Predictions of potential geographical distribution and quality of<i>Schisandra sphenanthera</i>under climate change

Guo, Yuxue; Wei, Haiyan; Lu, Chunxia; Gao, Bei; Gu, Wei

doi:10.7717/peerj.2554

Cited by 57 publications

(44 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, the distribution of plant species and the establishment of populations are closely related to geographic and climatic conditions [11,65,66]. In this study, considering the study area and species, we selected 19 bioclimatic factors and human activity factors.…”

Section: The Influence Of Major Variables On Adaptationmentioning

confidence: 99%

The Global Potential Distribution of Invasive Plants: Anredera cordifolia under Climate Change and Human Activity Based on Random Forest Models

et al. 2020

Self Cite

View full text Add to dashboard Cite

The potential distribution of the invasive plant Anredera cordifolia (Tenore) Steenis was predicted by Random Forest models under current and future climate-change pathways (i.e., RCP4.5 and RCP8.5 of 2050s and the 2070s). Pearson correlations were used to select variables; the prediction accuracy of the models was evaluated by using AUC, Kappa, and TSS. The results show that suitable future distribution areas are mainly in Southeast Asia, Eastern Oceania, a few parts of Eastern Africa, Southern North America, and Eastern South America. Temperature is the key climatic factor affecting the distribution of A. cordifolia. Important metrics include mean temperature of the coldest quarter (0.3 • C ≤ Bio11 ≤ 22.9 • C), max temperature of the warmest month (17.1 • C ≤ Bio5 ≤ 35.5 • C), temperature annual range (10.7 • C ≤ Bio7 ≤ 33 • C), annual mean air temperature (6.8 • C ≤ Bio1 ≤ 24.4 • C), and min temperature of coldest month (−2.8 • C ≤ Bio6 ≤ 17.2 • C). Only one precipitation index (Bio19) was important, precipitation of coldest quarter (7 mm ≤ Bio19 ≤ 631 mm). In addition, areas with strong human activities are most prone to invasion. This species is native to Brazil, but has been introduced in Asia, where it is widely planted and has escaped from cultivation. Under the future climate scenarios, suitable habitat areas of A. cordifolia will expand to higher latitudes. This study can provide a reference for the rational management and control of A. cordifolia.

show abstract

Section: The Influence Of Major Variables On Adaptationmentioning

confidence: 99%

The Global Potential Distribution of Invasive Plants: Anredera cordifolia under Climate Change and Human Activity Based on Random Forest Models

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The habitat suitability index (HSI) is widely used in species habitat evaluation and can be obtained from the model outputs [43][44][45][46]. For further analyses, HSI was classified into four levels of habitat suitability [47]: unsuitable (HSI < 0.3), marginally suitable (0.3 ≤ HSI < 0.5), moderately suitable (0.5 ≤ HSI < 0.7), and highly suitable (HSI ≥ 0.7).…”

Section: Data Analyses Of Key Bioclimatic Variables and Habitat Suitamentioning

confidence: 99%

Non-Pessimistic Predictions of the Distributions and Suitability of Metasequoia glyptostroboides under Climate Change Using a Random Forest Model

Zhang

Wei

Zhang

et al. 2020

Forests

Self Cite

View full text Add to dashboard Cite

Metasequoia glyptostroboides Hu & W. C. Cheng, which is a remarkable rare relict plant, has gradually been reduced to its current narrow range due to climate change. Understanding the comprehensive distribution of M. glyptostroboides under climate change on a large spatio-temporal scale is of great significance for determining its forest adaptation. In this study, based on 394 occurrence data and 10 bioclimatic variables, the global potential distribution of M. glyptostroboides under eight different climate scenarios (i.e., the past three, the current one, and the next four) from the Quaternary glacial to the future was simulated by a random forest model built with the biomod2 package. The key bioclimatic variables affecting the distribution of M. glyptostroboides are BIO2 (mean diurnal range), BIO1 (annual mean temperature), BIO9 (mean temperature of driest quarter), BIO6 (min temperature of coldest month), and BIO18 (precipitation of warmest quarter). The result indicates that the temperature affects the potential distribution of M. glyptostroboides more than the precipitation. A visualization of the results revealed that the current relatively suitable habitats of M. glyptostroboides are mainly distributed in East Asia and Western Europe, with a total area of approximately 6.857 × 10 6 km 2 . With the intensification of global warming in the future, the potential distribution and the suitability of M. glyptostroboides have a relatively non-pessimistic trend. Whether under the mild (RCP4.5) and higher (RCP8.5) emission scenarios, the total area of suitable habitats will be wider than it is now by the 2070s, and the habitat suitability will increase to varying degrees within a wide spatial range. After speculating on the potential distribution of M. glyptostroboides in the past, the glacial refugia of M. glyptostroboides were inferred, and projections regarding the future conditions of these places are expected to be optimistic. In order to better protect the species, the locations of its priority protected areas and key protected areas, mainly in Western Europe and East Asia, were further identified. Our results will provide theoretical reference for the long-term management of M. glyptostroboides, and can be used as background information for the restoration of other endangered species in the future.

show abstract

“…Excessive environmental variables involved in modeling can cause data redundancy [29]. In the previous study, the number of modeling variables selected by our research team was from 12 to 21 (Table 1).…”

Section: Eevs Screeningmentioning

confidence: 99%

“…The FME uses the specific matching relationship between species characteristics and EEVs, and the inferred parameters could be used to project the new areas in the current period or under climate scenarios in the future. Hence, FME has unique advantages over many models that predict the future distribution of species [29]. The incorporation of topographic factors and soil factors ( Table 2) has improved the accuracy of the model in predicting the habitat of G. pentaphyllum in the future scenarios.…”

Section: Model Accuracy For Predicting Future Suitabilitymentioning

confidence: 99%

“…et Wils. 19 schisantherin A Qinling Mountains [29] Based on a large number of actual operations of FME, we found that FME needs to be optimized and improved in the following aspects: (1) The Gaussian membership function often used by researchers might not reasonably describe the relationship between characteristic values of species and EEVs of sample sites in the low membership range. For the Gaussian membership function, since the range of the single-variable's membership value is 0 to 1, it is possible that the HSI may present the result of the accumulated comprehensive error.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of the Fuzzy Matter Element Method for Predicting Species Suitability Distribution Based on Environmental Data

et al. 2018

Self Cite

View full text Add to dashboard Cite

Over the years, with the efforts of many researchers, the field of species distribution model (SDM) has been well explored. The model of fuzzy matter elements (FME), which, combined with GIS to predict species distribution, has received extensive attention since its emergence. Based on previous studies, this paper improved FME, extended the scope of the membership degree and habitat suitability index, and explored the unsuitable areas of species. We have enhanced the limitation effect of key variables on species habitats, making the operation of FME more consistent with biological laws. By optimizing the FME, it could avoid the accumulation of predicted errors with multi-variables, and make the predicted results more reasonable. In this study, Gynostemma pentaphyllum (Thunb.) Makino was used as an example. The experimental process used several major environmental variables (climate, soil, and terrain variables) to predict the habitat suitability distribution of G. pentaphyllum in China for its current and future period, which includes the period of 2050s (average for 2041–2060) and 2070s (average for 2061–2080) under representative concentration pathways 4.5 (RCP4.5). The results of the analysis showed that the model performed well with a high accuracy by reducing the redundancy of the environmental data. The study could relieve the reliance on a large database of environmental information and propose a new approach for protecting the G. pentaphyllum in unsuitable areas under climate change.

show abstract

Predictions of potential geographical distribution and quality ofSchisandra sphenantheraunder climate change

Cited by 57 publications

References 40 publications

The Global Potential Distribution of Invasive Plants: Anredera cordifolia under Climate Change and Human Activity Based on Random Forest Models

The Global Potential Distribution of Invasive Plants: Anredera cordifolia under Climate Change and Human Activity Based on Random Forest Models

Non-Pessimistic Predictions of the Distributions and Suitability of Metasequoia glyptostroboides under Climate Change Using a Random Forest Model

Optimization of the Fuzzy Matter Element Method for Predicting Species Suitability Distribution Based on Environmental Data

Contact Info

Product

Resources

About