未来气候变化对广布种麻栎地理分布的影响和主导气候因子分析

为了解麻栎（Quercus acutissima）的潜在分布特征及其对未来气候的响应,运用最大熵模型（Maxent）模拟在当前气候条件下麻栎的潜在分布格局,预测未来不同温室气体排放情景下的格局变化,并分析影响其分布的主导因子。结果表明,Maxent模型有较好的预测能力,AUC值大于0.95。当前气候条件下,麻栎广泛分布于我国南方大部分地区和陕西、河南、山西、甘肃、北京、辽宁等北方省市。此外,在日本、朝鲜半岛、老挝、越南、缅甸、尼泊尔、不丹、印度、巴基斯坦等国家和地区亦存在不同程度和范围的麻栎适生区域,麻栎总适生区域面积达11.57×10⁵km²。在RCP2.6和RCP8.5情景下,麻栎适宜分布区域向北和西南方向扩展,新增适生区面积为（2.49~3.02）×10⁵km²;适生区域丧失主要集中在广西南部、广东南部和缅甸东部等地。影响其分布的主导气候因子为最暖季降水量、等温性、最干季均温、最冷月最低温,因子贡献率分别为54.2%、13.7%、8.8%和7.8%。这为麻栎的栽培和保育研究提供了参考依据。     

The MIAmaxent R package: Variable transformation and model selection for species distribution models

The widely used “Maxent” software for modeling species distributions from presence‐only data (Phillips et al., Ecological Modelling, 190, 2006, 231) tends to produce models with high‐predictive performance but low‐ecological interpretability, and implications of Maxent's statistical approach to variable transformation, model fitting, and model selection remain underappreciated. In particular, Maxent's approach to model selection through lasso regularization has been shown to give less parsimonious distribution models—that is, models which are more complex but not necessarily predictively better—than subset selection. In this paper, we introduce the MIAmaxent R package, which provides a statistical approach to modeling species distributions similar to Maxent's, but with subset selection instead of lasso regularization. The simpler models typically produced by subset selection are ecologically more interpretable, and making distribution models more grounded in ecological theory is a fundamental motivation for using MIAmaxent. To that end, the package executes variable transformation based on expected occurrence–environment relationships and contains tools for exploring data and interrogating models in light of knowledge of the modeled system. Additionally, MIAmaxent implements two different kinds of model fitting: maximum entropy fitting for presence‐only data and logistic regression (GLM) for presence–absence data. Unlike Maxent, MIAmaxent decouples variable transformation, model fitting, and model selection, which facilitates methodological comparisons and gives the modeler greater flexibility when choosing a statistical approach to a given distribution modeling problem.     

Using species distribution models to predict new occurrences for rare plants

Aim  To evaluate a suite of species distribution models for their utility as predictors of suitable habitat and as tools for new population discovery of six rare plant species that have both narrow geographical ranges and specialized habitat requirements.
Location  The Rattlesnake Creek Terrane (RCT) of the Shasta-Trinity National Forest in the northern California Coast Range of the United States.
Methods  We used occurrence records from 25 years of US Forest Service botanical surveys, environmental and remotely sensed climate data to model the distributions of the target species across the RCT. The models included generalized linear models (GLM), artificial neural networks (ANN), random forests (RF) and maximum entropy (ME). From the results we generated predictive maps that were used to identify areas of high probability occurrence. We made field visits to the top-ranked sites to search for new populations of the target species.
Results  Random forests gave the best results according to area under the curve and Kappa statistics, although ME was in close agreement. While GLM and ANN also gave good results, they were less restrictive and more varied than RF and ME. Cross-model correlations were the highest for species with the most records and declined with record numbers. Model assessment using a separate dataset confirmed that RF provided the best predictions of appropriate habitat. Use of RF output to prioritize search areas resulted in the discovery of 16 new populations of the target species.
Main conclusions  Species distribution models, such as RF and ME, which use presence data and information about the background matrix where species do not occur, may be an effective tool for new population discovery of rare plant species, but there does appear to be a lower threshold in the number of occurrences required to build a good model.     

Climate‐driven spatial mismatches between British orchards and their pollinators: increased risks of pollination deficits

Understanding how climate change can affect crop‐pollinator systems helps predict potential geographical mismatches between a crop and its pollinators, and therefore identify areas vulnerable to loss of pollination services. We examined the distribution of orchard species (apples, pears, plums and other top fruits) and their pollinators in Great Britain, for present and future climatic conditions projected for 2050 under the SRES A1B Emissions Scenario. We used a relative index of pollinator availability as a proxy for pollination service. At present, there is a large spatial overlap between orchards and their pollinators, but predictions for 2050 revealed that the most suitable areas for orchards corresponded to low pollinator availability. However, we found that pollinator availability may persist in areas currently used for fruit production, which are predicted to provide suboptimal environmental suitability for orchard species in the future. Our results may be used to identify mitigation options to safeguard orchard production against the risk of pollination failure in Great Britain over the next 50 years; for instance, choosing fruit tree varieties that are adapted to future climatic conditions, or boosting wild pollinators through improving landscape resources. Our approach can be readily applied to other regions and crop systems, and expanded to include different climatic scenarios.     

基于Maxent和GARP模型的日本双棘长蠹在中国的潜在地理分布分析

【目的】日本双棘长蠹Sinoxylon japonicum是一种重要的林木蛀干害虫。该虫自1981年首次报道于云南昆明以来,先后在中国的10多个省市被发现。近年来该害虫在北京、天津和河北等地对国槐Sophora japonica的危害日趋严重。【方法】在本研究中,作者按时间顺序梳理了日本双棘长蠹在中国的分布记录,根据日本双棘长蠹已有分布记录及其主要寄主植物国槐在我国的种植区域采用了Maxent和GARP两种生态位模型对日本双棘长蠹的潜在地理分布进行分析。【结果】日本双棘长蠹在中国南方地区呈零星分布,而在中国北方地区较为集中。分布记录的报道时间呈现出由南向北和自东向西的局势,推测这种分布格局是由于其寄主植物所导致的：中国北方地区危害严重可能与国槐在中国北方的集中种植有关,寄主树苗在不同地区间的调运是其种群扩散主要原因。基于生态位模拟的结果显示日本双棘长蠹在中国具有较大的适宜生态空间,潜在地理分布范围较广,从北到南其适生区主要有：辽宁西部、北京、天津、宁夏、河北、山西南部、山东、陕西、江苏、安徽、湖北、重庆、浙江、江西、湖南以及四川和贵州西北部。【结论】这些地区间的树苗转运需要做好检验检疫工作,以防止日本双棘长蠹的扩散。     

我国大陆黄檗潜在分布区及分布适宜性评价

黄檗为我国国家二级重点保护野生植物,多散生于阔叶林中,数量稀少,近年来,无论是从国家层面,还是地方政府层面都采取了一系列保护措施,人为破坏大大减少,然而其种群数量仍没有显著增加,拟从生态学的角度分析我国黄檗的潜在分布区,并进行了适宜性评价,为我国野生黄檗种群的就地保护和迁地保护提供技术支撑。通过实地调查和文献资料整理,获得69个包括准确经纬度信息的分布点,同时在世界气候数据库(World-Clim)中下载中国大陆的3个地形变量(alt、slo、asp),5个土壤变量(grav、pH、radi、rub、oc)和19个生物气候变量(bio1—bio19),利用多重共线性分析来检验环境因子之间的相关性,剔除出部分相关性高的环境因子,最终得到包括6个气候变量(bio1、bio3、bio4、bio12、bio15、prec1)、3个地形变量(alt、asp、slo)及5个土壤变量(grav、pH、radi、rub、oc)的14个环境因子作为环境变量,进而应用最大熵模型(MaxEnt)和地理信息系统(GIS)的空间分析功能,预测了黄檗在我国的潜在分布区,并评价分布区的适宜等级;分析了影响黄檗分布和适生性的关键因子及其适生区间。1)黄檗潜在分布区主要集中在我国东北地区和京津冀大部分区域,以及河南北部、内蒙古东南部等区域,黄檗潜在分布区总面积为117.51万km~2,占全国总面积的12.27%,其中高度适宜分布区面积为189400 km~2,占全国总面积的1.97%,主要包括黑龙江中东部,吉林大部分区域,辽宁东南部和北京大部分区域。2)温度季节性变化的标准差(35.7%)、年均降水量(28%)、坡度(6.5%)、年均温(6.7%)和有机碳含量(5.8%)是影响黄檗分布的5个最主要的环境因子,总的贡献率为82.7%。温度季节性变化标准差在14000—16000范围内,年均降水量在600—800 mm范围内,坡度在2°—8°范围内,年均温在1—7℃范围内,土壤有机碳含量在25—65 g/kg范围内为黄檗适宜分布的环境因子区间。我国野生黄檗分布还远远没有达到其潜在的分布范围,山东、山西、河南、内蒙古等目前资料显示分布并不广泛的省份也是进行野生黄檗迁地保护和人工种植的可选区域。对影响黄檗分布和适生性的关键因子进行分析后表明,温度季节性变化标准差越大其适生程度越高(14000—16000),说明黄檗对于温度的承受范围较大;年均降水量处于700 mm左右其适生程度最高,说明黄檗对于水分要求为中等水平;坡度为2°—8°范围内野生黄檗的适生程度最高,说明黄檗多分布在缓坡地带,年均温在4℃其适生程度最高;土壤有机碳含量在50 g/kg左右其适生程度最高。     

Potential distribution of orchid bees outside their native range: The cases of Eulaema polychroma (Mocsáry) and Euglossa viridissima Friese in the USA (Hymenoptera: Apidae)

Aim This study aimed to evaluate the probability of suitable habitats in the USA for two adventive orchid bee species (Eulaema polychroma (Mocsáry) and Euglossa viridissima Friese), one of which has become established in southern Florida despite the absence of its associated orchid hosts. Location North and Central America, northern South America and the Caribbean. Methods Using positive occurrence data within the native range of both orchid bee species, Maxent species distribution modelling was employed to evaluate the probability of suitable habitats in the USA. The power of predictability for the model was tested using partitions of the data. Results Our results show the absence of suitable habitat in southern Arizona for E. polychroma to maintain populations there, as well as establishing the northernmost limit for the species at around 29°N in north‐western Mexico. Suitable habitat was found for E. viridissima in various locations throughout southern Florida. This species is predicted to spread to occupy roughly the southern half of the Florida Peninsula. Main conclusions The findings indicate that species distribution modelling is useful for evaluating records of species occurrence outside of their native range. Our results indicate that the isolated record of a male of E. polychroma from southern Arizona should not be considered representative of an established population in the absence of further males and females from the same region. Conversely, E. viridissima has successfully become established in south‐eastern Florida after a seemingly accidental introduction first noticed in the summer of 2003. We discuss the naturalization of E. viridissima in Florida, the probability of suitable habitat across the Caribbean (where orchid bees are otherwise natively absent today) and the absence of perfume orchids (Orchidaceae). Lastly, we discuss the implications of these results for understanding the biology and biogeography of Euglossini.