空间尺度对地形异质性-物种多样性关系的影响:粒度和幅度

空间尺度是影响我们理解生态学格局和过程的关键因素。目前已有多种关于物种多样性分布格局形成机制的假说且研究者未达成共识,原因之一是空间尺度对物种多样性分布格局的环境影响因子的解释力和相对重要性有重要影响。地形异质性是物种多样性分布格局的重要影响因素。本文综述了在地形异质性-物种多样性关系的研究中,不同空间粒度和幅度对研究结果的影响,以及可能的原因。尽管已认识到地形异质性-物种多样性关系的空间尺度效应,但粒度和幅度的具体影响仍未有统一结论。当前物种多样性分布格局研究未能覆盖较完整的尺度变化梯度。未来对地形异质性-物种多样性关系的研究需要同时考虑幅度和粒度的影响。建议结合可靠的模型和统计分析方法开展多尺度格局比较分析,以进一步阐明研究尺度对地形异质性-物种多样性关系的影响以及地形异质性起主导作用的空间尺度。     

Scale‐dependent determinants of plant species richness in a semi‐arid fragmented agro‐ecosystem

Aims: (1) Understanding how the relationship between species richness and its determinants depends on the interaction between scales at which the response and explanatory variables are measured. (2) Quantifying the relative contributions of local, intermediate and large‐scale determinants of species richness in a fragmented agro‐ecosystem. (3) Testing the hypothesis that the relative contribution of these determinants varies with the grain size at which species richness is measured. Location: A fragmented agro‐ecosystem in the Southern Judea Lowland, Israel, within a desert–Mediterranean transition zone. Methods: Plant species richness was estimated using hierarchical nested sampling in 81 plots, positioned in 38 natural vegetation patches within an agricultural matrix (mainly wheat fields) among three land units along a sharp precipitation gradient. Explanatory variables included position along that gradient, patch area, patch isolation, habitat heterogeneity and overall plant density. We used general linear models and hierarchical partitioning of variance to test and quantify the effect of each explanatory variable on species richness at four grain sizes (0.0625, 1, 25 and 225 m²). Results: Species richness was mainly affected by position along a precipitation gradient and overall plant density, and to a lesser extent by habitat heterogeneity. It was also significantly affected by patch area and patch isolation, but only for small grain sizes. The contribution of each explanatory variable to explained variance in species richness varied with grain size, i.e. scale‐dependent. The influence of geographic position and habitat heterogeneity on species richness increased with grain size, while the influence of plant density decreased with grain size. Main conclusions: Species richness is determined by the combined effect of several scale‐dependent determinants. Ability to detect an effect and effect size of each determinant varies with the scale (grain size) at which it is measured. The combination of a multi‐factorial approach and multi‐scale sampling reveals that conclusions drawn from studies that ignore these dimensions are restricted and potentially misleading.     

Patterns of woody plant species richness in the Iberian Peninsula: environmental range and spatial scale

Aim Climate‐based models often explain most of the variation in species richness along broad‐scale geographical gradients. We aim to: (1) test predictions of woody plant species richness on a regional spatial extent deduced from macro‐scale models based on water–energy dynamics; (2) test if the length of the climate gradients will determine whether the relationship with woody species richness is monotonic or unimodal; and (3) evaluate the explanatory power of a previously proposed ‘water–energy’ model and regional models at two grain sizes. Location The Iberian Peninsula. Methods We estimated woody plant species richness on grid maps with c. 2500 and 22,500 km² cell size, using geocoded data for the individual species. Generalized additive models were used to explore the relationships between richness and climatic, topographical and substrate variables. Ordinary least squares regression was used to compare regional and more general water–energy models in relation to grain size. Variation partitioning by partial regression was applied to find how much of the variation in richness was related to spatial variables, explanatory variables and the overlap between these two. Results Water–energy dynamics generate important underlying gradients that determine the woody species richness even over a short spatial extent. The relationships between richness and the energy variables were linear to curvilinear, whereas those with precipitation were nonlinear and non‐monotonic. Only a small fraction of the spatially structured variation in woody species richness cannot be accounted for by the fitted variables related to climate, substrate and topography. The regional models accounted for higher variation in species richness than the water–energy models, although the water–energy model including topography performed well at the larger grain size. Elevation range was the most important predictor at all scales, probably because it corrects for ‘climatic error’ due to the unrealistic assumption that mean climate values are evenly distributed in the large grid cells. Minimum monthly potential evapotranspiration was the best climatic predictor at the larger grain size, but actual evapotranspiration was best at the smaller grain size. Energy variables were more important than precipitation individually. Precipitation was not a significant variable at the larger grain size when examined on its own, but was highly significant when an interaction term between itself and substrate was included in the model. Main conclusions The significance of range in elevation is probably because it corresponds to several aspects that may influence species diversity, such as climatic variability within grid cells, enhanced surface area, and location for refugia. The relative explanatory power of energy and water variables was high, and was influenced by the length of the climate gradient, substrate and grain size of the analysis. Energy appeared to have more influence than precipitation, but water availability is also determined by energy, substrate and topographic relief.     

Freshwater biodiversity at regional extent: determinants of macroinvertebrate taxonomic richness in headwater streams

We studied spatial variation of macroinvertebrate species richness in headwater streams at two spatial extents, within and across drainage systems, and assessed the relative importance of three groups of variables (local, landscape and regional) at each extent. We specifically asked whether the same variables proposed to control broad‐scale richness patterns of terrestrial organisms (temperature, topographic variability) are important determinants of species richness also in streams, or whether environmental factors effective at mainly local scales (in‐stream heterogeneity, potential productivity) constrain species richness in local communities. We used forward selection with two stopping criteria to identify the key environmental and spatial variables at each study extent. Eigenvector‐based spatial filtering was applied to evaluate spatial patterns in species richness, and variation partitioning was used to assess the amount of variation in richness attributable to purely environmental and spatial components. A prime regulator of richness variation at the bioregion extent was elevation range (increasing richness with higher topographic variability), whereas hydrological stability and temperature were unimportant. Water chemistry variables, particularly water color, exhibited strong spatially‐structured variation across drainage systems. Local environmental variables explained most of the variation in species richness at the drainage‐system extent, reflecting gradients in total phosphorus and water color (negative effect on richness). The importance of the pure spatial component was strongly region‐dependent, with a peak (60%) in one drainage system, suggesting the presence of unmeasured environmental factors. Our results emphasize the need for spatially‐explicit, regional studies to better understand geographical variation of freshwater biodiversity. Future studies need to relate species richness not only to local factors but also to broad‐scale climatic variables, recognizing the presence of spatially‐structured environmental variation.     

Multiple environmental determinants of regional species richness and effects of geographic range size

Understanding patterns of species richness at broad geographic extents remains one of the most challenging yet necessary scientific goals of our time. Many hypotheses have been proposed to account for spatial variation in species richness; among them, environmental determinants have played a central role. In this study, we use data on regional bat species richness in the New World to study redundancy and complementarity of three environmental hypotheses: energy, heterogeneity and seasonality. We accomplish this by partitioning variation in species richness among components associated with unique and combined effects of variables from each hypotheses, and by partitioning the overall richness gradient into gradients of species with varying breadths of geographic distribution. These three environmental hypotheses explain most variation in the species richness gradient of all bats, but do not account for all positive spatial autocorrelation at short distances. Although environmental predictors are highly redundant, energy and seasonality explain different and complementary fractions of variation in species richness of all bats. On the other hand, heterogeneity variables contribute little to explain this gradient. However, results change dramatically when richness is estimated for groups of species with different sizes of geographic distribution. First, the amount of variation explained by environment decreases with a decrease in range size; this suggests that richness gradients of small‐ranged species can not be explained as easily as those of broadly distributed species, as has been implied by analyses that do not consider differences in range size among species. Second, the relative contribution of environmental predictors to explained variation also changes with change in range size. Seasonality and energy are good predictors of species with broad distributions, but they loose almost all explanatory power for richness of species with small ranges. In contrast, heterogeneity, which is a relatively poor predictor of richness of species with large ranges, becomes the main predictor of richness gradients of species with restricted distributions. This suggests that range size is a different dimension on which heterogeneity and other environmental characteristics are complementary to each other. Our results suggest that determinants of species richness gradients might be complex, or at least more complex than many studies have previously suggested.     

The role of spatial scale and the perception of large-scale species-richness patterns

Despite two centuries of exploration, our understanding of factors determining the distribution of life on Earth is in many ways still in its infancy. Much of the disagreement about governing processes of variation in species richness may be the result of differences in our perception of species‐richness patterns. Until recently, most studies of large‐scale species‐richness patterns assumed implicitly that patterns and mechanisms were scale invariant. Illustrated with examples and a quantitative analysis of published data on altitudinal gradients of species richness (n = 204), this review discusses how scale effects (extent and grain size) can influence our perception of patterns and processes. For example, a hump‐shaped altitudinal species‐richness pattern is the most typical (c. 50%), with a monotonic decreasing pattern (c. 25%) also frequently reported, but the relative distribution of patterns changes readily with spatial grain and extent. If we are to attribute relative impact to various factors influencing species richness and distribution and to decide at which point along a spatial and temporal continuum they act, we should not ask only how results vary as a function of scale but also search for consistent patterns in these scale effects. The review concludes with suggestions of potential routes for future analytical exploration of species‐richness patterns.     

Type and spatial structure of distribution data and the perceived determinants of geographical gradients in ecology: the species richness of African birds

Aim Studies exploring the determinants of geographical gradients in the occurrence of species or their traits obtain data by: (1) overlaying species range maps; (2) mapping survey‐based species counts; or (3) superimposing models of individual species’ distributions. These data types have different spatial characteristics. We investigated whether these differences influence conclusions regarding postulated determinants of species richness patterns. Location Our study examined terrestrial bird diversity patterns in 13 nations of southern and eastern Africa, spanning temperate to tropical climates. Methods Four species richness maps were compiled based on range maps, field‐derived bird atlas data, logistic and autologistic distribution models. Ordinary and spatial regression models served to examine how well each of five hypotheses predicted patterns in each map. These hypotheses propose productivity, temperature, the heat–water balance, habitat heterogeneity and climatic stability as the predominant determinants of species richness. Results The four richness maps portrayed broadly similar geographical patterns but, due to the nature of underlying data types, exhibited marked differences in spatial autocorrelation structure. These differences in spatial structure emerged as important in determining which hypothesis appeared most capable of explaining each map's patterns. This was true even when regressions accounted for spurious effects of spatial autocorrelation. Each richness map, therefore, identified a different hypothesis as the most likely cause of broad‐scale gradients in species diversity. Main conclusions Because the ‘true’ spatial structure of species richness patterns remains elusive, firm conclusions regarding their underlying environmental drivers remain difficult. More broadly, our findings suggest that care should be taken to interpret putative determinants of large‐scale ecological gradients in light of the type and spatial characteristics of the underlying data. Indeed, closer scrutiny of these underlying data — here the distributions of individual species — and their environmental associations may offer important insights into the ultimate causes of observed broad‐scale patterns.     

Cross‐scale variation in species richness–environment associations

Aim The scale dependence of many ecological patterns and processes implies that general inference is reliant on obtaining scale‐response curves over a large range of grains. Although environmental correlates of richness have been widely studied, comparisons among groups have usually been applied at single grains. Moreover, the relevance of environment–richness associations to fine‐grain assemblages has remained surprisingly unclear. We present a first global cross‐scale assessment of environment–richness associations for birds, mammals and amphibians from 2000 km down to c. 20 km. Location World‐wide. Methods We performed an extensive survey of the literature for well‐sampled terrestrial vertebrate inventories over clearly defined small extents. Coarser grain richness was estimated from the intersection of extent‐of‐occurrence range maps with concentric equal‐distance circles around fine‐grain assemblage location centroids. General linear and simultaneous autoregressive models were used to relate richness at the different grains to environmental correlates. Results The ability of environmental variables to explain species richness decreases markedly toward finer grains and is lowest for fine‐grained assemblages. A prominent transition in importance occurs between productivity and temperature at increased grains, which is consistent with the role of energy affecting regional, but not local, richness. Variation in fine‐grained predictability across groups is associated with their purported grain of space use, i.e. highest for amphibians and narrow‐ranged and small‐bodied species. Main conclusions We extend the global documentation of environment–richness associations to fine‐grained assemblages. The relationship between fine‐grained predictability of a group and its ecological characteristics lends empirical support to the idea that variation in species fine‐grained space use may scale up to explain coarse‐grained diversity patterns. Our study exposes a dramatic and taxonomically variable scale dependence of environment–richness associations and suggests that environmental correlates of richness may hold limited information at the level of communities.     

A macroecological perspective of diversity patterns in the freshwater realm

1. The aim of this paper is to review literature on species diversity patterns of freshwater organisms and underlying mechanisms at large spatial scales. 2. Some freshwater taxa (e.g. dragonflies, fish and frogs) follow the classical latitudinal decline in regional species richness (RSR), supporting the patterns found for major terrestrial and marine organism groups. However, the mechanisms causing this cline in most freshwater taxa are inadequately understood, although research on fish suggests that energy and history are major factors underlying the patterns in total species and endemic species richness. Recent research also suggests that not all freshwater taxa comply with the decline of species richness with latitude (e.g. stoneflies, caddisflies and salamanders), but many taxa show more complex geographical patterns in across‐regions analyses. These complexities are even more profound when studies of global, continental and regional extents are compared. For example, clear latitudinal gradients may be present in regional studies but absent in global studies (e.g. macrophytes). 3. Latitudinal gradients are often especially weak in the across‐ecosystems analyses, which may be attributed to local factors overriding the effects of large‐scale factors on local communities. Nevertheless, local species richness (LSR) is typically linearly related to RSR (suggesting regional effects on local diversity), although saturating relationships have also been found in some occasions (suggesting strong local effects on diversity). Nestedness has often been found to be significant in freshwater studies, yet this pattern is highly variable and generally weak, suggesting also a strong beta diversity component in freshwater systems. 4. Both geographical location and local environmental factors contribute to variation in alpha diversity, nestedness and beta diversity in the freshwater realm, although the relative importance of these two groups of explanatory variables may be contingent on the spatial extent of the study. The mechanisms associated with spatial and environmental control of community structure have also been inferred in a number of studies, and most support has been found for species sorting (possibly because many freshwater studies have species sorting as their starting point), although also dispersal limitation and mass effects may be contributing to the patterns found. 5. The lack of latitudinal gradients in some freshwater taxa begs for further explanations. Such explanations may not be gained for most freshwater taxa in the near future, however, because we lack species‐level information, floristic and faunistic knowledge, and standardised surveys along extensive latitudinal gradients. A challenge for macroecology is thus to use the best possible species‐level information on well‐understood groups (e.g. fish) or use surrogates for species‐level patterns (e.g. families) and then develop hypotheses for further testing in the freshwater realm. An additional research challenge concerns understanding patterns and mechanisms associated with the relationships between alpha, beta and gamma components of species diversity. 6. Understanding the mechanistic basis of species diversity patterns should preferably be based on a combination of large‐scale macroecological and landscape‐scale metacommunity research. Such a research approach will help in elucidating patterns of species diversity across regional and local scales in the freshwater realm.     

Patterns of rare woody species richness: the influence of environment, landscape attributes and spatial structure across different spatial scales

The aim of this study was to evaluate the relative contributions of the environment, landscape patterns, and spatial structure to explaining the variation in richness of rare woody species at three levels of rarity (low, medium, and high) and at different grain sizes and spatial extents. We used herbarium records of 195 rare woody species to quantify species richness—overall and for three levels of rarity—of the Yucatan Peninsula, Mexico. We assessed relationships between rare species richness and different sets of explanatory variables (environmental, landscape patterns, and spatial structure of sampling units) using linear regression and variation partitioning analyses at three grain sizes (625, 400, and 225 km²). We also conducted a principle coordinates of neighbor matrices analysis to allow interpretation of the results in terms of different spatial extents. The percentage of variation in rare species richness explained by the models was highest for the largest grain size and spatial extent. At the larger extents, rare species richness was explained mainly by the environment, whereas landscape patterns played a more prominent role at the local extent. Landscape patterns also contributed more to explaining species richness at low to medium levels of rarity, whereas the richness of extremely rare species was better explained by spatial structure. We conclude that the relative contribution of the factors explaining the variation of rare species richness depends on both grain and extent, as well as on the level of rarity. These results underscore the importance of considering the different components of scale (grain and extent) as well as different levels of species rarity in order to better understand the patterns of distribution of rare species richness and to be able to frame appropriate conservation strategies.     

The significance of geographic range size for spatial diversity patterns in Neotropical palms

We examined the effect of range size in commonly applied macroecological analyses using continental distribution data for all 550 Neotropical palm species (Arecaceae) at varying grain sizes from 0.5° to 5°. First, we evaluated the relative contribution of range-restricted and widespread species on the patterns of species richness and endemism. Second, we analysed the impact of range size on the predictive value of commonly used predictor variables. Species sequences were produced arranging species according to their range size in ascending, descending, and random order. Correlations between the cumulative species richness patterns of these sequences and environmental predictors were performed in order to analyse the effect of range size. Despite the high proportion of rare species, patterns of species richness were found to be dominated by a minority of widespread species (∼20%) which contained 80% of the spatial information. Climatic factors related to energy and water availability and productivity accounted for much of the spatial variation of species richness of widespread species. In contrast, species richness of range-restricted species was to a larger extent determined by topographical complexity. However, this effect was much more difficult to detect due to a dominant influence of widespread species. Although the strength of different environmental predictors changed with spatial scale, the general patterns and trends proved to be relatively stabile at the examined grain sizes. Our results highlight the difficulties to approximate causal explanations for the occurrence of a majority of species and to distinguish between contemporary climatic factors and history.     

Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna

Aim This article aims to test for and explore spatial nonstationarity in the relationship between avian species richness and a set of explanatory variables to further the understanding of species diversity variation. Location Sub‐Saharan Africa. Methods Geographically weighted regression was used to study the relationship between species richness of the endemic avifauna of sub‐Saharan Africa and a set of perceived environmental determinants, comprising the variables of temperature, precipitation and normalized difference vegetation index. Results The relationships between species richness and the explanatory variables were found to be significantly spatially variable and scale‐dependent. At local scales > 90% of the variation was explained, but this declined at coarser scales, with the greatest sensitivity to scale variation evident for narrow ranging species. The complex spatial pattern in regression model parameter estimates also gave rise to a spatial variation in scale effects. Main conclusions Relationships between environmental variables are generally assumed to be spatially stationary and conventional, global, regression techniques are therefore used in their modelling. This assumption was not satisfied in this study, with the relationships varying significantly in space. In such circumstances the average impression provided by a global model may not accurately represent conditions locally. Spatial nonstationarity in the relationship has important implications, especially for studies of species diversity patterns and their scaling.     

When does diversity fit null model predictions? Scale and range size mediate the mid‐domain effect

Aim  Recently, a flurry of studies have focused on the extent to which geographical patterns of diversity fit mid-domain effect (MDE) null models. While some studies find strong support for MDE null models, others find little. We test two hypotheses that might explain this variation among studies: small-ranged groups of species are less likely than large-ranged species to show mid-domain peaks in species richness, and mid-domain null model predictions are less robust for smaller spatial extents than for larger spatial extents.
Location  We analyse data sets from elevational, riverine, continental and other domains from around the world.
Methods  We use a combination of Spearman rank correlations and binomial tests to examine whether differences within and among studies and domains in the predictive power of MDE null models vary with spatial scale and range size.
Results  Small-ranged groups of species are less likely to fit mid-domain predictions than large-ranged groups of species. At large spatial extents, diversity patterns of taxonomic groups with large mean range sizes fit MDE null model predictions better than did diversity patterns of groups with small mean range sizes. MDE predictions were more explanatory at larger spatial extents than at smaller extents. Diversity patterns at smaller spatial extents fit MDE predictions poorly across all range sizes. Thus, MDE predictions should be expected to explain patterns of species richness when ranges and the scale of analysis are both large.
Main conclusions  Taken together, the support for these hypotheses offers a more sophisticated model of when MDE predictions should be expected to explain patterns of species richness, namely when ranges and the scale of analysis are both large. Thus the circumstances in which the MDE is important are finite and apparently predictable.     

Multi-species richness of boreal agricultural landscapes: effects of climate, biotope, soil and geographical location

Aim To assess the relative importance of climate, biotope and soil variables as well as geographical location for the species richness of plants, butterflies, day‐active macromoths and wild bees in boreal agricultural landscapes. Location A total of 68 agricultural landscapes located in southern Finland. Methods Generalized linear mixed models were used to analyse the effects of environmental (climate, biotope and soil) and spatial (latitude and longitude) variables on species richness of four taxa in 136 study squares of 0.25 km². Using partial regression, the variation in species richness was decomposed into the purely environmental fraction; the spatially structured environmental fraction; and the purely spatial fraction, including variables retained in cubic trend surface regression. Results Species richness of all taxa was positively correlated with temperature. Species richness of plants and butterflies was also positively correlated with the heterogeneity of landscape. The extent of low‐intensity agricultural land and forest had a positive effect, and the extent of cultivated field a negative effect on the species richness of these taxa. The effect of soil characteristics on the number of observed species was negligible for all taxa. The greatest part of the explained variation for all taxa was accounted for by the pure effect of geographical location. To a somewhat lesser extent, the species richness of plants, butterflies and bees was also related to the effects of spatially structured environmental variables, and the species richness of macromoths to the effects of environmental variables. Main conclusions Multi‐species richness of boreal agricultural landscapes at the scale of 0.25 km² was associated with the heterogeneity of the local landscape. However, large‐scale geographical variation in species richness was also observed, which indicates the importance of climate and geographical location for the taxa studied. These results highlight the fact that, even on a landscape scale, geographical factors should be taken into account in biodiversity studies. Heterogeneous agricultural landscapes, including forest and non‐crop biotopes, should be preserved or restored to maintain biodiversity.     

Richness patterns, species distributions and the principle of extreme deconstruction

Aim To analyse the global patterns in species richness of Viperidae snakes through the deconstruction of richness into sets of species according to their distribution models, range size, body size and phylogenetic structure, and to test if environmental drivers explaining the geographical ranges of species are similar to those explaining richness patterns, something we called the extreme deconstruction principle. Location Global. Methods We generated a global dataset of 228 terrestrial viperid snakes, which included geographical ranges (mapped at 1° resolution, for a grid with 7331 cells world‐wide), body sizes and phylogenetic relationships among species. We used logistic regression (generalized linear model; GLM) to model species geographical ranges with five environmental predictors. Sets of species richness were also generated for large and small‐bodied species, for basal and derived species and for four classes of geographical range sizes. Richness patterns were also modelled against the five environmental variables through standard ordinary least squares (OLS) multiple regressions. These subsets are replications to test if environmental factors driving species geographical ranges can be directly associated with those explaining richness patterns. Results Around 48% of the total variance in viperid richness was explained by the environmental model, but richness sets revealed different patterns across the world. The similarity between OLS coefficients and the primacy of variables across species geographical range GLMs was equal to 0.645 when analysing all viperid snakes. Thus, in general, when an environmental predictor it is important to model species geographical ranges, this predictor is also important when modelling richness, so that the extreme deconstruction principle holds. However, replicating this correlation using subsets of species within different categories in body size, range size and phylogenetic structure gave more variable results, with correlations between GLM and OLS coefficients varying from –0.46 up to 0.83. Despite this, there is a relatively high correspondence (r = 0.73) between the similarity of GLM‐OLS coefficients and R² values of richness models, indicating that when richness is well explained by the environment, the relative importance of environmental drivers is similar in the richness OLS and its corresponding set of GLMs. Main conclusions The deconstruction of species richness based on macroecological traits revealed that, at least for range size and phylogenetic level, the causes underlying patterns in viperid richness differ for the various sets of species. On the other hand, our analyses of extreme deconstruction using GLM for species geographical range support the idea that, if environmental drivers determine the geographical distribution of species by establishing niche boundaries, it is expected, at least in theory, that the overlap among ranges (i.e. richness) will reveal similar effects of these environmental drivers. Richness patterns may be indeed viewed as macroecological consequences of population‐level processes acting on species geographical ranges.     

Scale,disturbance and productivity control the native‐exotic richness relationship

The relationship between native and exotic species richness may be highly context‐dependent. Spatial scale, including both plot size (grain) and study area (extent), is likely to influence this relationship, as are environmental conditions such as resource availability and disturbance intensity. We used experimental manipulations of soil fertility and disturbance in a California coastal grassland to directly examine the sensitivity of the native–exotic richness relationship (NERR) to these factors across five grain sizes and two spatial extents. The slope of the NERR was a function of grain size, extent and treatment. Over small spatial extents, native and exotic richness were usually uncorrelated. Across a larger extent, NERRs were negative in control plots, neutral in disturbance plots, and positive in plots with experimentally reduced soil fertility. These patterns were strongest for small grain sizes. We verify the importance of spatial grain in determining the NERR, and emphasize the role of spatial extent.     

Spatial species-richness gradients across scales: a meta-analysis

Aim We surveyed the empirical literature to determine how well six diversity hypotheses account for spatial patterns in species richness across varying scales of grain and extent. Location Worldwide. Methods We identified 393 analyses (‘cases’) in 297 publications meeting our criteria. These criteria included the requirement that more than one diversity hypothesis was tested for its relationship with species richness. We grouped variables representing the hypotheses into the following ‘correlate types’: climate/productivity, environmental heterogeneity, edaphics/nutrients, area, biotic interactions and dispersal/history (colonization limitation or other historical or evolutionary effect). For each case we determined the ‘primary’ variable: the one most strongly correlated with taxon richness. We defined ‘primacy’ as the proportion of cases in which each correlate type was represented by the primary variable, relative to the number of times it was studied. We tested for differences in both primacy and mean coefficient of determination of the primary variable between the hypotheses and between categories of five grouping variables: grain, extent, taxon (animal vs. plant), habitat medium (land vs. water) and insularity (insular vs. connected). Results Climate/productivity had the highest overall primacy, and environmental heterogeneity and dispersal/history had the lowest. Primacy of climate/productivity was much higher in large‐grain and large‐extent studies than at smaller scales. It was also higher on land than in water, and much higher in connected systems than in insular ones. For other hypotheses, differences were less pronounced. Throughout, studies on plants and animals showed similar patterns. Coefficients of determination of the primary variables differed little between hypotheses and across the grouping variables, the strongest effects being low means in the smallest grain class and for edaphics/nutrients variables, and a higher mean for water than for land in connected systems but vice versa in insular systems. We highlight areas of data deficiency. Main conclusions Our results support the notion that climate and productivity play an important role in determining species richness at large scales, particularly for non‐insular, terrestrial habitats. At smaller extents and grain sizes, the primacy of the different types of correlates appears to differ little from null expectation. In our analysis, dispersal/history is rarely the best correlate of species richness, but this may reflect the difficulty of incorporating historical factors into regression models, and the collinearity between past and current climates. Our findings are consistent with the view that climate determines the capacity for species richness. However, its influence is less evident at smaller spatial scales, probably because (1) studies small in extent tend to sample little climatic range, and (2) at large grains some other influences on richness tend to vary mainly within the sampling unit.