Shifting ranges of eleven invasive alien plants in China in the face of climate change

Guan, Bi-Cai; Guo, Han-jing; Chen, Sisi; Li, Dongming; Liu, Xiang; Gong, Xiaohua; Ge, Gang

doi:10.1016/j.ecoinf.2019.101024

Cited by 32 publications

(22 citation statements)

References 45 publications

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“…It was noted that RF produced the highest accuracy followed by MaxEnt and BRT with marginal differences. Similar studies by (Guan et al 2020) and (Stohlgren et al 2010) showed the same pattern with the latter models predicting IAPs habitat suitability at relatively high accuracy across the models, although based on different algorithms (Downie, Von Numers, and Boström 2013;Mohammadi et al 2019;Pearce and Boyce 2006). The robustness of these models was further evident in the spatial distribution of predicted suitable habitats.…”

Section: Evaluation Of the Model Performancessupporting

confidence: 63%

“…As such, the ensemble model was successfully used to produce predictions by including only models with a TSS > 0.7 as opposed to AUC due to associated criticisms to ensure only strong models are included (Allouche et al). The advantage of ensembles is their ability to minimize the spatial uncertainties of the models for each climate scenario to enable reliable spatial estimates (Downie, Von Numers, and Boström 2013;Guan et al 2020;Pearce and Boyce 2006).…”

Section: Evaluation Of the Model Performancesmentioning

confidence: 99%

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Modeling the geographic spread and proliferation of invasive alien plants (IAPs) into new ecosystems using multi-source data and multiple predictive models in the Heuningnes catchment, South Africa

Mtengwana

Dube

Mudereri

et al. 2021

GIScience & Remote Sensing

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The geographic spread and proliferation of Invasive Alien Plants (IAPs) into new ecosystems requires accurate, constant, and frequent monitoring particularly under the changing climate to ensure the integrity and resilience of affected as well as vulnerable ecosystems. This study thus aimed to understand the distribution and shifts of IAPs and the factors influencing such distribution at the catchment scale to minimize their risks and impacts through effective management. Three machine learning Species Distribution Modeling (SDM) techniques, namely, Random Forest (RF), Maximum Entropy (MaxEnt), Boosted Regression Trees (BRT) and their respective ensemble model were used to predict the potential distribution of IAPs within the catchment. The current and future bioclimatic variables, environmental and Sentinel-2 Multispectral Instrument satellite data were used to fit the models to predict areas at risk of IAPs invasions in the Heuningnes catchment, South Africa. The present and two future climatic scenarios from the Community Climate System Model (CCSM4) were considered in modeling the potential distribution of these species. The two future scenarios represented the minimum and maximum atmospheric carbon Representative Concentration Pathways (RCP) 2.6 and 8.5 for 2050 (average for 2041-2060). The results show that IAPs are predicted to expand under the influence of climate change in the catchment. Concurrently, riparian zones, bare areas, and the native vegetation which is rich in biodiversity will greatly be affected. The mean diurnal range (Bio2), warmest quarter maximum temperature (Bio5), and the warmest quarter precipitation (Bio18) were the most important bioclimatic variables in modeling the spatial distribution of IAPs in the catchment. Comparatively, all the models were successful in predicting the potential distribution of IAPs for all the scenarios. The BRT, MaxEnt, and RF predicted the spatial distribution of IAPs with an Area Under Curve (AUC) of 0.89, 0.92, and 0.94, respectively. The study highlighted the importance of multi-source data and multiple predictive models in predicting the current and potential future IAP distribution. The results from this study provide baseline information for effective land management, planning, and continuous monitoring of the further spread of IAPs within the Heuningnes catchment.

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Section: Evaluation Of the Model Performancessupporting

confidence: 63%

Section: Evaluation Of the Model Performancesmentioning

confidence: 99%

Modeling the geographic spread and proliferation of invasive alien plants (IAPs) into new ecosystems using multi-source data and multiple predictive models in the Heuningnes catchment, South Africa

Mtengwana

Dube

Mudereri

et al. 2021

GIScience & Remote Sensing

View full text Add to dashboard Cite

show abstract

“…For each model, 70% of the data were randomly assigned for model calibration and 30% for the performance of the algorithms. To prevent bias from the splitting of the total records, each model algorithm was run 10 times (Ancillotto et al., 2020; Ashrafzadeh et al., 2019; Gilani et al., 2020; Guan et al., 2020; Zhang et al., 2021). True skill statistic (TSS: score >0.8), the area under the receiver operating characteristic curve (AUC: score >0.8), and Cohen's Kappa (KAPPA: score >0.8) used the predictive performance of each model (Guisan et al., 2017).…”

Section: Methodsmentioning

confidence: 99%

Historic range dynamics in Kaiser's mountain newt (Neurergus kaiseri): Insights from phylogeographic analyses and species distribution modeling

Vaissi

2021

Ecology and Evolution

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“…The wettest climatic scenario, CNRM (RCP 4.5 and 8.5), projected a reduction in invasive species richness, whereas the driest scenario, MIROC (RCP 4.5), projected an increase. Contrary to the negative effects that extreme climatic conditions such as those generated by climatic scenario MIROC (Lenihan et al 2003;Sandel and Dangremond 2012); however, the magnitude and direction of these impacts might depend on the climate change scenario and the species-specific responses to climatic conditions (Bellard et al 2013;Finch et al 2021;Guan et al 2020;Petitpierre et al 2016).…”

Section: Species Risk Assessmentmentioning

confidence: 99%

“…Increasing temperature and precipitation combined with more frequent and intense climatic events (very wet and very dry years) are likely to favor the establishment and spread of some invasive species (Bradley et al 2010;Guan et al 2020;Hellmann et al 2008). Climate change may increase the probability of invasive species establishment by providing more favorable climatic conditions in areas where exotic species are currently unable to survive (e.g., ornamental exotic plants that currently depend on the artificial climate of a garden).…”

Section: Introductionmentioning

confidence: 99%

Assessing the risk of plant species invasion under different climate change scenarios in California

Rentería

Darin²,

Grosholz

2021

Invasive plant sci. manag.

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Using Species Distribution Models (SDMs), we predicted the distribution of 170 plant species under different climate scenarios (current and future climatic conditions) and used this information to create invasion risk maps to identify potential hotspots of invasion in California. Using species’ predicted area in combination with some biological traits associated with invasiveness (growth form, reproduction mechanisms and age of maturity), the risk of invasion by individual species was also assesed. A higher number of species would find suitable climatic conditions along the coast; the Central Western (CW) and South Western (SW) were ecoregions where a higher number of species was predicted. Overall, hotspots of species distribution were similar under current and future climatic conditions; however, individual species’ predicted area (increase or decrease) was variable depending on the climate change scenario and the greenhouse gas emission. Out of the 170 species assessed, 22% ranked as high-risk species, with herbs, grasses and vines accounting for 78% within this risk class and a high proportion (67%) of Asteraceae species ranked as high risk. This study suggests that current climatic conditions of the central and south coastal regions of California would be considered as hotspots of new invasions, and for some species this risk might increase with hotter and drier future climatic conditions.

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Shifting ranges of eleven invasive alien plants in China in the face of climate change

Cited by 32 publications

References 45 publications

Modeling the geographic spread and proliferation of invasive alien plants (IAPs) into new ecosystems using multi-source data and multiple predictive models in the Heuningnes catchment, South Africa

Modeling the geographic spread and proliferation of invasive alien plants (IAPs) into new ecosystems using multi-source data and multiple predictive models in the Heuningnes catchment, South Africa

Historic range dynamics in Kaiser's mountain newt (Neurergus kaiseri): Insights from phylogeographic analyses and species distribution modeling

Assessing the risk of plant species invasion under different climate change scenarios in California

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