共查询到20条相似文献，搜索用时 15 毫秒
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Gábor Várbíró Judit Görgényi Béla Tóthmérész Judit Padisák Éva Hajnal Gábor Borics 《Ecology and evolution》2017,7(23):9905-9913
Although species–area relationship (SAR ) is among the most extensively studied patterns in ecology, studies on aquatic and/or microbial systems are seriously underrepresented in the literature. We tested the algal SAR in lakes, pools and ponds of various sizes (10?2–108 m2) and similar hydromorphological and trophic characteristics using species‐specific data and functional groups. Besides the expectation that species richness increases monotonously with area, we found a right‐skewed hump‐shaped relationship between the area and phytoplankton species richness. Functional richness however did not show such distortion. Differences between the area dependence of species and functional richness indicate that functional redundancy is responsible for the unusual hump‐backed SAR . We demonstrated that the Small Island Effect, which is a characteristic for macroscopic SAR s can also be observed for the phytoplankton. Our results imply a so‐called large lake effect, which means that in case of large lakes, wind‐induced mixing acts strongly against the habitat diversity and development of phytoplankton patchiness and finally results in lower phytoplankton species richness in the pelagial. High functional redundancy of the groups that prefer small‐scale heterogeneity of the habitats is responsible for the unusual humpback relationship. The results lead us to conclude that although the mechanisms that regulate the richness of both microbial communities and communities of macroscopic organisms are similar, their importance can be different in micro‐ and macroscales. 相似文献
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Thomas J. Matthews Kostas A. Triantis Robert J. Whittaker Franois Guilhaumon 《Ecography》2019,42(8):1446-1455
The species–area relationship (SAR) constitutes one of the most general ecological patterns globally. A number of different SAR models have been proposed. Recent work has shown that no single model universally provides the best fit to empirical SAR datasets: multiple models may be of practical and theoretical interest. However, there are no software packages available that a) allow users to fit the full range of published SAR models, or b) provide functions to undertake a range of additional SAR‐related analyses. To address these needs, we have developed the R package ‘sars’ that provides a wide variety of SAR‐related functionality. The package provides functions to: a) fit 20 SAR models using non‐linear and linear regression, b) calculate multi‐model averaged curves using various information criteria, and c) generate confidence intervals using bootstrapping. Plotting functions allow users to depict and scrutinize the fits of individual models and multi‐model averaged curves. The package also provides additional SAR functionality, including functions to fit, plot and evaluate the random placement model using a species–sites abundance matrix, and to fit the general dynamic model of oceanic island biogeography. The ‘sars’ R package will aid future SAR research by providing a comprehensive set of simple to use tools that enable in‐depth exploration of SARs and SAR‐related patterns. The package has been designed to allow other researchers to add new functions and models in the future and thus the package represents a resource for future SAR work that can be built on and expanded by workers in the field. 相似文献
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Itamar Giladi Felix May Michael Ristow Florian Jeltsch Yaron Ziv 《Journal of Biogeography》2014,41(6):1055-1069
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Jürgen Dengler 《Diversity & distributions》2010,16(2):256-266
Aim The small island effect (SIE), i.e. the hypothesis that species richness below a certain threshold area varies independently of island size, has become a widely accepted part of the theory of island biogeography. However, there are doubts whether the findings of SIEs were based on appropriate methods. The aim of this study was thus to provide a statistically sound methodology for the detection of SIEs and to show this by re‐analysing data in which an SIE has recently been claimed ( Sfenthourakis & Triantis, 2009 , Diversity and Distributions, 15 , 131–140). Location Ninety islands of the Aegean Sea (Greece). Methods First, I reviewed publications on SIEs and evaluated their methodology. Then, I fitted different species–area models to the published data of area (A) and species richness (S) of terrestrial isopods (Oniscidea), with log A as predictor and both S (logarithm function) and log S (power function) as response variables: (i) linear; (ii) quadratic; (iii) cubic; (iv) breakpoint with zero slope to the left (SIE model); (v) breakpoint with zero slope to the right; (vi) two‐slope model. I used non‐linear regression with R2adj., AICc and BIC as goodness‐of‐fit measures. Results Many different methods have been applied for detecting SIEs, all of them with serious shortcomings. Contrary to the claim of the original study, no SIE occurs in this particular dataset as the two‐slope variants performed better than the SIE variants for both the logarithm and power functions. Main conclusions For the unambiguous detection of SIEs, one needs to (i) include islands with no species; (ii) compare all relevant models; and (iii) account for different model complexities. As none of the reviewed SIE studies met all these criteria, their findings are dubious and SIEs may be less common than reported. Thus, conservation‐related predictions based on the assumption of SIEs may be unreliable. 相似文献
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Simone Fattorini Diana M. P. Galassi Giovanni Strona 《Insect Conservation and Diversity》2016,9(4):369-373
- Human presence can affect biodiversity in many ways. If anthropization is one of the major drivers of species extinctions, at the same time, human induced increase in environmental heterogeneity may also increase species richness.
- In many cases, however, heterogeneity is not enough to explain the unexpectedly high biodiversity found in some densely populated areas.
- A possible explanation to such situations is the partial overlap in resource requirements between man and other species, which promotes a tendency for humans to settle in sites characterised by environmental conditions that are particularly favourable also for many other organisms.
- To test this hypothesis, we investigated the relationships between human population and species richness of native (non‐synanthropic) tenebrionid beetles in the Mediterranean islands, many of which have been inhabited by humans for millennia.
- Using partial correlation analyses, we found that tenebrionid diversity increased not only with island area and maximum elevation (used herein as a measure of environmental heterogeneity), but also with human population.
- This may suggest that the islands that were (and are) more accessible and hospitable to humans are also those which can be more easily colonised by tenebrionids, owing to their larger areas and higher environmental heterogeneity.
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JOS H. SCHOEREDER TATHIANA G. SOBRINHO CARLA R. RIBAS RENATA B. F. CAMPOS 《Austral ecology》2004,29(4):391-398
Abstract In this paper we tested the assumption that smaller and more isolated remnants receive fewer ant colonizers and lose more species. We also tested hypotheses to explain such a pattern. We sampled ants in Brazil for 3 years in 18 forest remnants and in 10 grasslands between them. We tested the influence of remnant area and isolation on colonization rate, as well as the effect of remnant area on extinction rate. We tested the correlation between remnant area and isolation to verify the landscape design. Colonization rate was not affected by remnant area or isolation. Extinction rate, however, was smaller in larger remnants. Remnant area and isolation were negatively correlated. We tested two hypotheses related to the decrease in ant species extinction rate with increased remnant area: (i) small remnants support smaller and more extinction‐prone populations; and (ii) small remnants are more often invaded by generalist species, which suffer higher extinction inside remnants. The density of ant populations significantly increased with area. Generalist species presented a lower colonization rate in larger remnants, contrary to the pattern observed in forest species. Generalist species suffered more extinction than expected inside remnants. The lack of response of colonization rate to remnant area can be explained by the differential colonization by generalist and forest species. The decrease of ant population density in smaller remnants could be related to loss of habitat quality or quantity. The higher colonization by generalist ant species in the smaller remnants could be related to landscape design, because smaller remnants are more similar to the matrix than larger ones. Our results have important implications for conservation strategies because small remnants seem to be more affected by secondary effects of fragmentation, losing more forest species and being invaded more often by generalist species. Studies that compare only species richness between remnants cannot detect such patterns in species composition. 相似文献
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K. A. Triantis K. Vardinoyannis E. P. Tsolaki I. Botsaris K. Lika M. Mylonas 《Journal of Biogeography》2006,33(5):914-923
Aim To propose a new approach to the small island effect (SIE) and a simple mathematical procedure for the estimation of its upper limit. The main feature of the SIE is that below an upper size threshold an increase of species number with increase of area in small islands is not observed. Location Species richness patterns from different taxa and insular systems are analysed. Methods Sixteen different data sets from 12 studies are analysed. Path analysis was used for the estimation of the upper limit of the SIE. We studied each data set in order to detect whether there was a certain island size under which the direct effects of area were eliminated. This detection was carried out through the sequential exclusion of islands from the largest to the smallest. For the cases where an SIE was detected, a log‐log plot of species number against area is presented. The relationships between habitat diversity, species number and area are studied within the limits of the SIE. In previous studies only area was used for the detection of the SIE, whereas we also encompass habitat diversity, a parameter with well documented influence on species richness, especially at small scales. Results An SIE was detected in six out of the 16 studied cases. The upper limit of the SIE varies, depending on the characteristics of the taxon and the archipelago under study. In general, the values of the upper limit of the SIE calculated according to the approach undertaken in our study differ from the values calculated in previous studies. Main conclusions Although the classical species–area models have been used to estimate the upper limit of the SIE, we propose that the detection of this phenomenon should be undertaken independently from the species–area relationship, so that the net effects of area are calculated excluding the surrogate action of area on other variables, such as environmental heterogeneity. The SIE appears when and where area ceases to influence species richness directly. There are two distinct SIE patterns: (1) the classical SIE where both the direct and indirect effects of area are eliminated and (2) the cryptic SIE where area affects species richness indirectly. Our approach offers the opportunity of studying the different factors influencing biodiversity on small scales more accurately. The SIE cannot be considered a general pattern with fixed behaviour that can be described by the same model for different island groups and taxa. The SIE should be recognized as a genuine but idiosyncratic phenomenon. 相似文献
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Species–area curves from islands and other isolates often differ in shape from sample‐area curves generated from mainlands or sections of isolates (or islands), especially at finer scales. We examine two explanations for this difference: (1) the small‐island effect (SIE), which assumes the species–area curve is composed of two distinctly different curve patterns; and (2) a sigmoid or depressed isolate species–area curve with no break‐points (in arithmetic space). We argue that the application of Ockham’s razor – the principle that the simplest, most economical explanation for a hypothesis should be accepted over less parsimonious alternatives – leads to the conclusion that the latter explanation is preferable. We hold that there is no reason to assume the ecological factors or patterns that affect the shapes of isolate (or island) curves cause two distinctly different patterns. This assumption is not required for the alternative, namely that these factors cause a single (though depressed) isolate species–area curve with no break‐points. We conclude that the theory of the small‐island effect, despite its present standing as an accepted general pattern in nature, should be abandoned. 相似文献
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Thomas J. Matthews H. Eden Cottee‐Jones Robert J. Whittaker 《Diversity & distributions》2014,20(10):1136-1146
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小岛屿效应(small-island effect, SIE)描述了种-面积关系(species-area relationship, SAR)的一种特殊现象: 在面积低于某个阈值时, 物种数不随岛屿面积的增加而增加或以一种比大岛屿低的速率增加的现象。由于小岛屿效应的面积阈值可能是多种生物地理格局和生态学过程在空间尺度上的转折点, 另外该现象在生物多样性保护领域具有重要指导意义, 因此小岛屿效应已经成为岛屿生物地理学和生物多样性研究领域的一种重要格局。目前主要有5种分析和检验小岛屿效应的方法, 包括种-面积关系形状比较法(SAR shape comparison)、断点回归法(breakpoint/piecewise regression)、零模型法(null model)、路径分析法(path analysis)和树模型法(tree-based model)。本文首先简要介绍了小岛屿效应与种-面积关系的关联, 然后重点总结了文献中记载的小岛屿效应检测的5种方法的优点和不足。在SAR形状比较法中, 受大岛屿离群值效应的影响, SAR的形状往往很难呈现出“S”形曲线。在断点回归法中, 数据的对数转换使检测到的SIE可能只是一种假象。在零模型法中, 随机化过程忽略了物种之间生态特征的差异, 降低了岛屿物种丰富度预期值的可信度。在路径分析法中, 生境多样性难以量化以及SIE范围内如果SAR具有斜率则会降低该方法的适用性。在树模型法中, 如果面积不是物种丰富度变异的最佳预测因子, 树模型不会首先选择面积对样本进行拆分; 另外如果SAR存在两个面积阈值, 树模型可能不会从SIE阈值处进行拆分。因此, 为了避免因某种方法的自身不足而造成的错误判断, 我们建议: 首先应同时使用多种方法来进行SIE检测, 当至少有两种方法同时检测出SIE时, 方可认为系统中存在SIE; 其次, 针对现有检测方法中存在的缺陷加以改进也是今后重要的发展方向。最后, 本文针对国内学者从事较多的生境岛屿中SIE的特征以及导致全球变化的人类活动如何影响SIE的出现等问题给出了一些启发性建议。本文将为小岛屿效应的准确检测提供参考依据并为完善小岛屿效应的理论框架起到推动作用。 相似文献
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Mingjian Yu Guang Hu Kenneth J. Feeley Jianguo Wu Ping Ding 《Journal of Biogeography》2012,39(6):1124-1133
Aim To test relationships between the richness and composition of vascular plants and birds and attributes of habitat fragments using a model land‐bridge island system, and to investigate whether the effects of fragmentation differ depending on species natural history traits. Location Thousand Island Lake, China. Methods We compiled presence/absence data of vascular plant and bird species through exhaustive surveys of 41 islands. Plant species were assigned to two categories: shade‐intolerant and shade‐tolerant species; bird species were assigned to three categories: edge, interior, and generalist species. We analysed the relationships between island attributes (area, isolation, elevation, shape complexity, and perimeter to area ratio) and species richness using generalized linear models (GLMs). We also investigated patterns of composition in relation to island attributes using ordination (redundancy analysis). Results We found that island area explained a high degree of variation in the species richness of all species groups. The slope of the species–area relationship (z) was 0.16 for all plant species and 0.11 for all bird species. The lowest z‐value was for generalist birds (0.04). The species richness of the three plant species groups was associated with island area per se, while that of all, generalist, and interior birds was explained mainly by elevation, and that of edge bird species was associated primarily with island shape. Patterns of species composition were most strongly related to elevation, island shape complexity, and perimeter to area ratio rather than to island area per se. Species richness had no significant relationship with isolation, but species composition did. We also found differential responses among the species groups to changes in island attributes. Main conclusions Within the Thousand Island Lake system, the effects of fragmentation on both bird and plant species appear to be scale‐dependent and taxon‐specific. The number of plant species occurring on an island is strongly correlated with island area, and the richness of birds and the species composition of plants and birds are associated with variables related to habitat heterogeneity. We conclude that the effects of fragmentation on species diversity and composition depend not only on the degree of habitat loss but also on the specific patterns of habitat fragmentation. 相似文献
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In some island systems, an ‘anomalous’ feature of species richness on smaller islands, in comparison with larger ones, has been observed. This has been described as the small island effect (SIE). The precise meaning of the term remains unresolved, as does the explanation for the phenomenon and even whether it exists. Dengler (2010 ; Diversity Distrib, 16 , 256–266.) addresses a number of conceptual and methodological issues concerning the nature and the detection of the SIE but fails to settle conclusively most of the issues he raises. We contend that his approach is theoretically flawed, especially in its treatment of habitat diversity. We offer a few suggestions of what is needed to advance understanding of the SIE. 相似文献
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Aim Anolis lizard invasions are a serious threat world‐wide, and information about how this invasive predator affects the diversity of prey assemblages is important for many strategic conservation goals. It is hypothesized that these predators reduce the slope of species–area relationships (SARs) of their prey assemblages. The effects of island area and predation by anolis lizards on the species richness of insular insect assemblages were investigated. Location Twenty‐four isles around Staniel Cay, Exuma Cays, Bahamas. Methods Flying insects were sampled using half‐sized Malaise traps for three consecutive days on each island in May 2007. First, the effect of island area on the probability of lizard presence was evaluated. Then, the effects of the presence–absence of predatory lizards on SARs were analysed for the overall insect assemblage and for the assemblages of five dominant insect orders. Results Our results indicated that anolis lizards occurred primarily on larger islands. The species richness of the overall insect assemblage and five dominant insect orders significantly increased with island area. The interaction between island area and predator presence–absence significantly affected the overall insect assemblage and Diptera and Hymenoptera assemblages (but not Coleoptera, Hemiptera and Lepidoptera assemblages). The presence of predators caused decreases in the slope of the SARs. Main conclusions The presence of predatory lizards strongly affects species richness of insular insect assemblages with the island area being a crucial determinant of the species richness. Therefore, the slope of the SAR can serve as a measure of the consequence of invasive predatory species on native insect assemblages. 相似文献