Using Consensus Land Cover Data to Model Global Invasive Tree Species Distributions

Abstract

Invasive tree species threaten ecosystems, natural resources, and managed land worldwide. Land cover has been widely used as an environmental variable for predicting global invasive tree species distributions. Recent studies have shown that consensus land cover data can be an effective tool for species distribution modelling. In this paper, consensus land cover data were used as prediction variables to predict the distribution of the 11 most aggressive invasive tree species globally. We found that consensus land cover data could indeed contribute to modelling the distribution of invasive tree species. According to the contribution rate of land cover to the distribution of invasive tree species, we inferred that the cover classes of open water and evergreen broadleaf trees have strong explanatory power regarding the distribution of invasive tree species. Under consensus land cover changes, invasive tree species were mainly distributed near equatorial, tropical, and subtropical areas. In order to limit the damage caused by invasive tree species to global biodiversity, human life, safety, and the economy, strong measures must be implemented to prevent the further expansion of invasive tree species. We suggest the use of consensus land cover data to model global invasive tree species distributions, as this approach has strong potential to enhance the performance of species distribution modelling. Our study provides new insights into the risk assessment and management of invasive tree species globally.

Comparing the Performance of CMCC-BioClimInd and WorldClim Datasets in Predicting Global Invasive Plant Distributions

“…It was also observed that the 11 IPS mainly distributed in tropical rainforests and grasslands, subtropical evergreen broad-leaved forests, and the Mediterranean region (Figure 2). These climate regions have abundant species resources, sufficient rain, heat, and forest resources [77]. Biodiversity is also richer in places with abundant plant resources [77].…”

“…These climate regions have abundant species resources, sufficient rain, heat, and forest resources [77]. Biodiversity is also richer in places with abundant plant resources [77]. For example, the invasive Morella faya is highly scattered throughout a narrow distribution area [78].…”

“…Therefore, CMCC-BioClimInd can predict potential alterations for invasive species with respect to future climate according to the 35 climate variables and help to implement timely preventive measures [46]. Otherwise, invasive plants' economic loss and negative impacts on food security, biodiversity, and ecosystem services may soon sharply increase [1,77].…”

Integrating Different Scales into Species Distribution Models: A Case for Evaluating the Risk of Plant Invasion in Chinese Protected Areas under Climate Change

“…For instance, we can use the results of modeling to develop actionable recommendations for biological conservation and risk prevention and control [4][5][6]. Despite their important uses, there are still many technical issues involved with the use of SDMs [7,8]. Addressing these issues will greatly improve the forecasting accuracy of SDMs, improve the feasibility of environmental management/policymaking, and create a bridge between modelers and decision-makers.…”