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1.
  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.
  相似文献   
2.
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。  相似文献   
3.
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。  相似文献   
4.
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。  相似文献   
5.
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。  相似文献   
6.
基于MaxEnt模型的毛红椿适生区预测   总被引:1,自引:0,他引:1       下载免费PDF全文
[目的]了解环境因子对毛红椿适生区分布的影响,为其资源保护、引种及其人工林的发展提供参考。[方法]基于MaxEnt模型能利用现存不完整、小样本、离散型分布数据构建物种适生区预测模型,用受试者工作曲线线下面积(AUC)检验预测模型的精度,面积越大精度越高等优点,本研究应用毛红椿在云南的分布数据及1个地型因子和6个气候因子,来构建其适生区分布模型。[结果]毛红椿适生区分布MaxEnt模型平均训练AUC和平均测试AUC分别为0.891、0.885,说明对毛红椿适生区的预测是可靠的;降水量变异系数和最干季度降水量是决定毛红椿适生区分布的主要因子,年均气温变化范围、最冷季度平均气温、最湿季度降水量、最冷季度降水量是次要因子。在当代和未来(2050S、2070S)气候变暖条件下(RCP2.6情景),云南省和全国适生区面积计算结果直观、定量的反应了全球变暖对毛红椿适生区变迁的影响。[结论]预测云南省及全国的毛红椿适生区随全球变暖而小幅萎缩。  相似文献   
7.
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.  相似文献   
8.
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.  相似文献   
9.
基于黄花红砂与五柱红砂的现有分布数据,利用地理信息系统(ArcGIS)技术与最大熵(MaxEnt)模型软件,对2种红砂属植物在3个时期的潜在适宜分布区进行模拟。结果表明:1)五柱红砂的潜在适宜分布区主要在青海地区、新疆南疆地区以及甘肃部分地区;黄花红砂的潜在适宜分布区主要集中在宁夏地区、内蒙古地区、甘肃地区以及新疆与青海部分地区。2种红砂属植物在3个时期的潜在适宜分布区有所增加。黄花红砂的潜在适宜区朝着东北方向移动,五柱红砂的潜在适宜区出现收缩现象。2种红砂属的低适宜区相比较高适宜区与较适宜区变化较大。2)黄花红砂与五柱红砂通过MaxEnt模型运算出的AUC值均>0.9,表明MaxEnt模型预测精度很高。可以用于2种红砂属植物的潜在适宜分布区的预测。3)控制黄花红砂潜在分布的关键环境变量为最湿月降水量、最暖季度降水量和最湿季度降水量,影响五柱红砂潜在分布的关键环境变量为海拔、等温性和温度季节性变化。上述研究结果对2种红砂属植物资源保护利用与未来分布趋势提供了重要的指导意义。  相似文献   
10.
为探究毛竹的潜在分布及其动态变化,并确定对其分布起主导作用的环境变量,文章基于MaxEnt预测模型,利用环境变量图层及316个毛竹分布点数据研究毛竹地理分布的变化。结果显示:1)限制毛竹分布的主要环境变量为最干月降水量、年均降水量、最冷月最低温、年平均温、温度年较差和海拔;2)当前毛竹的潜在适生区主要位于中国东南部,与亚热带季风气候区大致重叠,高适生区主要位于总适生区的东部和西北部;3)在未来气候情景下,毛竹总适生区面积有所缩减,各不同等级适生区面积变化大,其中高适生区面积大幅度缩减。研究获得的毛竹适生区变化趋势可为毛竹培育、引种以及入侵防治提供参考。  相似文献   
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