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1.
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. 相似文献
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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. 相似文献
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Aim The goal of our study was to build a global model of island biogeography explaining bird species richness that combines MacArthur and Wilson's area–isolation theory with the species–energy theory. Location Global. Methods We assembled a global data set of 346 marine islands representing all types of climate, topography and degree of isolation on our planet, ranging in size from 10 ha to 800,000 km2. We built a multiple regression model with the number of non‐marine breeding bird species as the dependent variable. Results We found that about 85–90% of the global variance in insular bird species richness can be explained by simple, contemporary abiotic factors. On a global scale, the three major predictors — area, average annual temperature and the distance separating the islands from the nearest continent — all have constraining (i.e. triangular rather than linear) relationships with insular bird species richness. We found that the slope of the species–area curve depends on both average annual temperature and total annual precipitation, but not on isolation. Insular isolation depends not only on the distance of an island from the continent, but also on the presence or absence of other neighbouring islands. Range in elevation — a surrogate for diversity of habitats — showed a positive correlation with bird diversity in warmer regions of the world, while its effect was negative in colder regions. We also propose a global statistical model to quantify the isolation‐reducing effect of neighbouring islands. Main conclusions The variation in avian richness among islands worldwide can be statistically explained by contemporary environmental variables. The equilibrium theory of island biogeography of MacArthur and Wilson and the species–energy theory are both only partly correct. Global variation in richness depends about equally upon area, climate (temperature and precipitation) and isolation. The slope of the species richness–area curve depends upon climate, but not on isolation, in contrast to MacArthur and Wilson's theory. 相似文献
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M. Lussu;P. Zannini;R. Testolin;D. Dolci;M. Conti;S. Martellos;A. Chiarucci; 《Journal of Biogeography》2024,51(5):869-877
Despite the research on orchid in insular conditions, few studies are focused on the spatial distribution of their reproductive syndromes across complex insular systems. By using island species–area relationships (ISAR), we explore orchid biogeography in the Central Western-Mediterranean islands. In this study, we aim to investigate variation in ISARs using orchid pollination mechanisms as proxies to establish permanent populations explaining how the c and z parameters of ISARs vary among island types and pollination strategies and defining the most influential factors in shaping orchids' distribution. 相似文献
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本文在排除了“平衡假说”中的“岛屿效应”的情况下,估算了世界部分国家兽类,鸟类,爬行类和两栖类的物种－面积,物种－纬度及物种－面积－纬度关系式中的参数。研究发现,大陆连续栖息地性的ｚ值并不比岛屿或栖息地“岛屿”性的ｚ值小,ｚ值与面积样本大小和范围有关。栖息地异质性对ｚ值的大小也起着很重要的作用。本文建立了全球脊推动物物种－面积－纬度相关模型,即Ｌｏｇｓ＝ｂ＿ｏ＋ｂ＿１·ＬｏｇＡ＋ｂ＿２·Ｌ,总复合相关系数达０．９０２８（ｐ＜０．０１）,可用于预测或评估全球脊推动物种数分布或由于栖息地破坏后物种数消失的情况。 相似文献
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Kim C. Diver 《Journal of Biogeography》2008,35(6):1040-1048
Aim To evaluate the role of island isolation in explaining the distribution of vascular plant species in a dense freshwater archipelago, specifically comparing conventional measures of island isolation with landscape measures of island isolation. Location Data were collected from 35 islands within Massasauga Provincial Park on the eastern shores of the Georgian Bay, Ontario, Canada. Methods Sampled islands were located using stratified random selection based on location and size variation. The number of species was recorded along stratified random transects. Island isolation variables included distance to the mainland, distance to the nearest island, largest gap in a stepping‐stone sequence, distance to the closest upwind point of land, and a landscape measure of island isolation. The landscape measure of isolation was quantified as the percentage of the land area within 100, 250, 500, 1000, 1500 and 2000 m of each island’s perimeter. The isolation variables were calculated within a geographical information system (GIS). Dependent variables in the regression analyses included species richness, the logarithm of species richness and residuals of the species–area relationship. Independent variables included island isolation variables and their logarithmic transformations. Results Isolation plays a role, albeit small, in explaining species richness in the study area. In the regression analyses, the landscape measure of isolation provided a better fit than conventional measures of island isolation. Islands with less land than water within a 250‐m buffer were more effectively isolated and had fewer species present than islands surrounded by a greater proportion of water. Main conclusions Consistent with the species–isolation relationship, fewer species were present on more isolated islands within the Massasauga study area, as elucidated using a series of island buffers in a GIS. Applying a landscape measure of isolation to similar dense, freshwater archipelagos may elucidate species–isolation patterns not evident through conventional, straight‐line distance measurements of island isolation. The low value of the regression coefficients as well as the isolation history and high density of the Massasauga islands suggests caution in extending the results, especially to dissimilar archipelagos. 相似文献
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To demonstrate a new and more general model of the species–area relationship that builds on traditional models, but includes the provision that richness may vary independently of island area on relatively small islands (the small island effect).Location
We analysed species–area patterns for a broad diversity of insular biotas from aquatic and terrestrial archipelagoes.Methods
We used breakpoint or piecewise regression methods by adding an additional term (the breakpoint transformation) to traditional species–area models. The resultant, more general, species–area model has three readily interpretable, biologically relevant parameters: (1) the upper limit of the small island effect (SIE), (2) an estimate of richness for relatively small islands and (3) the slope of the species–area relationship (in semi‐log or log–log space) for relatively large islands.Results
The SIE, albeit of varying magnitude depending on the biotas in question, appeared to be a relatively common feature of the data sets we studied. The upper limit of the SIE tended to be highest for species groups with relatively high resource requirements and low dispersal abilities, and for biotas of more isolated archipelagoes.Main conclusions
The breakpoint species–area model can be used to test for the significance, and to explore patterns of variation in small island effects, and to estimate slopes of the species–area (semi‐log or log–log) relationship after adjusting for SIE. Moreover, the breakpoint species–area model can be expanded to investigate three fundamentally different realms of the species–area relationship: (1) small islands where species richness varies independent of area, but with idiosyncratic differences among islands and with catastrophic events such as hurricanes, (2) islands beyond the upper limit of SIE where richness varies in a more deterministic and predictable manner with island area and associated, ecological factors and (3) islands large enough to provide the internal geographical isolation (large rivers, mountains and other barriers within islands) necessary for in situ speciation.11.
Islands harbour a significant portion of all plant species worldwide. Their biota are often characterized by narrow distributions and are particularly susceptible to biological invasions and climate change. To date, the global richness pattern of islands is only poorly documented and factors causing differences in species numbers remain controversial. Here, we present the first global analysis of 488 island and 970 mainland floras. We test the relationship between island characteristics (area, isolation, topography, climate and geology) and species richness using traditional and spatial models. Area is the strongest determinant of island species numbers ( R 2 = 0.66) but a weaker predictor for mainlands ( R 2 = 0.25). Multivariate analyses reveal that all investigated variables significantly contribute to insular species richness with area being the strongest followed by isolation, temperature and precipitation with about equally strong effects. Elevation and island geology show relatively weak yet significant effects. Together these variables account for 85% of the global variation in species richness. 相似文献
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The species–area relationship (SAR) provides the foundation for much of theoretical ecology and conservation practice. However, by ignoring time the SAR offers an incomplete model for biodiversity dynamics. We used long‐term data from permanent plots in Kansas grasslands, USA, to show that the increase in the number of species found with increasing periods of observation takes the same power‐law form as the SAR. A statistical model including time, area, and their interaction explains 98% of variation in mean species number and demonstrates that while the effect of time depends on area, and vice versa, time has strong effects on species number even at relatively broad spatial scales. Our results suggest equivalence of underlying processes in space and time and raise questions about the diversity estimates currently used by basic researchers and conservation practitioners. 相似文献
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1. Despite the growing view that biodiversity provides a unifying theme in river ecology, global perspectives on richness in riverine landscapes are limited. As a result, there is little theory or quantitative data on features that might have influenced global patterns in riverine richness, nor are there clear indications of which riverine landscapes are important to conservation at the global scale. As conspicuous elements of the vertebrate fauna of riverine landscapes, we mapped the global distributions of all of the world's specialist riverine birds and assessed their richness in relation to latitude, altitude, primary productivity and geomorphological complexity (surface configuration). 2. Specialist riverine birds, typical of high‐energy riverine landscapes and dependent wholly or partly on production from river ecosystems, occur in 16 families. They are represented by an estimated 60 species divided equally between the passerines and non‐passerines. Major radiation has occurred among different families on different continents, indicating that birds have evolved several times into the niches provided by riverine landscapes. 3. Continental richness varies from four species in Europe to 28 in Asia, with richness on the latter continent disproportionately larger than would be expected from a random distribution with respect to land area. Richness is greatest in mountainous regions at latitudes of 20–40°N in the riverine landscapes of the Himalayan mountains, where 13 species overlap in range. 4. Family, genus and species richness in specialist riverine birds all increase significantly with productivity and surface configuration (i.e. relief). However, family richness was the best single predictor of the numbers of species or genera. In keeping with the effect of surface configuration, river‐bird richness peaks globally at 1300–1400 m altitude, and most species occur typically on small, fast rivers where they feed predominantly on invertebrates. Increased lengths of such streams in areas of high relief and rainfall might have been responsible for species–area effects. 5. We propose the hypothesis that the diversity in channel forms and habitats in riverine landscapes, in addition to high temperature and primary productivity, have been prerequisites to the development of global patterns in the richness of specialist riverine organisms. We advocate tests of this hypothesis in other taxonomic groups. We draw attention, however, to the challenges of categorically defining riverine organisms in such tests because (i) rivers grade into many other habitat types across several different ecotones and (ii) `terrestrialisation' processes in riverine landscapes means that they offer habitat for organisms whose evolutionary origins are not exclusively riverine. 相似文献
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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. 相似文献
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Aim We studied the relationship between the size and isolation of islands and bat species richness in a near‐shore archipelago to determine whether communities of vagile mammals conform to predictions of island biogeography theory. We compared patterns of species richness in two subarchipelagos to determine whether area per se or differences in habitat diversity explain variations in bat species richness. Location Islands in the Gulf of California and adjacent coastal habitats on the Baja California peninsula in northwest Mexico. Methods Presence–absence surveys for bats were conducted on 32 islands in the Gulf of California using acoustic and mist‐net surveys. We sampled for bats in coastal habitats of four regions of the Baja peninsula to characterize the source pool of potential colonizing species. We fitted a semi‐log model of species richness and multiple linear regression and used Akaike information criterion model selection to assess the possible influence of log10 area, isolation, and island group (two subarchipelagos) on the species richness of bats. We compared the species richness of bats on islands with greater vegetation densities in the southern gulf (n = 20) with that on drier islands with less vegetation in the northern gulf (n = 12) to investigate the relationship between habitat diversity and the species richness of bats. Results Twelve species of bats were detected on islands in the Gulf of California, and 15 species were detected in coastal habitats on the Baja peninsula. Bat species richness was related to both area and isolation of islands, and was higher in the southern subarchipelago, which has denser vegetation. Log10 area was positively related to bat species richness, which increased by one species for every 5.4‐fold increase in island area. On average, richness declined by one species per 6.25 km increase in isolation from the Baja peninsula. Main conclusions Our results demonstrate that patterns of bat species richness in a near‐shore archipelago are consistent with patterns predicted by the equilibrium theory of island biogeography. Despite their vagility, bats may be more sensitive to moderate levels of isolation than previously expected in near‐shore archipelagos. Differences in vegetation and habitat xericity appear to be associated with richness of bat communities in this desert ecosystem. Although observed patterns of species richness were consistent with those predicted by the equilibrium theory, similar relationships between species richness and size and isolation of islands may arise from patch‐use decision making by individuals (optimal foraging strategies). 相似文献
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Gunnar Keppel Thomas W. Gillespie Paul Ormerod Geoffrey A. Fricker 《Journal of Biogeography》2016,43(12):2332-2342
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ERICA FLEISHMAN GEORGE T. AUSTIN DENNIS D. MURPHY 《Biological journal of the Linnean Society. Linnean Society of London》2001,74(4):501-515
We used comprehensive data on butterfly distributions from six mountain ranges in the Great Basin to explore three connected biogeographic issues. First, we examined species richness and occurrence patterns both within and among mountain ranges. Only one range had a significant relationship between species richness and area. Relationships between species richness and elevation varied among mountain ranges. Species richness decreased as elevation increased in one range, increased as elevation increased in three ranges, and was not correlated in two ranges. In each range, distributional patterns were nested, but less vagile species did not always exhibit greater nestedness. Second, we compared our work with similar studies of montane mammals. Results from both taxonomic groups suggest that it may be appropriate to modify existing general paradigms of the biogeography of montane faunas in the Great Basin. Third, we revisited and refined previous predictions of how butterfly assemblages in the Great Basin may respond to climate change. The effects of climate change on species richness of montane butterflies may vary considerably among mountain ranges. In several ranges, few if any species apparently would be lost. Neither local species composition nor the potential order of species extirpations appears to be generalizable among ranges. 相似文献
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Athanasios S. Kallimanis Antonis D. Mazaris Joseph Tzanopoulos John M. Halley John D. Pantis Stefanos P. Sgardelis 《Global Ecology and Biogeography》2008,17(4):532-538
Aim To examine the way in which 'area' and 'habitat diversity' interact in shaping species richness and to find a simple and valid way to express this interaction.
Location The Natura 2000 network of terrestrial protected areas in Greece, covering approximately 16% of the national territory.
Methods We used the Natura 2000 framework, which provides a classification scheme for natural habitat types, to quantify habitat heterogeneity. We analysed data for the plant species composition in 16,143 quadrats in which 5044 species and subspecies of higher plants were recorded. We built a simple mathematical model that incorporates the effect of habitat diversity on the species–area relationship (SAR).
Results Our analysis showed that habitat diversity was correlated with area. However, keeping habitat diversity constant, species richness was related to area; while keeping area constant, species richness was related to habitat diversity. Comparing the SAR of the 237 sites we found that the slope of the species–area curve was related to habitat diversity.
Main conclusions Discussion of the causes of the SAR has often focused on the primacy of area per se versus habitat heterogeneity, even though the two mechanisms are not mutually exclusive and should be considered jointly. We find that increasing habitat diversity affects the SAR in different ways, but the dominant effect is to increase the slope of the SAR. While a full model fit typically includes a variety of terms involving both area and habitat richness, we find that the effect of habitat diversity can be reduced to a linear perturbation of the slope of the species accumulation curve. 相似文献
Location The Natura 2000 network of terrestrial protected areas in Greece, covering approximately 16% of the national territory.
Methods We used the Natura 2000 framework, which provides a classification scheme for natural habitat types, to quantify habitat heterogeneity. We analysed data for the plant species composition in 16,143 quadrats in which 5044 species and subspecies of higher plants were recorded. We built a simple mathematical model that incorporates the effect of habitat diversity on the species–area relationship (SAR).
Results Our analysis showed that habitat diversity was correlated with area. However, keeping habitat diversity constant, species richness was related to area; while keeping area constant, species richness was related to habitat diversity. Comparing the SAR of the 237 sites we found that the slope of the species–area curve was related to habitat diversity.
Main conclusions Discussion of the causes of the SAR has often focused on the primacy of area per se versus habitat heterogeneity, even though the two mechanisms are not mutually exclusive and should be considered jointly. We find that increasing habitat diversity affects the SAR in different ways, but the dominant effect is to increase the slope of the SAR. While a full model fit typically includes a variety of terms involving both area and habitat richness, we find that the effect of habitat diversity can be reduced to a linear perturbation of the slope of the species accumulation curve. 相似文献
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An index (Ci*E) combining the number of line‐of‐sight islands (Ci) within a radius i and target island elevation (E) has been proposed as an improved predictive model of plant species richness (St) in the Galápagos Archipelago. We examined this index critically and found that several major flaws preclude it from being a useful predictive tool for the archipelago. Although the number of collecting trips to an island was reported over 20 years ago to have substantial predictive value for reported plant species richness in the Galápagos Islands, this relationship was ignored in multiple regression analyses of the index. When we included the number of collecting trips in different multiple regression analyses of the index, Ci*E had less predictive power than collecting trips or ceased to be significant at all. Additionally, the strong significant relationship between elevation and area in the Galápagos Archipelago results in area having a major confounding influence on the Ci*E index. When elevation is removed from the Ci*E index, the predictive power of Ci is far less than area alone. Finally, the data used to construct and correlate the Ci*E index with (St) were based only on a subset of the islands and species lists that were incomplete or out of date. Species richness on islands can be related to the interaction of different factors, so development and testing of indices like Ci*E is not inappropriate. However, it is important to examine the interrelationships among the components of these indices thoroughly, and not ignore the effect of factors already known to have high predictive power. We propose several ways in which more meaningful indices of source pool(s) capacity can be constructed. 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