Projections of anthropogenically-induced global climate change and its impacts on potential distributions of invasive species are crucial for implementing effective conservation and management strategies. Lantana camara L., a popular ornamental plant native to tropical America, has become naturalized in some 50 countries and is considered one of the world's worst weeds. To increase our understanding of its potential extent of spread and examine the responses of global geographic distribution, predictive models incorporating global distribution data of L. camara were generated. These models were used to identify areas of environmental suitability and project the effects of future climate change based on an ensemble of the four global climate models (GCMs) within the Inter-Sectoral Impact Model Intercomparis on Project (ISI-MIP). Each model was run under the four emission scenarios (Representative Concentration Pathways, RCPs) using the Maximum entropy (Maxent) approach. Future model predictions through 2050 indicated an overall expansion of L. camara, despite future suitability varying considerably among continents. Under the four RCP scenarios, the range of L. camara expanded further inland in many regions (e.g. Africa, Australia), especially under the RCP85 emission scenario. The global distribution of L. camara, though restricted within geographical regions of similar latitude as at present (35°N~35°S), was projected to expand Climatic Change

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Understanding temporal and spatial distributions of naturally occurring total organic carbon (TOC) in sediments is critical because TOC is an important feature of surface water quality. This study investigated temporal and spatial distributions of sediment TOC and its relationships to sediment contaminants in the Cedar and Ortega Rivers, Florida, USA, using three-dimensional kriging analysis and field measurement. Analysis of field data showed that large temporal changes in sediment TOC concentrations occurred in the rivers, which reflected changes in the characteristics and magnitude of inputs into the rivers during approximately the last 100 yr. The average concentration of TOC in sediments from the Cedar and Ortega Rivers was 12.7% with a maximum of 22.6% and a minimum of 2.3%. In general, more TOC accumulated at the upper 1.0 m of the sediment in the southern part of the Ortega River although the TOC sedimentation varied with locations and depths. In contrast, high concentrations of sediment contaminants, that is, total polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs), were found in sediments from the Cedar River. There was no correlation between TOC and PAHs or PCBs in these river sediments. This finding is in contradiction to some other studies which reported that the sorption of hydrocarbons is highly related to the organic matter content of sediments. This discrepancy occurred because of the differences in TOC and hydrocarbon source input locations. It was found that more TOC loaded into the southern part of the Ortega River, while almost all of the hydrocarbons entered into the Cedar River. This study suggested that the locations of their input sources as well as the land use patterns should also be considered when relating hydrocarbons to sediment TOC.

Pseudomonas syringae pv. actinidiae (Psa) is a causal agent of kiwifruit bacterial canker worldwide, which has affected kiwifruit vines in China since 1996 and has subsequently spread to the main cultivation areas. Based on occurrence of Psa and pseudo‐absences randomly generated in China, the consensus‐based modelling technique was used to estimate the spatial spread of Psa epidemics within China. Environmental variables that related to Psa development were identified, and their contributions to Psa development were evaluated. Three modelling algorithms, namely generalized boosting models (GBM), random forests (RF) and classification tree analysis (CTA) within the BIOMOD2 framework, were employed to construct the model. The ensemble models weighted by the true skill statistic (TSS) value were used to predict the current habitat suitability of Psa, and were projected using the four general circulation models (GCMs) to assess range shifts under two types of representative concentration pathways (RCP 4.5 and RCP 8.5) by 2050. The results indicated that precipitation in March and mean temperature of warmest quarter were the most important limiting factors for distribution of Psa. The predictive accuracy of the ensemble model showed acceptable predictive powers (TSS = 0.852). Under future climate conditions, substantial net loss of suitability for Psa was estimated to be 3.03–12.5% under RCP 4.5 (except one GCM), and 2.46–9.89% under RCP 8.5. Shrinkage of suitable habitats was detected mainly in the areas currently infected by Psa. Special attention should be given to recent infectious regions in south and southwest China, considering the locally expanding kiwifruit commercial plantations.