贵州花溪大学城破碎化林地中鸟类群落的小岛屿效应

小岛屿效应打破了传统的种-面积关系认知,是当前岛屿生物地理学与生境破碎化领域的研究热点之一。然而,目前的研究缺乏以人类干扰度较高的城市破碎化生境为载体来探究小岛屿效应问题。本研究以贵州花溪大学城30个面积0.25 ~ 290.40 hm2的残存自然林地为研究区,在2017至2021年的鸟类繁殖季对林地中的鸟类进行调查。共记录到鸟类98种,隶属于11目41科。剔除高空飞行、非森林鸟类及偶然出现的物种后,不同斑块中的鸟类物种数介于12至49种之间,平均每个斑块24种。在R软件中利用“sars”包构建4种关键种-面积回归模型发现,先平后升的两段式回归模型是预测种-面积关系的最佳模型。该模型显示,在面积阈值1.16 hm2之上,物种丰富度随着面积的增加逐渐增多,符合传统岛屿生物地理学提出的面积效应;但是,在面积阈值1.16 hm2之下,物种丰富度不随面积发生显著变化,表现出小岛屿效应的特征。小岛屿效应的形成可能与喀斯特特殊地貌环境、食物资源或“中转站”、“垫脚石”等生态功能相关,其具体发生机制尚有待进一步研究。根据本研究结果,建议在城市规划建设时尽可能保护自然林地并设计绿色过渡带,在优先保护大林地斑块的同时不应忽视对具有重要生态价值的小林地斑块的保护。     

千岛湖岛屿小型兽类群落的多样性

2007 年秋季和2008 年春季,选取千岛湖地区14 个岛屿和2 个半岛作为样地,采用夹夜法进行小型兽类群落组成调查。两季度共布夹20 400 个,捕获小型兽类1 141 只,隶属2 目3 科9 属13 种,啮齿目(Rodentia)鼠科(Muridae)10 种和仓鼠科(Cricetidae)1 种,食虫目(Insectivora)鼩鼱科(Soricidae)2 种。利用以上结果分析其群落多样性,结果显示:14 个岛屿小型兽类群落春、秋两季的多样性指数、均匀度指数和优势度指数均呈现极显著差异且优势种发生变化;对可能影响岛屿小型兽类群落多样性的岛屿面积、距最近陆地距离、距最近大岛距离和植物丰富度等因素进行逐步回归分析,发现只有植物丰富度对小型兽类群落的物种丰富度有显著影响;对16 个样地按照物种组成比进行聚类,许源半岛样地与14 个岛屿聚为一类,姚家半岛样地单独归为一类,相似性指数比较结果亦显示姚家半岛样地与其它样地的相似性指数偏低。结论:景观破碎化导致千岛湖岛屿小型兽类群落的稳定性下降,物种多样性季节变化强烈;随岛屿面积的增加,小型兽类物种丰富度并非总是增加的,而是出现反复,呈现明显的小岛效应;14 个岛屿的物种与许源半岛样地物种构成比接近,推断在水库未形成前属同一生境。     

岛屿栖息地鸟类群落的丰富度及其影响因子

1997年1月至1997年12月间,以杭州市的园林鸟类群落为研究对象,对岛屿栖息地鸟类群落的丰富度与面积,人为干扰,内部结构和周围景观结构等多种因素的关系进行了系统的分析和检验。在杭州市各园林中共观察到82种鸟类。园林单次调查的鸟类物种数（S）与园林全年总物种数（Sy)与园林面积（A）的最佳回归拟合方程分别为;S＝ 2.7432A^0.3846,Sy=10.6574A^0.3669。杭州市园林鸟类群落物种－面积关系的成因不支持平衡假说,随机取样假说,栖息地多样性假说和干扰假说,岛屿栖息地鸟类群落的丰富度是多因素综合作用的结果,包括取样面积效应（排除了取样面积效应之后,小园林具有更高的物种密度）,栖息地结构的多样性（其中树种多样性是最主要的影响因子）,干扰因素,物种因素和研究尺度等几个方面。     

温州沿海小型海岛植物丰富度和β多样性及其影响因子

于2012-2015年调查了温州沿海20个小型无居民海岛的植物组成,共记录到维管束植物366种,隶属于95科244属,其中草本植物226种木本植物140种。拟合了5个种-面积关系模型,采用赤池信息量AIC对模型进行选择,发现种-面积-生境类型关系模型SAH_nR权重系数最大,为40.26%,两种断点回归种-面积关系模型BR-SAR权重系数分别仅为6.94%和0.43%,表明基于这20个海岛拟合的种-面积关系不存在小岛屿效应。岛屿植物物种丰富度主要受面积A影响,离大陆距离,I_m对丰富度无显著作用;偏相关分析表明除A外,周长/面积比PAR和岛屿生境多样性指数H_d显著影响了植物丰富度,其逐步回归方程分别为:植物总丰富度S=76.714+1.696A-0.046PAR,R~2=0.839;木本植物丰富度S_(-woody)=6.525+0.455A+24.544H_d,R~2=0.697;草本植物丰富度S_(-herbaceous)=66.899+1.285A-0.04PAR-23.434H_d,R~2=0.865。偏最小二乘回归PLS分析中岛屿空间特征参数对岛屿物种相似性指数重要性排序为:I_m(0.61)I_i(0.56)PAR(0.49)A(0.20)岸线长度Per(0.14)生境类型H(0.072)岛屿高程E(0.065)岛屿形状指数SI(0.05)。由此可见,近岸的小型海岛植物丰富度并不总是由岛屿面积来决定;隔离度对岛屿植物β多样性影响较大。     

新疆喀纳斯自然保护区藓类植物物种多样性与区系调查

从1995～2009年,作者对新疆喀纳斯自然保护区藓类植物进行了全面的调查、采集、鉴定,分析研究。结果表明,该自然保护区藓类植物共有37科113属253种（含1变种）,优势科主要体现寒、温带地区分布类型,该区藓类植物生境的多样性可以从物种多样性,如曲尾藓科、木灵藓科、泥炭藓科、紫萼藓科、丛藓科等的广泛分布反映出来。通过与该区相邻的6个地区的物种丰富度系数比较,该自然保护区综合系数S i排第三位,说明此区藓类植物物种多样性丰富,这与本区的水分条件有很大关系。藓类植物区系成分以北温带成分为绝对优势,占总种数的83.33%;东亚成分占总种数的5.10%;东亚-北美成分占总种数的4.17%,各区系成分交汇。主成分分析和聚类分析表明,喀纳斯自然保护区藓类植物区系与新疆东部天山、内蒙古大青山、吉林长白山、河北木兰围场藓类植物区系关系较接近,与贵州梵净山、云南鸡足山藓类植物区系关系疏远,体现了我国北方藓类植物分布特征。     

温带次生林的岛屿化对鸟类物种多样性及密度的影响

由于自然事件的影响和人类活动的干扰,越来越多的大片森林破碎成彼此孤立、面积不一的森林岛屿,这种变化无疑会对某些动物的分布模式及行为特征产生影响。于2000和2001年的春夏季,在吉林省左家自然保护区及土门岭地区,采用点样法对18块森林岛屿(面积范围:4.3–76.9hm2)中的鸟类物种多样性及密度进行了调查。主要目的是检测森林岛屿的面积效应是否对鸟类物种多样性及密度产生影响,同时分析经典的岛屿生物地理理论是否可以解释破碎化后的森林岛屿面积与物种的关系。结果表明,鸟类物种多样性在年间没有显著变化,但鸟类的密度在不同年间变化较大。不同面积森林岛屿中的鸟类物种多样性有所差异,所包含的鸟类物种数从12种到43种不等。尽管有些面积较大的斑块所包含的物种数较少,但鸟类物种数的总体趋势是随着斑块面积的增大而增多。不同鸟类对森林岛屿面积的反应并不相同,灰椋鸟(Sturnuscineraceus)、红尾伯劳(Laniuscristatus)、灰头鹀(Embrizaspodocephala)等在面积较小的斑块中密度较大,而山鹡鸰(Dendronanthusindicus)、树鹨(Anthushodg-soni)、灰背鸫(Turdushortulorum)等几乎不分布于小面积斑块之中。森林岛屿中鸟类物种随着面积变化的变异方式符合经典的岛屿生物地理理论的基本模式,但Z值和C值差异较大     

福建平潭无居民岛屿空间特征对植物多样性的影响

为了明确岛屿空间特征对植物物种多样性的影响,该研究应用样线法和样方法对福建省平潭主岛周边19座无居民海岛进行植被调查,统计分析每座岛屿的物种丰富度和典型植物群落的Margalef丰富度指数、Shannon-Wiener多样性指数、Simpson多样性指数和Pielou均匀度指数;采用多种函数对种-面积关系进行拟合,并利用冗余分析影响岛屿典型植物群落物种多样性的空间因素,以探究岛屿植物物种多样性的形成与维持机制。结果表明：(1)平潭主岛周边19座无居民岛屿中有12座岛屿以典型的岛屿森林群落为主,有2座岛屿以灌草丛为主且缺乏乔木层,有5座岛屿只有草丛覆盖。(2)19座岛屿的植物物种丰富度均随岛屿面积的增加而增加,当岛屿面积增加到7.184 hm²时,物种数量增加的速率减慢。(3)岛屿的面积、周长、周长面积比、近岸距离是影响物种多样性的主要空间特征,其中岛屿的面积、周长越大,周长面积比、近岸距离越小,岛屿的物种丰富度越高。(4)在岛屿典型植物群落中,乔木层的物种多样性主要受岛屿面积、周长、周长面积比的影响,而灌、草层的物种多样性主要受近岸距离的影响。研究认为,岛屿面积达...     

千岛湖岛屿化对植物多样性的影响初探

选取千岛湖典型破碎化区域,研究了水库形成后引起的岛屿化对植物物种多样性的影响.在18个大中小型岛屿和一处陆地对照中设立了26个样方,调查乔木和灌木的种类和数量.乔木物种丰富度的单因素方差分析显示:F=13.0,P=0.000,说明各类岛屿间乔木物种差异极显著.多重比较发现大岛上乔木物种丰富度显著高于小岛和中岛,与对照陆地差别不大;灌木的分析显示:F=1.31,P=0.29,说明小、中、大岛和对照陆地灌木物种丰富度差异不显著.Spearman相关性分析显示乔木物种与岛屿面积显著相关,随岛屿面积增大而增加,而灌木物种相关性不显著.Shannon多样性指数分析表明,无论乔木还是灌木其多样性都是大岛最大,陆地次之,而小岛上灌木多样性指数大于中岛.Simpson优势度和Pielou均匀度分析显示,乔木样地中大岛的物种分布均匀性最好,优势种的优势度最低,而灌木样地中小岛的均匀度最高,优势种的优势度最不明显.     

植物物种多样性与岛屿面积的关系

由于水库蓄水导致千岛湖原有生境的破碎化和岛屿化.研究选取了50个岛屿,共设立样方70个.调查这些岛屿上乔木和灌木的种类及数量,选择9种曲线拟合岛屿面积与物种多样性指数之间的数学关系.结果发现:乔木、灌木和木本物种数与岛屿面积关系拟合较好的是对数函数、幂函数和S型曲线,其中对数函数为最优模型;乔木、木本Shannon-Wiener多样性指数与岛屿面积关系拟合较好的是S型曲线和逆函数,灌木Shannon-Wiener多样性指数与岛屿面积关系拟合不显著,乔木和木本Shannon-Wiener多样性指数与较小岛屿(y小于1 hm2)面积拟合呈S形曲线和逆函数,而灌木Shannon-Wiener多样性指数与较大岛屿(y大于1 hm2)面积拟合呈S形曲线和逆函数;均匀度、优势度指数与面积拟合关系不显著. 在岛屿面积较小时,物种多样性指数随着面积的增加而迅速增加,但在面积增加到一定限度时,物种多样性指数增加的速率就逐渐变缓.植物物种数增加速率的转折点约为4 hm2,乔木、木本Shannon-Wiener多样性指数增加速率的转折点约为1 hm2,对面积小于的1 hm2的岛屿进行拟合时发现,乔木、木本Shannon-Wiener多样性指数增加速率的转折点在0.15～0.2 hm2之间.     

海洋岛屿生物多样性保育研究进展

海洋岛屿生态系统因具有明显的海域地理隔离而区别于陆地生态系统,被誉为生物地理与进化生态学研究的"天然实验室".陆地或其它邻近岛屿的种源物种迁移到新的岛屿后,经历地理隔离、特征置换或适应辐射等一系列的岛屿进化过程,形成与种源物种具有显著遗传差异的岛屿特有种.岛屿在小面积范围内分化形成大量的特有种,是岛屿生物多样性最为重要的特点之一.但是,岛屿种群由于分布范围局限、生境脆弱且种群规模较小,岛屿种群较陆地种群具有更高的灭绝风险.本文通过对海洋岛屿物种的起源与演化、遗传结构以及岛屿物种的濒危与保护3个热点问题的讨论,阐述岛屿生物多样性的形成机制、濒危肇因以及岛屿生物多样性保育的重要性.     

The role of habitat diversity in generating the small-island effect

The small-island effect (SIE) has become a widespread pattern in island biogeography and biodiversity research. However, in most previous studies only area is used for the detection of the SIE, while other causal factors such as habitat diversity is rarely considered. Therefore, the role of habitat diversity in generating SIEs is poorly known. Here, we compiled 86 global datasets that included the variables of habitat diversity, area and species richness to systematically investigate the prevalence and underlying factors determining the role of habitat diversity in generating SIEs. For each dataset, we used both path analysis and breakpoint regressions to identify the existence of an SIE. We collected a number of system characteristics and employed logistic regression models and an information–theoretic approach to determine which combination of variables was important in determining the role of habitat diversity in generating SIEs. Among the 61 datasets with adequate fits, habitat diversity was found to influence the detection of SIEs in 32 cases (52.5%) when using path analysis. By contrast, SIEs were detected in 26 of 61 cases (42.6%) using breakpoint regressions. Model selection and model-averaged parameter estimates showed that Number of sites, Habitat range and Species range were three key variables that determined the role of habitat diversity in generating SIEs. However, Area range, Taxon group and Site type received considerably less support. Our study demonstrates that the effect of habitat diversity on generating SIEs is quite prevalent. The inclusion of habitat diversity is important because it provides a causal factor for the detection of SIEs. We conclude that for a better understanding of the causes of SIEs, habitat diversity should be included in future studies.     

Small island biogeography in the Gulf of California: lizards, the subsidized island biogeography hypothesis, and the small island effect

Aim We used insular lizard communities to test the predictions of two hypotheses that attempt to explain patterns of species richness on small islands. We first address the subsidized island biogeography (SIB) hypothesis, which predicts that spatial subsidies may cause insular species richness to deviate from species–area predictions, especially on small islands. Next, we examine the small island effect (SIE), which suggests small islands may not fit the traditional log‐linear species–area curve. Location Islands with arthropodivorous lizard communities throughout the Gulf of California. Methods To evaluate the SIB hypothesis, we first identified subsidized and unsubsidized islands based on surrogate measures of allochthonous productivity (i.e. island size and bird presence). Subsequently, we created species–area curves from previously published lizard species richness and island area data. We used the residuals and slopes from these analyses to compare species richness on subsidized and unsubsidized islands. To test for an SIE, we used breakpoint regression to model the relationship between lizard species richness and island area. We compared results from this model to results from the log‐linear regression model. Results Subsidized islands had a lower slope than unsubsidized islands, and the difference between these groups was significant when small islands were defined as < 1 km². In addition to comparing slopes, we tested for differences in the magnitude of the residuals (from the species–area regression of all islands) for subsidized vs. unsubsidized islands. We found no significant patterns in the residual values for small vs. large islands, or between islands with and without seabirds. The SIE was found to be a slightly better predictor of lizard species richness than the traditional log‐linear model. Main conclusions Predictions of the SIB hypothesis were partially supported by the data. The absence of a significant SIE may be a result of spatial subsidies as explained by the SIB hypothesis and data presented here. We conclude by suggesting potential scenarios to test for interactions between these two small island hypotheses. Future studies considering factors affecting species richness should examine the possible role of spatial subsidies, an SIE, or a synergistic effect of the two in data sets with small islands.     

Robust methods for detecting a small island effect

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 R²_adj., AIC_c 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.     

Geographical,Temporal and Environmental Determinants of Bryophyte Species Richness in the Macaronesian Islands

Species richness on oceanic islands has been related to a series of ecological factors including island size and isolation (i.e. the Equilibrium Model of Island Biogeography, EMIB), habitat diversity, climate (i.e., temperature and precipitation) and more recently island ontogeny (i.e. the General Dynamic Model of oceanic island biogeography, GDM). Here we evaluate the relationship of these factors with the diversity of bryophytes in the Macaronesian region (Azores, Madeira, Canary Islands and Cape Verde). The predictive power of EMIB, habitat diversity, climate and the GDM on total bryophyte richness, as well as moss and liverwort richness (the two dominant bryophyte groups), was evaluated through ordinary least squares regressions. After choosing the best subset of variables using inference statistics, we used partial regression analyses to identify the independent and shared effects of each model. The variables included within each model were similar for mosses and liverworts, with orographic mist layer being one of the most important predictors of richness. Models combining climate with either the GDM or habitat diversity explained most of richness variation (up to 91%). There was a high portion of shared variance between all pairwise combinations of factors in mosses, while in liverworts around half of the variability in species richness was accounted for exclusively by climate. Our results suggest that the effects of climate and habitat are strong and prevalent in this region, while geographical factors have limited influence on Macaronesian bryophyte diversity. Although climate is of great importance for liverwort richness, in mosses its effect is similar to or, at least, indiscernible from the effect of habitat diversity and, strikingly, the effect of island ontogeny. These results indicate that for highly vagile taxa on oceanic islands, the dispersal process may be less important for successful colonization than the availability of suitable ecological conditions during the establishment phase.     

Vascular plant species richness in relation to habitat diversity and island area in the Finnish Archipelago

Aim The aim of this study is to explore the interrelationships between island area, species number and habitat diversity in two archipelago areas. Location The study areas, Brunskär and Getskär, are located in an archipelago in south‐western Finland. Methods The study areas, 82 islands in Brunskär and 78 in Getskär, were classified into nine habitat types based on land cover. In the Brunskär area, the flora (351 species) was surveyed separately for each individual habitat on the islands. In the Getskär area, the flora (302 species) was surveyed on a whole‐island basis. We used standard techniques to analyse the species–area relationship on a whole‐island and a habitat level. We also tested our data for the small island effect (SIE) using breakpoint and path analysis models. Results Species richness was significantly associated with both island area and habitat diversity. Vegetated area in particular, defined as island area with the rock habitat subtracted, proved to be a strong predictor of species richness. Species number had a greater association with island area multiplied by the number of habitats than with island area or habitat number separately. The tests for a SIE in the species–area relationship showed the existence of a SIE in one of the island groups. No SIE could be detected for the species–vegetated area relationship in either of the island groups. The strength of the species–area relationship differed considerably between the habitats. Main conclusions The general principles of island biogeography apply well to the 160 islands in this study. Vascular plant diversity for small islands is strongly influenced by physiographic factors. For the small islands with thin and varying soil cover, vegetated area was the most powerful predictor of species richness. The species–area curves of various habitats showed large variations, suggesting that the measurement of habitat areas and establishment of habitat‐based species lists are needed to better understand species richness on islands. We found some evidence of a SIE, but it is debatable whether this is a ‘true’ SIE or a soil cover/habitat characteristics feature.     

Morphology,ultrastructure and phylogenetic affinities of the single-island endemic Anthoceros cristatus Steph. (Ascension Island)

Oceanic islands, due to their geographical isolation, number, precisely defined boundaries and their geomorphological and climatic diversity, have provided enormous insights into speciation, dispersal, adaptive radiations and macroecological processes. One of the key components of these island studies is the role of single-island endemics (SIEs) as, in many instances, island biogeography models use the proportion of SIEs to infer evolutionary processes. It is, therefore, imperative to undertake critical taxonomic revisions to evaluate SIEs because changes in the number of SIEs have a key impact on downstream biogeographic analyses. We revise the special case of a putative SIE Anthoceros cristatus on Ascension Island using light and electron microscopy, as well phylogenomic tools. A. cristatus lies within the A. agrestis/A. punctatus complex but differs from the sister species A. agrestis and A. punctatus in spore morphology and gametophytic lamellae fringed with caducous marginal cells. The present confirmation, from both molecules and morphology, of the SIE status of Anthoceros cristatus and its restricted distribution on the Island makes the preservation of its habitat a conservation priority. Ascension Island is the tip of an undersea volcano that is thought to have emerged from the ocean 1 million years ago with an area of approximately 91?km², with Green Mountain as the highest elevation (～859?m a.s.l.). Ascension has a relative low bryophyte species diversity of 87 spp but this includes 12 endemics (～14%); a much higher level of endemism than on the far more speciose Macaronesian Islands.     

The effects of land-use change on arthropod richness and abundance on Santa Maria Island (Azores): unmanaged plantations favour endemic beetles

We study how endemic, native and introduced arthropod species richness, abundance, diversity and community composition vary between four different habitat types (native forest, exotic forest of Cryptomeria japonica, semi-natural pasture and intensive pasture) and how arthropod richness and abundance change with increasing distance from the native forest in adjacent habitat types in Santa Maria Island, the Azores. Arthropods were sampled in four 150 m long transects in each habitat type. Arthropods were identified to species level and classified as Azorean endemic, single-island endemic (SIE), native, or introduced. The native forest had the highest values for species richness of Azorean endemics, SIEs and natives; and also had highest values of Azorean endemic diversity (Fisher’s alpha). In contrast, the intensive pasture had the lowest values for endemic and native species richness and diversity, but the highest values of total arthropod abundance and introduced species richness and diversity. Arthropod community composition was significantly different between the four habitat types. In the semi-natural pasture, the number of SIE species decreased with increasing distance from the native forest, and in the exotic forest the abundance of both Azorean endemics and SIEs decreased with increasing distance from the native forest. There is a gradient of decreasing arthropod richness and abundance from the native forest to the intensive pasture. Although this study demonstrates the important role of the native forest in arthropod conservation in the Azores, it also shows that unmanaged exotic forests have provided alternative habitat suitable for some native species of forest specialist arthropods, particularly saproxylic beetles.     

Re‐approaching the small island effect

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.     

No evidence for the small‐island effect in avian communities on islands of an inundated lake

The small‐island effect (SIE), i.e. the hypothesis that species richness on islands below a certain threshold area varies independently of area, has become more and more part of the theoretical framework of biogeography and biodiversity research. However, existing SIE studies are extraordinarily biased taxonomically: plants and other animal taxonomic groups are predominantly studied, while birds are almost completely overlooked. Furthermore, previous methods for the detection of SIE are flawed in one or another way, including not accounting for model complexity, not comparing all relevant models, not including islands with no species, and ignoring the effects of logarithmic data transformations and habitat diversity in generating SIE. Therefore, the existence and the prevalence of the SIE may be dubious. In this study, after controlling for all these methodological shortcomings in detecting the SIE, we test for the existence of the SIE using bird data collected on islands in the Thousand Island Lake, China. We used the line‐transect method to survey bird occupancy and abundance on 42 islands from 2007 to 2011. We used three broad sets of analyses, regression‐based analyses, path analyses and null model analyses, to overcome potential methodological problems in detecting the SIE. We found no evidence for an SIE in avian communities in the Thousand Island Lake. Model selection based on AIC_c identified the simple power model without SIE as the most parsimonious model. In contrast, there was little support for the three breakpoint regression models with SIE. Path analyses and null model analyses also did not detect an SIE. We conclude that, for the robust detection of SIE, future study should carefully take all these methodological pitfalls into account.     

Evolutionary species–area curves as revealed by single-island endemics: insights for the inter-provincial species–area relationship

The species–area relationship (SAR) between different biological provinces is one of the most interesting, but least explored aspects of the well-known species–area pattern. Following the usage that a biological province is a region whose species have for the most part evolved within it, rather than immigrating from somewhere else, we propose that islands can be considered equivalent to biological provinces for single island endemic species (SIEs). Hence, analyses of the relationships between numbers of SIEs and island area can be used as model systems to explore the form of inter-provincial SARs. We analyzed 13 different data sets derived from 11 sources, using the power (log–log) model. Eleven of the SIE–area relationships were statistically significant, explaining high proportions of the variance in SIE numbers (R² 0.57–0.95). The z-values (slopes) of the statistically significant SIE–area relationships ranged from 0.47 to 1.13, with a mean value of 0.80 (SD±0.24).
All the island systems in which SIE represent >50% of species exhibited z-values for the SARs of native species higher than those deemed typical of archipelagic SARs. The SIE–area slopes are quite similar to those previously calculated for inter-provincial SARs, indicating that, when speciation becomes the dominant process adding to the species richness of assemblages, high z-values should be anticipated, regardless of the biogeographical scale of the study system.