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Biotic interactions have been controversial in distributional ecology, mainly in regards to whether they have effects over broad extents, with the negative view known as the Eltonian noise hypothesis (ENH). In this study, we evaluated the ENH for Phytotoma raimondii, a restricted‐range Peruvian endemic bird species: we developed models based on 1) only abiotic conditions, 2) only host plant distributions, and 3) both abiotic conditions and host plant distributions; models were evaluated with partial receiver operating characteristic test and Akaike information criteria metrics. We rejected the ENH for this case: biotic interactions improved the model. The frequency with which exceptions to the ENH are detected has important implications for distributional ecology and methods for estimating distributions of species.  相似文献   

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: 物种丰富度分布格局及其形成机制的研究对于生物多样性保护具有重要意义。为了解中国两栖动物物种丰富度分布格局,本文利用中国省级尺度两栖动物物种分布数据和环境信息,结合GIS和数理统计方法,探讨两栖动物物种丰富度的地理分布格局与环境因子之间的关系。研究结果表明:(1)物种丰富度随纬度增加呈逐渐递减趋势,但缺乏显著的经度梯度。丰富度最高的地区主要集中在南方,我国北方、西北干旱区和青藏高原北部地区丰富度较低;(2)最优模型由年均温、最冷月均温、净初级生产力、年降水量变化范围、月均降水量标准差组成,多层次方差分解表明,最冷月均温的独立解释能力(17.6%)高于年均温(11.5%);(3)方差分解表明,季节性因子的独立解释能力(5.6%)低于热量因子(6.1%),但高于水分因子(4.5%),因此我们认为季节性因子也是限制中国两栖动物分布的重要因素。  相似文献   

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Studies have found that biotic information can play an important role in shaping the distribution of species even at large scales. However, results from species distribution models are not always consistent among studies and the underlying factors that influence the importance of biotic information to distribution models, are unclear. We studied wild bees and plants, and cleptoparasite bees and their hosts in the Netherlands to evaluate how the inclusion of their biotic interactions affects the performance of species distribution models. We assessed model performance through spatial block cross-validation and by comparing models with interactions to models where the interacting species was randomized. Finally, we evaluated how, 1) spatial resolution, 2) taxonomic rank (genus or species), 3) degree of specialization, 4) distribution of the biotic factor, 5) bee body size and 6) type of biotic interaction, affect the importance of biotic interactions in shaping the distribution of wild bee species using generalized linear models (GLMs). We found that the models of wild bees improved when the biotic factor was included. The model performance improved the most for parasitic bees. Spatial resolution, taxonomic rank, distribution range of the biotic factor and degree of specialization of the modelled species all influenced the importance of the biotic interaction to the models. We encourage researchers to include biotic interactions in species distribution models, especially for specialized species and when the biotic factor has a limited distribution range. However, before adding the biotic factor we suggest considering different spatial resolutions and taxonomic ranks of the biotic factor. We recommend using single species or genus data as a biotic factor in the models of specialist species and for the generalist species, we recommend using an approximate measure of interactions, such as flower richness.  相似文献   

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Species distribution models (SDMs) can be correlative or mechanistic, which have very different assumptions, leading to potentially different estimates of the ecological niches and distributions of the species. The model predictions from correlative and mechanistic approaches are incomparable due to their distinct assumptions. Yet, seeking their agreements can identify robust predictions that are relatively independent of the assumptions used to generate them. However, the search for robust model predictions among SDM models remains understudied and rarely considers the effect of biotic interactions. It is essential to identify robust predictions from SDMs for policy making.  相似文献   

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物种分布模型(SDMs)通过量化物种分布和环境变量之间的关系,并将其外推到未知的景观单元,模拟、预测地理空间中生物的潜在分布,是生态学、生物地理学、保护生物学等研究领域的重要工具.然而,目前物种分布模型主要采用非生物因素作为预测变量,由于数据量化和建模表达困难,生物因素特别是种间作用在物种分布模型中常被忽略,将种间作用...  相似文献   

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以辽东山区次生林为研究对象,分析4 hm2样地Gleason丰富度指数、Simpson优势度指数、Shannon多样性指数和Pielou均匀度指数的空间分布特征及其与尺度的关系.结果表明: 4个多样性指数的空间分布均表现出较高的空间异质性;4个多样性指数的方差随尺度的增加其变化趋势有所差异;4个多样性指数的变异系数随尺度的增加呈下降趋势;乔木层的4个多样性指数值高于灌木层,且随尺度增加其变化趋势有所差异.在分析辽东山区次生林物种多样性时应考虑尺度效应.  相似文献   

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Climate change is affecting the geographic distributions of many species and researchers are increasingly relying on species distribution models (SDMs) to forecast species' redistributions under climate change. Such modelling studies, however, often ignore biotic interactions that shape species' geographic ranges. This is especially problematic for coral reefs, which host a high diversity of species and interactions. We tested how biotic interactions affect the distribution patterns of obligate coral-dwelling Trapezia crabs.  相似文献   

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The extent that biotic interactions and dispersal influence species ranges and diversity patterns across scales remains an open question. Answering this question requires framing an analysis on the frontier between species distribution modelling (SDM), which ignores biotic interactions and dispersal limitation, and community ecology, which provides specific predictions on community and meta‐community structure and resulting diversity patterns such as species richness and functional diversity. Using both empirical and simulated datasets, we tested whether predicted occurrences from fine‐resolution SDMs provide good estimates of community structure and diversity patterns at resolutions ranging from a resolution typical of studies within reserves (250 m) to that typical of a regional biodiversity study (5 km). For both datasets, we show that the imprint of biotic interactions and dispersal limitation quickly vanishes when spatial resolution is reduced, which demonstrates the value of SDMs for tracking the imprint of community assembly processes across scales.  相似文献   

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Although long-standing theory suggests that biotic variables are only relevant at local scales for explaining the patterns of species' distributions, recent studies have demonstrated improvements to species distribution models (SDMs) by incorporating predictor variables informed by biotic interactions. However, some key methodological questions remain, such as which kinds of interactions are permitted to include in these models, how to incorporate the effects of multiple interacting species, and how to account for interactions that may have a temporal dependence. We addressed these questions in an effort to model the distribution of the monarch butterfly Danaus plexippus during its fall migration (September–November) through Mexico, a region with new monitoring data and uncertain range limits even for this well-studied insect. We estimated species richness of selected nectar plants (Asclepias spp.) and roosting trees (various highland species) for use as biotic variables in our models. To account for flowering phenology, we additionally estimated nectar plant richness of flowering species per month. We evaluated three types of models: climatic variables only (abiotic), plant richness estimates only (biotic) and combined (abiotic and biotic). We selected models with AICc and additionally determined if they performed better than random on spatially withheld data. We found that the combined models accounting for phenology performed best for all three months, and better than random for discriminatory ability but not omission rate. These combined models also produced the most ecologically realistic spatial patterns, but the modeled response for nectar plant richness matched ecological predictions for November only. These results represent the first model-based monarch distributional estimates for the Mexican migration route and should provide foundations for future conservation work. More generally, the study demonstrates the potential benefits of using SDM-derived richness estimates and phenological information for biotic factors affecting species distributions.  相似文献   

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气候变化情景下物种适宜生境预测研究进展   总被引:2,自引:0,他引:2  
气候变化能够引起物种分布范围、生物物候等一系列生态现象和过程的变化,进而加速物种灭绝的速率。气候变化被认为是21世纪全球生物多样性面临的最主要威胁之一,将给未来的生物多样性保护工作带来严峻的挑战。利用物种分布模型预测气候变化情景下物种适宜生境的变化正成为当前的研究热点。本研究总结目前气候变化情景下物种适宜生境预测的最新方法及取得的主要成果。在研究方法上,多物种分布模型、多气候情景基础上的集合预测方法正成为目前研究采用的主要手段;在研究结果上,未来气候变化将有可能导致物种适宜生境面积减少,范围向高纬度、高海拔地区移动。最后本研究指出目前气候变化情景下物种适宜生境预测研究中存在的主要不足及今后的发展方向。  相似文献   

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森林群落物种多样性格局和动态一直是生态学的研究热点,人工林弃管后演替进程中物种多样性变化也很值得研究。杉木(Cunninghamia lanceolata)作为我国南方林区人工栽培最广、经济价值最高的用材树种之一,其人工林分布面积很大,通常群落结构简单、物种多样性低,然而群落中杉木数量如何影响植物物种多样性,迄今缺乏研究。在浙江省自然保护区内,选择不同疏伐强度和弃管时间的杉木人工林,建立了6个1 hm~2长期动态监测样地,在10 m×10 m、20 m×20 m、50 m×50 m和100 m×100 m尺度下,探究群落物种多样性(物种丰富度、Simpson指数、Shannon-Wiener指数和Pielou均匀度指数)的变化规律,分析杉木数量(多度和相对多度)对物种多样性的影响。结果显示:弃管前对杉木林的疏伐强度越高,演替恢复后的群落物种多样性越高。相同疏伐程度下,物种多样性随演替时间的延长有先升高后降低的趋势。取样尺度小于100 m×100 m时,杉木数量与物种多样性呈极显著负相关;100 m×100 m尺度下仅杉木相对多度与3种多样性指数呈显著负相关,杉木多度与各物种多样性均无显...  相似文献   

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空间异质性对样地数据空间外推的影响   总被引:1,自引:0,他引:1  
应用模型结合的方法模拟了3个空间异质性等级预案下反应变量(气候变化下景观水平的树种分布面积)的变化情况,并分析模拟结果在预案之间的差异性,探讨了环境空间异质性对样地观测到的树种对气候变化响应向更大空间尺度外推的影响.结果表明:空间异质性在一般情况下对样地数据向土地类型尺度外推没有影响,而对样地尺度外推到海拔带尺度的影响则有较复杂的情况.对于对气候变化不敏感的树种以及非地带性树种,空间异质性对样地数据向海拔带尺度外推没有影响;对于大多数对气候变化敏感的地带性树种而言,空间异质性对样地数据向海拔带尺度外推则有影响.  相似文献   

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空间尺度是影响我们理解生态学格局和过程的关键因素.目前已有多种关于物种多样性分布格局形成机制的假说且研究者未达成共识,原因之一是空间尺度对物种多样性分布格局的环境影响因子的解释力和相对重要性有重要影响.地形异质性是物种多样性分布格局的重要影响因素.本文综述了在地形异质性-物种多样性关系的研究中,不同空间粒度和幅度对研究...  相似文献   

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The symposium ‘What is Macroecology?’ was held in London on 20 June 2012. The event was the inaugural meeting of the Macroecology Special Interest Group of the British Ecological Society and was attended by nearly 100 scientists from 11 countries. The meeting reviewed the recent development of the macroecological agenda. The key themes that emerged were a shift towards more explicit modelling of ecological processes, a growing synthesis across systems and scales, and new opportunities to apply macroecological concepts in other research fields.  相似文献   

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Biotic homogenisation is defined as decreasing dissimilarity among ecological assemblages sampled within a given spatial area over time. Biotic differentiation, in turn, is defined as increasing dissimilarity over time. Overall, changes in the spatial dissimilarities among assemblages (termed ‘beta diversity’) is an increasingly recognised feature of broader biodiversity change in the Anthropocene. Empirical evidence of biotic homogenisation and biotic differentiation remains scattered across different ecosystems. Most meta-analyses quantify the prevalence and direction of change in beta diversity, rather than attempting to identify underlying ecological drivers of such changes. By conceptualising the mechanisms that contribute to decreasing or increasing dissimilarity in the composition of ecological assemblages across space, environmental managers and conservation practitioners can make informed decisions about what interventions may be required to sustain biodiversity and can predict potential biodiversity outcomes of future disturbances. We systematically reviewed and synthesised published empirical evidence for ecological drivers of biotic homogenisation and differentiation across terrestrial, marine, and freshwater realms to derive conceptual models that explain changes in spatial beta diversity. We pursued five key themes in our review: (i) temporal environmental change; (ii) disturbance regime; (iii) connectivity alteration and species redistribution; (iv) habitat change; and (v) biotic and trophic interactions. Our first conceptual model highlights how biotic homogenisation and differentiation can occur as a function of changes in local (alpha) diversity or regional (gamma) diversity, independently of species invasions and losses due to changes in species occurrence among assemblages. Second, the direction and magnitude of change in beta diversity depends on the interaction between spatial variation (patchiness) and temporal variation (synchronicity) of disturbance events. Third, in the context of connectivity and species redistribution, divergent beta diversity outcomes occur as different species have different dispersal characteristics, and the magnitude of beta diversity change associated with species invasions also depends strongly on alpha and gamma diversity prior to species invasion. Fourth, beta diversity is positively linked with spatial environmental variability, such that biotic homogenisation and differentiation occur when environmental heterogeneity decreases or increases, respectively. Fifth, species interactions can influence beta diversity via habitat modification, disease, consumption (trophic dynamics), competition, and by altering ecosystem productivity. Our synthesis highlights the multitude of mechanisms that cause assemblages to be more or less spatially similar in composition (taxonomically, functionally, phylogenetically) through time. We consider that future studies should aim to enhance our collective understanding of ecological systems by clarifying the underlying mechanisms driving homogenisation or differentiation, rather than focusing only on reporting the prevalence and direction of change in beta diversity, per se.  相似文献   

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