. Most SDMs, however, rely on an assumption of equilibrium with the environment, which an emerging pathogen, by definition, has not reached. To determine if some SDM approaches work better than others for modelling the spread of emerging, non-equilibrium pathogens, we studied time-sensitive predictive performance of SDMs for Batrachochytrium dendrobatidis, a devastating infectious fungus of amphibians, using multiple methods trained on time-incremented subsets of the available data. We split our data into timeline-based training and testing sets, and evaluated models on each set using standard performance criteria, including AUC, kappa, false negative rate and the Boyce index. Of eight models examined, we found that boosted regression trees and random forests performed best, closely followed by MaxEnt. As expected, predictive performance generally improved with the length of time series used for model training. These results provide information on how quickly the potential extent of an emerging disease may be determined, and identify which modelling frameworks are likely to provide useful information during the early phases of pathogen expansion.

No MeSH data available.


Boyce index values, with 95% CIs, from five-part chronological analysis, averaged over time steps.
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RSOS160975F4: Boyce index values, with 95% CIs, from five-part chronological analysis, averaged over time steps.

Mentions: The Boyce index (figure 4) was highly variable and uncorrelated with AUC values. We noticed this contradiction most strikingly in the five-part chronological analysis for RF and BRT. Boyce index values for both models dropped sharply at the final time step (electronic supplementary material, figure S3), while AUC values increased (figure 5b). Overall, we saw poorest performance with respect to the Boyce index for models that are not usually run in presence-only contexts (e.g. GLM and PPG; figure 4).Figure 4.


Accuracy of climate-based forecasts of pathogen spread
Boyce index values, with 95% CIs, from five-part chronological analysis, averaged over time steps.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5383844&req=5

RSOS160975F4: Boyce index values, with 95% CIs, from five-part chronological analysis, averaged over time steps.
Mentions: The Boyce index (figure 4) was highly variable and uncorrelated with AUC values. We noticed this contradiction most strikingly in the five-part chronological analysis for RF and BRT. Boyce index values for both models dropped sharply at the final time step (electronic supplementary material, figure S3), while AUC values increased (figure 5b). Overall, we saw poorest performance with respect to the Boyce index for models that are not usually run in presence-only contexts (e.g. GLM and PPG; figure 4).Figure 4.

View Article: PubMed Central - PubMed

ABSTRACT

Species distribution models (SDMs) are a tool for predicting the eventual geographical range of an emerging pathogen. Most SDMs, however, rely on an assumption of equilibrium with the environment, which an emerging pathogen, by definition, has not reached. To determine if some SDM approaches work better than others for modelling the spread of emerging, non-equilibrium pathogens, we studied time-sensitive predictive performance of SDMs for Batrachochytrium dendrobatidis, a devastating infectious fungus of amphibians, using multiple methods trained on time-incremented subsets of the available data. We split our data into timeline-based training and testing sets, and evaluated models on each set using standard performance criteria, including AUC, kappa, false negative rate and the Boyce index. Of eight models examined, we found that boosted regression trees and random forests performed best, closely followed by MaxEnt. As expected, predictive performance generally improved with the length of time series used for model training. These results provide information on how quickly the potential extent of an emerging disease may be determined, and identify which modelling frameworks are likely to provide useful information during the early phases of pathogen expansion.

No MeSH data available.