Abstract
Accurate information on the distribution of existing species is crucial to assess regional biodiversity. However, data inventories are insufficient in many areas. We examine the ability of Multivariate Adaptive Regression Splines (MARS) multi-response species distribution model to overcome species’ data limitations and portray plant species distribution patterns for 199 South Korean plant species. The study models species with two or more observations, examines their contribution to national patterns of species richness, provides a sensitivity analysis of different range threshold cutoff approaches for modeling species’ ranges, and presents considerations for species modeling at fine spatial resolution. We ran MARS models for each species and tested four threshold methods to transform occurrence probabilities into presence or absence range maps. Modeled occurrence probabilities were extracted at each species’ presence points, and the mean, median, and one standard deviation (SD) calculated to define data-driven thresholds. A maximum sum of sensitivity and specificity threshold was also calculated, and the range maps from the four cutoffs were tested using independent plant survey data. The single SD values were the best threshold tested for minimizing omission errors and limiting species ranges to areas where the associated occurrence data were correctly classed. Eight individual species range maps for rare plant species were identified that are potentially affected by resampling predictor variables to fine spatial scales. We portray spatial patterns of high species richness by assessing the combined range maps from three classes of species: all species, endangered and endemic species, and range-size rarity of all species, which could be used in conservation planning for South Korea. The MARS model is promising for addressing the common problem of few species occurrence records. However, projected species ranges are highly dependent on the threshold and scale criteria, which should be assessed on a per-project basis.
Highlights
Species distribution model (SDM) applications use the climate and occasionally the environmental characteristics of known species occurrence locations or abundance to estimate a likelihood of occurrence at other locations where no occurrence information is available [1]
This study identified biodiversity hotspot areas for vascular plants in South Korea by using an inventory that includes many species with too few observations to reliably project a range map with standard SDM techniques
We addressed this weakness by deploying a multi-response SDM to take advantage of occurrence data from more common species that frequently cooccur with the rare species for which we have sparse data
Summary
Species distribution model (SDM) applications use the climate and occasionally the environmental characteristics of known species occurrence locations or abundance to estimate a likelihood of occurrence at other locations where no occurrence information is available [1]. Multi-response SDMs may offer benefits for applications with large numbers of species that include species with few records ([5]; ‘Multi-response’ models were previously called ‘community’ models). The potential benefits of a multi-response SDM include a possibly more accurate representation of biodiversity, and a method to synthesize complex data into a simpler form. This method may be useful for modeling rarely recorded species of conservation concern which otherwise would be excluded from regional analyses, because significant predictor variables for some species identified by the MARS algorithm may help to inform how species with few presence points react to the same predictor variables. Multi-response SDMs produce information on spatial patterns of biodiversity at a multi-species level as well as at an individual level [5,6,7]
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