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Journal ArticleDOI: 10.1007/S11356-021-13121-3

Assessing the impact of climate change on the distribution of Osmanthus fragrans using Maxent

02 Mar 2021-Environmental Science and Pollution Research (Springer Berlin Heidelberg)-Vol. 28, Iss: 26, pp 34655-34663
Abstract: Models that evaluate the potential geographic distribution of species can be used with a variety of important applications in conservation biology. Osmanthus fragrans has high ornamental, culinary, and medicinal value, and is widely used in landscaping. However, its preferred habitat and the environmental factors that determine its distribution remain largely unknown; the environmental factors that shape its suitability also require analysis. Based on 89 occurrence records and 30 environmental variables, this study constructed Maxent models for current as well as future appropriate habitats for O. fragrans. The results indicate that UV-B seasonality (19.1%), precipitation seasonality (18.8%), annual temperature range (13.1%), and mean diurnal temperature range (12.5%) were the most important factors used for interpreting the environmental demands for this species. Highly appropriate habitats for O. fragrans were mainly distributed in southwestern Jiangsu, southern Anhui, Shanghai, Zhejiang, Fujian, northern Guangdong, Guangxi, southern Hunan, southern Hubei, Sichuan, and Taiwan. Under climate change scenarios, the spatial extent of the area of suitable distribution will decrease, and the distribution center of O. fragrans will shift to the southwest. The results of this study will help land managers to avoid blindly introducing this species into inappropriate habitat while improving O. fragrans yield and quality.

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Topics: Osmanthus fragrans (65%)
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6 results found


Open accessJournal ArticleDOI: 10.1038/S41598-021-96041-Z
Yang Zhang, Jieshi Tang1, Gang Ren, Kaixin Zhao  +1 moreInstitutions (1)
16 Aug 2021-Scientific Reports
Abstract: Alien invasive plants pose a threat to global biodiversity and the cost of control continues to rise. Early detection and prediction of potential risk areas are essential to minimize ecological and socio-economic costs. In this study, the Maxent model was used to predict current and future climatic conditions to estimate the potential global distribution of the invasive plant Xanthium italicum. The model consists of 366 occurrence records (10 repeats, 75% for calibration and 25% for verification) and 10 climate prediction variables. According to the model forecast, the distribution of X. italicum was expected to shrink in future climate scenarios with human intervention, which may be mainly caused by the rise in global average annual temperature. The ROC curve showed that the AUC values of the training set and the test set are 0.965 and 0.906, respectively, indicating that the prediction result of this model was excellent. The contribution rates of annual mean temperature, monthly mean diurnal temperature range, standard deviation of temperature seasonal change and annual average precipitation to the geographical distribution of X. italicum were 65.3%, 11.2%, 9.0%, and 7.7%, respectively, and the total contribution rate was 93.2%. These four variables are the dominant environmental factors affecting the potential distribution of X. italicum, and the influence of temperature is greater than that of precipitation. Through our study on the potential distribution prediction of X. italicum under the future climatic conditions, it has contribution for all countries to strengthen its monitoring, prevention and control, including early warning.

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2 Citations


Open accessJournal ArticleDOI: 10.3390/F12060752
07 Jun 2021-Forests
Abstract: Cunninghamia lanceolata (Lamb.) Hook. (Chinese fir) is one of the main timber species in Southern China, which has a wide planting range that accounts for 25% of the overall afforested area. Moreover, it plays a critical role in soil and water conservation; however, its suitability is subject to climate change. For this study, the appropriate distribution area of C. lanceolata was analyzed using the MaxEnt model based on CMIP6 data, spanning 2041–2060. The results revealed that (1) the minimum temperature of the coldest month (bio6), and the mean diurnal range (bio2) were the most important environmental variables that affected the distribution of C. lanceolata; (2) the currently suitable areas of C. lanceolata were primarily distributed along the southern coastal areas of China, of which 55% were moderately so, while only 18% were highly suitable; (3) the projected suitable area of C. lanceolata would likely expand based on the BCC-CSM2-MR, CanESM5, and MRI-ESM2-0 under different SSPs spanning 2041–2060. The increased area estimated for the future ranged from 0.18 to 0.29 million km2, where the total suitable area of C. lanceolata attained a maximum value of 2.50 million km2 under the SSP3-7.0 scenario, with a lowest value of 2.39 million km2 under the SSP5-8.5 scenario; (4) in combination with land use and farmland protection policies of China, it is estimated that more than 60% of suitable land area could be utilized for C. lanceolata planting from 2041–2060 under different SSP scenarios. Although climate change is having an increasing influence on species distribution, the deleterious impacts of anthropogenic activities cannot be ignored. In the future, further attention should be paid to the investigation of species distribution under the combined impacts of climate change and human activities.

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Topics: Cunninghamia (52%), Species distribution (51%)

1 Citations


Open accessJournal ArticleDOI: 10.3390/F12081126
22 Aug 2021-Forests
Abstract: Global climate change has created a major threat to biodiversity. However, little is known about the habitat and distribution characteristics of Cinnamomum camphora (Linn.) Presl., an evergreen tree growing in tropical and subtropical Asia, as well as the factors influencing its distribution. The present study employed Maxent and a GARP to establish a potential distribution model for the target species based on 182 known occurrence sites and 17 environmental variables. The results indicate that Maxent performed better than GARP. The mean diurnal temperature range, annual precipitation, mean air temperature of driest quarter and sunshine duration in growing season were important environmental factors influencing the distribution of C. camphora and contributed 40.9%, 23.0%, 10.5%, and 7.2% to the variation in the model contribution, respectively. Based on the models, the subtropical and temperate regions of Eastern China, where the species has been recorded, had a high suitability for this species. Under each climate change scenario, the potential geographical distribution shifted farther north and toward a higher elevation. The predicted spatial and temporal distribution patterns of this species can provide guidance for the development strategies for forest management and species protection.

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Topics: Cinnamomum camphora (53%), Temperate climate (52%), Subtropics (51%) ... read more

1 Citations


Journal ArticleDOI: 10.1016/J.ECOINF.2021.101459
Xinggang Tang1, Yingdan Yuan2, Lingjian Wang1, Sirun Chen1  +2 moreInstitutions (2)
Abstract: As an important plant resource in China, medicinal plants dominate the Chinese herbal medicine market. The intensified human activities and the deteriorated ecological environment have caused the reduction or even extinction of medicinal plants nationwide. The artificial bionic cultivation of medicinal plants has become an important way for the healthy development of the Chinese medicine industry, given the increasing demand for Chinese medicinal materials. However, the blind introductions of medical plants ignoring the planting area's environmental suitability will waste many human and financial resources. Currently, species distribution models, widely used to predict the potential geographic distribution of species, enable the proper planning of prioritized planting areas with fully considered climatic factors. To scientifically and reasonably determine the best planting area of medicinal materials under current and future climate, we used the MaxEnt model to predict the suitable habitat for Thesium chinense Turcz., and determined the potential migration trends of its suitable areas. In addition, we also evaluated the main environmental variables that affect the distribution of T. chinense. In all the suitable habitat predictions, the training and testing area under the curve (AUC) values were greater than 0.9, indicating the robust performance of our model. Meanwhile, we found that annual mean temperature (Bio1), the maximum temperature of the warmest month (Bio5), annual temperature range (Bio7) and annual precipitation (Bio12) are the main environmental variables determining the T. chinense distribution, with the temperature being the most important factor under bionic cultivation conditions. The potential distribution areas of T. chinense are mainly the provinces along the middle and lower reaches of the Yangtze River. Under the future climate scenario, the highly suitable areas of T. chinense will generally increase, with the distribution ranges extending to higher latitudes. The Yellow River Basin may become another important planting area of T. chinense. Overall, the analysis provided the scientific basis for planning prioritized planting areas and improving bionic cultivation management techniques.

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Journal ArticleDOI: 10.1016/J.FORECO.2021.119696
Chunping Xie1, Boyang Huang2, Chi Yung Jim3, Weidong Han1  +1 moreInstitutions (3)
Abstract: Rhodomyrtus tomentosa, with edible and medicinal values, is a key shrub species in south China's forest understory. It maintains ecological balance, soil and water conservation, and biodiversity in the widely degraded mountain ecosystems. The distribution and population of R. tomentosa have shrunk recently due to anthropogenic impacts. At present, wild communities of R. tomentosa are rare in China's low-altitude areas. A comprehensive understanding of its current and future spatial patterns vis-a-vis changing climatic conditions can inform co-management for economic use and conservation. Based on 213 validated distribution records and nine selected environmental variables, the potential biogeographical range of R. tomentosa in China was predicted by Maxent and QGIS modeling under current and three future climate-change scenarios. The limiting factors for distribution were evaluated by Jackknife, per cent contribution and permutation importance. We found that the present actual biogeographical range was concentrated in tropical and south-subtropical China with some extensions to mid-subtropical east and southwest China, with the main occurrence in the core range of Guangdong, Guangxi, and Hainan provinces. The modeling results indicated temperature as the clinching determinant of distribution patterns, including the minimum temperature of coldest month, mean temperature of warmest quarter, and temperature seasonality. Moisture was a necessary but not critical secondary factor. Under future climate-change scenarios, habitats with excellent suitability index will expand and shift towards southwest China and high-altitude areas. The findings provide science-based evidence to adjust management and conservation plans in response to climate change protect and use R. tomentosa in suitability habitats.

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Topics: Population (52%)

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24 results found


Journal ArticleDOI: 10.1002/JOC.1276
Abstract: We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950–2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledgebased methods and inclusion of additional co-variates, particularly layers obtained through remote sensing. Copyright  2005 Royal Meteorological Society.

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Topics: Spatial variability (54%), Elevation (53%), Weather station (53%) ... read more

16,411 Citations


Open accessJournal ArticleDOI: 10.1016/J.ECOLMODEL.2005.03.026
Abstract: The availability of detailed environmental data, together with inexpensive and powerful computers, has fueled a rapid increase in predictive modeling of species environmental requirements and geographic distributions. For some species, detailed presence/absence occurrence data are available, allowing the use of a variety of standard statistical techniques. However, absence data are not available for most species. In this paper, we introduce the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data. Maxent is a general-purpose machine learning method with a simple and precise mathematical formulation, and it has a number of aspects that make it well-suited for species distribution modeling. In order to investigate the efficacy of the method, here we perform a continental-scale case study using two Neotropical mammals: a lowland species of sloth, Bradypus variegatus, and a small montane murid rodent, Microryzomys minutus. We compared Maxent predictions with those of a commonly used presence-only modeling method, the Genetic Algorithm for Rule-Set Prediction (GARP). We made predictions on 10 random subsets of the occurrence records for both species, and then used the remaining localities for testing. Both algorithms provided reasonable estimates of the species’ range, far superior to the shaded outline maps available in field guides. All models were significantly better than random in both binomial tests of omission and receiver operating characteristic (ROC) analyses. The area under the ROC curve (AUC) was almost always higher for Maxent, indicating better discrimination of suitable versus unsuitable areas for the species. The Maxent modeling approach can be used in its present form for many applications with presence-only datasets, and merits further research and development. © 2005 Elsevier B.V. All rights reserved.

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11,058 Citations


Open accessJournal ArticleDOI: 10.1111/J.2006.0906-7590.04596.X
01 Apr 2006-Ecography
Abstract: Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

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6,718 Citations


Journal ArticleDOI: 10.1016/S0304-3800(00)00354-9
Abstract: With the rise of new powerful statistical techniques and GIS tools, the development of predictive habitat distribution models has rapidly increased in ecology. Such models are static and probabilistic in nature, since they statistically relate the geographical distribution of species or communities to their present environment. A wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management. In this paper, we present a review of predictive habitat distribution modeling. The variety of statistical techniques used is growing. Ordinary multiple regression and its generalized form (GLM) are very popular and are often used for modeling species distributions. Other methods include neural networks, ordination and classification methods, Bayesian models, locally weighted approaches (e.g. GAM), environmental envelopes or even combinations of these models. The selection of an appropriate method should not depend solely on statistical considerations. Some models are better suited to reflect theoretical findings on the shape and nature of the species’ response (or realized niche). Conceptual considerations include e.g. the trade-off between optimizing accuracy versus optimizing generality. In the field of static distribution modeling, the latter is mostly related to selecting appropriate predictor variables and to designing an appropriate procedure for model selection. New methods, including threshold-independent measures (e.g. receiver operating characteristic (ROC)-plots) and resampling techniques (e.g. bootstrap, cross-validation) have been introduced in ecology for testing the accuracy of predictive models. The choice of an evaluation measure should be driven primarily by the goals of the study. This may possibly lead to the attribution of different weights to the various types of prediction errors (e.g. omission, commission or confusion). Testing the model in a wider range of situations (in space and time) will permit one to define the range of applications for which the model predictions are suitable. In turn, the qualification of the model depends primarily on the goals of the study that define the qualification criteria and on the usability of the model, rather than on statistics alone. © 2000 Elsevier Science B.V. All rights reserved.

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6,248 Citations


Journal ArticleDOI: 10.1111/J.1461-0248.2005.00792.X
Antoine Guisan1, Wilfried Thuiller2Institutions (2)
01 Sep 2005-Ecology Letters
Abstract: In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.

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Topics: Population (53%)

5,098 Citations