基于MaxEnt模型的广西黑颈长尾雉潜在栖息地预测与保护空缺分析

了解物种生境的空间分布以及和环境因子的关系,对物种的保护有重要意义。黑颈长尾雉Syrmaticus humiae作为在广西壮族自治区分布的鸟类,通过查阅文献与分布名录得到其分布点,以GIS空间技术运用Max Ent模型对区内黑颈长尾雉的生境适应性和其主要影响因子进行分析,模型评价结果为优秀水平。通过划分出的适宜生境和现有保护区进行保护空缺分析。模型预测黑颈长尾雉的生境适宜区主要分布在广西的西北区域,适宜生境总面积为6 771.84 km~2,已建立的保护区覆盖了16.72%的适宜生境,尚有4 985.86 km2的适宜生境在保护区之外。为更有效地实行保护,应对保护空缺区域进行填补。     

基于MaxEnt模型的四川小寨子沟国家级自然保护区大熊猫化学通讯位点适宜性评价

为揭示影响野生大熊猫Ailuropoda melanoleuca化学通讯位点选择的生态因素,于2016年3月和10月在四川小寨子沟国家级自然保护区,利用样线法对大熊猫化学通讯位点的适宜性进行了初步研究。调查共发现大熊猫化学通讯位点33个,基于这些位点和海拔、坡度、竹林分布等10个环境变量,使用最大熵(Max Ent)模型对该区域大熊猫化学通讯位点适宜性进行综合评价。结果表明:竹林分布(56.90%)和距山脊距离(29.90%)2个环境变量的累积贡献率达86.80%,是影响该区域大熊猫化学通讯位点的主要生态因子。依据Max Ent模型最大约登指数,将结果分为适宜和不适宜2种类型,其中,适宜面积为15 187.50 hm~2,占研究区域面积的34.22%,主要位于保护区中部、保护区南北部分别与宝顶沟和白河自然保护区交界区域;不适宜面积为29 197.20 hm~2,占65.78%。在人力、物力有限的情况下,本研究为保护区合理安排保护行动的时段和区域、提升大熊猫的保护成效提供了数据支撑,对进一步推动野生大熊猫保护实践具有一定意义。     

《中国野生植物资源》征稿简约

1.《中国野生植物资源》是中华全国供销合作总社主管,南京野生植物综合利用研究院主办的综合性科技双月刊,1982年创刊,面向国内外公开发行。其宗旨是报道野生经济植物最新科研成果,介绍野生植物综合利用,栽培、引种技术。主要栏目     

基于MaxEnt濒危植物独叶草的中国潜在适生分布区预测

独叶草(Kingdonia uniflora Balf.f.et W.W.Smith)属毛茛科独叶草属多年生草本植物,属国家二级濒危保护植物。近年来,随着森林采伐和人类活动加剧生境的破碎化,独叶草自然分布区迅速缩减,存在濒临灭绝的风险。预测独叶草潜在的适宜分布区,对于合理保护和利用独叶草具有重要意义。本文结合64份独叶草的标本地理信息和14个环境因子参数,应用最大熵模型(Max Ent)和地理信息技术(GIS),对独叶草在中国的潜在适生分布区和影响分布的关键环境因子进行了预测。受试者工作特性曲线(ROC)分析法的AUC值为0.990,表明Max Ent模型预测可靠性极高。预测结果显示,独叶草最适潜在分布区主要在陕西秦岭北坡(眉县,太白县)、四川省的邛崃山(理县,马尔康县)和大凉山(马边彝族自治县),云南东北部和贵州西北部交界的大娄山(金沙县)和乌蒙山(赫章县)部分地区(适生指数0.5)。刀切法检测(Jackknife test)分析表明,影响独叶草适生分布的关键环境因子包括年均降水量(贡献率33.1%)、海拔(22.3%)、温度季节性变化的标准差(11.4%)、降水量变异系数(7.2%)、土壤p H(5.4%)、1月最低温(5.1%)和土壤碎石百分比(4.9%)。适生区环境参数综合统计分析表明,独叶草最适宜生长在高海拔(1646~2810 m)、年均降水量大(856 mm)、1月最低温适中(-7.2℃)和土壤偏酸性(p H 6.89)的地区。上述研究结果将为在最适生区通过合理规划自然保护区来保护独叶草野生资源提供理论依据。     

《中国野生植物资源》投稿须知

1.《中国野生植物资源》是由中华全国供销合作总社主管,南京野生植物综合利用研究院主办的综合性科技双月刊,1982年创刊,面向国内外公开发行。其宗旨是报道野生经济植物最新科研成果,介绍野生植物综合利用,栽培、引种技术。主要栏目包括综述、研究报告、资源研究、应用开发、裁培技术、信息报道等。2.主要读者对象为农、林、食品、医药、土特产、轻化工等部门科研、教学及生产决策人员。     

吉林通化地区杨树栽培的生态区划

选取通化地区与杨树生长有密切关系的、具典型代表和鲜明时空分布规律的气候因子和自然地理因子作为统计指标,运用聚类分析方法对全区林业生态环境进行分类区划,划分为4个气候小区;调查全区26个引种的杨树生长状况,重点针对其抗寒性进行分类分级;针对杨树新品种与该地区环境适应性关系,确定杨树栽培的生态区划结果,并对每一气候小区的特殊情况具体分析,提出杨树引种栽培的科学依据．     

朝鲜淫羊藿于中国东三省的潜在分布预测

了解物种分布格局及其主要影响因子是进行科学合理保护和利用野生资源的重要基础和前提。朝鲜淫羊藿(Epimedium koreanum Nakai)作为东北地区重要的药用植物资源,对其潜在分布格局进行预测,探讨决定其分布格局的主要环境因子,可为其种群保护和开发利用提供科学依据。本文基于朝鲜淫羊藿的15个地理分布点数据和19个气候以及6个土壤指标,利用最大熵(Max Ent)模型,对朝鲜淫羊藿在东北三省的潜在分布区和适宜等级进行预测。结果表明:朝鲜淫羊藿的潜在分布区主要位于辽宁省东部和吉林省南部,面积达170270 km2;核心适生区位于辽宁省东部以及吉林省南部的温带落叶阔叶林区域,面积达80102 km2。最干月降水量和表层土壤有机碳含量分别是影响朝鲜淫羊藿分布的主要气候和土壤因子。本研究可为朝鲜淫羊藿药用植物资源的生境保护与人工栽培用地的合理布局提供科学依据。     

《中国野生植物资源》征稿简约

1.《中国野生植物资源》是中华全国供销合作总社主管,南京野生植物综合利用研究院主办的综合性科技双月刊,1982年创刊,面向国内外公开发行。其宗旨是报道野生经济植物最新科研成果,介绍野生植物综合利用,栽培、引种技术。主要栏目包括资源介绍、综合开发、加工新工艺、新技术等。     

西洞庭湖湿地越冬期野生植物多样性

本研究采用定点取样法,于2008～2009年越冬期对西洞庭湖湿地野生植物多样性进行了调查统计分析.结果表明:西洞庭湖湿地野生植物种类共73种,隶属29科,植物种类丰富,其中以菊科、十字花科野生植物为主.西洞庭湖区野生植物群落具有较大的物种丰富度,分布均匀,且不同种之间相遇几率较大,具有较高的物种多样性.为保护西洞庭湖区野生植物多样性提供了科学依据.     

《中国野生植物资源》征稿简约

1．《中国野生植物资源》是中华全国供销合作总社主管,南京野生植物综合利用研究院主办的综合性科技双月刊,1982年创刊,面向国内外公开发行。其宗旨是报道野生经济植物最新科研成果,介绍野生植物综合利用,栽培、引种技术。主要栏目包括资源介绍、综合开发、加工新工艺、新技术等。     

基于生态位模型预测野生油茶的潜在分布

油茶(Camellia oleifera)是我国第一大木本油料作物, 野生油茶是油茶育种的宝贵遗传资源。本研究从中国数字植物标本馆(CVH, http://www.cvh.org.cn/)获得可靠的野生油茶分布点数据, 结合气象和土壤数据, 分别应用最大熵(MaxEnt)模型和规则集遗传算法(GARP)模型构建了野生油茶的生态位模型, 预测了野生油茶的潜在分布区, 并分析了影响野生油茶分布的主要环境变量。根据生态位模型预测的分布概率值, 对野生油茶的潜在分布区划分适生等级, 并与主要油茶产地的实际分布数据进行比较, 以验证适生等级划分的可靠性。结果表明, 两种模型的预测结果均能较好地反映油茶的分布情况。GARP模型预测的潜在分布区更广, 而MaxEnt模型的预测结果更精确。两种模型的预测结果均显示, 野生油茶的潜在分布区大部分位于中国, 但在中南半岛也有部分分布。MaxEnt模型预测的野生油茶在中国的潜在分布区与我国亚热带常绿阔叶林的分布区基本吻合, 高适生区主要可以分为3大区域: (1)东北-西南走向的武夷山脉及附近的群山区域; (2)东西走向的南岭山脉及附近的群山区域; (3)东北-西南走向的武陵山脉及附近的群山区域。MaxEnt模型分析显示, 影响野生油茶分布的主要环境变量是昼夜温差月均值、最干季降水量与最暖季降水量。油茶生长面积较大的地区绝大部分都位于MaxEnt模型预测的中、高适生区, 说明适生等级的划分较可靠。实地考察显示, 生态位模型的预测结果对于寻找野生油茶资源具有较高的参考价值。此外, 本研究也充分显示, 利用中国数字植物标本馆的植物分布数据, 结合相应的环境数据构建生态位模型, 有助于了解作物野生近缘种的地理分布。     

Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

Species distribution models for Amazonian trees have mostly been produced at scales and resolutions that are too broad and coarse for practical use in either conservation or forestry. On the other hand, several studies have shown that elevation and the medium‐resolution remote sensing data available via Landsat imagery can be successfully used to detect differences in plant species composition in Amazonia. Therefore, it seems likely that the same data can also be used to predict geographical distributions of individual taxa. Here we use remotely sensed data and a maximum entropy algorithm (MaxEnt) to generate landscape‐scale distribution models at 30‐m‐resolution for five economically important timber tree genera (Apuleia, Amburana, Crepidospermum, Dipteryx, and Manilkara). Individual Landsat Thematic Mapper bands and normalized difference vegetation index yielded acceptable model performance, and the use of averaging filters (3 × 3 and 5 × 5 pixel low‐pass filters) improved model performance further. Including elevation as a predictor also improved model performance for all the genera. Our results suggest that it is possible to use Landsat bands and elevation as predictors for modeling the potential distribution of tree species in lowland Amazonia at a fine enough resolution to facilitate the practical management of forest resources.     

Multi-scale approach for predicting fish species distributions across coral reef seascapes

Two of the major limitations to effective management of coral reef ecosystems are a lack of information on the spatial distribution of marine species and a paucity of data on the interacting environmental variables that drive distributional patterns. Advances in marine remote sensing, together with the novel integration of landscape ecology and advanced niche modelling techniques provide an unprecedented opportunity to reliably model and map marine species distributions across many kilometres of coral reef ecosystems. We developed a multi-scale approach using three-dimensional seafloor morphology and across-shelf location to predict spatial distributions for five common Caribbean fish species. Seascape topography was quantified from high resolution bathymetry at five spatial scales (5–300 m radii) surrounding fish survey sites. Model performance and map accuracy was assessed for two high performing machine-learning algorithms: Boosted Regression Trees (BRT) and Maximum Entropy Species Distribution Modelling (MaxEnt). The three most important predictors were geographical location across the shelf, followed by a measure of topographic complexity. Predictor contribution differed among species, yet rarely changed across spatial scales. BRT provided ‘outstanding’ model predictions (AUC = >0.9) for three of five fish species. MaxEnt provided ‘outstanding’ model predictions for two of five species, with the remaining three models considered ‘excellent’ (AUC = 0.8–0.9). In contrast, MaxEnt spatial predictions were markedly more accurate (92% map accuracy) than BRT (68% map accuracy). We demonstrate that reliable spatial predictions for a range of key fish species can be achieved by modelling the interaction between the geographical location across the shelf and the topographic heterogeneity of seafloor structure. This multi-scale, analytic approach is an important new cost-effective tool to accurately delineate essential fish habitat and support conservation prioritization in marine protected area design, zoning in marine spatial planning, and ecosystem-based fisheries management.     

A statistical explanation of MaxEnt for ecologists

MaxEnt is a program for modelling species distributions from presence‐only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links between the structure of the model, decisions required in producing a modelled distribution, and knowledge about the species and the data that might affect those decisions. To begin we discuss the characteristics of presence‐only data, highlighting implications for modelling distributions. We particularly focus on the problems of sample bias and lack of information on species prevalence. The keystone of the paper is a new statistical explanation of MaxEnt which shows that the model minimizes the relative entropy between two probability densities (one estimated from the presence data and one, from the landscape) defined in covariate space. For many users, this viewpoint is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts. We then step through a detailed explanation of MaxEnt describing key components (e.g. covariates and features, and definition of the landscape extent), the mechanics of model fitting (e.g. feature selection, constraints and regularization) and outputs. Using case studies for a Banksia species native to south‐west Australia and a riverine fish, we fit models and interpret them, exploring why certain choices affect the result and what this means. The fish example illustrates use of the model with vector data for linear river segments rather than raster (gridded) data. Appropriate treatments for survey bias, unprojected data, locally restricted species, and predicting to environments outside the range of the training data are demonstrated, and new capabilities discussed. Online appendices include additional details of the model and the mathematical links between previous explanations and this one, example code and data, and further information on the case studies.     

末次盛冰期以来红豆树在不同气候变化情景下的分布动态

红豆树(Ormosia hosiei Hemsl. et Wils.)是中国的特有种,具有极高的经济价值、景观价值和药用价值。由于木材珍贵,人工盗伐严重,其种群数量和分布范围不断减少,被世界自然保护联盟(IUCN)濒危物种红色名录列为近危物种。气候变化会对物种的分布造成严重影响,理解该影响将有助于物种保护策略的制定,尤其是为濒危物种未来的保护提供重要参考。本研究基于红豆树在中国的地理分布数据,借助经相关性分析后筛选出的9个变量因子,利用最大熵模型(MaxEnt)模拟红豆树在末次盛冰期、全新世中期、当代和未来气候情景下的潜在分布区和影响其分布的主导环境因子,并且通过空间分析模拟其在不同气候变化情景下空间分布格局的变化。结果表明MaxEnt在各时期训练集和测试集的AUC(受试者工作特征曲线下的面积)均值均达到0.9以上,表明模型有很好的预测能力。刀切法(Jackknife)表明年均降水量、温度季节性变化标准差和昼夜温差月均值是主导其分布的三大因子,累计贡献率达到91.8%。将模拟结果导入到ArcGIS后,处理得到不同时期红豆树适生区空间分布格局变化。结果表明,自末次盛冰期以来,红豆树的适生...     

Search for the wild ancestor of buckwheat III. The wild ancestor of cultivated common buckwheat, and of tatary buckwheat

By surveying wild Fagopyrum species and their distribution in southern China and the Himalayan hills, I arrived at the conclusion that the newly discovered subspecies F. esculentum ssp. ancestralis Ohnishi is the wild ancestor of cultivated common buckwheat, while previously known wild tatary buckwheat,F. tataricum ssp. potanini Batalin is the wild ancestor of tatary buckwheat. Their original birthplace is revealed to be northwestern corner of Yunnan province for common buckwheat judging from the distribution of wild ancestor, and to be the northwest part of Sichuan province for tatary buckwheat judging from allozyme variability in wild tatary buckwheat. F. cymosum is not the ancestor of cultivated buckwheat; it is only distantly related to cultivated buckwheat, in morphology, isozymes and cpDNA. Several genetic, ecological and taxonomic categories which should be taken into consideration in examining the origin of buckwheat were discussed. Key Words: Fagopyrum esculentum ssp. ancestralis; Fagopyrum tataricum ssp. potanini; southern China theory of origin of buckwheat. Contribution from Plant Germ-Plasm Institute, Faculty of Agriculture, Kyoto University. No. 78.     

The distribution of cultivated species of Porophyllum (Asteraceae) and their wild relatives under climate change

The geographic distribution of plant species is already being affected by climate change. Cropping patterns of edible plant species and their wild relatives will also be affected, making it important to predict possible changes to their distributions in the future. Currently, species distribution models are valuable tools that allow the estimation of species’ potential distributions, in the recent past as well as during other time spans for which climate data have been obtained. With the aim of evaluating how species distributions respond to current and future climate changes, in this work species distribution models were generated for two cultivated species of the Porophyllum genus (Asteraceae), known commonly as ‘pápalos' or ‘pápaloquelites', as well as their Mexican wild relatives, at five points in time (21,000 years ago, present, 2020, 2050, and 2080). Using a database of 1442 entries for 16 species of Porophyllum and 19 environmental variables, species distribution models were constructed for each time period using the Maxent modelling algorithm; those constructed for the future used a severe climate change scenario. The results demonstrate contrasting effects between the two cultivated species; for P. linaria, the future scenario suggests a decrease in distribution area, while for P. macrocephalum distribution is predicted to increase. Similar trends are observed in their wild relatives, where 11 species will tend to decrease in distribution area, while three are predicted to increase. It is concluded that the most important agricultural areas where the cultivated species are grown will not be greatly affected, while the areas inhabited by the wild species will. However, while the results suggest that climate change will affect the distribution of the cultivated species in contrasting ways, evaluations at finer scales are recommended to clarify the impact within cultivation zones.     

基于MaxEnt模型的云南干热河谷适生树种选择

基于MaxEnt模型识别和预测云南干热河谷适生树种,对于改善和恢复其生态治理能力具有重要意义。收集40种具有代表性的潜在适生树种地理分布数据,结合气候、地形和土壤等环境因子,利用MaxEnt模型筛选适生树种。预测当前和2021-2040年四种气候情景下(SSP126、SSP245、SSP370和SSP585)适生树种适生区的分布格局,划分优先种植区域,并明确MaxEnt模型用于树种选择的可行性。结果表明:(1)当前气候情景下,影响干热河谷潜在适生树种分布的主导因子是气候因子,其次是海拔、植物归一化指数、地表太阳辐射量和人类足迹。(2)未来,24种适生树种适生区稳定,发生概率与海拔关系呈单峰分布且高海拔下适生树种丰富度将降低。(3)干热河谷适生树种优先种植区域沿干热河谷呈狭长分布;实际调查发现,元谋县适生树种实际分布区域面积略高于最佳种植区域面积。应用MaxEnt模型筛选干热河谷适生树种选择是可行的,但在应用之前必须通过实地调查来验证树种实际生存情况与预测结果的差异。在干热河谷生态修复造林时,可优先考虑白枪杆、车桑子等24种树种。     

Machine learning of large‐scale spatial distributions of wild turkeys with high‐dimensional environmental data

Species distribution modeling often involves high‐dimensional environmental data. Large amounts of data and multicollinearity among covariates impose challenges to statistical models in variable selection for reliable inferences of the effects of environmental factors on the spatial distribution of species. Few studies have evaluated and compared the performance of multiple machine learning (ML) models in handling multicollinearity. Here, we assessed the effectiveness of removal of correlated covariates and regularization to cope with multicollinearity in ML models for habitat suitability. Three machine learning algorithms maximum entropy (MaxEnt), random forests (RFs), and support vector machines (SVMs) were applied to the original data (OD) of 27 landscape variables, reduced data (RD) with 14 highly correlated covariates being removed, and 15 principal components (PC) of the OD accounting for 90% of the original variability. The performance of the three ML models was measured with the area under the curve and continuous Boyce index. We collected 663 nonduplicated presence locations of Eastern wild turkeys (Meleagris gallopavo silvestris) across the state of Mississippi, United States. Of the total locations, 453 locations separated by a distance of ≥2 km were used to train the three ML algorithms on the OD, RD, and PC data, respectively. The remaining 210 locations were used to validate the trained ML models to measure ML performance. Three ML models had excellent performance on the RD and PC data. MaxEnt and SVMs had good performance on the OD data, indicating the adequacy of regularization of the default setting for multicollinearity. Weak learning of RFs through bagging appeared to alleviate multicollinearity and resulted in excellent performance on the OD data. Regularization of ML algorithms may help exploratory studies of the effects of environmental factors on the spatial distribution and habitat suitability of wildlife.     

AFLP based assessment of genetic relationships among shiitake (<Emphasis Type="Italic">Lentinula</Emphasis> ssp.) mushrooms

Despite the economical importance of shiitake (Lentinula ssp.) mushrooms, until the present date little information exists on cultivated and wild species in correlation with geographic origin applying molecular techniques. Use of a high resolution molecular tool like AFLP for assessing genetic similarity and geographical diversity would be an important step towards understanding of different Lentinula species. Thirteen wild and 17 cultivated accessions of 3 Lentinula species were analysed with 64 EcoRI–MseI primer combinations and finally 32 reproducible and polymorphic primer combinations were considered for the analysis. A total of 816 informative AFLP markers were generated and scored as binary data. These data were analysed using various method packages for cluster analysis, genetic diversity and genetic differentiation. Percentage polymorphism was high (62.99%) among the species studied. Different clustering analysis segregated the wild and the cultivated species into two major branches, with the wild samples being further grouped according to their geographic location. Overall polymorphisms among cultivated strains in the USA were higher than that of the cultivated strains in Japan (58.9%).