Comparing the accuracy of predictive distribution models for Fagus crenata forests in Japan

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  • ブナ林分布予測モデルの精度比較

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Abstract

We developed three different types of predictive distribution models for the presence/absence of Fagus crenata forests with four climatic parameters, and compared their performance. Generalised Linear Models (GLMs), Generalised Additive Models (GAMs), and Tree-Based Models (TMs) were compared in this way, due to their popularity in predicting plant species distributions. Four climatic factors; the minimum temperature of the coldest month (TMC), the warmth index (WI), winter precipitation (PRW), and summer precipitation (PRS); were used as explanatory variables in the model development. For GLMs, two sets of explanatory variables were applied, one of which was based solely on the four climatic terms (GLM-Simple), while the other included two-level interaction terms and quadratic polynomial terms (GLM-Complex). The models' performance was compared with AIC (Akaike's information criterion), residual deviance, and accuracy measures, including Kohen's kappa statistic and overall prediction success, which are often used in predictive modelling studies. The resulting values all indicated that TMs performed best, followed by GAMs, GLM-Complex, and GLM-Simple. We envisaged that the superiority of the TMs may be due to their binary recursive partitioning nature, which appears to give them a high capacity to capture the non-homogeneous Japanese climatic patterns nationwide. The model can explain the relationships between F. crenata forest distribution and climate factors well, although the forests are widely distributed under non-homogeneous climatic systems in Japan. We therefore support the use of TMs in predicting the presence/absence distributions of widely distributed forest types or plant species under the Japanese climate systems.

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