The Relic Trochodendron aralioides Siebold & Zucc. (Trochodendraceae) in Taiwan: Ensemble Distribution Modeling and Climate Change Impacts

Abstract

Trochodendron aralioides Siebold & Zuccarini (Trochodendraceae) is a famous relic tree species. Understanding the comprehensive spatial distribution and likely impacts of climate change on T. aralioides in its main habitat—Taiwan—is of great importance. We collected occurrence data and bioclimatic data to predict the current and future (year 2050) distribution by ensemble distribution modeling on the BIOMOD2 platform. Visualization of occurrence point data revealed that the main population of T. aralioides was concentrated at medium altitudes and extended to both ends of Taiwan, being especially rich in the northern low mountains. A similar distribution pattern of occurrence probability was shown by ensemble prediction of the true skill statistic >0.8 models. Comparing the current and future distribution of T. aralioides, the overlay analysis with profile display demonstrated spatial turnover that revealed a discrepancy between different latitudes and altitudes. In the future climate, T. aralioides at the middle altitudes of central Taiwan could migrate upward, but its population in northern Taiwan could lose most of its habitat. Consequently, T. aralioides in the low mountains of northern Taiwan could be particularly in need of further conservation research, which is urgently required to mitigate climate change impacts.

Responses of four dominant dryland plant species to climate change in the Junggar Basin, northwest China

“…For each species, we used ten modeling algorithms embedded in the biomod2 package: generalized linear models (GLM), boosted regression trees (GBM), generalized additive model (GAM), classification tree analysis (CTA), artificial neural network (ANN), surface range envelop or BIOCLIM (SRE), flexible discriminant analysis (FDA), multiple adaptive regression splines (MARS), random forests (RF), and maximum entropy (MaxEnt). These algorithms were chosen because they have shown good ability to predict current species distribution and have been widely used in ecological modeling (Hamid et al, ; Lin & Chiu, ; Thuiller, ). Combining the predictions of individual algorithms to make ensemble prediction provides results that are more robust and also enables the assessment of the uncertainty generated from the modeling procedure.…”

“…For each species, we used ten modeling The standard deviation from the mean AUC of all models (10 modeling algorithms × 10 replicates). good ability to predict current species distribution and have been widely used in ecological modeling (Hamid et al, 2019;Lin & Chiu, 2019;Thuiller, 2003). Combining the predictions of individual algorithms to make ensemble prediction provides results that are more robust and also enables the assessment of the uncertainty generated from the modeling procedure.…”

Distribución potencial de Euphorbia antisyphilitica Zucc. en México

“…Its presence is subject to strong pressures, both from harvesting and land-use change and the effect of climate change. Given this risky scenario for these populations, it is important to know the potential distribution; for this purpose, the use of modeling based on environmental variables is a useful tool, because changes have an impact on biotic interactions (Lin & Chiu, 2018).…”

A chromosome‐level genome assembly for the tertiary relict plant Tetracentron sinense oliv. (trochodendraceae)

“…sinense showed habitat loss in low‐altitude areas under future climate warming, and this scenario is consistent with a previous study on Tr. aralioides (Lin & Chiu, 2019), which indicated that the central Taiwanese population will migrate upward and the northern Taiwanese population could lose most of its current habitats. Low‐altitude regions are therefore critical for the future conservation of both species.…”