Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China

Aili, Qin; Liu, Bo; Guo, Qiqiang; Bussmann, Rainer W.; Fanqiang, Ma; Jian, Zunji; Xu, Guangdong; Pei, Shunxiang

doi:10.1016/j.gecco.2017.02.004

Cited by 288 publications

(237 citation statements)

References 34 publications

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“…Maxent has been widely used in the prediction of potential distribution of species in recent years [34,85]. The model assumes that species will be present in all areas with suitable climate conditions and will not present in any area with unsuitable conditions; it is believed that the larger the entropy of a species under known conditions, the closer it is to the reality [27].…”

Section: Discussionmentioning

confidence: 99%

“…The model simulates the potential geographical distribution of a species using information on its current (present day) distribution as well as various environmental data [27,30]. The Maxent model is relatively simple and quick to run, has a small sample demand, provides stable operation results, and allows prediction results to be tested [27,31,32]; thus, it is favored by many researchers [14,33,34]. However, the model is often employed using the default parameters or those published previously without consideration of the details of the algorithm or input parameters, and all the environment variables that can be collected are included in the model indiscriminately.…”

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Optimized Maxent Model Predictions of Climate Change Impacts on the Suitable Distribution of Cunninghamia lanceolata in China

et al. 2020

Forests

136

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Climate change significantly influences changes in ecological phenomena and processes, such as species distribution and phenology, thus accelerating the rate of species extinction or prosperity. Climate change is considered to be one of the most important threats to global biodiversity in the 21st century and will pose significant challenges to biodiversity conservation in the future. The use of niche modelling to predict changes in the suitable distribution of species under climate change scenarios is becoming a hot topic of biological conservation. In this study, we use data from China's National Forest Continuous Inventory as well as specimen collection data of Cunninghamia lanceolata (Lamb.) Hook to run optimized Maxent models to predict potential suitable distribution of the species in the present day, 2050s, and 2070s under different climate change scenarios in China. In the modeling process, the most important uncorrelated variables were chosen, and the sample-size-adjusted Akaike information criterion (AICc) was used to select the optimal combination of feature type and regularization multiplier. Variable selection reduced the number of variables used and the complexity of the model, and the use of the AICc reduced overfitting. Variables relating to precipitation were more important than temperature variables in predicting C. lanceolata distribution in the optimal model. The predicted suitable distribution areas of C. lanceolata were different for the different periods under different climate change scenarios, with the centroids showing a degree of northward movement. The suitable distribution area is predicted to become more fragmented in the future. Our results reveal the climate conditions required for the suitable distribution of C. lanceolata in China and the likely changes to its distribution pattern in the future, providing a scientific basis for the sustainable management, protection, and restoration of the suitable habitat of this economically important tree species in the context of climate change.Forests 2020, 11, 302 2 of 25 have shown that climate change has caused or is causing significant changes to ecological processes, including distribution ranges, morphological characteristics, and phenology that are accelerating species extinction or prosperity [7][8][9]. Because of this, quantitative analysis of climate factors and predictions of climate change impacts on potential suitable species distributions have become key to biogeography and global change research [8,10,11].Niche modelling (also known as species distribution modelling or habitat suitability modelling) uses statistical or biophysical methods to infer the environmental requirements for the survival of a species based on the environmental variables taken from the known distribution areas, and then predicts their potential or future distribution [12][13][14]. Niche modelling has been widely used in ecology, biogeography, and conservation biology studies, such as the adaptive analysis of invasive alien species [15,16], conservation o...

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Section: Discussionmentioning

confidence: 99%

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Optimized Maxent Model Predictions of Climate Change Impacts on the Suitable Distribution of Cunninghamia lanceolata in China

et al. 2020

Forests

136

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“…SDMs identify relationships between the known occurrence of a species (presence or presence/absence data) and environmental data, and use these relationships to make predictions for all unsampled areas in the study region [14,15]. Most studies have been directed at invasive [16], endangered [17], medicinal [18,19], bioenergy [20] and ornamental plants [21]. Nevertheless, studies on suitable areas for Sphagnum bogs and their relationships with climate change in China are weak.…”

Section: Introductionmentioning

confidence: 99%

Predicting the dynamic distribution of Sphagnum bogs in China under climate change since the last interglacial period

Cong

Tang

et al. 2020

PLoS ONE

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Sphagnum bogs possess irreplaceable ecological and economic value, and they are scarce in China, with a fragmented distribution. Based on 19 high-resolution bioclimatic environmental datasets and 71 bog center point locations, we employed a maximum entropy model (MaxEnt) to reconstruct and predict the spatial-temporal geographical distribution patterns of Sphagnum bogs from the last interglacial (LIG) period to two typical CO 2 representative concentration pathway scenarios (RCP2.6, RCP8.5) in the future. We further computed the migratory paths of the distribution center points. Finally, a jackknife test was used to uncover the crucial environmental factors restricting the geographical distribution of the bogs. Our data indicated that the MaxEnt niche model had a high simulation precision with an area under the ROC curve value of 0.957. Spatially, the suitable bog habitats are currently centralized in northeastern China, including the Greater Khingan Mountains, the Lesser Khingan Mountains, and the Changbai Mountains, as well as peripheral areas of the Sichuan Basin. Temporally, the contours of Sphagnum bogs were similar to the present and rendered from the last glacial maximum (LMG) period, and had much more total area than the current. The total area in LIG was nearly the same as the current because of the similar climate. It was worth noting that there would be a reduction of the total area in the future. Loss of area occurred at the edges of bogs, especially under RCP8.5. The distribution center of bogs will shift to the northwest in the immediate future. The precipitation of driest month, the mean temperature of warmest quarter and the precipitation of warmest quarter were identified as crucial climatic factors affecting the distribution of Sphagnum bogs. Overall, our research provides scientific evidence for the long-term protection and effective management of these rare, precious natural resources and suggestions for in situ conservation.

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“…Species distribution models (SDMs) have found an important application in biodiversity research [36-38]. They are employed in studying habitat suitability [39-41], identifying environmental drivers of species distribution [42-45] and predicting impacts of climate change on biodiversity [46-51]. SDMs are successfully used to identify suitable habitats of species even in areas with no distribution records [52-54].…”

Section: Introductionmentioning

confidence: 99%

Developing snakebite risk model using venomous snake habitat suitability as an indicating factor: An application of species distribution models in public health research

Yousefi

Kafash

Khani³

et al. 2020

Preprint

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22Snakebite envenoming is an important public health problem in Iran, despite its risk not 23 being quantified. This study aims to use venomous snakes' habitat suitability as an 24 indicator of snakebite risk, to identify high-priority areas for snakebite management 25 across the country. Thus, an ensemble approach using five distribution modeling 26 methods: Generalized Boosted Models, Generalized Additive Models, Maximum Entropy 27 Modeling Generalized Linear Models, and Random Forest was applied to produce a 28 spatial snakebite risk model for Iran. To achieve this, four venomous snakes' habitat 29 suitability (Macrovipera lebetina, Echis carinatus, Pseudocerastes persicus and Naja 30 oxiana) were modeled and then multiplied. These medically important snakes are 31 responsible for the most snakebite incidents in Iran. Multiplying habitat suitability 32 models of the four snakes showed that the northeast of Iran (west of Khorasan-e-Razavi 33 province) has the highest snakebite risk in the country. In addition, villages that were at 34 risk of envenoming from the four snakes were identified. Results revealed that 51,112 35 villages are at risk of envenoming from M. lebetina, 30,339 from E. carinatus, 51,657 36 from P. persicus and 12,124 from N. oxiana. This paper demonstrates application of 37 species distribution modeling in public health research and identified potential snakebite 38 risk areas in Iran by using venomous snakes' habitat suitability models as an indicating 39 factor. Results of this study can be used in snakebite and human-snake conflict 40 management in Iran. We recommend increasing public awareness of snakebite 3 41 envenoming and education of local people in areas which identified with the highest 42 snakebite risk. 43 4 62 identifying environmental drivers of species distribution [42−45] and predicting impacts 63 of climate change on biodiversity [46−51]. SDMs are successfully used to identify 64 suitable habitats of species even in areas with no distribution records [52−54]. Thus, 65 these models can be used to identify suitable habitats of venomous snakes as proxies of 66 snakebite risk [12, 55−57] in data poor regions like Iran. 67 The main goal of this paper was to apply SDMs and produce a spatial risk model 68 for snakebite in Iran. To do this, firstly, five distribution modeling methods [38]: 69 Generalized Boosted Models, Generalized Additive Models, Maximum Entropy 70 Modeling Generalized Linear Models, and Random Forest and distribution data of M. 71 lebetina, E. carinatus, P. persicus and N. oxiana were to produce their habitat suitability 72 models. Secondly, the five habitat suitability models [38−58] of each species were 73combined by ensemble approach and finally the four species ensemble models were 74 multiplied to identify potential snakebite risk. We also in addition, number of villages 75 that are at risk of envenoming by these four snakes determined in Iran. 76 77 Materials and methods 78 Occurrence data 79 Distribution records of the M. lebetina, E. carinatus, P. per...

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Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China

Cited by 288 publications

References 34 publications

Optimized Maxent Model Predictions of Climate Change Impacts on the Suitable Distribution of Cunninghamia lanceolata in China

Optimized Maxent Model Predictions of Climate Change Impacts on the Suitable Distribution of Cunninghamia lanceolata in China

Predicting the dynamic distribution of Sphagnum bogs in China under climate change since the last interglacial period

Developing snakebite risk model using venomous snake habitat suitability as an indicating factor: An application of species distribution models in public health research

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