Abstract
Aim To evaluate a suite of species distribution models for their utility
as predictors of suitable habitat and as tools for new population
discovery of six rare plant species that have both narrow geographical
ranges and specialized habitat requirements.
Location The Rattlesnake Creek Terrane (RCT) of the Shasta-Trinity
National Forest in the northern California Coast Range of the United
States.
Methods We used occurrence records from 25 years of US Forest Service
botanical surveys, environmental and remotely sensed climate data
to model the distributions of the target species across the RCT.
The models included generalized linear models (GLM), artificial neural
networks (ANN), random forests (RF) and maximum entropy (ME). From
the results we generated predictive maps that were used to identify
areas of high probability occurrence. We made field visits to the
top-ranked sites to search for new populations of the target species.
Results Random forests gave the best results according to area under
the curve and Kappa statistics, although ME was in close agreement.
While GLM and ANN also gave good results, they were less restrictive
and more varied than RF and ME. Cross-model correlations were the
highest for species with the most records and declined with record
numbers. Model assessment using a separate dataset confirmed that
RF provided the best predictions of appropriate habitat. Use of RF
output to prioritize search areas resulted in the discovery of 16
new populations of the target species.
Main conclusions Species distribution models, such as RF and ME, which
use presence data and information about the background matrix where
species do not occur, may be an effective tool for new population
discovery of rare plant species, but there does appear to be a lower
threshold in the number of occurrences required to build a good model.
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