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1.
中国梧桐属(Firmiana)在世界梧桐属中占比较大,且除梧桐外其余种均为中国特有且分布范围狭窄的植物种,灭绝风险大,研究气候变化对中国梧桐属树种的影响对于维护生物多样性具有重要的意义。结合多时期第六次国际气候耦合模式比较计划(CMIP6)气候变量数据和中国八种梧桐属树种的分布数据,基于R语言kuenm程序包优化的最大熵(Maxent)模型模拟分析中国八种梧桐属树种在多尺度下的潜在适生区,得出梧桐属最适宜的模拟尺度、潜在适生区的面积变化和迁移方向、梧桐属多样性保护关键区域及保护空缺。结果表明:(1)梧桐属最适宜的模拟尺度为亚洲;(2) Maxent模型的接收者操作特征曲线下面积(AUC)值均大于0.9,表明模型对梧桐属潜在适生区预测结果具有较高准确度;(3)气候变化影响下除云南梧桐(Firmiana major)外其它树种的潜在适生区都将在未来有所扩大;(4)中国八种梧桐属树种潜在适生区迁移方向主要为东西向,南北向大跨度迁移较少,纬度变化不大;(5)丹霞梧桐(Firmiana danxiaensis)的稳定潜在适生区最小;(6)中国梧桐属多样性保护关键区域主要分布于广西壮族自治区及云南、广东、海南等省区;(7)中国梧桐属多样性保护空缺区域主要分布于广西壮族自治区中部及海南省北部;(8)梧桐属多样性保护关键区域正在为人造地表所侵蚀。研究分析气候变化对中国八种梧桐属树种的影响及其潜在适生区变化、中国梧桐属多样性保护状态,可为中国梧桐属建立多样性保护廊道提供相关建议,为制定多样性保护规划及相应措施提供参考。  相似文献   
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番荔枝实蝇Ceratitis artortoe(Graham)是一种重要的外来人侵性检疫害虫.在广东口岸,其幼虫连续从入境旅客所携带的水果中被榆出.目前关于番荔枝实蝇潜在适生性分布的研究进行得很少,但对于我国的生物生态安全却有重要意义.本研究中,我们使用3种牛态位模型(ENFA模型,马氏典型性模型和Maxent模型)对番荔枝实蝇在中斟以及全球范尉内的潜在适生性分布区域进行了预测分析.结果显示:Maxent模型拥有最好的预测精确度,马氏典型件模型次之,而ENFA模型的预测精确度最差;Maxent模型和马氏典型性模型的预测精确度无显著性差异;根据Maxent模型的预测结果,番荔枝实蝇在中国的潜在适生区主要是广西、广东、海南以及云南的少部分地区.分析结果显示,番荔枝实蝇从境外传人中国南部地区并最终在上述地区定殖的风险可能性存在,但风险较小.另外,折刀法(Jackknife)分析显示,6种环境因子,例如地面霜冻频率、年平均降雨量、十月降雨量、四月降雨量、年最低温度以及蒸气压,对于番荔枝实蝇在全球和局部地区的分布模式有显著的影响.  相似文献   
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Leveraging existing presence records and geospatial datasets, species distribution modeling has been widely applied to informing species conservation and restoration efforts. Maxent is one of the most popular modeling algorithms, yet recent research has demonstrated Maxent models are vulnerable to prediction errors related to spatial sampling bias and model complexity. Despite elevated rates of biodiversity imperilment in stream ecosystems, the application of Maxent models to stream networks has lagged, as has the availability of tools to address potential sources of error and calculate model evaluation metrics when modeling in nonraster environments (such as stream networks). Herein, we use Maxent and customized R code to estimate the potential distribution of paddlefish (Polyodon spathula) at a stream‐segment level within the Arkansas River basin, USA, while accounting for potential spatial sampling bias and model complexity. Filtering the presence data appeared to adequately remove an eastward, large‐river sampling bias that was evident within the unfiltered presence dataset. In particular, our novel riverscape filter provided a repeatable means of obtaining a relatively even coverage of presence data among watersheds and streams of varying sizes. The greatest differences in estimated distributions were observed among models constructed with default versus AICC‐selected parameterization. Although all models had similarly high performance and evaluation metrics, the AICC‐selected models were more inclusive of westward‐situated and smaller, headwater streams. Overall, our results solidified the importance of accounting for model complexity and spatial sampling bias in SDMs constructed within stream networks and provided a roadmap for future paddlefish restoration efforts in the study area.  相似文献   
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Climate change poses a serious threat to biodiversity. Predicting the effects of climate change on the distribution of a species' habitat can help humans address the potential threats which may change the scope and distribution of species. Pterocarya stenoptera is a common fast‐growing tree species often used in the ecological restoration of riverbanks and alpine forests in central and eastern China. Until now, the characteristics of the distribution of this species' habitat are poorly known as are the environmental factors that influence its preferred habitat. In the present study, the Maximum Entropy Modeling (Maxent) algorithm and the Genetic Algorithm for Ruleset Production (GARP) were used to establish the models for the potential distribution of this species by selecting 236 sites with known occurrences and 14 environmental variables. The results indicate that both models have good predictive power. Minimum temperature of coldest month (Bio6), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12), and precipitation of driest month (Bio14) were important environmental variables influencing the prediction of the Maxent model. According to the models, the temperate and subtropical regions of eastern China had high environmental suitability for this species, where the species had been recorded. Under each climate change scenario, climatic suitability of the existing range of this species increased, and its climatic niche expanded geographically to the north and higher elevation. GARP predicted a more conservative expansion. The projected spatial and temporal patterns of P. stenoptera can provide reference for the development of forest management and protection strategies.  相似文献   
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Predictive potential distribution modeling is crucial in outlining habitat usage and establishing conservation management priorities. In this paper we provide detailed data on the distribution of the Caucasian rock agama Para- laudakia caucasia, and use species distribution models (MAXENT) to evaluate environmental suitability and potential distribution at a broad spatial scale. Locality data on the distribution of P. caucasia have been gathered over nearly its entire range by various authors from field surveys. The distribution model ofP caucasia showed good performance (AUC = 0.887), and predicted high suitability in regions mainly located in Tajikistan, north Pakistan, Afghanistan, southeast Turkmenistan, northeast Iran along the Elburz mountains, Transcaueasus (Azerbajan, Armenia, Georgia), northeastern Turkey and northward along the Caspian Sea coast in Daghestan, Russia. The identification of suitable areas for this species will help to assess conservation status of the species, and to set up management programs.  相似文献   
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The EU 2020 Biodiversity Strategy requires the gathering of information on biodiversity to aid in monitoring progress towards its main targets. Common species are good proxies for the diversity and integrity of ecosystems, since they are key elements of the biomass, structure, functioning of ecosystems, and therefore of the supply of ecosystem services. In this sense, we aimed to develop a spatially-explicit indicator of habitat quality (HQI) at European level based on the species included in the European Common Bird Index, also grouped into their major habitat types (farmland and forest). Using species occurrences from the European Breeding Birds Atlas (at 50 km × 50 km) and the maximum entropy algorithm, we derived species distribution maps using refined occurrence data based on species ecology. This allowed us to cope with the limitations arising from modelling common and widespread species, obtaining habitat suitability maps for each species at finer spatial resolution (10 km × 10 km grid), which provided higher model accuracy. Analysis of the spatial patterns of local and relative species richness (defined as the ratio between species richness in a given location and the average richness in the regional context) for the common birds analysed demonstrated that the development of a HQI based on species richness needs to account for the regional species pool in order to make objective comparisons between regions. In this way, we proved that relative species richness compensated for the bias caused by the inherent heterogeneous patterns of the species distributions that was yielding larger local species richness in areas where most of the target species have the core of their distribution range. The method presented in this study provides a robust and innovative indicator of habitat quality which can be used to make comparisons between regions at the European scale, and therefore potentially applied to measure progress towards the EU Biodiversity Strategy targets. Finally, since species distribution models are based on breeding birds, the HQI can be also interpreted as a measure of the capacity of ecosystems to provide and maintain nursery/reproductive habitats for terrestrial species, a key maintenance and regulation ecosystem service.  相似文献   
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Forest undergrowth plants are tightly connected with the shady and humid conditions that occur under the canopy of tropical forests. However, projected climatic changes, such as decreasing precipitation and increasing temperature, negatively affect understory environments by promoting light‐demanding and drought‐tolerant species. Therefore, we aimed to quantify the influence of climate change on the spatial distribution of three selected forest undergrowth plants, Dracaena Vand. ex L. species, D. afromontana Mildbr., D. camerooniana Baker, and D. surculosa Lindl., simultaneously creating the most comprehensive location database for these species to date. A total of 1,223 herbarium records originating from tropical Africa and derived from 93 herbarium collections worldwide have been gathered, validated, and entered into a database. Species‐specific Maxent species distribution models (SDMs) based on 11 bioclimatic variables from the WorldClim database were developed for the species. HadGEM2‐ES projections of bioclimatic variables in two contrasting representative concentration pathways (RCPs), RCP2.6 and RCP8.5, were used to quantify the changes in future potential species distribution. D. afromontana is mostly sensitive to temperature in the wettest month, and its potential geographical range is predicted to decrease (up to ?63.7% at RCP8.5). Optimum conditions for D. camerooniana are low diurnal temperature range (6–8°C) and precipitation in the wettest season exceeding 750 mm. The extent of this species will also decrease, but not as drastically as that of D. afromontana. D. surculosa prefers high precipitation in the coldest months. Its potential habitat area is predicted to increase in the future and to expand toward the east. This study developed SDMs and estimated current and future (year 2050) potential distributions of the forest undergrowth Dracaena species. D. afromontana, naturally associated with mountainous plant communities, was the most sensitive to predicted climate warming. In contrast, D. surculosa was predicted to extend its geographical range, regardless of the climate change scenario.  相似文献   
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The ability to identify the spatial distribution of economically important fungal species is crucial for understanding the environmental factors that affect them and for conservation management. A potentially valuable approach for this is maximum entropy (Maxent) spatial distribution modeling, which was applied here to map the potential distribution of three “Sanghuang” mushrooms in China, which include Phellinus baumii, Phellinus igniarius and Phellinus vaninii. Nineteen WorldClim bioclimatic variables, with corresponding altitude data, and 89 spatially well-dispersed species occurrence records were used in the modeling. The relative importance of the environmental variables was evaluated by Jackknife tests in the modeling analysis. The maximum entropy models obtained have high Area Under Receiver Operating Characteristic Curve (AUC) values: 0.956, 0.967 and 0.960, for P. baumii, P. igniarius and P. vaninii, respectively. The bioclimatic variable that most strongly affected distributions of P. baumii and P. vaninii was precipitation in the warmest quarter, while the mean temperature in the warmest quarter affected the distribution of P. igniarius most strongly. Overall, these models could provide valuable help in searching for the target species in areas where it is hitherto unknown, and be the reference of conservation measures for these medicinal fungal species.  相似文献   
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