The species–area relationship in centipedes (Myriapoda: Chilopoda): a comparison between Mediterranean island groups

The present study article examines the shapes of centipede species–area relationships (SARs) in the Mediterranean islands, compares the results of the linear form of the power model between archipelagos, discusses biological significance of the power model parameters with other taxa on the Aegean archipelago, and tests for a significant small‐island effect (SIE). We used 11 models to test the SARs and we compared the quality‐of‐fit of all candidate models. The power function ranked first and Z‐values was in the range 0.106–0.334. We assessed the presence of SIEs by fitting both a continuous and discontinuous breakpoint regression model. The continuous breakpoint regression functions never performed much better than the closest discontinuous model as a predictor of centipede species richness. We suggest that the relatively low Z‐values in our data partly reflect better dispersal abilities in centipedes than in other soil invertebrate taxa. Longer periods of isolation and more recent island formation may explain the somewhat lower constant c in the western Mediterranean islands compared to the Aegean islands. Higher breakpoint values in the western Mediterranean may also be a result of larger distance to the mainland and longer separation times. Despite the differences in the geological history and the idiosyncratic features of the main island groups considered, the overall results are quite similar and this could be assigned to the ability of centipedes to disperse across isolation barriers. © 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, 105 , 146–159.     

mmSAR: an R‐package for multimodel species–area relationship inference

The species–area relationship (SAR) is one of the most fundamental tools in ecology. After almost a century of quantitative ecology, however, the quest for a “best SAR model” still remains elusive, with a substantial uncertainty about the best fitting SAR model frequently being observed. Recent research has required that this uncertainty be addressed, and a multimodel SAR framework has been devised. Here we introduce the mmSAR R‐package, which is a flexible and scalable implementation of the multimodel SAR framework for species‐area datasets, and provide some examples of its use. This R‐package provides functions for fitting SAR models, performing model selection, and the build up of multimodel SARs.     

Fragmentation,grazing and the species–area relationship

Habitat loss is one of the greatest threats to species persistence. Gauging the scale of this problem requires quantitative methods that can predict the number of extinctions resulting from habitat loss. For the past three decades, the species–area relationship, an empirical relationship between the number of species present in an area and the size of that area, has been this tool. However, it fails to incorporate threats to species aside from habitat loss and the heterogeneous distribution of these threats across habitats. Recent studies have improved species–area predictions by incorporating not only direct effects of area on richness, but also indirect effects of area (through area‐mediated predator abundance), on prey species richness. We extend this work to test the hypotheses that the indirect effects of the multiple threats of grazing and trampling in addition to fragmentation will amplify the effect of area on species richness and that this effect will be greatest in zones closest to the fragment edge. Further, we test for species and population level effects of fragmentation and grazing, including the non‐random pattern of species loss and the decline in population sizes. We test our hypotheses with a field study of land snail richness in fragments with and without the additional threats of grazing and trampling. Our study supports the hypotheses that fragments with multiple threats in addition to habitat loss harbour fewer species than fragments without these threats, and that this effect is non‐uniform across fragments, populations and species.     

Functional redundancy modifies species–area relationship for freshwater phytoplankton

Although species–area relationship (SAR ) is among the most extensively studied patterns in ecology, studies on aquatic and/or microbial systems are seriously underrepresented in the literature. We tested the algal SAR in lakes, pools and ponds of various sizes (10^?2–10⁸ m²) and similar hydromorphological and trophic characteristics using species‐specific data and functional groups. Besides the expectation that species richness increases monotonously with area, we found a right‐skewed hump‐shaped relationship between the area and phytoplankton species richness. Functional richness however did not show such distortion. Differences between the area dependence of species and functional richness indicate that functional redundancy is responsible for the unusual hump‐backed SAR . We demonstrated that the Small Island Effect, which is a characteristic for macroscopic SAR s can also be observed for the phytoplankton. Our results imply a so‐called large lake effect, which means that in case of large lakes, wind‐induced mixing acts strongly against the habitat diversity and development of phytoplankton patchiness and finally results in lower phytoplankton species richness in the pelagial. High functional redundancy of the groups that prefer small‐scale heterogeneity of the habitats is responsible for the unusual humpback relationship. The results lead us to conclude that although the mechanisms that regulate the richness of both microbial communities and communities of macroscopic organisms are similar, their importance can be different in micro‐ and macroscales.     

sars: an R package for fitting,evaluating and comparing species–area relationship models

The species–area relationship (SAR) constitutes one of the most general ecological patterns globally. A number of different SAR models have been proposed. Recent work has shown that no single model universally provides the best fit to empirical SAR datasets: multiple models may be of practical and theoretical interest. However, there are no software packages available that a) allow users to fit the full range of published SAR models, or b) provide functions to undertake a range of additional SAR‐related analyses. To address these needs, we have developed the R package ‘sars’ that provides a wide variety of SAR‐related functionality. The package provides functions to: a) fit 20 SAR models using non‐linear and linear regression, b) calculate multi‐model averaged curves using various information criteria, and c) generate confidence intervals using bootstrapping. Plotting functions allow users to depict and scrutinize the fits of individual models and multi‐model averaged curves. The package also provides additional SAR functionality, including functions to fit, plot and evaluate the random placement model using a species–sites abundance matrix, and to fit the general dynamic model of oceanic island biogeography. The ‘sars’ R package will aid future SAR research by providing a comprehensive set of simple to use tools that enable in‐depth exploration of SARs and SAR‐related patterns. The package has been designed to allow other researchers to add new functions and models in the future and thus the package represents a resource for future SAR work that can be built on and expanded by workers in the field.     

Global pattern and local variation in species–area relationships

Aim We conducted a meta‐analysis of species–area relationships (SARs) by combining several data sets and important covariates such as types of islands, taxonomic groups, latitude and spatial extent, in a hierarchical model framework to study global pattern and local variation in SARs and its consequences for prediction. Location One thousand nine hundred and eighteen islands from 94 SAR studies from around the world. Methods We developed a generalization of the power‐law SAR model, the HSARX model, which allows: (1) the inclusion of multiple focal parameters (intercept, slope, within‐study variance), (2) use of multiple effect modifiers based on a collection of SAR studies, and (3) modelling of the between‐ and within‐study variability. Results The global pattern in the SAR was the average of local SARs and had wide confidence intervals. The global SAR slope was 0.228 with 90% confidence limits of 0.059 and 0.412. The intercept, slope and within‐study variability of local SARs showed great heterogeneity as a result of the interaction of modifying covariates. Confidence intervals for these SAR parameters were narrower when other covariates in addition to area were accounted for, thus increasing the accuracy of the predictions for species richness. The significant effect of latitude and the interaction of latitude, taxa and island type on the SAR slope indicated that the ‘typical’ latitudinal diversity gradient can be reversed in isolated systems. Main conclusions The power‐law relationship underlying the HSARX model provides a good fit for non‐nested SARs across vastly different spatial scales by taking into account other covariates. The HSARX framework allows researchers to explore the complex interactions among SAR parameters and modifying variables, to explicitly study the scale dependence, and to make robust predictions on multiple levels (island, study, global) with associated prediction intervals. From a prediction perspective, it is not the global pattern but the local variation that matters.     

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.     

Effects of community structure on the species–area relationship in China's forests

The species–area relationship (SAR) is the oldest and most frequently documented law in ecology. In a community, the SAR is regulated by the abiotic environment and biotic interactions and depends on the individual–spatial distribution of species (ISD) and the species–abundance distribution (SAD). In this study, we explored the effects of aggregation of ISDs and unevenness of SADs on SARs in forests of China by comparing the empirical and simulated SARs of 32 nested plots distributed along an extensive latitudinal gradient. Both aggregation and unevenness affected the shape of SARs significantly: ISDs accounted for 12.6 ± 4.0% of the incremental increase in species richness with area, and SADs accounted for 18.7 ± 3.8 and 23.5 ± 3.9% under the broken‐stick model and even abundance model, respectively. Effects of both aggregation and unevenness decreased as temperature increased, suggesting that individuals of a species were spatially more aggregated than random, and the individuals among species were more discrepant from the null distribution (broken‐stick model and even abundance model in this study), in the cold than in the warm areas. Taken together, our results demonstrate that ISDs and SADs within communities can shape SARs, but these effects vary along latitudinal gradients, and are likely mediated by temperature.     

Fruit development: new directions for an old pathway

A recent study investigating the molecular mechanisms of seed pod shattering has shown that the basic helix-loop-helix (bHLH) proteins INDEHISCENT and?ALCATRAZ appear to regulate fruit patterning through gibberellic acid (GA)-DELLA signalling, revealing a central role for bHLH family members in GA?response specificity.     

Biodiversity on urban roundabouts—Hemiptera, management and the species–area relationship

The biodiversity of insects within urban areas has been relatively little studied. Given the large and ever increasing extent of urban areas, and that the insect species richness there can be high, it is important to know the factors determining that aspect of biodiversity. In this study two of these factors, namely habitat management and area, were considered. Arboreal and grassland Hemiptera, and grassland plants, were sampled on 18 roundabouts and other road enclosed sites in the town of Bracknell. Hemiptera were sampled using suction sampling and tree beating. A significant species–area relationship was found for arboreal Hemiptera, which was strongly related to habitat diversity. For both grassland plants and Hemiptera, grassland management, by mowing, had a significant effect on species richness. Despite the management grassland plants showed a significant species–area relationship. However the effect of management on Hemiptera was great enough to outweigh any area effect. As the size of open spaces is often constrained in urban areas, altering habitat management has a greater potential for enhancing biodiversity. For arboreal Hemiptera choice of trees for planting is of particular importance, while for grassland Hemiptera diversity would be increased with a reduction in the intensity of management, such a reduction in the frequency of mowing.

Zusammenfassung

Die Biodiversität der Insekten auf urbanen Flächen ist relativ wenig untersucht. Angesichts der großen und der immer größer werdenden Ausdehnung urbaner Gebiete und angesichts dessen, dass der Artenreichtum der Insekten dort groß sein kann, ist es wichtig die Faktoren zu kennen, die diesen Aspekt der Biodiversität bestimmen. In dieser Untersuchung wurden zwei dieser Faktoren, nämlich Habitatmanagement und Fläche, betrachtet. Baum- und wiesenbewohnende Hemiptera sowie Wiesenpflanzen wurden in 18 Kreisverkehren und anderen straßenumschlossenen Orten innerhalb der Stadt Bracknell gesammelt. Die Hemiptera wurden mit Saugproben und Klopfproben an den Bäumen gesammelt. Für die baumbewohnenden Hemiptera wurde eine signifikante Art-Areal-Beziehung gefunden, die in enger Beziehung zur Habitatdiversität stand. Sowohl für die Wiesenpflanzen als auch für die Hemiptera hatte das Wiesenmanagement in Form von Mahd einen signifikanten Einfluss auf den Artenreichtum. Trotz des Managements zeigten die Wiesenpflanzen eine signifikante Art-Areal-Beziehung. Die Auswirkungen des Managements auf die Hemiptera waren jedoch groß genug, um den Arealeffekt zu überwiegen. Da die Größe offener Flächen in städtischen Gebieten oft beschränkt ist, hat die Änderung des Habitatmanagements ein größeres Potenzial die Biodiversität zu erhöhen. Für baumbewohnende Hemiptera ist die Auswahl der Bäume für die Bepflanzung von besonderer Wichtigkeit, während für die wiesenbewohnenden Hemiptera die Diversität durch eine Verringerung der Managementintensität erhöht würde, wie z. B. durch die Verringerung der Mahdfrequenz.     

Species–area and species–sampling effort relationships: disentangling the effects

Species numbers tend to increase with both the area surveyed (species–area relationship, SAR) and the number of samples taken (species–sampling effort relationship, SSER). These two relationships differ in their nature and underlying mechanisms but are not clearly distinguished in field studies. To discriminate the effects of area (spatial extent) and sampling effort (SE) on species richness, several models explicitly involving both variables were proposed and tested against 13 datasets from marine micro‐, meio‐ and macrobenthos. A combination of power SSER and piecewise power SAR terms was found to have the best fit. The effects of area and SE were both significant, but the former one was noticeably weaker. The SSERs were roughly linear in log‐log space, whereas the SARs demonstrated scale‐dependent behavior with a noticeable threshold (slope breakpoint). Species richness was almost area‐independent below this threshold (the “small area effect”, SAE) but followed typical power‐law SAR beyond the threshold. This effect was similar to the “small island effect” but occurred for arbitrarily delineated areas within continuous habitats. Parameters of the SAR curves depended on organism size. The upper limit of the SAE increased from microorganisms to meiofauna to macrofauna. Also, SAR curves for unicellular groups had significantly lower slopes. SAE is supposed to indicate a spatial range of statistical homogeneity in species composition. Its upper limit corresponds to the characteristic size of a local community (a single habitat occupied by a common species pool). Interpretations of SAR and SSER parameters in terms of α‐ and β‐diversity are proposed. Both SAR and SSER slopes obtained from univariate regressions are overestimated. This upward bias depends on sampling design, decreasing for SAR but increasing for SSER with more unequally spaced samples. Both spatial extent and sampling effort should be taken into account to disentangle properly their effects on diversity.