Modeling the distribution of Zanthoxylum armatum in China with MaxEnt modeling

Xu, Danping; Zhuo, Zhihang; Wang, Rulin; Ye, Meng; Biao, PU

doi:10.1016/j.gecco.2019.e00691

Cited by 102 publications

(89 citation statements)

References 26 publications

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“…There are several reports on the prediction of suitable species distributions under climate change using the MaxEnt model. These reports mainly focus on energy plants, medicinal plants, invasive species, endangered species, and the migration routes of species [ 4 , 38 , 39 , 42 , 45 , 46 , 47 , 48 ]. Different plants have different requirements for their growth environments, resulting in obvious differences in suitable distributions.…”

Section: Discussionmentioning

confidence: 99%

“…The geographical distribution of species, which is an important field of biogeography research, mainly reveals the spatial distribution of species and its relationship with environmental factors [ 1 , 2 , 3 ]. Climatic and environmental factors (terrain and soil, among others) are the decisive factors affecting species distribution [ 3 , 4 , 5 ]. Global warming is expected to continue, with the average temperature of the earth increasing by 0.3–4.5 °C by 2100 compared with the average temperature from 1986 to 2005.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Suitable Geographical Distribution of Sinadoxa Corydalifolia under Different Climate Change Scenarios in the Three-River Region Using the MaxEnt Model

Huang

Chen

et al. 2020

Plants

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Sinadoxa corydalifolia is a perennial grass with considerable academic value as a rare species owing to habitat destruction and a narrow distribution. However, its distribution remains unclear. In this study, we predicted the distribution of Sinadoxa corydalifolia in the three-river region (the source of the Yangtze River, Yellow River, and Lancang River) under the context of climate change using the maximum entropy (MaxEnt) model. Under the current climate scenario, the suitable distribution mainly occurred in Yushu County and Nangqian County. The suitable distribution area of Sinadoxa corydalifolia covered 3107 km2, accounting for 0.57% of the three-river region. The mean diurnal air temperature range (Bio2), temperature seasonality (Bio4), and mean air temperature of the driest quarter (Bio9) contributed the most to the distribution model for Sinadoxa corydalifolia, with a cumulative contribution of 81.4%. The highest suitability occurred when air temperature seasonality (Bio4) ranged from 6500 to 6900. The highest suitable mean air temperature of the driest quarter ranged from −5 to 0 °C. The highest suitable mean diurnal temperature (Bio2) ranged from 8.9 to 9.7 °C. In future (2041–2060) scenarios, the suitable distribution areas of Sinadoxa corydalifolia from high to low are as follows: representative concentration pathway (RCP)26 (6171 km2) > RCP45 (6017 km2) > RCP80 (4238 km2) > RCP60 (2505 km2). In future (2061–2080) scenarios, the suitable distribution areas of Sinadoxa corydalifolia from high to low are as follows: RCP26 (18,299 km2) > RCP60 (11,977 km2) > RCP45 (10,354 km2) > RCP80 (7539 km2). In general, the suitable distribution will increase in the future. The distribution area of Sinadoxa corydalifolia will generally be larger under low CO2 concentrations than under high CO2 concentrations. This study will facilitate the development of appropriate conservation measures for Sinadoxa corydalifolia in the three-river region.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting the Suitable Geographical Distribution of Sinadoxa Corydalifolia under Different Climate Change Scenarios in the Three-River Region Using the MaxEnt Model

Huang

Chen

et al. 2020

Plants

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“…Fruticella are dormant in winter and their physiological activities are weak. However, a certain amount of precipitation and temperature accumulation in this period are required for dormancy breaking, vegetative bud development, and flower bud differentiation (Xu et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Predictive modelling of the distribution of Clematis sect. Fruticella s. str. under climate change reveals a range expansion during the Last Glacial Maximum

Zhao

Lyu

et al. 2020

PeerJ

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Background. The knowledge of distributional dynamics of living organisms is a prerequisite for protecting biodiversity and for the sustainable use of biotic resources. Clematis sect. Fruticella s. str. is a small group of shrubby, yellow-flowered species distributed mainly in arid and semi-arid areas of China. Plants in this section are both horticulturally and ecologically important. Methods. Using past, present, and future environmental variables and data with Maximum Entropy (Maxent) modeling, we evaluated the importance of the environmental variables on the section's estimated distributions, thus simulating its distributional dynamics over time. The contractions and expansions of suitable habitat between the past and future scenarios and the present were then compared. Results and Discussion. The models revealed that the areas with high and moderate suitability currently encompass about 725,110 km 2 . The distribution centroid location varies between points in Ningxia and Inner Mongolia during the different scenarios. Elevation, Mean UV-B of Lowest Month, Precipitation of Coldest Quarter, and Mean Temperature of Driest Quarter were major factors determining the section's distribution. Our modeling indicated that Clematis sect. Fruticella underwent a significant range contraction during the last interglacial period, and then expanded during the last glacial maximum (LGM) to amounts like those of the present. Cold, dry, and relatively stable climate, as well as steppe or desert steppe environments may have facilitated range expansion of this cold-adapted, drought-resistant plant taxon during the LGM. Predicted future scenarios show little change in the amounts of suitable habitat for Clematis sect. Fruticella. This study aids understanding of the distributional dynamics of Clematis sect. Fruticella, and the results will help the conservation and sustainable use of these important woody plants in Chinese arid and semiarid areas. How to cite this article Li M, He J, Zhao Z, Lyu R, Yao M, Cheng J, Xie L. 2020. Predictive modelling of the distribution of Clematis sect. Fruticella s. str. under climate change reveals a range expansion during the Last Glacial Maximum. PeerJ 8:e8729 An LZ, Feng HY, Tang XD, Wang XL. 2000. Changes of microsomal membrane properties in spring wheat leaves (Triticum aestivum L.) exposed to enhanced ultraviolet-B radiation. Journal of Photochemistry and Photobiology B: Biology . Arar A, Tabet S, Nouidjem Y, Bounar R, Chenchouni H. 2019. Projected smallscale range reductions of Cedrus atlantica forests due to climate change at the Belezma National Park (Algeria). In: Exploring the nexus of geoecology, geography, geoarcheology and geotourism: advances and applications for sustainable development in environmental sciences and agroforestry research. Cham: Springer, 15-19. Austin MP, Smith TM, Niel KPV, Wellington AB. 2009. Physiological responses and statistical models of the environmental niche: a comparative study of two cooccurring eucalyptus species. Journal of Ecology 97:469-507. Babar S,...

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“…The MaxEnt setting selection should not be an arbitrary procedure, since each parameter represents and responds against the combinations of environmental predictors incorporated into the algorithm (Radosavljevic & Anderson, 2014;Xu, Zhuo, Wang, Ye, & Pu, 2019).…”

Section: Enmeval Processesmentioning

confidence: 99%

“…However, the fact is that working with presence-only data allows for the temporal representation of all those possible areas of historical occurrences of the species, allowing for a better characterisation of the M component. On the other hand, both metrics continue to be used currently and allow for the correct evaluation of MaxEnt (Xu et al, 2019). For this reason and because the TSS values we obtained were low, we chose AUC to evaluate our results.…”

Section: Model Evaluationmentioning

confidence: 99%

Effects of different variable sets on the potential distribution of fish species in the Amazon Basin

Alvarez

Gerhard

Silva

et al. 2020

Ecology of Freshwater Fish

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Estimating species’ potential distribution is one of the main objectives of macroecology, especially when sampling biases can affect knowledge on how environmental variables affect species distribution. Ecological niche models estimate species’ environmental niches from different variables and their occurrences. Using the presence‐only data from eight Amazonian fish species, which inhabit rivers and streams, we aimed to (a) explore the effect of different sets variables on the spatial distributions of target species and (b) evaluate the predictive responses of MaxEnt to sets of variables with different degrees of complexity. MaxEnt has high flexibility in relation to the input data and its performance is influenced by a moderate number of adjustable parameters, allowing for high precision results when balancing underestimation and overestimation errors. We used environmental predictors in MaxEnt the principal components of climatic, topographic and edaphic variables as inputs. The combination of topographic and edaphic variables produced more precise and spatially restricted distribution ranges for all species when compared to those generated with climatic variables. All models reached high AUC values, especially for stream species. Modelled range sizes were broader for the river species, suggesting different tolerance thresholds and habitat preferences when compared to stream species. The complexity of the different variables sets did not affect MaxEnt's prediction capacity. However, for stream species, MaxEnt showed a greater predictive power. This work increases the knowledge with regards to the influence of different environmental predictors on the spatial patterns of the distribution of Amazonian fish.

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Modeling the distribution of Zanthoxylum armatum in China with MaxEnt modeling

Cited by 102 publications

References 26 publications

Predicting the Suitable Geographical Distribution of Sinadoxa Corydalifolia under Different Climate Change Scenarios in the Three-River Region Using the MaxEnt Model

Predicting the Suitable Geographical Distribution of Sinadoxa Corydalifolia under Different Climate Change Scenarios in the Three-River Region Using the MaxEnt Model

Predictive modelling of the distribution of Clematis sect. Fruticella s. str. under climate change reveals a range expansion during the Last Glacial Maximum

Effects of different variable sets on the potential distribution of fish species in the Amazon Basin

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