On the general dynamic model of oceanic island biogeography

Aim To investigate the biological meaning of equations used to apply the general dynamic model (GDM) of oceanic island biogeography proposed by R. J. Whittaker, K. A. Triantis and R. J. Ladle. Location Analyses are presented for 17 animal groups living on the Aeolian Islands, a volcanic archipelago in the central Mediterranean, near Sicily. Methods In addition to the mathematical implementation of the GDM proposed by Whittaker, Triantis and Ladle, and termed here logATT² (, where S is species number or any other diversity metric, t is island age, A is island area, and a, b, c and d are fitted parameters), a new implementation based on the Arrhenius equation of the species–area relationship (SAR) is investigated. The new model (termed powerATT²) is: . For logATT² and powerATT² models, equations were developed to calculate (1) the expected number of species at equilibrium (i.e. when the island has reached maturity) per unit area (S_eq), and (2) the time required to obtain this value (t_eq). Whereas the intercept in the Gleason model (S = C + z log A) or the coefficient of the Arrhenius power model (S = CA^z) of the SAR can be considered measures of the expected number of species per unit area, this is not the case for the parameter a of the ATT² models. However, values of S_eq can be used for this purpose. The index of ‘colonization ability’ (CAB), calculated as the ratio , may provide a measure of the mean number of species added per unit area per unit time. Results Both ATT² models fitted most of the data well, but the powerATT² model was in most cases superior. Equilibrial values of species richness (S_eq) varied from c. 3 species km^?2 (reptiles) to 100 species km^?2 (mites). The fitted curves for the powerATT² model showed large variations in d, from 0.03 to 3. However, most groups had values of d around 0.2–0.4, as commonly observed for the z‐values of SARs modelled by a power function. Equilibration times ranged from about 170,000 years to 400,000 years. Mites and springtails had very high values of CAB, thus adding many more species per unit area per unit time than others. Reptiles and phytophagous scarabs showed very low values, being the groups that added fewest species per unit area per unit time. Main conclusions Values of equilibrial species richness per unit area are influenced by species biology (e.g. body size and ecological specialization). Theoretical and empirical evidence suggests that higher immigration rates should increase the z‐values of the Arrhenius model. Thus, in the same archipelago, groups with larger z‐values should be characterized by higher dispersal ability. Results obtained here for the parameter d conform to this prediction.     

Which function describes the species–area relationship best? A review and empirical evaluation

Aim The aims of this study are to resolve terminological confusion around different types of species–area relationships (SARs) and their delimitation from species sampling relationships (SSRs), to provide a comprehensive overview of models and analytical methods for SARs, to evaluate these theoretically and empirically, and to suggest a more consistent approach for the treatment of species–area data. Location Curonian Spit in north‐west Russia and archipelagos world‐wide. Methods First, I review various typologies for SARs and SSRs as well as mathematical models, fitting procedures and goodness‐of‐fit measures applied to SARs. This results in a list of 23 function types, which are applicable both for untransformed (S) and for log‐transformed (log S) species richness. Then, example data sets for nested plots in continuous vegetation (n = 14) and islands (n = 6) are fitted to a selection of 12 function types (linear, power, logarithmic, saturation, sigmoid) both for S and for log S. The suitability of these models is assessed with Akaike’s information criterion for S and log S, and with a newly proposed metric that addresses extrapolation capability. Results SARs, which provide species numbers for different areas and have no upper asymptote, must be distinguished from SSRs, which approach the species richness of one single area asymptotically. Among SARs, nested plots in continuous ecosystems, non‐nested plots in continuous ecosystems, and isolates can be distinguished. For the SARs of the empirical data sets, the normal and quadratic power functions as well as two of the sigmoid functions (Lomolino, cumulative beta‐P) generally performed well. The normal power function (fitted for S) was particularly suitable for predicting richness values over ten‐fold increases in area. Linear, logarithmic, convex saturation and logistic functions generally were inappropriate. However, the two sigmoid models produced unstable results with arbitrary parameter estimates, and the quadratic power function resulted in decreasing richness values for large areas. Main conclusions Based on theoretical considerations and empirical results, I suggest that the power law should be used to describe and compare any type of SAR while at the same time testing whether the exponent z changes with spatial scale. In addition, one should be aware that power‐law parameters are significantly influenced by methodology.     

Habitat reduction and patterns of species loss

This paper uses data of The Distribution Atlas of Polish Butterflies to simulate the effect of four different types of area loss (aggregated, fractal, random, and nested) on species diversity and species–area relationships (SARs). We found that power function and logarithmic SAR models overestimated species loss in the case of aggregated, fractal, and random patterns of area reduction. Instead, the modification of the power function by Plotkin et al. (Proc. Natl. Acad. Sci. USA 97 (2000b) 10850) (S=S₀A^ze^−kA) with k being a shape-adjusting parameter worked better and gave sufficient predictions of species loss. The net effects of the aggregated, fractal, and random types of area loss on species diversity were very similar with an aggregated pattern of area loss leading to slightly higher rates of species loss than both other processes. We conclude that SARs might be useful tools for biodiversity forecasting if they are constructed in a case-specific manner. The use of standard models instead might be misleading.     

Local–regional boundary shifts in oribatid mite (Acari: Oribatida) communities: species–area relationships in arboreal habitat islands of a coastal temperate rain forest, Vancouver Island, Canada

Aim This study investigates the species–area relationship (SAR) for oribatid mite communities of isolated suspended soil habitats, and compares the shape and slope of the SAR with a nested data set collected over three spatial scales (core, patch and tree level). We investigate whether scale dependence is exhibited in the nested sampling design, use multivariate regression models to elucidate factors affecting richness and abundance patterns, and ask whether the community composition of oribatid mites changes in suspended soil patches of different sizes. Location Walbran Valley, Vancouver Island, Canada. Methods A total of 216 core samples were collected from 72 small, medium and large isolated suspended soil habitats in six western redcedar trees in June 2005. The relationship between oribatid species richness and habitat volume was modelled for suspended soil habitat isolates (type 3) and a nested sampling design (type 1) over multiple spatial scales. Nonlinear estimation parameterized linear, power and Weibull function regression models for both SAR designs, and these were assessed for best fit using R² and Akaike's information criteria (ΔAIC) values. Factors affecting oribatid mite species richness and standardized abundance (number per g dry weight) were analysed by anova and linear regression models. Results Sixty‐seven species of oribatid mites were identified from 9064 adult specimens. Surface area and moisture content of suspended soils contributed to the variation in species richness, while overall oribatid mite abundance was explained by moisture and depth. A power‐law function best described the isolate SAR (S = 3.97 × A^0.12, R² = 0.247, F_1,70 = 22.450, P < 0.001), although linear and Weibull functions were also valid models. Oribatid mite species richness in nested samples closely fitted a power‐law model (S = 1.96 × A^0.39, R² = 0.854, F_1,18 = 2693.6, P < 0.001). The nested SAR constructed over spatial scales of core, patch and tree levels proved to be scale‐independent. Main conclusions Unique microhabitats provided by well developed suspended soil accumulations are a habitat template responsible for the diversity of canopy oribatid mites. Species–area relationships of isolate vs. nested species richness data differed in the rate of accumulation of species with increased area. We suggest that colonization history, stability of suspended soil environments, and structural habitat complexity at local and regional scales are major determinants of arboreal oribatid mite species richness.     

Decomposing the process of species accumulation into area dependent and time dependent parts

In 1960, Preston predicted that the process of species accumulation in time (species–time relationship, STR) should be similar to the species–area relationship (SAR) and follow a power function with a slope of about 0.26. Here these two conjectures are tested using data of the spatiotemporal species accumulation in a local community of beech forest Hymenoptera. A power function species–area–time model of the form S = S₀ A^z t gave better fits to observed species numbers than a simple power function SAR model, and was able to predict similar species turnover rates (about 9% per year) to those inferred by other methods. The STR was well fitted by a power function, although due the limited time span (8 years) a logarithmic STR pattern cannot be ruled out. STR slopes ranged between 0.01 and 0.23 and were lower than predicted by Preston. Temporal species turnover appeared to be negatively correlated to species densities and positively correlated to species body weights. Ecological guild and taxon membership did not significantly influence temporal species turnover.     

Dispersal and establishment: spatial patterns and species–area relationships

According to the equilibrium theory of island biogeography, high colonization ability of species is associated with low exponents (z) of the species–area relationship (SAR) and weak spatial patterns in species number and dissimilarity. However, the relationship between z and the strength of these spatial patterns has not been investigated systematically. We used a multispecies metapopulation model to investigate these relationships in an archipelago of islands. We conclude that this relationship can only be predicted if either the dispersal ability or the power of establishment of species is known. With species richness limited by establishment, we generated high z‐values associated with weak spatial patterns in species number and dissimilarity. If species richness was constrained by the dispersal ability of species, we observed low to medium z‐values but strong spatial patterns. If the dispersal ability and the abilities of species to establish were both high, z‐values and spatial pattern tend to be low and species numbers were limited by the size of the regional species pool.     

Arrhenius and Gleason revisited: new hybrid models resolve an old controversy

Aim Studies have typically employed species–area relationships (SARs) from sample areas to fit either the power relationship or the logarithmic (exponential) relationship. However, the plots from empirical data often fall between these models. This article proposes two complementary and hybrid models as solutions to the controversy regarding which model best fits sample‐area SARs. Methods The two models are and , where S_A is number of species in an area, A, where z, b, c₁ and c₂ are predetermined parameters found by calculation, and where d and n are parameters to be fitted. The number of parameters is reduced from six to two by fixing the model at either end of the scale window of the data set, a step that is justified by the condition that the error or the bias, or both, in the first and the last data points is negligible. The new hybrid models as well as the power model and the logarithmic model are fitted to 10 data sets. Results The two proposed models fit well not only to Arrhenius’ and Gleason’s data sets, but also to the other six data sets. They also provide a good fit to data sets that follow a sigmoid (or triphasic) shape in log–log space and to data sets that do not fall between the power model and the logarithmic model. The log‐transformation of the dependent variable, S, does not affect the curve fit appreciably, although it enhances the performance of the new models somewhat. Main conclusions Sample‐area SARs have previously been shown to be convex upward, convex downward (concave), sigmoid and inverted sigmoid in log–log space. The new hybrid models describe successfully data sets with all these curve shapes, and should therefore produce good fits also to what are termed triphasic SARs.     

Speciation-rate dependence in species–area relationships

The general tendency for species number (S) to increase with sampled area (A) constitutes one of the most robust empirical laws of ecology, quantified by species–area relationships (SAR). In many ecosystems, SAR curves display a power-law dependence, S∝A^z. The exponent z is always less than one but shows significant variation in different ecosystems. We study the multitype voter model as one of the simplest models able to reproduce SAR similar to those observed in real ecosystems in terms of basic ecological processes such as birth, dispersal and speciation. Within the model, the species–area exponent z depends on the dimensionless speciation rate ν, even though the detailed dependence is still matter of controversy. We present extensive numerical simulations in a broad range of speciation rates from ν=10^-3 down to ν=10^-11, where the model reproduces values of the exponent observed in nature. In particular, we show that the inverse of the species–area exponent linearly depends on the logarithm of ν. Further, we compare the model outcomes with field data collected from previous studies, for which we separate the effect of the speciation rate from that of the different species lifespans. We find a good linear relationship between inverse exponents and logarithm of species lifespans. However, the slope sets bounds on the speciation rates that can hardly be justified on evolutionary basis, suggesting that additional effects should be taken into account to consistently interpret the observed exponents.     

北京山区河岸带植物群落种-面积关系

种-面积关系是群落生态学的基本问题之一,是了解植物群落结构的重要途径。为摸清北京山区河流河岸带植物群落调查最小样方面积,在北京市怀柔区怀九河河岸带沿线,采用基于河岸带立地条件逐步扩大样地面积的方法布设50个80m长样地,调查计算并拟合不同类型河岸带所需的最小样地面积。研究结果表明:北京市怀柔区怀九河河岸带植物种数255种,隶属于70科185属。通过聚类分析将怀九河河岸带分为自然河岸带、近自然河岸带、人工岸坡乔灌草河岸带、人工岸坡观赏性乔灌草河岸带、人工岸坡疏乔灌草干砌石河岸带和人工岸坡浆砌石河岸带6种类型。根据赤池信息量准则AIC可知自然河岸带、近自然河岸带、人工岸坡乔灌草河岸带和人工岸坡疏乔灌草干砌石河岸带优先选取S=c-ae~(-bA),人工岸坡观赏性乔灌草河岸带优先选取S=aA/(1+bA),人工岸坡浆砌石河岸带优先选取S=c/(1+ae~(-bA))。满足相同比例植物种调查,不同类型河岸带所需最小样地面积存在明显差异,当满足河岸带植物调查80%植物种时,人工岸坡浆砌石河岸带(84m~2)和自然河岸带(217m~2)所需样地面积较小,其次是人工岸坡疏乔灌草干砌石河岸带(362m~2),近自然河岸带(450m~2)和人工岸坡乔灌草河岸带(460m~2)所需样地面积相似,而人工岸坡观赏性乔灌草河岸带所需样地面积最大为571m~2。所得出的河岸带植物调查最小样地面积对于河岸带生物多样性保护和指导河岸带生态修复具有重要意义。     

Spatial patterns in species–area relationships and species distribution in a West African forest–savanna mosaic

Aim To investigate the relationship between the slope z of the species–area relationship (SAR) and the intensity of spatial patterns in species number and dissimilarity for woody plants with different modes of seed dispersal. According to island theory we expect, for any given archipelago, steeper slopes and more pronounced spatial patterns for groups of less dispersive species. Location Ivory Coast, West Africa. Methods In a West African forest–savanna mosaic we collected presence–absence data for woody plant species in 49 forest islands. The parameters of the SARs were fitted by nonlinear regressions and then compared for plant species aggregated according to their mode of seed dispersal. We used the Mantel test to calculate the intensity of spatial patterns in species number, i.e. residual deviation from SAR, and species dissimilarity. Results The z‐value for bird‐dispersed species was lower (0.11) than that for wind‐dispersed species (0.27), with mammal‐dispersed species taking an intermediate value (0.16). This result suggests that, as a group, bird‐dispersed species are better colonizers. The spatial pattern in species number as well as species similarity was more pronounced for bird‐ compared with wind‐dispersed species. Main conclusions The standard interpretation of the theory of island biogeography claims that shallow slopes in the SAR imply low isolation of islands, i.e. good dispersal abilities of species. The results of our study appear to contradict this statement. The contradiction can eventually be resolved by a more detailed account of the colonization process, i.e. by distinguishing between dispersal and consecutive establishment of populations.     

Pitfalls in Small-Scale Species-Area Sampling and Analysis

Analyses of the dependency of species richness (S) on area (A), the so-called species-area relationships (SARs), are widespread approaches to characterize and compare biodiversity patterns. This article highlights – with a focus on small-scale SARs of plants in continuous ecosystems – how inappropriate sampling methods or theoretical misconceptions can create artifacts and thus may lead to wrong conclusions. Most of these problems have been recognized before but continue to appear regularly in the scientific literature. The following main points are reviewed and discussed: i) Species richness values and SARs depend on the measurement method (any-part vs. grid-point system); ii) Species-richness values depend on the shape of the analyzed plots; iii) Many published SARs are not true SARs but instead represent species sampling curves or their data points consist of richness totals for incontiguous subplots; iv) Nested-plot design is the preferred sampling method for SARs (the claim that this approach would cause pseudoreplication is erroneous); v) SARs should be constructed using mean values of several counts for the smaller areas; vi) SAR functions can be fitted and selected both in the S- and the log S-space but this must be done consistently for all compared function types. It turns out that the finding of non-power function SARs in many studies is due to a lack of awareness of one or several of the named points. Thus, power-function SARs are even more widespread than a recent review would suggest. I therefore propose to use the power law as a universal model for all types of SARs but to test whether the slope z varies with spatial scale. Finally, I urge readers to be aware of the many pitfalls in SAR studies, to fully disclose methodology, and to apply a meaningful and consistent terminology, especially by restricting the terms “species-area relationship” and “species density” to situations in which each data point represents a contiguous area.     

Detecting biodiversity hotspots using species–area and endemics–area relationships: the case of butterflies

Using data of the Red Data Book of European Butterflies we establish the species–area relationship (S = 8.5 A^0.23) and the endemics–area relationship (S = 0.5 A^0.18) of European butterflies. Applying confidence limits as tools for the identification of hotspot countries we show that in the case of butterflies hotspots of endemism and hotspots of overall species richness do not coincide. We introduce plots of residuals from species–area relationships shifted upwards by the 95% confidence limits of the intercept (α-values) as a new tool for identifying and ranking of hotspots.     

Orchids of the West Indies: predictability of diversity and endemism

Aim We examined phytogeographical patterns of West Indian orchids, and related island area and maximum elevation with orchid species richness and endemism. We expected strong species–area relationships, but that these would differ between low and montane island groups. In so far as maximum island elevation is a surrogate for habitat diversity, we anticipated a strong relationship with maximum elevation and both species richness and endemism for montane islands. Location The West Indies. Methods Our data included 49 islands and 728 species. Islands were classified as either montane (≥ 300 m elevation) or low (< 300 m). Linear and multivariate regression analyses were run to detect relationships between either area or maximum island elevation and species richness or the number of island endemic species. Results For all 49 islands, the species–area relationship was strong, producing a z‐value of 0.47 (slope of the regression line) and explaining 46% of the variation. For 18 relatively homogeneous, low islands we found a non‐significant slope of z = −0.01 that explained only 0.1% of the variation. The 31 montane islands had a highly significant species–area relationship, with z = 0.49 and accounting for 65% of the variation. Species numbers were also strongly related to maximum island elevation. For all islands < 750 km², we found a small‐island effect, which reduced the species–area relationship to a non‐significant z = 0.16, with only 5% of the variation explained by the model. Species–area relationships for montane islands of at least 750 km² were strong and significant, but maximum elevation was the best predictor of species richness and accounted for 79% of the variation. The frequency of single‐island endemics was high (42%) but nearly all occurred on just nine montane islands (300 species). The taxonomic distribution of endemics was also skewed, suggesting that seed dispersability, while remarkable in some taxa, is very limited in others. Montane island endemics showed strong species–area and species–elevation relationships. Main conclusions Area and elevation are good predictors of orchid species diversity and endemism in the West Indies, but these associations are driven by the extraordinarily strong relationships of large, montane islands. The species richness of low islands showed no significant relationship with either variable. A small‐island effect exists, but the montane islands had a significant relationship between species diversity and maximum elevation. Thus, patterns of Caribbean orchid diversity are dependent on an interplay between area and topographic diversity.     

Linking species richness curves from non-contiguous sampling to contiguous-nested SAR: An empirical study

The species–area relationship (SAR) is one of the few generalizations in ecology. However, many different relationships are denoted as SARs. Here, we empirically evaluated the differences between SARs derived from nested-contiguous and non-contiguous sampling designs, using plants, birds and butterflies datasets from Great Britain, Greece, Massachusetts, New York and San Diego. The shape of SAR depends on the sampling scheme, but there is little empirical documentation on the magnitude of the deviation between different types of SARs and the factors affecting it. We implemented a strictly nested sampling design to construct nested-contiguous SAR (SA_CR), and systematic nested but non-contiguous, and random designs to construct non-contiguous species richness curves (SA_SRs for systematic and SACs for random designs) per dataset. The SA_CR lay below any SA_SR and most of the SACs. The deviation between them was related to the exponent f of the power law relationship between sampled area and extent. The lower the exponent f, the higher was the deviation between the curves. We linked SA_CR to SA_SR and SAC through the concept of “effective” area (A_e), i.e. the nested-contiguous area containing equal number of species with the accumulated sampled area (A_S) of a non-contiguous sampling. The relationship between effective and sampled area was modeled as log(A_e) = klog(A_S). A Generalized Linear Model was used to estimate the values of k from sampling design and dataset properties. The parameter k increased with the average distance between samples and with beta diversity, while k decreased with f. For both systematic and random sampling, the model performed well in predicting effective area in both the training set and in the test set which was totally independent from the training one. Through effective area, we can link different types of species richness curves based on sampling design properties, sampling effort, spatial scale and beta diversity patterns.     

The theory of the nested species–area relationship: geometric foundations of biodiversity scaling

The relationship between sampled area and the number of species within that area, the species–area relationship (SAR), is a major biodiversity pattern and one of a few law‐like regularities in ecology. While the SAR for isolated units (islands or continents) is assumed to result from the dynamics of species colonization, speciation and extinction, the SAR for contiguous areas in which smaller plots are nested within larger sample areas can be attributed to spatial patterns in the distribution of individuals. The nested SAR is typically triphasic in logarithmic space, so that it increases steeply at smaller scales, decelerates at intermediate scales and increases steeply again at continental scales. I will review current theory for this pattern, showing that all three phases of the SAR can be derived from simple geometric considerations. The increase of species richness with area in logarithmic space is generally determined by overall species rarity, so that the rarer the species are on average, the higher is the local slope z. Rarity is scale‐dependent: species occupy only a minor proportion of area at broad spatial scales, leading to upward accelerating shape of the SAR at continental scales. Similarly, species are represented by only a few individuals at fine spatial scales, leading to high SAR slope also at small areas. Geometric considerations reveal links of the SAR to other macroecological patterns, namely patterns of β‐diversity, the species–abundance distribution, and the relationship between energy availability (or productivity) and species richness. Knowledge of the regularities concerning nested SARs may be used for standardizing unequal areas, upscaling species richness and estimating species loss due to area loss, but all these applications have their limits, which also follow from the geometric considerations.     

The species-area relationship of European Lumbricidae (Annelida,Oligochaeta)

Summary Studies throughout Europe reporting species lists of lumbricid earthworms and ranging from 100 m² to >500000 km² are analysed for the regression of species number S on size of area A [km²]. This species-area relation is described by: S=7.9*A ^0.09 (r=0.76).Revised version of a poster presented at the Wilhelm Michaelsen Memorial Symposium (International Symposium on Terrestrial Oligochaeta), Hamburg Sept. 14–18th 1987     

The power of time: spatiotemporal scaling of species diversity

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.     

The terminology and use of species–area relationships: a response to Dengler (2009)

Dengler ( Journal of Biogeography , 2009, 36 , 728–744) addresses issues regarding species–area relationships (SARs), but fails to settles those issues. He states that only certain types of sampling schemes should be used to construct SARs, but is not consistent in the criteria that he uses to include some sampling schemes but not others. He argues that a sampling scheme of contiguous plots will be more accurate in extrapolating beyond the sampled area, but logic tells us that a dispersed sampling scheme is likely to be more accurate. Finally, he concludes that the 'true' SAR is a power function, but this conclusion is inconsistent with his results and with the results of others. Rather than defining a narrow framework for SARs, we need to recognize that the relationship between area and species richness is scale- and system-dependent. Different sampling schemes serve different purposes, and a variety of functional relationships are likely to hold. Further theoretical and empirical work is needed to resolve these issues fully.     

The effect of area on local and regional elevational patterns of species richness

Aim To calculate the degree to which differences between local and regional elevational species richness patterns can be accounted for by the effects of regional area. Location Five elevational transects in Costa Rica, Ecuador, La Réunion, Mexico and Tanzania. Methods We sampled ferns in standardized field plots and collated regional species lists based on herbarium and literature data. We then used the Arrhenius function S = cA^z to correct regional species richness (S) for the effect of area (A) using three slightly different approaches, and compared the concordance of local and regional patterns prior to and after accounting for the effect of area on regional richness using linear regression analyses. Results We found a better concordance between local and regional elevational species richness after including the effect of area in the majority of cases. In several cases, local and regional patterns are very similar after accounting for area. In most of the cases, the maximum regional richness shifted to a higher elevation after accounting for area. Different approaches to correct for area resulted in qualitatively similar results. Main conclusions The differences between local and regional elevational richness patterns can at least partly be accounted for by area effects, suggesting that the underlying causes of elevational richness patterns might be the same at both spatial scales. Values used to account for the effect of area differ among the different study locations, showing that there is no generally applicable elevational species–area relationship.     

吉林蛟河42 hm2针阔混交林样地植物种-面积关系

 种-面积关系是生态学中的基本问题, 其构建方式对种-面积关系的影响以及最优种-面积模型的选择仍然存在争议。该文利用吉林蛟河42 hm²针阔混交林样地数据, 分别采用巢式样方法和随机样方法建立对数模型、幂函数模型和逻辑斯蒂克模型, 并通过赤池信息量准则(AIC)检验种-面积模型优度。结果表明, 种-面积关系受到取样方法的影响, 随机样方法的拟合效果优于巢式样方法。采用随机样方法构建的幂指数模型(AIC = 89.11)和逻辑斯蒂克模型(AIC = 71.21)优于对数模型(AIC = 113.81)。根据AIC值可知, 随机样方法构建的逻辑斯蒂克模型是拟合42 hm²针阔混交林样地种-面积关系的最优模型。该研究表明: 在分析种-面积关系时不仅应考虑尺度效应, 还需注意生境变化及群落演替的影响。