Climate Change and the Potential Distribution of an Invasive Shrub, Lantana camara L

Self Cite

Merremia peltata is a species with uncertain status in the island nations of the Pacific region. It has been designated introduced and invasive in some countries whereas it is considered native in others. Recent increase in its abundance across some island landscapes have led to calls for its designation as an invasive species of environmental concern with biological control being suggested as a control strategy. Climate change will add to the complications of managing this species since changes in climate will influence its range limits. In this study, we develop a process‐oriented niche model of M. peltata using CLIMEX to investigate the impacts of climate change on its potential distribution. Information on the climatic requirements of M. peltata and its current geographic distribution were used to calibrate the model. The results indicate that under current climate, 273,132 km2 of the land area in the region is climatically unsuitable or marginal for M. peltata whereas 664,524 km2 is suitable to highly suitable. Under current climate, areas of climatic suitability for M. peltata were identified on the archipelagos of Fiji, Papua New Guinea, Solomon Islands and Vanuatu. By the end of the century, some archipelagos like Fiji, Hawaii, New Caledonia and Vanuatu will probably become more suitable while PNG and Solomon Islands become less suitable for M. peltata. The results can be used to inform biosecurity planning, management and conservation strategies on islands.

“…2012b). The uncertainty related to future global greenhouse gas emission patterns also introduce further uncertainty to model projections (Kriticos et al.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…2011; Taylor et al. 2012b). This software was used in the present study to investigate the impacts of climate change on the potential distribution of Merremia peltata .…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…These two mechanisms ensured that the potential distribution was restricted to the known northern and southern limits (Taylor et al. 2012b). …”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Will climate change impact the potential distribution of a native vine (Merremia peltata) which is behaving invasively in the Pacific region?

Taylor

Kumar

2016

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A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area

Shabani

Kumar

Ahmadi

2016

Self Cite

230

170

To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models (SDMs) including Generalized Linear Model (GLM), MaxEnt, Random Forest (RF), Boosted Regression Tree (BRT), Bioclim, together with CLIMEX (CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic (ROC) curves (AUC), true skill statistic (TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a “large” number of species into novel environments or in an independent area, the selection of the “best” model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models (CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models.

Modeling the distribution of pine wilt disease in China using the ensemble models MaxEnt and CLIMEX

Chen,

Lu,

Lamont

et al. 2024

Pine wilt disease (PWD) is a devastating plant disease caused by the pinewood nematode (PWN, Bursaphelenchus xylophilus) that is transmitted by several beetle species in the genus, Monochamus. Once present, the disease is difficult to control. Prevention rather than control is regarded as an effective strategy for PWD management. Central to this prevention strategy is the ability to predict the potential distribution of the disease. Here, we employed an integrated MaxEnt and CLIMEX approach to model the potential distribution of PWD under various climate‐change scenarios. Our results indicate that rising temperatures and lower humidity under climate change will render some of the northern regions of China more suitable for the nematode and these beetles, causing the gradual northward movement of PWD. Furthermore, suitable habitats for three pine species, Pinus massoniana, P. taiwanensis and P. shurbergia, overlap with PWN and Monochamus, suggesting that these three species are potentially at high risk of PWD. Thus, PWD management should target the northern regions of China and the three pine species that are most susceptible to PWD.