Monday, September 7, 2015

Dan Warren with donuts for Mainali et al. 2015

Dan bringing some donuts for a new paper out in Global Change Biology: Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling, by Kumar P Mainali, Dan L Warren, Kunjithapatham Dhileepan, Andrew McConnachie, Lorraine Strathie, Gul Hassan, Debendra Karki, Bharat B Shrestha, and Camille Parmesan.

Here's the abstract:

Modeling the distributions of species, especially of invasive species in non-native ranges,
involves multiple challenges. Here, we developed some novel approaches to species
distribution modeling aimed at reducing the influences of such challenges and improving the
realism of projections. We estimated species-environment relationships with four modeling
methods run with multiple scenarios of (1) sources of occurrences and geographically
isolated background ranges for absences, (2) approaches to drawing background (absence)
points, and (3) alternate sets of predictor variables. We further tested various quantitative
metrics of model evaluation against biological insight. Model projections were very sensitive
to the choice of training dataset. Model accuracy was much improved by using a global
dataset for model training, rather than restricting data input to the species’ native range. AUC
score was a poor metric for model evaluation and, if used alone, was not a useful criterion for
assessing model performance. Projections away from the sampled space (i.e. into areas of
potential future invasion) were very different depending on the modeling methods used,
raising questions about the reliability of ensemble projections. Generalized linear models
gave very unrealistic projections far away from the training region. Models that efficiently fit
the dominant pattern, but exclude highly local patterns in the dataset and capture interactions
as they appear in data (e.g. boosted regression trees), improved generalization of the models.
Biological knowledge of the species and its distribution was important in refining choices
about the best set of projections. A post-hoc test conducted on a new Partenium dataset from
Nepal validated excellent predictive performance of our “best” model. We showed that vast
stretches of currently uninvaded geographic areas on multiple continents harbor highly
suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However,
discrepancies between model predictions and parthenium invasion in Australia indicate
successful management for this globally significant weed.

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