气候变化情景下流苏香竹潜在分布区预测

流苏香竹（Chimonocalamus fimbriatus）是云南特有珍稀竹种，主要分布于云南西南部。文章以野外调查获取的流苏香竹分布信息为主，运用最大熵模型（MaxEnt）同时结合地理信息系统（ArcGIS），基于19个气候因子，预测其在当前及未来气候变化情景下的潜在分布区。结果表明：当前流苏香竹的高适生区和中适生区主要分布于德宏州、保山市和临沧市等地，除迪庆州、丽江市和昭通市外，云南其他区域均有低适生区零星分布。在未来2050s和2070s的2个时间段，基于2种不同共享社会经济路径（SSP1-2.6和SSP5-8.5），流苏香竹的高适生区面积呈减少的趋势，尤其是SSP5-8.5路径下，高适生区面积仅为当前的12.51%（2050s）和18.63%（2070s）；中、低适生区在SSP1-2.6路径下，显著扩张（2050s）或略微扩张（2070s），在SSP5-8.5路径下，则大幅收缩。流苏香竹野外实际分布区及其潜在分布区均以斑块状为主，可能与云南特殊的地形、地貌有关。影响流苏香竹分布的主导气候因子为最湿月份降水量、最暖月份最高温度、最干季度降水量和平均气温日较差。流苏香竹对气候变化比较敏感，根据其野外分布状况，建议以就地保护为主、迁地保护为辅，在其潜在适生区内适当引种栽培。     

Mapping the current and future distributions of Onosma species endemic to Iran

Climate change may cause shifts in the natural range of species especially for those that are geographically restricted and/or endemic species. In this study, the spatial distribution of five endemic and threatened species belonging to the genus Onosma (including O. asperrima, O. bisotunensis, O. kotschyi, O. platyphylla, and O. straussii) was investigated under present and future climate change scenarios: RCP2.6 (RCP, representative concentration pathway; optimistic scenario) and RCP8.5 (pessimistic scenario) for the years 2050 and 2080 in Iran. Analysis was conducted using the maximum entropy (MaxEnt) model to provide a basis for the protection and conservation of these species. Seven environmental variables including aspect, depth of soil, silt content, slope, annual precipitation, minimum temperature of the coldest month, and annual temperature range were used as main predictors in this study. The model output for the potential habitat suitability of the studied species showed acceptable performance for all species (i.e., the area under the curve (AUC)>0.800). According to the models generated by MaxEnt, the potential current patterns of the species were consistent with the observed areas of distributions. The projected climate maps under optimistic and pessimistic scenarios (RCP2.6 and RCP8.5, respectively) of 2050 and 2080 resulted in reductions and expansions as well as positive range changes for all species in comparison to their current predicted distributions. Among all species, O. bisotunensis showed the most significant and highest increase under the pessimistic scenario of 2050 and 2080. Finally, the results of this study revealed that the studied plant species have shown an acute adaptability to environmental changes. The results can provide useful information to managers to apply appropriate strategies for the management and conservation of these valuable Iranian medicinal and threatened plant species in the future.     

Predicting the potential geographic distribution of Bactrocera bryoniae and Bactrocera neohumeralis(Diptera: Tephritidae) in China using MaxEnt ecological niche modeling

Bactrocera bryoniae and Bactrocera neohumeralis are highly destructive and major biosecurity/quarantine pests of fruit and vegetable in the tropical and subtropical regions in the South Pacific and Australia. Although these pests have not established in China, precautions must be taken due to their highly destructive nature. Thus, we predicted the potential geographic distribution of B. bryoniae and B. neohumeralis across the world and in particular China by ecological niche modeling of the Maximum Entropy(Max Ent) model with the occurrence records of these two species. Bactrocera bryoniae and B. neohumeralis exhibit similar potential geographic distribution ranges across the world and in China, and each species was predicted to be able to distribute to over 20% of the globe. Globally, the potential geographic distribution ranges for these two fruit fly species included southern Asia, the central and the southeast coast of Africa, southern North America, northern and central South America, and Australia. While within China, most of the southern Yangtze River area was found suitable for these species. Notably, southern China was considered to have the highest risk of B. bryoniae and B. neohumeralis invasions. Our study identifies the regions at high risk for potential establishment of B. bryoniae and B. neohumeralis in the world and in particular China, and informs the development of inspection and biosecurity/quarantine measures to prevent and control their invasions.     

基于MaxEnt模型的可可潜在适宜分布研究

根据野外采样和文献查阅,系统整理了可可的地理分布记录,并利用MaxEnt生态位模型和ArcGIS软件对可可的潜在适宜分布范围进行预测。结果表明:北美洲南部、中南美洲北部、非洲西部、亚洲东南部以及太平洋美拉尼西亚群岛地区均是可可的潜在适宜分布区域。其中,中国海南、台湾南部、云南西双版纳、广东雷州半岛也属于可可的适生范围。经ROC(Receiver operating characteristic)曲线分析法验证,MaxEnt模型的AUC(Area under curve)值为0.977,表明预测结果具有较高的可信度。各环境变量重要性的Jacknife检验表明,极端最低温度、年降雨量、年温度变化范围、最暖季降雨量对可可的潜在分布影响最大。     

基于最大熵模型预测小火蚁在我国的适生区

小火蚁是中国大陆新发现的重要外来入侵害虫, 目前对该虫的传入来源和在我国的适生区范围尚不明确?为明确我国小火蚁适生区范围, 有效防控该虫在我国的扩散和蔓延, 本研究通过该虫全球已有的分布数据, 采用最大熵模型对其适生区进行了预测?研究表明, 最大熵模型预测小火蚁适生区精度较高?预测结果显示, 我国小火蚁的潜在适生区主要分布于南方, 其中, 高适生区分布在台湾?海南?云南南部边境?广西西南局部?福建西南部?广东南部及其沿海地区?预测结果与该物种现有地理范围的生态条件一致?年降水量对小火蚁的适生性影响最大, 理论年降水量为2 040 mm时小火蚁分布的概率最高?随着全球气候变暖, 未来我国小火蚁的适生区有向北扩大的趋势, 但主要适生区还是以南方为主?     

基于MaxEnt模型预测梨火疫病菌的潜在地理分布

为探究梨火疫病菌解淀粉欧文氏菌Erwinia amylovora在全球的潜在地理分布,基于其全球分布数据和筛选得到的环境变量,利用MaxEnt模型对其在当前气候和未来气候条件下的潜在地理分布进行预测,并利用刀切法和皮尔逊相关性分析法筛选对梨火疫病菌分布有重要影响的环境变量。结果显示,对梨火疫病菌分布有重要影响的环境变量包括2月平均最高温度、1月平均降水量、7月平均最低温度、温度变化方差、昼夜温差月均值和7月平均降水量,表明春季和夏季的温度和降水对梨火疫病菌的分布有较大影响。在当前气候条件下,梨火疫病菌在全球的适生区分布较广,适生区总面积达到5.58×10⁷ km²,且高适生区主要分布在北美洲沿海地区、地中海沿岸和亚洲中部及东部的部分地区;梨火疫病菌在我国的适生区总面积为7.36×10⁶ km²,占全国陆地总面积的76.70%;在未来气候SSP126和SSP585情景下,梨火疫病菌在全球的适生区总面积分别为5.52×10⁷ km²和5.24×10⁷ km²。表明梨火疫病菌对我国大部分地区有潜在威胁,应加强监测与防控。     

珍稀植物楠木的地理分布及潜在分布区的预测

以植物地理分布与气候变化间的关系为研究基础,通过MaxEnt模型,应用贡献率、置换重要值以及Jackknife检验、主成分分析,模拟研究影响楠木Phoebe zhennan地理分布的主要因子,分析楠木在当前的地理分布格局,并预测未来楠木在中国的潜在分布格局。结果表明,采用MaxEnt模型预测其潜在适宜分布区准确度极高,楠木在ROC曲线下的AUC值达0.995;当前(2019)适生区预测表明,四川东南部、重庆和贵州是其分布最集中的地方,其次云南、广西、广东、福建、浙江、江西、湖北也有零散分布;随着未来(2050年)气温与降雨量的变化,江苏将有较大范围适合楠木分布,但是总的适生区面积比当前减少,例如贵州西南部的适生区面积将变小,福建、江西中度适生分布面积将变大,其余地区的适生区和当前持平。本研究结果表明,影响楠木分布的主要气候因子是温度和降雨量,可为楠木资源的科学保护和合理利用提供理论依据。     

气候变化下沙冬青适宜分布区预测

通过135个地理分布数据,利用MaxEnt生态位模型,选择19项环境因子预测沙冬青未来的分布格局及面积。结果表明:1)模型模拟的训练集和测试集的AUC值分别为0.990和0.988,模型的预测结果可靠;2)沙冬青主要分布于内蒙古西部地区、宁夏北部、甘肃东部地区以及蒙古国南部;3)模型模拟当前沙冬青的分布面积为6.3×10⁵ km²,模拟2050年沙冬青的分布面积为9.1×10⁵ km²,沙冬青分布范围和种群面积将呈增加趋势;4)影响沙冬青分布的主要因子为最冷季降水量、最冷月最低气温、最热季降水量、年平均气温、最湿月降水量和年降水。研究结果不仅提供了沙冬青适生分布的划分,也为沙冬青调查与监测提供了重要的科学基础。     

气候变化下花榈木在我国的潜在分布区预测

针对花榈木的种质资源保护利用问题,基于花榈木在中国的188条地理分布记录和9个气候变量,于 SSP1-2.6、SSP2-4.5、SSP5-8.5 3种气候情景模式下,利用ENMeval数据包优化后的MaxEnt模型和ArcGIS对其当前和未来(2050s和2070s)的生态位进行模拟,研究影响花榈木地理分布的主导环境因子,分析其在不同气候情景模式下的空间分布格局和迁移趋势。结果表明,当模型的特征组合（FC）为线性、二次型、片段化、乘积型和阈值性,正则化乘数(RM)为2时,模型复杂度和过拟合程度较低,此时训练集的平均AUC和平均标准偏差分别为0.954和0.004,表明模型预测精度很高。花榈木在当前气候下的潜在分布区主要在我国东南部,核心分布区位于江西、湖南、福建、浙江等省份及其周边区域。刀切法(Jackknife)表明最干季度降水量、最湿季度降水量、最冷季度平均温度是影响其分布的主导气候因子。未来3种气候情景下花榈木总体适生区分布相对稳定,核心适生区主要表现为东西向稳定,于南北向中部迁移,尤以广东中部及江西中部收缩显著,这些迁移的核心适生区是花榈木应对气候变化的敏感区域,需引起重视。     

Predicting summer fin whale distribution in the Pelagos Sanctuary (north-western Mediterranean Sea) to identify dynamic whale–vessel collision risk areas

1. Mediterranean fin whales aggregating in the Pelagos Sanctuary in summer to feed are exposed to vessel collision risk, particularly from high-speed ferries.
2. This study developed models to predict summer fin whale distribution using a generalized additive model (GAM) and MaxEnt, with the aim of providing a tool to identify potential high whale–ferry collision risk areas along ferry routes within the Pelagos Sanctuary during summertime.
3. Models were trained using sightings data collected in the summer months of 2009–2018 on board ferries crossing the central area of the Pelagos Sanctuary. Environmental predictors were bathymetry and mean sea surface chlorophyll concentration of the annual spring bloom period.
4. The predictive ability of GAM and MaxEnt was assessed using existing knowledge of summer fin whale distribution in the region. GAM (deviance explained = 20.2%) predictions matched documented distributions more closely than that of MaxEnt, with highest predicted fin whale occurrence in deep offshore waters (>2000 m) encompassing the central north-western and western regions, and in the south-eastern region, consistent with known fin whale habitats within the Pelagos Sanctuary. Inter-annual variability was evident, influencing collision risk areas.
5. Collision risk was estimated as a function of the overlap between the predicted probability of fin whale occurrence and ferry density estimated from Automated Identification System data. Ferry routes that cross the northern and eastern regions of the Pelagos Sanctuary presented relatively higher collision risk.
6. Areas with changes in risk intensity between the years were temporally and spatially dynamic: some appeared intermittently throughout the study period while others persisted over consecutive years or recurred in different years.
7. Due to the vastness of the Pelagos Sanctuary, vessel speed reduction maybe a more practical measure to manage collision risk than re-routing shipping lanes. A combination of Seasonal Management Areas and Dynamic Management Areas approaches could be adopted for high-risk areas.