Spatial and temporal variability in species richness in a temperate intertidal community

Understanding changes in estuarine benthic communities has important implications for conservation and yet it is a challenge due to the high natural variability of these systems. We addressed this challenge through the study of temporal and spatial patterns of species richness in an intertidal benthic community in New Zealand North Island. Five different locations within the estuary were monitored seasonally over 12 yr. This data set allowed the study of species–time–area relationships (STAR) and the delineation of patterns in species richness, heterogeneity and turnover in space and time. The site with the highest species richness also had the highest within‐site heterogeneity in species richness, a high number of species occurring infrequently in time, the lowest mud content and the most variable wave climate. Similarities and differences between sites were generally maintained over time, although seasonal and multi‐year patterns in species richness occurred at all sites. The STAR showed a significant negative interaction between space and time, with species accumulation rates in space and time being equivalent at 4 spatial replicates (250 m²) and 2 temporal replicates (6 months). The lowest source of variability in species turnover was within site, and the highest source was over years. This was reflected in the lack of an asymptotic relationship in the species accumulation curve despite the 12 yr of monitoring. These results contribute to the knowledge of the variability in diversity patterns in estuaries and have important implications for long‐term monitoring of natural communities and the estimation of diversity for conservation.     

Hot,dry and different: Australian lizard richness is unlike that of mammals,amphibians and birds

Aims (1) To map the species richness of Australian lizards and describe patterns of range size and species turnover that underlie them. (2) To assess the congruence in the species richness of lizards and other vertebrate groups. (3) To search for commonalities in the drivers of species richness in Australian vertebrates. Location Australia. Methods We digitized lizard distribution data to generate gridded maps of species richness and β‐diversity. Using similar maps for amphibians, mammals and birds, we explored the relationship between species richness and temperature, actual evapotranspiration, elevation and local elevation range. We used spatial eigenvector filtering and geographically weighted regression to explore geographical patterns and take spatial autocorrelation into account. We explored congruence between the species richness of vertebrate groups whilst controlling for environmental effects. Results Lizard richness peaks in the central deserts (where β‐diversity is low) and tropical north‐east (where β‐diversity is high). The intervening lowlands have low species richness and β‐diversity. Generally, lizard richness is uncorrelated with that of other vertebrates but this low congruence is strongly spatially structured. Environmental models for all groups also show strong spatial heterogeneity. Lizard richness is predicted by different environmental factors from other vertebrates, being highest in dry and hot regions. Accounting for environmental drivers, lizard richness is weakly positively related to richness of other vertebrates, both at global and local scales. Main conclusions Lizard species richness differs from that of other vertebrates. This difference is probably caused by differential responses to environmental gradients and different centres of diversification; there is little evidence for inter‐taxon competition limiting lizard richness. Local variation in habitat diversity or evolutionary radiations may explain weak associations between taxa, after controlling for environmental variables. We strongly recommend that studies of variation in species richness examine and account for non‐stationarity.     

Effect of species richness and relative abundance on the shape of the species accumulation curve

Abstract We explain how species accumulation curves are influenced by species richness (total number of species), relative abundance and diversity using computer‐generated simulations. Species richness defines the boundary of the horizontal asymptote value for a species accumulation curve, and the shape of the curve is influenced by both relative abundance and diversity. Simulations with a high proportion of rare species and a few abundant species have a species accumulation curve with a low ‘shoulder’ (inflection point on the ordinate axis) and a long upward slope to the asymptote. Simulations with a high proportion of relatively abundant species have a steeply rising initial slope to the species accumulation curve and plateau early. Diversity (as measured by Simpson's and Shannon–Weaver indices) for simulations is positively correlated with the initial slope of the species accumulation curve. Species accumulation curves cross when one simulation has a high proportion of both rare and abundant species compared with another that has a more even distribution of abundance among species.     

Ecological biogeography of Mexican bats: the relative contributions of habitat heterogeneity,beta diversity,and environmental gradients to species richness and composition patterns

Mexico has higher mammalian diversity than expected for its size and geographic position. High environmental hetero geneity throughout Mexico is hypothesized to promote high turnover rates (β‐diversity), thus contributing more to observed species richness and composition than within‐habitat (α) diversity. This is true if species are strongly associated with their environments, such that changes in environmental attributes will result in changes in species composition. Also, greater heterogeneity in an area will result in greater species richness. This hypothesis has been deemed false for bats, as their ability to fly would reduce opportunities for habitat specialization. If so, we would expect no significant relationships between 1) species composition and environmental variables, 2) species richness and environmental heterogeneity, 3) β‐diversity and environmental heterogeneity. We tested these predictions using 31 bat assemblages distributed across Mexico. Using variance partitioning we evaluated the relative contribution of vegetation, climate, elevation, horizontal heterogeneity (a variate including vegetation, climate, and elevational heterogeneity), spatial variation (lat‐long), and vertical hetero geneity (of vegetation strata) to variation in bat species composition and richness. Variation in vegetation explained 92% of the variation in species composition and was correlated with all other variables examined, indicating that bats respond directly to habitat composition and structure. Beta‐diversity and vegetational heterogeneity were significantly correlated. Bat species richness was significantly correlated with vertical, but not horizontal, heterogeneity. Nonetheless, neither horizontal nor vertical heterogeneity were random; both were related to latitude and to elevation. Variation in bat community composition and richness in Mexico were primarily explained by local landscape heterogeneity and environmental factors. Significant relationships between β‐diversity and environmental variation reveal differences in habitat specialization by bats, and explain their high diversity in Mexico. Understanding mechanisms acting along environmental or geographic gradients is as important for understanding spatial variation in community composition as studying mechanisms that operate at local scales.     

Environmental heterogeneity as a universal driver of species richness across taxa,biomes and spatial scales

Environmental heterogeneity is regarded as one of the most important factors governing species richness gradients. An increase in available niche space, provision of refuges and opportunities for isolation and divergent adaptation are thought to enhance species coexistence, persistence and diversification. However, the extent and generality of positive heterogeneity–richness relationships are still debated. Apart from widespread evidence supporting positive relationships, negative and hump‐shaped relationships have also been reported. In a meta‐analysis of 1148 data points from 192 studies worldwide, we examine the strength and direction of the relationship between spatial environmental heterogeneity and species richness of terrestrial plants and animals. We find that separate effects of heterogeneity in land cover, vegetation, climate, soil and topography are significantly positive, with vegetation and topographic heterogeneity showing particularly strong associations with species richness. The use of equal‐area study units, spatial grain and spatial extent emerge as key factors influencing the strength of heterogeneity–richness relationships, highlighting the pervasive influence of spatial scale in heterogeneity–richness studies. We provide the first quantitative support for the generality of positive heterogeneity–richness relationships across heterogeneity components, habitat types, taxa and spatial scales from landscape to global extents, and identify specific needs for future comparative heterogeneity–richness research.     

A test of multiple hypotheses for the species richness gradient of South American owls

Many mechanisms have been proposed to explain broad scale spatial patterns in species richness. In this paper, we evaluate five explanations for geographic gradients in species richness, using South American owls as a model. We compared the explanatory power of contemporary climate, landcover diversity, spatial climatic heterogeneity, evolutionary history, and area. An important aspect of our analyses is that very different hypotheses, such as history and area, can be quantified at the same observation scale and, consequently can be incorporated into a single analytical framework. Both area effects and owl phylogenetic history were poorly associated with richness, whereas contemporary climate, climatic heterogeneity at the mesoscale and landcover diversity explained ca. 53% of the variation in species richness. We conclude that both climate and environmental heterogeneity should be retained as plausible explanations for the diversity gradient. Turnover rates and scaling effects, on the other hand, although perhaps useful for detecting faunal changes and beta diversity at local and regional scales, are not strong explanations for the owl diversity gradient.     

Environmental drivers of ant species richness and composition across the Argentine Pampas grassland

Understanding the underlying mechanisms causing diversity patterns is a fundamental objective in ecology and science‐based conservation biology. Energy and environmental‐heterogeneity hypotheses have been suggested to explain spatial changes in ant diversity. However, the relative roles of each one in determining alpha and beta diversity patterns remain elusive. We investigated the main factors driving spatial changes in ant (Hymenoptera, Formicidae) species richness and composition (including turnover and nestedness components) along a 500 km longitudinal gradient in the Pampean region of Argentina. Ants were sampled using pitfall traps in 12 sample sites during the summer. We performed a model selection approach to analyse responses of ant richness and composition dissimilarity to environmental factors. Then, we computed a dissimilarity partitioning of the contributions of spatial turnover and nestedness to total composition dissimilarity. Temporal habitat heterogeneity and temperature were the primary factors explaining spatial patterns of epigean ant species richness across the Pampas. The distance decay in species composition similarity was best accounted by temperature dissimilarity, and turnover had the greatest contribution to the observed beta diversity pattern. Our findings suggest that both energy and environmental‐heterogeneity‐related variables are key factors shaping richness patterns of ants and niche‐based processes instead of neutral processes appear to be regulating species composition of ant assemblages. The major contribution of turnover to the beta diversity pattern indicated that lands for potential reconversion to grassland should represent the complete environmental gradient of the Pampean region, instead of prioritizing a single site with high species richness.     

Intransitive competition is widespread in plant communities and maintains their species richness

Intransitive competition networks, those in which there is no single best competitor, may ensure species coexistence. However, their frequency and importance in maintaining diversity in real‐world ecosystems remain unclear. We used two large data sets from drylands and agricultural grasslands to assess: (1) the generality of intransitive competition, (2) intransitivity–richness relationships and (3) effects of two major drivers of biodiversity loss (aridity and land‐use intensification) on intransitivity and species richness. Intransitive competition occurred in > 65% of sites and was associated with higher species richness. Intransitivity increased with aridity, partly buffering its negative effects on diversity, but was decreased by intensive land use, enhancing its negative effects on diversity. These contrasting responses likely arise because intransitivity is promoted by temporal heterogeneity, which is enhanced by aridity but may decline with land‐use intensity. We show that intransitivity is widespread in nature and increases diversity, but it can be lost with environmental homogenisation.     

Predicting patterns of plant species richness in megadiverse South Africa

Using new tools (boosted regression trees) in predictive biogeography, with extensive spatial 23 distribution data for >19 000 species, we developed predictive models for South African plant species richness patterns. Further, biome level analysis explored possible functional determinants of country‐wide regional species richness. Finally, to test model reliability independently, we predicted potential alien invasive plant species richness with an independent dataset. Amongst the different hypotheses generally invoked to explain species 30 diversity (energy, favorableness, topographic heterogeneity, irregularity and seasonality), results revealed topographic heterogeneity as the most powerful single explanatory variable for indigenous South African plant species richness. Some biome‐specific responses were observed, i.e. two of the five analyzed biomes (Fynbos and Grassland) had richness best explained by the “species‐favorableness” hypothesis, but even in this case, topographic heterogeneity was also a primary predictor. This analysis, the largest conducted on an almost exhaustive species sample in a species‐rich region, demonstrates the preeminence of topographic heterogeneity in shaping the spatial pattern of regional plant species richness. Model reliability was confirmed by the considerable predictive power for alien invasive species richness. It thus appears that topographic heterogeneity controls species richness in two main ways: firstly, by providing an abundance of ecological niches in contemporary space (revealed by alien invasive species richness relationships) and secondly, by facilitating the persistence of ecological niches through time. The extraordinary richness of the South African Fynbos biome, a world‐renowned hotspot of biodiversity with the steepest environmental gradients in South Africa, may thus have arisen through both mechanisms. Comparisons with similar regions of the world outside South Africa are needed to confirm the generality of topographic heterogeneity and favorableness as predictors of plant richness.     

Species richness correlates of raw and standardized co‐occurrence metrics

Measuring β‐diversity and changes in species composition across multiple sites and environments is a major research focus in macroecology, and a variety of metrics have been proposed to quantify species co‐occurrence patterns in a species × site occurrence matrix. However, indices of β‐diversity and species co‐occurrence are often statistically dependent on the number of species in an assemblage. We compared the results of several common co‐occurrence metrics with patterns generated by a spatially explicit neutral model simulation. We found that all measures of co‐occurrence and β‐diversity, whether raw, rescaled or standardized by a null model expectation, were highly correlated with the total species richness of the landscape. The one important exception were the effect sizes of the fixed–fixed null model algorithm, which preserves row and column sums of the original matrix during matrix randomization. Our results call for a careful interpretation of meta‐analyses of assemblages that differ widely in species richness. At a minimum, observed species richness should be used as a statistical covariate in regression analyses, and results of the fixed–fixed algorithm should be compared carefully with the results of other randomization tests.     

Distribution of vascular plant species richness and endemic richness along the Himalayan elevation gradient in Nepal

Aim Species richness and endemic richness vary along elevation gradients, but not necessarily in the same way. This study tests if the maxima in gamma diversity for flowering plants and the endemic subset of these plants are coherent or not. Location The study was conducted in Nepal, between 1000 and 5000 m a.s.l. Methods We used published data on distribution and elevational ranges of the Nepalese flora to interpolate presence between maximum and minimum elevations. Correlation, regression and graphical analyses were used to evaluate the diversity pattern between 1000 and 5000 m a.s.l. Results The interval of maximum species endemic to Nepal or the Himalayas (3800–4200 m) is above the interval of maximum richness (1500–2500 m). The exact location of maximum species density is uncertain and its accuracy depends on ecologically sound estimates of area in the elevation zones. There is no positive statistically significant correlation between log‐area and richness (total or endemic). Total richness is positively correlated with log‐area‐adjusted, i.e. estimated area adjusted for the degree of topographic heterogeneity. The proportion of endemic species increases steadily from low to high elevations. The peak in endemism (c. 4000 m) corresponds to the start of a rapid decrease in species richness above 4000 m. This may relate to the last glacial maximum (equilibrium line at c. 4000 m) that penetrated down to 2500–3000 m. This dynamic hard boundary may have caused an increase in the extinction rate above 4000 m, and enhanced the probability of isolation and facilitated speciation of neoendemics, especially among genera with a high proportion of polyploids. Main conclusions The results reject the idea of corresponding maxima in endemic species and species richness in the lowlands tentatively deduced from Stevens’ elevational Rapoport effect. They confirm predictions based on hard boundary theory, but hard‐boundaries should be viewed as dynamic rather than static when broad‐scale biogeographical patterns with a historical component are being interpreted.     

Size-dependent species richness: trends within plant communities and across latitude

We examine how species richness and species‐specific plant density (number of species and number of individuals per species, respectively) vary within community size frequency distributions and across latitude. Communities from Asia, Africa, Europe, and North, Central and South America were studied (60°4′N–41°4′S latitude) using the Gentry data base. Log–log linear stem size (diameter) frequency distributions were constructed for each community and the species richness and species‐specific plant density within each size class were determined for each frequency distribution. Species richness in the smallest stem size class correlated with the Y‐intercepts (β‐values) of the regression curves describing each log–log linear size distributions. Two extreme community types were identified (designated as type A and type B). Type A communities had steep size distributions (i.e. large β‐values), log–log linear species‐richness size distributions, low species‐specific plant density distributions, and a small size class (2–4 cm) containing the majority of all species but rarely conspecifics of the dominant tree species. Type B communities had shallow size distributions (i.e. small β‐values), more or less uniform (and low) size class species‐ richness and species‐specific density distributions and size‐dominant species resident in the smallest size class. Type A communities were absent in the higher latitudes but increased in number towards the equator, i.e. in the smallest size class, species richness increased (and species‐specific density decreased) towards the tropics. Based on our survey of type A and type B communities (and their intermediates), species richness evinces size‐dependent and latitudinal trends, i.e. species richness increased with decreasing body size and most species increasingly reside in the smallest plant size class towards the tropics. Across all latitudes, a trade‐off exists between the number of species and the number of individuals per species residing in the smaller size classes.     

Anthropogenic impacts upon plant species richness and net primary productivity in California

We assess the importance of anthropogenic land‐use, altered productivity, and species invasions for observed productivity–richness relationships in California. To this end, we model net primary productivity (NPP) c. 1750 AD and at present (1982–1999) and map native and exotic vascular plant richness for 230 subecoregions. NPP has increased up to 105% in semi‐arid areas and decreased up to 48% in coastal urbanized areas. Exotic invasions have increased local species diversity up to 15%. Human activities have reinforced historical gradients in species richness but reduced the spatial heterogeneity of NPP. Structural equation modelling suggests that, prior to European settlement, NPP and richness were primarily controlled by precipitation and other abiotic variables, with NPP mediating richness. Abiotic variables remain the strongest predictors of present NPP and richness, but intermodel comparisons indicate a significant anthropogenic impact upon statewide distributions of NPP and richness. Exotic and native species each positively correlate to NPP after controlling for other variables, which may help explain recent reports of positively associated native and exotic richness.     

Multi‐scale patterns of moss and lichen richness on the Antarctic Peninsula

Mosses and lichens are the dominant macrophytes of the Antarctic terrestrial ecosystem. Using occurrence data from existing databases and additional published records, we analyzed patterns of moss and lichen species diversity on the Antarctic Peninsula at both a regional scale (1°latitudinal bands) and a local scale (52 and 56 individual snow‐ and ice‐free coastal areas for mosses and lichens, respectively) to test hypothesized relationships between species diversity and environmental factors, and to identify locations whose diversity may be particularly poorly represented by existing collections and online databases. We found significant heterogeneity in sampling frequency, number of records collected, and number of species found among analysis units at the two spatial scales, and estimated species richness using projected species accumulation curves to account for potential biases stemming from sample heterogeneity. Our estimates of moss and lichen richness for the entire Antarctic Peninsula region were within 20% of the total number of known species. Area, latitude, spatial isolation, mean summer temperature, and penguin colony size were considered as potential covariates of estimated species richness. Moss richness was correlated with isolation and latitude at the local scale, while lichen richness was correlated with summer mean temperature and, for 17 sites where penguins where present with <20 000 breeding pairs, penguin colony size. At the regional scale, moss richness was correlated with temperature and latitude. Lichen richness, by contrast, was not significantly correlated with any of the variables considered at the regional scale. With the exception of temperature, which explained 91% of the variation in regional moss diversity, explained variance was very low. Our results show that patterns of moss and lichen biodiversity are highly scale‐dependent and largely unexplained by the biogeographic variables found important in other systems.     

Effects of sampling protocol on the shapes of species richness curves

Aim Scheiner (Journal of Biogeography, 2009, 36 , 2005–2008) criticized several issues regarding the typology and analysis of species richness curves that were brought forward by Dengler (Journal of Biogeography, 2009, 36 , 728–744). In order to test these two sets of views in greater detail, we used a simulation model of ecological communities to demonstrate the effects of different sampling schemes on the shapes of species richness curves and their extrapolation capability. Methods We simulated five random communities with 100 species on a 64 × 64 grid using random fields. Then we sampled species–area relationships (SARs, contiguous plots) as well as species–sampling relationships (SSRs, non‐contiguous plots) from these communities, both for the full extent and the central quarter of the grid. Finally, we fitted different functions (power, quadratic power, logarithmic, Michaelis–Menten, Lomolino) to the obtained data and assessed their goodness‐of‐fit (Akaike weights) and their extrapolation capability (deviation of the predicted value from the true value). Results We found that power functions gave the best fit for SARs, while for SSRs saturation functions performed better. Curves constructed from data of 32² grid cells gave reasonable extrapolations for 64² grid cells for SARs, irrespective of whether samples were gathered from the full extent or the centre only. By contrast, SSRs worked well for extrapolation only in the latter case. Main conclusions SARs and SSRs have fundamentally different curve shapes. Both sampling strategies can be used for extrapolation of species richness to a target area, but only SARs allow for extrapolation to a larger area than that sampled. These results confirm a fundamental difference between SARs and area‐based SSRs and thus support their typological differentiation.     

Energy–water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperm species richness in China

Studying the pattern of species richness is crucial in understanding the diversity and distribution of organisms in the earth. Climate and human influences are the major driving factors that directly influence the large‐scale distributions of plant species, including gymnosperms. Understanding how gymnosperms respond to climate, topography, and human‐induced changes is useful in predicting the impacts of global change. Here, we attempt to evaluate how climatic and human‐induced processes could affect the spatial richness patterns of gymnosperms in China. Initially, we divided a map of the country into grid cells of 50 × 50 km² spatial resolution and plotted the geographical coordinate distribution occurrence of 236 native gymnosperm taxa. The gymnosperm taxa were separated into three response variables: (a) all species, (b) endemic species, and (c) nonendemic species, based on their distribution. The species richness patterns of these response variables to four predictor sets were also evaluated: (a) energy–water, (b) climatic seasonality, (c) habitat heterogeneity, and (d) human influences. We performed generalized linear models (GLMs) and variation partitioning analyses to determine the effect of predictors on spatial richness patterns. The results showed that the distribution pattern of species richness was highest in the southwestern mountainous area and Taiwan in China. We found a significant relationship between the predictor variable set and species richness pattern. Further, our findings provide evidence that climatic seasonality is the most important factor in explaining distinct fractions of variations in the species richness patterns of all studied response variables. Moreover, it was found that energy–water was the best predictor set to determine the richness pattern of all species and endemic species, while habitat heterogeneity has a better influence on nonendemic species. Therefore, we conclude that with the current climate fluctuations as a result of climate change and increasing human activities, gymnosperms might face a high risk of extinction.     

Predicting mountain plant richness and rarity from space using satellite-derived vegetation indices

Can species richness and rarity be predicted from space? If satellite‐derived vegetation indices can provide us with accurate predictions of richness and rarity in an area, they can serve as an excellent tool in diversity and conservation research, especially in inaccessible areas. The increasing availability of high‐resolution satellite images is enabling us to study this question more carefully. We sampled plant richness and rarity in 34 quadrats (1000 m²) along an elevation gradient between 300 and 2200 m focusing on Mount Hermon as a case study. We then used 10 Landsat, Aster, and QuickBird satellite images ranging over several seasons, going up to very high resolutions, to examine the relationship between plant richness, rarity, and vegetation indices calculated from the images. We used the normalized difference vegetation index (NDVI), one of the most commonly used vegetation indexes, which is strongly correlated to primary production both globally and locally (in more seasonal and in drier and/or colder environments that have wide ranges of NDVI values). All images showed a positive significant correlation between NDVI and both plant species richness and percentage tree cover (with R² as high as 0.87 between NDVI and total plant richness and 0.89 for annual plant richness). The high resolution images enabled us to examine spatial heterogeneity in NDVI within our quadrats. Plant richness was significantly correlated with the standard deviation of NDVI values (but not with their coefficient of variation) within quadrats and between images. Contrary to richness, relative range size rarity was negatively correlated with NDVI in all images, this result being significant in most cases. Thus, given that they are validated by fieldwork, satellite‐derived indices can shed light on richness and even rarity patterns in mountains, many of which are important biodiversity centres.     

Determinants of avian species richness at different spatial scales

ABSTRACT. Studies of factors influencing avian biodiversity yield very different results depending on the spatial scale at which species richness is calculated. Ecological studies at small spatial scales (plot size 0.0025–0.4 km²) emphasize the importance of habitat diversity, whereas biogeographical studies at large spatial scales (quadrat size 400–50,000 km²) emphasize variables related to available energy such as temperature. In order to bridge the gap between those two approaches the bird atlas data set of Lake Constance was used to study factors determining avian species diversity at the intermediate spatial scales of landscapes (quadrat size 4–36 km²). At these spatial scales bird species richness was influenced by habitat diversity and not by variables related to available energy probably because, at the landscape scale, variation in available energy is small. Changing quadrat size between 4 and 36 km², but keeping the geographical extension of the study constant resulted in profound changes in the degree to which the amount of different habitat types was correlated with species richness. This suggests that high species diversity is achieved by different management regimes depending on the spatial scale at which species richness is calculated. However, generally, avian species diversity seems to be determined by spatial heterogeneity at the corresponding spatial scale. Thus, protecting the diversity of landscapes and ecosystems appears to ensure also high levels of species diversity.