喀斯特高原峡谷优势种叶片功能性状分析

该研究对喀斯特石漠化高原峡谷地区优势种的叶片功能性状进行了分析,调查了17种植物的叶片厚度、叶面积、叶片鲜重、叶片干重、叶干物质含量、比叶面积、叶组织密度等能反映植物生存策略且易于测量的叶片功能性状,并采用逐步回归的方法探究了叶片功能性状与土壤养分之间的关系。结果表明:(1)不同优势种叶片功能性状差异明显,叶片的厚度为0.18～0.78 mm、鲜重为0.07～6.51 g、干重为0.04～3.19 g、叶面积为3.07～325.64 cm~2、叶干物质含量为318.61～573.22 mg·g~(-1)、比叶面积为60.98～236.90 cm~2·g~(-1)、叶组织密度为0.022 1～0.036 g·cm~(-3)。(2)植物通过较小的比叶面积与较大的叶干物质含量来减少水分散失、增加养分储存,以适应高温、缺水少土的环境。(3)叶片功能性状之间存在广泛的相关关系,且均达到极显著水平。叶片厚度对鲜重、干重均表现为促进效应,比叶面积与叶干物质含量及叶组织密度之间均存在极显著的抑制效应。(4)叶片功能性状随土壤养分的变化产生规律性变化。土壤全氮、全钾和有机碳对叶片功能性状产生显著影响,尤以土壤有机碳的影响更为显著。进一步分析发现,喀斯特高原峡谷地区植被恢复及重建的优势种或建群种为翅荚香槐、清香木、枇杷等;施用有机肥能够提高花椒、金银花等经济林树种的适应能力。     

天坑森林植物群落叶功能性状、物种多样性和功能多样性特征

以天坑内部-边缘-外部森林植物群落为研究对象,通过调查植物的群落结构、叶功能性状,探究天坑内外森林植物群落叶功能性状、物种多样性和功能多样性变化特征及其内在关联,为深入了解负地形森林生态系统的功能和恢复退化喀斯特地区的植被提供一定参考。研究结果如下：（1）比叶面积（SLA: 198.75 cm2/g)）、叶面积（LA: 42.70 cm2）、叶磷含量（LPC: 1.70 g/kg）和叶钾含量（LKC: 10.27 g/kg）在天坑内部最高,叶组织密度（LTD: 0.32 g/cm3）和叶干物质含量（LDMC: 0.41 g/g）在天坑外部最高,天坑内外森林均易受到磷限制,表明随天坑内部-边缘-外部生境变化,植物对环境的适应机制和生存策略发生了部分调整,物种的防御策略增强,生长投入策略减弱。（2）Shannon-Wiener指数（2.82）、Simpson指数（0.92）和Pielou’s均匀度指数（0.87）均以天坑外部最高,功能丰富度（1.05）、功能离散度（1.88）和Rao’s二次熵（4.52）以天坑内部最高,表明随天坑内部-边缘-外部生境的变化,植物功能性状的差异减少,物种分布及其功能性状分布总体上更为均匀、物种数量增多。（3）物种多样性指数之间、功能多样性指数之间存在较强的相关性,表明物种多样性指数之间、功能多样性指数之间存在不同的制约关系。（4）叶功能性状与物种多样性、功能多样性的相关性强,物种多样性和功能多样性之间相关性较弱,表明叶性状对生态学过程的变化较为敏感,叶功能性状与物种多样性之间存在较强的耦合关系。     

Effect of chronological addition of records to species distribution maps: The case of Tonatia saurophila maresi (Chiroptera,Phyllostomidae) in South America

Ecological niche models have become very popular for analysing the potential distribution of species. Nevertheless, models are strongly influenced by many factors, such as spatial resolution, environmental variables and the quality of distribution records. In this paper, we evaluated how ecological niche models changed with the addition of records accumulated over four decades. Our model species was the stripe‐headed round‐eared bat (Tonatia saurophila). Thus, with data organized in chronological order, we could observe how the models changed in predicting distributions over time in comparison with all known point locations. We tested if partial models could predict the occurrence of new unpublished records for savannah areas in central Brazil, considering that the species is typically associated with forest environments. Our results indicate a high omission rate for models built with point localities from the 1970s and 1980s (58.5% and 50.0% of all known points respectively), and predicted that the species could occur in central Brazil. Although T. saurophila has indeed been recorded recently in central Brazil, it was found in places different from those predicted by the models using these restricted earlier data. Nevertheless, the environmental suitability of such areas is significantly different from sites largely described in earlier records from the Amazonia region, as shown by principal components analysis. We argue that populations of T. saurophila that occupy open habitats in central South America (including Caatinga, Cerrado, Chaco and semi‐deciduous interior forests) deserve further study at the genetic level to determine if bats in these very different habitats are taxonomically distinct from Amazonian populations. Our results also suggest that models based on very limited datasets for species occurrence can lead conservationists or decision makers to wrong conclusions.     

Incorporating biotic interactions in the distribution models of African wild silk moths (Gonometa species,Lasiocampidae) using different representations of modelled host tree distributions

Biotic interactions influence species niches and may thus shape distributions. Nevertheless, species distribution modelling has traditionally relied exclusively on environmental factors to predict species distributions, while biotic interactions have only seldom been incorporated into models. This study tested the ability of incorporating biotic interactions, in the form of host plant distributions, to increase model performance for two host‐dependent lepidopterans of economic interest, namely the African silk moth species, Gonometa postica and Gonometa rufobrunnea (Lasiocampidae). Both species are dependent on a small number of host tree species for the completion of their life cycle. We thus expected the host plant distribution to be an important predictor of Gonometa distributions. Model performance of a species distribution model trained only on abiotic predictors was compared to four species distribution models that additionally incorporated biotic interactions in the form of four different representations of host plant distributions as predictors. We found that incorporating the moth–host plant interactions improved G. rufobrunnea model performance for all representations of host plant distribution, while for G. postica model performance only improved for one representation of host plant distribution. The best performing representation of host plant distribution differed for the two Gonometa species. While these results suggest that incorporating biotic interactions into species distribution models can improve model performance, there is inconsistency in which representation of the host tree distribution best improves predictions. Therefore, the ability of biotic interactions to improve species distribution models may be context‐specific, even for species which have obligatory interactions with other organisms.     

Incorporating intraspecific variation into species distribution models improves distribution predictions,but cannot predict species traits for a wide-spread plant species

The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its potential distribution under future climate scenarios. A core, rarely tested, assumption of SDMs is that all populations will respond equivalently to climate. Few studies have examined this assumption, and those that have rarely dissect the reasons for intraspecific differences. Focusing on the arctic-alpine cushion plant Silene acaulis, we compared predictive accuracy from SDMs constructed using the species’ full global distribution with composite predictions from separate SDMs constructed using subpopulations defined either by genetic or habitat differences. This is one of the first studies to compare multiple ways of constructing intraspecific-level SDMs with a species-level SDM. We also examine the contested relationship between relative probability of occurrence and species performance or ecological function, testing if SDM output can predict individual performance (plant size) and biotic interactions (facilitation). We found that both genetic- and habitat-informed SDMs are considerably more accurate than a species-level SDM, and that the genetic model substantially differs from and outperforms the habitat model. While SDMs have been used to infer population performance and possibly even biotic interactions, in our system these relationships were extremely weak. Our results indicate that individual subpopulations may respond differently to climate, although we discuss and explore several alternative explanations for the superior performance of intraspecific-level SDMs. We emphasize the need to carefully examine how to best define intraspecific-level SDMs as well as how potential genetic, environmental, or sampling variation within species ranges can critically affect SDM predictions. We urge caution in inferring population performance or biotic interactions from SDM predictions, as these often-assumed relationships are not supported in our study.     

植物叶片功能性状及其环境适应研究

植物叶片是连接植物与外界环境的重要桥梁,其功能性状变化受外界环境和系统发育的共同影响。充分了解不同环境下叶片功能性状的变化对探讨植物对环境的适应性具有重要意义。本文阐述了叶片功能性状的类型及其功能意义,综述了影响叶片功能性状2个主要因素（环境因子和系统发育历史）的相关研究,探讨了叶片功能性状对植物适应环境的意义。最后,对叶片功能性状研究的现状及未来趋势进行了展望。     

Using avatar species to model the potential distribution of emerging invaders

Aim Anticipating the potential distributions of emerging invasive species is complicated by the tendency for species distribution models to perform better when both native and invasive range data are available for model development. If invasive range data are lacking, species models are liable to under‐estimate distributions for emerging invaders, particularly for species that are not at equilibrium with their native range environment due to historical factors, dispersal limitation and/or ecological interactions. We demonstrate the potential to use well‐quantified niche shifts from established ‘avatar’ (i.e. the remote or virtual manifestation of an entity) invaders to develop plausible distributions for data‐poor emerging invaders contingent on niche shifts of similar magnitude or character. Location Global. Methods Using the globally invasive crayfishes Pacifastacus leniusculus and Procambarus clarkii as our avatar invaders, we quantify how niche position, size and structure differs between native and total ranges using Mahalanobis distance (a measure of multivariate similarity) and the climate predictors of annual minimum and maximum air temperature. We then generalize patterns of niche shift from these species to the emerging crayfish invader Cherax quadricarinatus. Results Some patterns of niche shifts were similar for Pacifastacus leniusculus and Procambarus clarkii, but niche shifts were of considerably greater magnitude for P. clarkii. When a native range model for C. quadricarinatus was modified with generalized niche shifts similar to Pacifastacus leniusculus and Procambarus clarkii, the potential global distribution for this species increased considerably, including many areas not identified by the native range model. Main conclusions We illustrate the potential to use avatar invaders to provide cautionary, niche shift‐assuming species distribution models for emerging invaders. Many theoretical and applied implications of the avatar species concept require additional investigation, including the development of frameworks to select appropriate avatar species and evaluate the performance of avatar‐derived models for emerging invaders. Despite these research needs, we believe this concept will have considerable utility for predicting vulnerability to invasion by data‐poor species; this is a critical management need because shifting pathways of introduction and climate change will produce many novel, emerging invasive species in the future.     

践踏干扰对碧塔海高寒草甸植被茎叶性状、物种多样性和功能多样性的影响

高寒草甸具有重要的生态服务功能,然而固有脆弱性使其极易遭受气候变化和人为干扰等多重因素的影响。作为滇西北旅游资源中重要的组分之一,高寒草甸吸引了大批游客前往开展徒步旅行活动,但伴随着的践踏干扰作用会不可避免地对高寒草甸生态系统带来负面影响。然而,目前关注践踏干扰对滇西北高寒草甸植被的影响,特别是植被功能性状和功能多样性如何发生变化方面的研究还十分欠缺。以云南省香格里拉市碧塔海自然保护区内的典型高寒草甸生态系统为研究对象,采用实验践踏的方式(一共5种不同强度的践踏处理)来模拟旅游活动对草甸植被的干扰作用,并以草甸植被的茎叶性状特征为切入点,重点探讨践踏干扰对茎叶性状的平均大小和变异程度的影响,以及物种丰富度(以物种形态分类为基础)和功能丰富度(以功能性状为基础)之间的关系。研究结果显示,随着践踏强度的增加,植株高度和叶片大小的平均值,而不是茎叶性状的变异程度,出现明显下降趋势。此外,物种丰富度和功能丰富度均随践踏强度的增强而减小,且两者之间呈现显著正相关关系。然而,较之轻度践踏实验组,重度践踏实验组中的功能均匀度和功能分离度水平均有所增加,表明践踏干扰可能会在短期内打破优势种对资源的绝对占有格局和减少物种间的生态位重叠程度。尽管高寒草甸对人类践踏活动有一定的承受能力,但气候变化和人为干扰等多重因素势必会改变和影响高寒草甸群落的结构和功能可持续性,这也对高寒草甸的保护与管理工作提出了更加紧迫的要求。     

基于MaxEnt模型的延河流域草本植物适生分布与功能性状分析

明确延河流域常见草本植物的潜在适生区分布,是植被恢复工作持续推进的基础。本研究收集了延河流域8种常见草本植物的地理分布信息和13个环境变量,采用MaxEnt和ArcGIS模拟了延河流域常见草本植物在当前气候下的潜在适生性分布,进而研究这8种不同草本植物适生性分布与功能性状变异特征之间的相关关系。研究结果显示：根据物种-性状排序图的分布格局判断,本研究选择的七个功能性状在植物所属科之间发生了明显趋异分化现象,在PC1右侧为禾本科植物,PC1左侧为菊科、豆科和唇形科植物。对物种适生性分布模拟结果表明,达乌里胡枝子在研究区内的适生性最高,百里香的适生性最低,表明达乌里胡枝子比其他常见草本物种更适合被选择为该流域的植被恢复的先锋物种。在功能性状变异特征相关性分析中,物种适生区大小与比叶面积变异系数呈显著正相关,与其他植物功能性状变异特征不显著。因此,比叶面积的变异系数更适合作为指示延河流域草本植物适生区大小的性状。     

物种分布模型理论研究进展

利用物种分布模型估计物种的真实和潜在分布区,已成为区域生态学与生物地理学中非常活跃的研究领域。然而,到目前为止,这项技术的理论基础仍然存在不足之处,一些关键的生态过程未能被有效纳入到物种分布模型的理论框架中,从而为解释物种分布模型预测的结果带来了诸多困惑。鉴于此,总结了物种分布模型的理论基础;系统探讨了物种分布模型与物种分布区的关系;特别指出了物种分布模型研究中存在的理论问题;重点阐述了物种分布模型未来的发展方向。研究认为,物种分布模型与生态位理论、源-库理论、种群动态理论、集合种群理论、进化理论等具有重要的联系;正确理解物种分布模型的预测结果与物种分布区的关系,有赖于对影响物种分布的3个主要因素(环境条件、物种相互作用与物种迁移能力)做出定量的分离;目前物种分布模型主要存在的问题是未能将物种的相互作用和物种的迁移能力有效纳入到模型的构建过程中;未来物种分布模型的发展应该加强模型背后理论框架的研究,并进一步加强整合物种相互作用过程、种群动态过程、迁移过程和物种进化过程等内容。研究还认为,从更高的理论层次模拟功能群和群落结构将是未来物种分布模型的重要发展方向。     

PSDS 2.0: 一个基于GIS和多个模型的生物潜在分布地预测系统

根据对生物分布地预测模型和软件发展现状的分析和总结, 本研究在PSDS 1.0的基础上提出并实现一个基于GIS且具有多个代表性模型的生物分布地预测系统(PSDS 2.0)。PSDS 2.0系统继承了1.0的环境包络和聚类包络模型, 进一步引入了限制因子包络、马氏距离、支持向量机等新模型, 并针对本领域中模型比较与选择的难点增加了迭代交叉验证的多模型选择功能。系统还实现了灵活定制和评估伪负样本的功能, 通过用只需要正样本的I类模型预测的结果对随机产生的伪负样本进行评估, 减小其落入适宜地区的概率, 进一步提高需要正负样本的II类模型的准确率。GIS功能在PSDS 2.0中也得到加强, 被应用于数据准备及结果分析等重要环节。文章最后以白冠长尾雉(Syrmaticus reevesii)为例, 运用PSDS 2.0系统预测其在中国范围内的潜在分布地, 并对各种模型的预测结果进行评估和比较。     

Using a species distribution model to guide NSW surveys of the long‐footed potoroo (Potorous longipes)

Knowledge of threatened species’ distributions is essential for effective conservation decision‐making. Species distribution models (SDMs) are widely used to map species’ geographic ranges, identify new areas of suitable habitat and guide field surveys. In New South Wales (NSW), Australia, there are grave doubts about whether populations of the critically endangered long‐footed potoroo (Potorous longipes) remain extant, and identification of occupied sites is a high priority for its conservation. We used an SDM (Maxent) to identify regions in NSW that may have suitable habitat for the potoroo. The SDM was built with seven climate layers and had strong predictive performance (cross‐validated AUC = 0.94). We then combined this information on habitat suitability with vegetation and topography, to identify 58 survey sites across NSW. From April 2016 to May 2017, we undertook six field trips deploying six to eight cameras at each site for 52–63 days, resulting in 25 120 camera trap nights. A total of 215 759 images captured 43 native and feral animal species, but no long‐footed potoroos. Following the survey, newly available, independent presence and absence data were used to validate our model. A Kruskal–Wallis H test indicated that habitat suitability values were significantly higher at presence locations than absence locations (H = 58.66, d.f. = 1, P < 0.001). Finally, we refitted the Maxent model with the new data and identified additional regions that future surveys could explore. We conclude, however, that if the long‐footed potoroo remains extant in NSW, it is extremely rare.     

南亚热带森林常见种和偶见种的功能多样性特征

群落特征可能取决于物种在功能实体功能特征的独特组合（FE）中的分布。了解这些分布是保护生物多样性和维持生态系统功能的基础。鼎湖山南亚热带常绿阔叶林群落物种丰富,根据物种的多度来区分常见种（每hm²超过一个个体）和偶见种（每hm²少于一个个体）。基于功能实体,分别计算功能实体数量、功能冗余度、功能脆弱度和功能过度冗余度来描述每个样方中常见种和偶见种的功能多样性。采用Wilcoxon检验检测常见种和偶见种功能性状的差异,通过线性回归分析常见种和偶见种的功能多样性与物种数量的关系,采用冗余分析（RDA）影响常见种和偶见种的功能多样性的环境因子。研究结果表明：（1）鼎湖山南亚热带常绿阔叶林群落中功能性状在常见种和偶见种间差异显著。（2）群落中数量较低的偶见种保持着较高比例的功能实体数量。功能实体数量、功能冗余度与常见种和偶见种数量显著正相关,功能过度冗余度与偶见种数量显著正相关,功能脆弱度与常见种和偶见种的物种数量呈显著负相关。（3）海拔是影响常见种功能多样性最关键的地形因子,凹凸度是影响偶见种功能多样性最关键的地形因子。土壤含水量是影响常见种功能多样性最关键的土壤因子,速效钾是影响偶见种功能多样性最关键的环境因子。研究结果表明,尽管物种丰富的热带森林具有较高的功能冗余,但其提供的保险效应不能抵消生态系统的功能脆弱性。减少具有独特功能实体和偶见种的损失是避免热带森林生态系统功能损失的有效和必要的方法。     

Untangling the relationships among regional occupancy,species traits,and niche characteristics in stream invertebrates

The regional occupancy and local abundance of species are affected by various species traits, but their relative effects are poorly understood. We studied the relationships between species traits and occupancy (i.e., proportion of sites occupied) or abundance (i.e., mean local abundance at occupied sites) of stream invertebrates using small‐grained data (i.e., local stream sites) across a large spatial extent (i.e., three drainage basins). We found a significant, yet rather weak, linear relationship between occupancy and abundance. However, occupancy was strongly related to niche position (NP), but it showed a weaker relationship with niche breadth (NB). Abundance was at best weakly related to these explanatory niche‐based variables. Biological traits, including feeding modes, habit traits, dispersal modes and body size classes, were generally less important in accounting for variation in occupancy and abundance. Our findings showed that the regional occupancy of stream invertebrate species is mostly related to niche characteristics, in particular, NP. However, the effects of NB on occupancy were affected by the measure itself. We conclude that niche characteristics determine the regional occupancy of species at relatively large spatial extents, suggesting that species distributions are determined by environmental variation among sites.