基于模糊数学的秦岭地区山茱萸生境适宜性评价

山茱萸(Cornus officinalis)是我国传统常用药材,本文采用模糊数学分析方法,对采自秦岭地区的山茱萸中马钱苷含量与21个评价因子的隶属函数进行拟合,同时采用最大信息熵模型确定各个评价因子的权重,利用ArcGIS 10空间分析模块模拟研究区域适宜山茱萸生长的潜在分布生境。结果表明,在山茱萸生境的21个评价因子中,主要影响因子为气候,其次是土壤和地形因子;所有评价因子中,土壤质地(TTEX)的权重最大,其次是果实生长期降水量(PG)、年降水量(AP)和降水季节性变化(PS)。研究区内山茱萸高适宜区面积占总面积的19.94%,主要分布在甘肃东南部、陕西南部和河南西部,这些区域温度适中、气候湿润、光照充足,适宜山茱萸生长;适宜区面积占总面积的11.85%,低适宜区面积占总面积的16.31%,不适宜区面积占总面积的51.90%。本研究基于GIS与模糊数学的生境适宜性评价模型,对秦岭地区山茱萸生境适宜性做出了科学划分,同时量化了不同生境区的评价因子对山茱萸的影响,可为山茱萸的管理和保护以及人工种植提供科学依据。     

Ecological effects on phenotypic,cytological and biochemical diversity of Cornus officinalis germplasm resources in China and USA

Cornus officinalis is a small deciduous tree with significant medicinal values. To explore the ecological effects on phenotypic, cytological and biochemical diversity of medicinal plants, we estimated the loganin content, C-values and fruit and seed phenotypic traits of 80 C. officinalis germplasms collected from across three genuine producing areas of China and Madison, USA. Our results showed that most fruit and seed phenotypic traits of C. officinalis germplasms from Madison, USA were significantly larger than those from China, but the loganin contents and 2C values of germplasms from China (0.9% and 5.92 pg) were significantly higher than those from Madison (0.6% and 5.29 pg) (P < 0.01). These data highlight the fact that there was a tight association between temperature, precipitation and loganin content in C. officinalis. Based on the above results, we found that C. officinalis had a tendency to decline in genome size (GS) and loganin content during the long period of adaptation and evolution with new ecological variables. We are able to presume that the ecological variable is the key factor in determining phenotypic diversity, GS and active ingredient of medicinal plants.     

美国大陆外来入侵物种斑马纹贻贝(Dreissena polymorpha)潜在生境预测模型

防止外来生物入侵造成危害的重要手段是阻止可能造成入侵的物种进入适合其生存的地区.论文以1864个美国外来入侵物种斑马纹贻贝定点发生数据和开放式基础地理信息数据库Daymet的34个环境变量为主要信息源,采用逻辑斯蒂回归(LR)、分类与回归树模型(CART)、基于规则的遗传算法(GARP)、最大熵法(Maxent)4种途径,建立美国大陆部分潜在生境预测模型,从接受者运行特征曲线下面积(AUC)、Pearson相关系数、Kappa值3个方面来检验模型预测精度,在此基础上分析斑马纹贻贝的空间分布规律及其环境影响因素.研究结果表明:在3个评价指标中,4个生态位模型预测精度均达到优良水平,其中Maxent在物种现实生境模拟、主要生态环境因子筛选、环境因子对物种生境影响的定量描述方面都表现出了优越的性能;距水源距离、海拔高度、降水频率、太阳辐射是影响物种空间分布的主要环境因子.论文提出的研究方法对中国外来入侵物种生境预测具有较强的借鉴意义,研究结果对中国海洋外来入侵物种沙筛贝的预测与防治,具有一定的指导作用.     

Species distribution models for predicting the habitat suitability of Chinese fire‐bellied newt Cynops orientalis under climate change

Climate change influences species geographical distribution and diversity pattern. The Chinese fire‐bellied newt (Cynops orientalis) is an endemic species distributed in East‐central China, which has been classified as near‐threatened species recently due to habitat destruction and degradation and illegal trade in the domestic and international pet markets. So far, little is known about the spatial distribution of the species. Based on bioclimatic data of the current and future climate projections, we modeled the change in suitable habitat for C. orientalis by ten algorithms, evaluated the importance of environmental factors in shaping their distribution, and identified distribution shifts under climate change scenarios. In this study, 46 records of C. orientalis from East China and 8 bioclimatic variables were used. Among the ten modeling algorithms, four (GAM, GBM, Maxent, and RF) were selected according to their predictive abilities. The current habitat suitability showed that C. orientalis had a relatively wide but fragmented distribution, and it encompassed 41,862 km². The models suggested that precipitation of warmest quarter (bio18) and mean temperature of wettest quarter (bio6) had the highest contribution to the model. This study revealed that C. orientalis is sensitive to climate change, which will lead to a large range shift. The projected spatial and temporal pattern of range shifts for C. orientalis should provide a useful reference for implementing long‐term conservation and management strategies for amphibians in East China.     

Prediction of Potential Geological Distribution of Asarum in China by Maxent Model

Genus Asarum contains several plant species that are mostly used as precious drug resources. In this study, 126 distribution records of 3 Asarum species and relevant 28 environmental factors data were collected, then a geographical distribution model of the genus medicinal plants in China was made by Maxent and the ArcGIS spatial analysis technique. The results showed that the 3 Asarum species have a wide potential distribution region. High suitable region and suitable region were 3125km2 and 276042km2 respectively. Among the environmental factors, 3 precipitation factors (annual precipitation, precipitations in the most dry season and the most warm season) are the main ones that can affect the distribution of the 3 Asarum medicinal plants. This study can provide a useful reference for the collection and cultivation of Asarum medicinal plants.     

全球气候变化下贵州省青冈林的潜在生境动态

为了解贵州省青冈林在全球气候变化下的潜在分布特征,基于现状分布数据,结合当前气候数据和未来气候变化情景(RCP8.5情景,2070-2099年)构建Maxent潜在分布模型,预测贵州省青冈林的潜在分布变化。结果表明,最冷季均温(bio11)、最冷月最低温度(bio6)和年均降水量(bio12)为控制贵州省青冈林潜在生境的主导气候因子;RCP8.5情景下贵州省青冈林的潜在分布面积相较当前气候条件增加,中度适宜生境增加19 419 km2,高度适宜生境增加9 944 km2;中度适宜生境平均海拔较当前气候条件上升126 m,高度适宜生境平均上升85 m。总的来说,贵州省青冈林对全球气候变化的响应不十分敏感。     

Dealing with vagueness in complex forest landscapes: A soft classification approach through a niche-based distribution model

The increasing interest in biodiversity conservation has led to the development of new approaches to facilitate ecologically based conservation policies and management plans. In this context, the development of effective methods for the classification of forest types constitutes a crucial issue as forests represent the most widespread vegetation structure and play a key role in ecosystem functioning. In this study a maximum entropy approach (Maxent) to forest type classification in a complex Mediterranean area, has been investigated. Maxent, a niche-based model of species/habitat distribution, allowed researchers to estimate the potential distribution of four forest types: Holm oak, Mixed oak, Mixed broadleaved and Riparian forests. The Maxent model's internal tests have proved a powerful tool for estimating the model's accuracy and analyzing the effects of the most important variables in the produced models. Moreover the comparison with a spectral response-based fuzzy classification, showed a higher accuracy in the Maxent outputs, demonstrating how the use of environmental variables, combined with spectral information in the classification of natural or semi-natural land cover classes, improves map accuracies. The modeling approach followed by this study, taking into account the uncertainty proper of the natural ecosystems and the use of environmental variables in land cover classification, can represent a useful approach to making more efficient and effective field inventories and to developing effective conservation policies.     

Assassin bugs of the subfamily Diaspidiinae (Heteroptera: Reduviidae): distribution and potential ecological niches

The prediction and definition of the conditions for the potentially suitable ecological niche of the subfamily Diaspidiinae was the main goal of this study. Our research was based on 283 specimens of all known species of assassin bugs belonging to the subfamily Diaspidiinae stored in European museum collections and a set of 21 environmental variables in the form of a 1 × 1 km grid covering Africa and Madagascar. Based on occurrence localities, as well as a digital elevation model and layer of the tree cover‐continuous fields, information about the distribution of each species is given. Using Maxent software, potentially useful ecological niches were modeled, which allowed for the creation of a map of the potential distribution of the members of this subfamily and for determining their climatic preferences. A jackknife test showed that annual precipitation, annual temperature range and tree cover‐continuous fields were the most important environmental variables affecting the distribution of the subfamily Diaspidiinae. An analysis of climatic preferences suggested that the representatives of the subfamily were linked mainly to the tropical climate. An analysis of environmental variables also showed that the subfamily preferred areas with herbaceous vegetation and some trees, and this preference is probably caused by the food preferences of their prey. On the basis of the museum data on the species occurrence, as well as ecological niche modeling methods, we provided new and valuable information on potentially suitable habitat and the possible range of distribution of the subfamily Diaspidiinae along with its climatic preferences.     

应用Maxent生态位模型预测淡水钩虾(甲壳纲,端足目)的分布(英文)

预测物种的适生区对于物种资源的评估、保护以及生物多样性的管理非常重要。由于全球气候变化和人类的过度开发,冷水性无脊椎动物的衰减速度比在陆地和海洋生活的无脊椎动物都要高。目前关于中国淡水钩虾分布方面的研究很少,本研究基于103个地表淡水钩虾的不同分布位点和广布种湖泊钩虾Gammarus lacustris 23个不同分布位点以及32个环境因子数据,使用生态位模型(Maxent)预测了淡水钩虾和湖泊钩虾在我国的适生分布区域。结果显示淡水钩虾非常适合分布在我国的一些偏远山区,如长白山、太行山、横断山、天山、昆仑山和祁连山,而青藏高原的东部、西部边缘地区和南部分布地区、尼泊尔、不丹和朝鲜半岛也是淡水钩虾的潜在适生区域,但淡水钩虾在我国华南、华中和华北的平原地区分布却很少,其在我国的潜在分布区与-10℃和5℃1月平均气温线间的区域相似。淡水钩虾是典型的狭温性物种,在不适宜温度条件下很难存活,这可能也是限制其扩散和存活的关键性因素。     

Evaluating sampling bias correction methods for invasive species distribution modeling in Maxent

As globalization continues, the spread of invasive species is accelerating, posing a severe threat to native biodiversity. To manage such species, reduce their negative impact on native biota and utilize management costs efficiently, a profound understanding of their geographical distribution pattern is mandatory. In this study, the species distribution model Maxent was used to predict the potential spatial distribution of U. europaeus. To account for sampling bias, three bias correction methods were applied, including a novel approach to increase the number of presence points by sampling occurrences based on satellite images. Furthermore, a decision structured process was used to evaluate and select optimal Maxent parameterization and account for limitations of single evaluation criteria. The currently suitable area of U. europaeus is primarily distributed in the coastal and central regions of Chilean natural region Zona Sur in south-central Chile. Annual mean temperature (bio1), annual precipitation (bio12), and precipitation seasonality (bio15) were the most important environmental variables that affected the distribution of U. europaeus. The sampling of additional presence points could effectively correct for sampling bias in species occurrence data. The use of a decision structured process for model evaluation proved to be useful in determining optimal model parameterization for decreased model complexity. This study highlights the importance of optimized Maxent calibrations to yield results as accurately as possible. The predicted suitable habitats can inform nature conservation planners and landscape managers to guide and prioritize conservation measures.     

Coral Reef Habitat Response to Climate Change Scenarios

Coral reef ecosystems are threatened by both climate change and direct anthropogenic stress. Climate change will alter the physico-chemical environment that reefs currently occupy, leaving only limited regions that are conducive to reef habitation. Identifying these regions early may aid conservation efforts and inform decisions to transplant particular coral species or groups. Here a species distribution model (Maxent) is used to describe habitat suitable for coral reef growth. Two climate change scenarios (RCP4.5, RCP8.5) from the National Center for Atmospheric Research’s Community Earth System Model were used with Maxent to determine environmental suitability for corals (order Scleractinia). Environmental input variables best at representing the limits of suitable reef growth regions were isolated using a principal component analysis. Climate-driven changes in suitable habitat depend strongly on the unique region of reefs used to train Maxent. Increased global habitat loss was predicted in both climate projections through the 21^st century. A maximum habitat loss of 43% by 2100 was predicted in RCP4.5 and 82% in RCP8.5. When the model is trained solely with environmental data from the Caribbean/Atlantic, 83% of global habitat was lost by 2100 for RCP4.5 and 88% was lost for RCP8.5. Similarly, global runs trained only with Pacific Ocean reefs estimated that 60% of suitable habitat would be lost by 2100 in RCP4.5 and 90% in RCP8.5. When Maxent was trained solely with Indian Ocean reefs, suitable habitat worldwide increased by 38% in RCP4.5 by 2100 and 28% in RCP8.5 by 2050. Global habitat loss by 2100 was just 10% for RCP8.5. This projection suggests that shallow tropical sites in the Indian Ocean basin experience conditions today that are most similar to future projections of worldwide conditions. Indian Ocean reefs may thus be ideal candidate regions from which to select the best strands of coral for potential re-seeding efforts.     

Modeling potential habitats for alien species Dreissena polymorpha in Continental USA

The effective measure to minimize the damage of invasive species is to block the potential invasive species to enter into suitable areas. 1864 occurrence points with GPS coordinates and 34 environmental variables from Daymet datasets were gathered, and 4 modeling methods, i.e., Logistic Regression (LR), Classification and Regression Trees (CART), Genetic Algorithm for Rule-Set Prediction (GARP), and maximum entropy method (Maxent), were introduced to generate potential geographic distributions for invasive species Dreissena polymorpha in Continental USA. Then 3 statistical criteria of the area under the Receiver Operating Characteristic curve (AUC), Pearson correlation (COR) and Kappa value were calculated to evaluate the performance of the models, followed by analyses on major contribution variables. Results showed that in terms of the 3 statistical criteria, the prediction results of the 4 ecological niche models were either excellent or outstanding, in which Maxent outperformed the others in 3 aspects of predicting current distribution habitats, selecting major contribution factors, and quantifying the influence of environmental variables on habitats. Distance to water, elevation, frequency of precipitation and solar radiation were 4 environmental forcing factors. The method suggested in the paper can have some reference meaning for modeling habitats of alien species in China and provide a direction to prevent Mytilopsis sallei on the Chinese coast line.     

Modeling potential habitats for alien species Dreissena polymorpha in Continental USA

The effective measure to minimize the damage of invasive species is to block the potential invasive species to enter into suitable areas. 1864 occurrence points with GPS coordinates and 34 environmental variables from Daymet datasets were gathered, and 4 modeling methods, i.e., Logistic Regression (LR), Classification and Regression Trees (CART), Genetic Algorithm for Rule-Set Prediction (GARP), and maximum entropy method (Maxent), were introduced to generate potential geographic distributions for invasive species Dreissena polymorpha in Continental USA. Then 3 statistical criteria of the area under the Receiver Operating Characteristic curve (AUC), Pearson correlation (COR) and Kappa value were calculated to evaluate the performance of the models, followed by analyses on major contribution variables. Results showed that in terms of the 3 statistical criteria, the prediction results of the 4 ecological niche models were either excellent or outstanding, in which Maxent outperformed the others in 3 aspects of predicting current distribution habitats, selecting major contribution factors, and quantifying the influence of environmental variables on habitats. Distance to water, elevation, frequency of precipitation and solar radiation were 4 environmental forcing factors. The method suggested in the paper can have some reference meaning for modeling habitats of alien species in China and provide a direction to prevent Mytilopsis sallei on the Chinese coast line.     

Distribution Pattern and Climate Preferences of the Representatives of the Cosmopolitan Genus Sirthenea Spinola, 1840 (Heteroptera: Reduviidae: Peiratinae)

The main goal of this study was to predict, through the use of GIS tool as ecological niche modelling, potentially suitable ecological niche and defining the conditions of such niche for the representatives of the cosmopolitan genus Sirthenea. Among all known genera of the subfamily Peiratinae, only Sirthenea occurs on almost all continents and zoogeographical regions. Our research was based on 521 unique occurrence localities and a set of environmental variables covering the whole world. Based on occurrence localities, as well as climatic variables, digital elevation model, terrestrial ecoregions and biomes, information about the ecological preferences is given. Potentially useful ecological niches were modelled using Maxent software, which allowed for the creation of a map of the potential distribution and for determining climatic preferences. An analysis of climatic preferences suggested that the representatives of the genus were linked mainly to the tropical and temperate climates. An analysis of ecoregions also showed that they preferred areas with tree vegetation like tropical and subtropical moist broadleaf forests biomes as well as temperate broadleaf and mixed forest biomes. Therefore, on the basis of the museum data on the species occurrence and ecological niche modelling method, we provided new and valuable information on the potentially suitable habitat and the possible range of distribution of the genus Sirthenea along with its climatic preferences.     

Simulation of the potential distribution of rare and endangered Satyrium species in China under climate change

Satyrium is an endangered and rare genus of plant that has various pharmacodynamic functions. In this study, optimized MaxEnt models were used in analyzing potential geographical distributions under current and future climatic conditions (the 2050s and 2070s) and dominant environmental variables influencing their geographic distribution. The results provided reference for implementation of long‐term conservation and management approaches for the species. The results showed that the area of the total suitable habitat for Satyrium ciliatum (S. ciliatum) in China is 32.51 × 10⁴ km², the total suitable habitat area for Satyrium nepalense (S. nepalense) in China is 61.76 × 10⁴ km², and the area of the total suitable habitat for Satyrium yunnanense (S. yunnanense) in China is 89.73 × 10⁴ km² under current climatic conditions. The potential suitable habitat of Satyrium is mainly distributed in Southwest China. The major environmental variables influencing the geographical distribution of S. ciliatum were isothermality (bio3), temperature seasonality (bio4), and mean temperature of coldest quarter (bio11). Environmental variables such as isothermality (bio3), temperature seasonality (bio4), and precipitation of coldest quarter (bio19) affected the geographical distribution of S. nepalense; and environmental variables such as isothermality (bio3), temperature seasonality (bio4), and lower temperature of coldest month (bio6) affected the geographical distribution of S. yunnanense. The distribution range of Satyrium was extended as global warming increased, showing emissions of greenhouse gases with lower concentration (SSP1‐2.6) and higher concentration (SSP5‐8.5). According to the study, the distribution of suitable habitat will shift with a change to higher elevation areas and higher latitude areas in the future.     

The ability of Nature Reserves to conserve medicinal plant resources: A case study in northeast China

Medicinal plants are important resources and are under serious threat due to human interference and climate change. We used species richness maps to find hotspots of medicinal plant localities and then modeled the environmental variables with a large effect on their distribution. We began by using a combination of species distribution models (SDMs) and geographic information system (GIS) tools to generate species richness maps of medicinal plants in northeast China. First, we conducted a detailed investigation of 2884 study plots in northeast China and selected 49 medicinal plant species for further analysis. The field surveys performed for this study spanned four years and identified a large number of new populations of medicinal plants in the forests of northeast China. We modeled and mapped the potential distributions of these 49 species and found that species richness hotspots are concentrated in the eastern and northeastern areas of the study region. We then analyzed the results of jackknife tests and found that the most important environmental variables on medicinal plant distribution are related to precipitation. Finally, we used the geographic distribution of medicinal plant richness to evaluate the ability of existing Nature Reserves to conserve these plants. By acquiring model data and using SDM and GIS to evaluate the current distribution and richness of medicinal plants, we are able to evaluate their current protection status and make recommendations about their utilization. This analysis could be expanded to assess medicinal plant populations in other regions where there are adequate records of the current distribution of medicinal plants.     

Modelling the geographic distributions of endemic genera in the eastern Central Asian desert

This study simulates the distributions of 13 endemic and near‐endemic genera (Ammopiptanthus, Sympegma, Iljinia, Elachanthemum, Potaninia, Tugarinovia, Kaschgaria, Sarcozygium, Timouria, Zollikoferia, Stilpnolepis, Synstemon and Tetraena) to indicate areas of plant diversity and conservation importance within the eastern Central Asian desert, and to identify the determinant environmental variables contributing to the spatial distribution patterns. Using known distribution localities and 14 environmental variables, the Maxent and Domain species distribution models were employed to map the patterns of geographic distribution. The power of predictability of the models was tested using the receiver operating characteristic method and the jackknife validation approach, according to the different number of species localities available. The estimated richness and the superimposed potential distributions of 13 genera were used to indicate endemic patterns of distribution. The comparison of Maxent and Domain further identified previously unknown areas of endemism and described the distribution for each taxon. Both observed species occurrence and the species occurrence predicted from the Maxent indicated that the eastern Alashan of Inner Mongolia is the most noticeable endemic area, and the northwestern and northern Tarim Basin of Xinjiang is the secondary center of plant diversity. These regions were then prioritized for conservation importance. Potential evapotranspiration ratio and precipitation seasonality played important roles in driving the observed patterns of endemic distribution.     

Analysis of the factors that affect the distribution and abundance of three Neobuxbaumia species (Cactaceae) that differ in their degree of rarity

We studied three species of columnar cacti in the genus Neobuxbaumia which differ in their degree of rarity: Neobuxbaumia macrocephala (the rarest), Neobuxbaumia tetetzo (intermediate), and Neobuxbaumia mezcalaensis (the most common). To investigate the ecological factors that limit their distribution and abundance, we surveyed 80 localities within the region of Tehuacan-Cuicatlán, in Central Mexico. At each locality we measured several environmental variables, and the density of the Neobuxbaumia populations present. We used a principal component analysis (PCA) to identify the factors that are associated to the presence/absence of each species. Additionally, we carried out multiple regressions between environmental variables and population density to test whether the variation in these variables was related to changes in abundance. The results show that factors significantly affecting the distribution of these species are mean annual temperature, altitude, rainfall, and soil properties such as texture and organic matter content. N. mezcalaensis reaches maximum population densities of 14,740 plants per ha (average density = 3943 plants per ha) and is associated with localities with relatively abundant rainfall. N. tetetzo shows maximum population densities of 14,060 plants per ha (average = 3070 plants per ha), and is associated with sites located at high latitudes and with high phosphorous content in the soil. The rarest species, N. macrocephala, shows maximum densities of 1180 plants per ha (average = 607 plants per ha) and is associated with localities with high soil calcium content. The distribution of this species is limited to sites with specific values of the environmental variables recorded, conferring it a high habitat specificity which accounts for its rarity.     

基于MaxEnt模式的白头叶猴栖息地评价——以广西崇左白头叶猴保护区为例

针对物种分布格局与其环境变量关系的研究,对于生态廊道规划与环境恢复研究具有重要意义.本文以白头叶猴(Trachypithecus leucocephalus)为研究对象,针对广西崇左白头叶猴国家级自然保护区51个白头叶猴分布点和11个环境变量数据,利用MaxEnt模型(maximum entropy modeling)...     

Habitat suitability of Gymnocladus assamicus - A critically endangered plant of Arunachal Pradesh,India using machine learning and statistical modeling

The present study sought to identify the potential distribution range of critically endangered Gymnocladus assamicus in Arunachal Pradesh based on published data and field collection. We used the Maxent model to estimate the range of distribution and the result was then compared with three other models, i.e., the Generalized Linear Model (GLM), the Bioclim and the Random Forest model to assess the species' habitat suitability. A total of 23 different environmental variables were used, including bioclimatic ones, monthly minimum and maximum temperature, monthly precipitation and elevation data. The Maxent output listed 12 variables explaining 99.9% variation in the model. In comparison, Maxent showed the maximum region under habitat suitability criteria (1884.48 km²), followed by Random Forest (70.73 km²) and Bioclim (11.62 km²) model. Except for the Maxent model, suitable habitats predicted by other models are highly restricted within and across the study species' current distribution range. The average model prediction shows an expanded distribution range for the species up to Tawang which is the closest district of currently known distribution of the species in the state. Thus, the present study recognizes the importance of the geographic range of G. assamicus, a critically endangered species with very limited spatial distribution range and also provides some specific details to explore possible habitats for the species in new areas of potential occurrence in Arunachal Pradesh, India.