Impacts of the spatial scale of climate data on the modeled distribution probabilities of invasive tree species throughout the world

Predicting the invasive trend of exotic plants in China based on the ensemble model under climate change: A case for three invasive plants of Asteraceae

Ageratina adenophora, Eupatorium odoratum, and Mikania micrantha are three highly destructive invasive plants of Compositae in China. Through the screening of SDMs, random forest (RF), gradient boosting model (GBM), artificial neural network (ANN), and flexible discriminant analysis (FDA) with TSS greater than 0.8 are selected to construct a high-precision ensemble model (EM) as the prediction model. We use specimen sites and environmental variables containing climate, soil, terrain, and human activities to simulate and predict the invasion trend of three invasive weeds in China in current, the 2050s, and the 2070s. Results indicate that the highly invasive risk area of three exotic plants is mostly distributed along the river in the provinces south of 30° N. In the future scenario, the three exotic plants obviously invade northwards Yunnan, Sichuan, Guizhou, Jiangxi and Fujian. Climate is the most important variable that affects the spread of three kinds of alien plant invasions. Temperature and precipitation variables have a similar effect on A. adenophora and E. odoratum, while M. micrantha is more sensitive to temperature. It has been reported that Ipomoea batatas and Vitex negundo can prevent the invasion of three invasive plants. Hence, we also simulate the suitable planting areas for I. batatas and V. negundo. The results show that I. batatas and V. negundo are suitable to be planted in the areas where the three weeds show invasion tendency. In the paper, predicting invasion trends of exotic plants and simulating the planting suitability of crops that can block invasion, to provide a practical significance reference and suggestion for the management, prevention, and control of the invasion of exotic plants in China.

Spatial Distribution of the Mexican Daisy, Erigeron karvinskianus, in New Zealand under Climate Change

The invasive species Erigeron karvinskianus or Mexican daisy is considered a significant weed that impacts native forest restoration efforts in New Zealand. Mapping the potential distribution of this species under current and future predicted climatic conditions provides managers with relevant information for developing appropriate management strategies. Using occurrences available from global and local databases, spatial distribution characteristics were analyzed using geostatistical tools in ArcMap to characterize current distribution. Species distribution modeling (SDM) using Maxent was conducted to determine the potential spatial distribution of E. karvinskianus worldwide and in New Zealand with projections into future climate conditions. Potential habitat suitability under future climatic conditions were simulated using greenhouse gas emission trajectories under the Representative Concentration Pathway (RCP) models RCP2.6, RCP4.5, RCP6.0 and RCP8.5 for years 2050 and 2070. Occurrence data were processed to minimize redundancy and spatial autocorrelation; non-correlated environmental variables were determined to minimize bias and ensure robust models. Kernel density, hotspot and cluster analysis of outliers show that populated areas of Auckland, Wellington and Christchurch have significantly greater concentrations of E. karvinskianus. Species distribution modeling results find an increase in the expansion of range with higher RCP values, and plots of centroids show a southward movement of predicted range for the species.

Enhancing the WorldClim data set for national and regional applications

Climatic change in the last few decades has had a widespread impact on both natural and human systems, observable on all continents. Ecological and environmental models using climatic data often rely on gridded data, such as WorldClim. The main aim of this study was to devise and evaluate a computationally efficient approach to produce new high resolution (100m) estimates of current and future climatic variables to be used at the national and regional scale. The test area was Great Britain, where local data are available and of good quality. Present and future climate surfaces were produced. For the present, the approach involved the integration, via spatial interpolation, of local climate information and WorldClim to reduce bias. For future climate scenarios the approach involved spatially downscaling of WorldClim (1km) to a finer resolution of 100m. The main advantages of the proposed approach are: 1. finer resolution, 2. locally adapted to the study area with use of higher number of meteorological stations and improved accuracy and bias, and 3. computationally efficient while making use of the existing resources provided by WorldClim. Two applications were presented to illustrate the practical consequences of improvements obtained with this method. The first is a measure of rainfall intensity, i.e. the R-factor, widely applied in erosion and catchment-scale studies. The second is an application to species distribution modelling, involving a range of bioclimatic variables. The results highlighted the importance of considering the spatial variability and structure of the data integrated in the modelling, and using data adapted to the geographical extent of the analysis, whenever possible. The results of the applications showed the advantage of using enhanced climatic data in applications such as the estimation of soil erosion, species range shift, carbon stocks and the provision of ecosystem services.