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

Rentería, Jorge L.; Darin, Gina M. Skurka; Grosholz, Edwin D.

doi:10.1017/inp.2021.23

Cited by 6 publications

(6 citation statements)

References 95 publications

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“…The current study sheds light on prediction modeling of distribution and invasion risk that can be applied to other invasive weed and plant species. This study could inform the central and federal governments and society about areas at high risk of invasion, guide invasive species management, and secure economic resources (Renteria et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Climate change-induced invasion risk of ecosystem disturbing alien plant species: An evaluation using species distribution modeling

et al. 2022

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Species distribution modeling is widely used for evaluating invasion risk, and for prioritizing areas for the control and management of invasive species. However, selecting a modeling tool that accurately predicts species invasion risk requires a systematic approach. In this study, five species distribution models (SDMs), namely, artificial neural network (ANN), generalized linear model (GLM), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt), and random forest (RF), were performed and evaluated their model performance using the mean value of area under the curve (AUC), true skill statistics (TSS), and Kappa scores of 12 ecosystem disturbing alien plant species (EDAPS). The mean evaluation metric scores were highest in RF (AUC = 0.924 ± 0.058, TSS = 0.789 ± 0.109, Kappa = 0.671 ± 0.096, n = 12) and lowest in ANN. The ANOVA of AUC, TSS, and Kappa metrics revealed the RF model was significantly different from other SDMs and was therefore selected as the relatively best model. The potential distribution area and invasion risk for each EDAPS were quantified. Under the current climate conditions of South Korea, the average potential distribution area of EDAPS was estimated to be 13,062 km2. However, in future climate change scenarios, the average percentage change of EDAPS distribution relative to the current climate was predicted to be increased over 219.93%. Furthermore, under the current climate, 0.16% of the area of the country was estimated to be under a very high risk of invasion, but this would increase to 60.43% by 2070. Invasion risk under the current climate conditions was highest in the northwestern, southern, and southeastern regions, and in densely populated cities, such as Seoul, Busan, and Daegu. By 2070, invasion risk was predicted to expand across the whole country except in the northeastern region. These results suggested that climate change induced the risk of EDAPS invasiveness, and SDMs could be valuable tools for alien and invasive plant species risk assessment.

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Section: Discussionmentioning

confidence: 99%

Climate change-induced invasion risk of ecosystem disturbing alien plant species: An evaluation using species distribution modeling

et al. 2022

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“…Since we only record presence data, we generated pseudo-absence observations assuming the same probability of occurrence of P. oocarpa [20]. Therefore, we use the "sdm" package [20] in the R software environment [21] to generate and use the "pseudo-absence" observations to compensate for the lack of "actual absence" data [22,23]. For analysis and modelling purposes, all data were divided for training (70%) and validation (30%) of the models.…”

Section: Field Datamentioning

confidence: 99%

“…Although computational improvements and programming efficiency facilitate the use of machine learning algorithms in ecological niche modelling, methods such as MaxEnt are still in use due to their simplicity of application and the flexibility of adaptation to several situations [22][23][24]. One good example is the Kuenm R package [38], which uses R and MaxEnt to enable detailed model calibration and selection, final model creation, and extrapolation risk analysis.…”

Section: Model Selection and Implicationsmentioning

confidence: 99%

Different Modelling Approaches to Determine Suitable Areas for Conserving Egg-Cone Pine (Pinus oocarpa Schiede) Plus Trees in the Central Part of Mexico

Sánchez¹,

Velasco-García²,

Miranda³

et al. 2022

Forests

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Various spatial modelling methods and tools have been used in ecology and biogeography. The application of these options serves a dual function: first, they offer information about the potential distribution of species to understand the richness and diversity of unassessed areas. Second, spatial modelling methods employ these predictions to select relevant sites to determine natural conservation areas. In this study, we compared three methods for modelling the spatial distribution of Egg-cone Pine (Pinus oocarpa Schiede), an important non-timber pine in Mexico. The final goal is to estimate suitable areas for the conservation and reproduction of superior individuals (plus trees) of P. oocarpa as a conservation strategy outside the known distribution since this species possesses a high ecological and economic value. The model used were a generalised linear model (GLM) as a parametric regression method, random forest (RF) as a machine-learning method, and the MaxEnt model, a standard procedure, implemented using the Kuenm R package. The results suggest that the models used performed well since the AUROC was between 0.95 and 0.98 in all cases. MaxEnt and random forest approaches provided more conservative predictions for the distribution of suitable areas of plus trees of P. oocarpa than the generalised linear model, but the random forest algorithm achieved the best performance. The results of the study allowed the determination of ex situ conservation areas for P. oocarpa plus trees outside of their known distribution.

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“…Among the factors that enhance biological invasion, climate change may alter the competitive potential of an exotic species [31] and growth in places where it currently does not occur [32]. Species distribution models (SDMs) are tools that establish relationships between species occurrence data and predictor variables [33], and they can be used to measure the impact of climate change on the distribution of organisms [34].…”

Section: Introductionmentioning

confidence: 99%

“…Studying the potential and future distribution of possibly aggressive invasive species such as U. panicoides allows for risk analyses, effective management, and invasion prevention [32]. The objective of this study was to use the CLIMEX software to predict suitable areas for the establishment of U. panicoides based on ecoclimatic conditions, determine potential regions subject to invasion of the species with risk analysis for China and Europe, and establish the most influential climatic parameters for the models.…”

Section: Introductionmentioning

confidence: 99%

Potential Distribution of and Sensitivity Analysis for Urochloa panicoides Weed Using Modeling: An Implication of Invasion Risk Analysis for China and Europe

Duque

Silva

Maciel

et al. 2022

Plants

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Urochloapanicoides P. Beauv. is considered one of the most harmful weeds in the United States and Australia. It is invasive in Pakistan, Mexico, and Brazil, but its occurrence is hardly reported in China and European countries. Species distribution models enable the measurement of the impact of climate change on plant growth, allowing for risk analysis, effective management, and invasion prevention. The objective of this study was to develop current and future climate models of suitable locations for U. panicoides and to determine the most influential climatic parameters. Occurrence data and biological information on U. panicoides were collected, and climatic parameters were used to generate the Ecoclimatic Index (EI) and to perform sensitivity analysis. The future projections for 2050, 2080, and 2100 were modeled under the A2 SRES scenario using the Global Climate Model, CSIRO-Mk3.0 (CS). The potential distribution of U. panicoides coincided with the data collected, and the reliability of the final model was demonstrated. The generated model identified regions where the occurrence was favorable, despite few records of the species. Sensitivity analysis showed that the most sensitive parameters of the model were related to temperature, humidity, and cold stress. Future projections predict reductions in climate suitability for U. panicoides in Brazil, Australia, India, and Africa, and an increase in suitability in Mexico, the United States, European countries, and China. The rise in suitability of China and Europe is attributed to predicted climate change, including reduction in cold stress. From the results obtained, preventive management strategies can be formulated against the spread of U. panicoides, avoiding economic and biodiversity losses.

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

Cited by 6 publications

References 95 publications

Climate change-induced invasion risk of ecosystem disturbing alien plant species: An evaluation using species distribution modeling

Climate change-induced invasion risk of ecosystem disturbing alien plant species: An evaluation using species distribution modeling

Different Modelling Approaches to Determine Suitable Areas for Conserving Egg-Cone Pine (Pinus oocarpa Schiede) Plus Trees in the Central Part of Mexico

Potential Distribution of and Sensitivity Analysis for Urochloa panicoides Weed Using Modeling: An Implication of Invasion Risk Analysis for China and Europe

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