A self-similarity model for the occupancy frequency distribution

The shapes of interspecific range-size distributions at scales finer than the geographic range are highly variable. However, no numerical model has been developed as a basis for understanding this variation. Using self-similarity conditions, we present an occupancy probability transition (OPT) model to investigate the effect of sampling scale (i.e. sample grain) and species saturation (strongly positively correlated with the fractal dimension) on the shape of occupancy frequency distributions (fine scale expression of range-size distributions). In accordance with empirical observations, the model showed that core-modes are likely to be rare in occupancy frequency distributions. The modal occupancy shifted from core to satellite with an increase in sample grain (from coarse scale to fine scale) at a linear rate after log-transformation of occupancy. Saturation coefficients above a particular threshold generated multimodality. Bimodal distributions arose from a combination of different occupancy probability distributions (OPDs), with species-specific saturation coefficients generating occupancy frequency distributions of the shape commonly observed empirically, i.e. bimodal with a dominant satellite mode. This is a consequence of the statistical properties of the OPD, and is also largely insensitive to species richness. The OPT model thus provides a null model for the shape of occupancy frequency distributions. Furthermore, it demonstrates that the sample grain of a study, sampling adequacy (based on a linear sampling assumption) and the distribution of species saturation coefficients in a community are together largely able to explain the patterns observed in empirical occupancy frequency distributions.     

Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition

Species abundance and community composition are affected not only by the local environment, but also by broader landscape and regional context. Yet, determining the spatial scales at which landscapes affect species remains a persistent challenge, hindering our ability to understand how environmental gradients shape communities. This problem is amplified by rare species and imperfect species detection. Here, we present a Bayesian framework that allows uncertainty surrounding the ‘true’ spatial scale of species’ responses (i.e. changes in presence/absence) to be integrated directly into a community hierarchical model. This scale‐selecting multispecies occupancy model (ssMSOM) estimates the scale of response, and shows high accuracy and correct levels of uncertainty in parameter estimates across a broad range of simulation conditions. An ssMSOM can be run in a matter of minutes, as opposed to the many hours required to run normal multispecies occupancy models at all queried spatial scales, and then conduct model selection – a problem that up to now has prohibited scale of response from being rigorously evaluated in an occupancy framework. Alternatives to the ssMSOM, such as GLM‐based approaches frequently fail to detect the correct spatial scale and magnitude of response, and are often falsely confident by favoring the incorrect parameter estimates, especially as species’ detection probabilities deviate from perfect. We further show how trait information can be leveraged to understand how individual species’ scales of response vary within communities. Integrating spatial scale selection directly into hierarchical community models provides a means of formally testing hypotheses regarding spatial scales of response, and more accurately determining the environmental drivers that shape communities.     

Ranked species occupancy curves reveal common patterns among diverse metacommunities

Aim Community ecologists often compare assemblages. Alternatively, one may compare species distributions among assemblages for macroecological comparisons of species niche traits and dispersal abilities, which are consistent with metacommunity theory and a regional community concept. The aim of this meta‐analysis is to use regressions of ranked species occupancy curves (RSOCs) among diverse metacommunities and to consider the common patterns observed. Location Diverse data sets from four continents are analysed. Methods Six regression models were translated from traditional occupancy frequency distributions (OFDs) and are distributed among four equation families. Each regression model was fitted to each of 24 data sets and compared using the Akaike information criterion. The analysed data sets encompass a wide range of spatial scales (5 cm–50 km grain, 2–7000 km extent), study scales (11–590 species, 6–5114 sites) and taxa. Observed RSOC regressions were tested for the differences in scale and taxa. Results Three RSOC models within two equation families (exponential and sigmoidal) are required to describe the very different data sets. This result is generally consistent with OFD research, but unlike OFD‐based expectations the simple RSOC patterns are not related to spatial scale or other factors. Species occupancy in diverse metacommunities is efficiently summarized with RSOCs, and multi‐model inference reliably distinguishes among alternative RSOCs. Main conclusions RSOCs are simple to generate and analyse and clearly identified surprisingly similar patterns among very different metacommunities. Species‐specific hypotheses (e.g. niche‐based factors and dispersal abilities) that depend on spatial scale may not translate to diverse metacommunities that sample regional communities. A novel set of three metacommunity succession and disturbance hypotheses potentially explain RSOC patterns and should be tested in subsequent research. RSOCs are an operational approach to the regional community concept and should be useful in macroecology and metacommunity ecology.     

Novel spatial analysis methods reveal scale‐dependent spread and infer limiting factors of invasion by Sahara mustard

Multiple scale‐dependent ecological processes influence species distributions. Uncovering these drivers of dynamic range boundaries can provide fundamental ecological insights and vital knowledge for species management. We develop a transferable methodology that uses widely available data and tools to determine critical scales in range expansion and to infer dominating scale‐dependent forces that influence spread. We divide a focal geographic region into different sized square cells, representing different spatial scales. We then used herbarium records to determine the species' occupancy of cells at each spatial scale. We calculated the growth in cell occupancy across scales to infer the scale dependent expansion rate. This is the first time such a ‘box‐counting’ method is used to study range expansion. We coupled this multi‐scale analysis with species distribution models to determine the range and spatial scales where suitable climate allows the species to spread, and where other factors may be influencing the expansion. We demonstrate our methodology by assessing the spread of invasive Sahara mustard in North America. We detect critical scales where its spread is limited (100–500 km) or unconstrained (5–50 km) by climatic variables. Using climate‐based models to assess the similarity of climate envelopes in its native and invaded range, we find that the climate in the invaded range generally predicts the native distribution, suggesting that either there has been little local adaptation to climate occurring since introduction or the biological interaction experienced in the invaded range has not driven the species to occupy climatic conditions much different from its native range. Our novel method can be broadly utilized in other studies to generate critical insights into the scale dependency of different ecological drivers that influence the spread and distribution limits, as well as to help parameterizing predictions of future spread, and thus inform management decisions.     

Spatial hierarchical approach in community ecology: a way beyond high context-dependency and low predictability in local phenomena

Patterns and functioning of communities, which are determined by a set of processes operating at a large variety of spatial and temporal scales, exhibit quite high context-dependency and low predictability at the fine spatial scales at which recent studies have concentrated. More attention to broader scale and across-scale phenomena may be useful to search for general patterns and rules in communities. In this context, it is effective to incorporate hierarchical spatial scale explicitly into the experimental and sampling design of field studies, an approach referred to here as the spatial hierarchical approach, focusing on a particular assemblage in which biological interaction and species life history are well known. The spatial hierarchical approach can provide insight into the effects of scale in operating processes and answers to a number of important questions in community ecology such as: (1) detection of patterns and processes in spatiotemporal variability in communities, including how to explain the partitioning of pattern information of species diversity at a broad scale into finer scales, and the pattern of spatial variability of community properties at the finest spatial scale; (2) evaluation of changes in patterns observed in macroecology at finer scales; (3) testing of models explaining the coexistence of competing species; and (4) detection of latitudinal patterns in spatiotemporal variability in communities and their causal processes.     

A dynamic multi‐scale occupancy model to estimate temporal dynamics and hierarchical habitat use for nomadic species

Distribution models are increasingly being used to understand how landscape and climatic changes are affecting the processes driving spatial and temporal distributions of plants and animals. However, many modeling efforts ignore the dynamic processes that drive distributional patterns at different scales, which may result in misleading inference about the factors influencing species distributions. Current occupancy models allow estimation of occupancy at different scales and, separately, estimation of immigration and emigration. However, joint estimation of local extinction, colonization, and occupancy within a multi‐scale model is currently unpublished. We extended multi‐scale models to account for the dynamic processes governing species distributions, while concurrently modeling local‐scale availability. We fit the model to data for lark buntings and chestnut‐collared longspurs in the Great Plains, USA, collected under the Integrated Monitoring in Bird Conservation Regions program. We investigate how the amount of grassland and shrubland and annual vegetation conditions affect bird occupancy dynamics and local vegetation structure affects fine‐scale occupancy. Buntings were prevalent and longspurs rare in our study area, but both species were locally prevalent when present. Buntings colonized sites with preferred habitat configurations, longspurs colonized a wider range of landscape conditions, and site persistence of both was higher at sites with greener vegetation. Turnover rates were high for both species, quantifying the nomadic behavior of the species. Our model allows researchers to jointly investigate temporal dynamics of species distributions and hierarchical habitat use. Our results indicate that grassland birds respond to different covariates at landscape and local scales suggesting different conservation goals at each scale. High turnover rates of these species highlight the need to account for the dynamics of nomadic species, and our model can help inform how to coordinate management efforts to provide appropriate habitat configurations at the landscape scale and provide habitat targets for local managers.     

Occupancy-abundance relationships and sampling scales

The area of occupancy of a species and its abundance are dependent on the spatial scale at which they are measured. However, it is less obvious how the scale of sampling affects their correlation. This study investigated and modeled the effects of sampling unit size and a real extent on the interspecific occupancy-abundance relationships for a tropical tree species assemblage at a local scale and a temperate bird species assemblage at a regional scale. The results showed that both sampling unit size and study extent had profound quantitative effects on the occupancy-abundance relationship, although it remained positive. Several properties of the occupancy-abundance relationship can result from the effects of scale: 1) the linearity of the relationship decreases with the increase of sampling unit size; 2) for a given abundance, the area of occupancy increases with sampling unit size; and 3) variation in the area of occupancy increases with the increase of both sampling unit size and extent, and if the extent is large enough may be sufficient that no occupancy-abundance relationship is observed. Although the occupancy-abundance relationship can be satisfactorily modeled, the parameters depend on the scale used. This suggests that a model derived from one scale cannot be applied to another. In other words, to estimate the rarity or commonness of species using such a model, the estimation must be strictly done using the same sampling scale for all the species.     

Decomposing the heterogeneity of species distributions into multiple scales: a hierarchical framework for large‐scale count surveys

We introduce a novel spatially explicit framework for decomposing species distributions into multiple scales from count data. These kinds of data are usually positively skewed, have non‐normal distributions and are spatially autocorrelated. To analyse such data, we propose a hierarchical model that takes into account the observation process and explicitly deals with spatial autocorrelation. The latent variable is the product of a positive trend representing the non‐constant mean of the species distribution and of a stationary positive spatial field representing the variance of the spatial density of the species distribution. Then, the different scales of emergent structures of the distribution of the population in space are modelled from the latent density of the species distribution using multi‐scale variogram models. Multi‐scale kriging is used to map the spatial patterns previously identified by the multi‐scale models. We show how our framework yields robust and precise estimates of the relevant scales both for spatial count data simulated from well‐defined models, and in a real case‐study based on seabird count data (the common guillemot Uria aalge) provided by large‐scale aerial surveys of the Bay of Biscay (France) performed over a winter. Our stochastic simulation study provides guidelines on the expected uncertainties of the scales estimates. Our results indicate that the spatial structure of the common guillemot can be modelled as a three‐level hierarchical system composed of a very broad‐scale pattern (～ 200 km) with a stable location over time that might be environmentally controlled, a broad‐scale pattern (～ 50 km) with a variable shape and location, that might be related to shifts in prey distribution, and a fine‐scale pattern (～ 10 km) with a rather stable shape and location, that might be controlled by behavioural processes. Our framework enables the development of robust, scale‐dependent hypotheses regarding the potential ecological processes that control species distributions.     

Spatial analyses of intertidal assemblages on sheltered rocky shores

Abstract Understanding processes in complex assemblages depends on good understanding of spatial and temporal patterns of structure at various spatial scales. There has been little quantitative information about spatial patterns and natural temporal changes in intertidal assemblages on sheltered rocky shores in temperate Australia. Natural changes and responses to anthropogenic disturbances in these habitats cannot be accurately measured and assessed without quantitative data on patterns of natural variability in space and through time. This paper describes some suitable quantitative methods for examining spatial and temporal patterns of diversity and abundances of highshore, midshore and lowshore intertidal assemblages and the important component species for a number of shores in a bay that has not been severely altered by human disturbance. Despite a diverse flora and fauna on these shores, the midshore and lowshore assemblages on sheltered shores were characterized by a few species which were also the most important in discriminating among assemblages on a shore and, for each assemblage, among different shores. The same set of species was also important for measuring small-scale patchiness within each assemblage (i.e. between replicate sites on a shore). Therefore, these data provide a rationale for selecting species that are useful for measuring differences and changes in abundance among places and times at different scales and, hence, can be used in the more complex sampling designs necessary to detect environmental impacts. There was considerable spatial variability in all assemblages and all species (or taxa) examined at scales of metres, tens of metres and kilometres. There were no clear seasonal trends for most measures, with as much or more variability at intervals of 3 months as from year to year. Most interactions between spatial and temporal measures were at the smallest scale, with different sites on the same shore generally showing different changes from time to time. The cause(s) of this apparently idiosyncratic variability1 were not examined, but some potential causes are discussed. These data are appropriate for testing hypotheses about the applicability of these findings to other relatively undisturbed sheltered shores, about effects of different anthropogenic disturbances on sheltered intertidal assemblages and to test hypotheses about differences in intertidal assemblages on sheltered versus wave-exposed shores.     

A consistent occupancy–climate relationship across birds and mammals of the Americas

At broad spatial scales, species richness is strongly related to climate. Yet, few ecological studies attempt to identify regularities in the individual species distributions that make up this pattern. Models used to describe species distributions typically model very complex responses to climate. Here, we test whether the variability in the distributions of birds and mammals of the Americas relates to mean annual temperature and precipitation in a simple, consistent way. Specifically, we test if simple mathematical models can predict, as a first approximation, the geographical variation in individual species’ probability of occupancy for 3277 non‐migratory bird and 1659 mammal species. We find a Gaussian model, where the probability of occupancy of a 10⁴ km² quadrat decreases symmetrically and gradually around a species ‘optimal’ temperature and precipitation, was generally the best model, explaining an average of 35% of the deviance in probability of occupancy. The inclusion of additional terms had very small and idiosyncratic effects across species. The Gaussian occupancy–climate relationship appears general among species and taxa and explains nearly as much deviance as complex models including many more parameters. Therefore, we propose that hypotheses aiming to explain the broad‐scale distribution of species or species richness must also predict generally Gaussian occupancy–climate relationships. Synthesis Science aims to identify regularities in a complex natural world. General patterns should be identified before one searches for potential mechanisms and contingencies. However, species geographic distributions are often modelled as complex (sometimes black box), species‐specific, functions of their environment. We asked whether a simple model could account for as much of the geographic variation in a species' probability of occupancy, and be widely applicable across thousands of species. As a first approximation, we found that a simple Gaussian occupancy‐climate relationship is very common in Nature, whether it be causal or not.     

Contribution of rare and common species to richness patterns at local scales

Several studies have recently reported that common species are more important for species richness patterns than rare species. However, most such studies have been based on broad‐scale atlas data. We studied the contribution of different species occupancy, i.e. number of plots occupied, to species richness patterns emerging from species data in 50 by 50 m plots within six 140–200 ha forests in Norway. The study included vascular plants, lichens, bryophytes, and polypore fungi. We addressed the following questions: 1) are common species more correlated with species richness than rare species? 2) How do occupancy classes combine at various levels of species richness? 3) Which occupancy class is best in identifying the overall most species‐rich sites (hotspots) by sampling? The results showed that rare species were better correlated with species richness than common species when the information content was accounted for, that high species richness was associated with a higher proportion of less frequent species, and that the best occupancy class for local hotspot identification was species present in 10–30% of the plots within a forest. We argue that the observed correlations between overall richness and sub‐assembly richness are primarily structured by the combination of the distributions of species richness and species occupancy. Although these distributions result from general ecological processes, they may also be strongly affected by idiosyncratic elements of the individual datasets caused by the specific environmental composition of a study area. Hence, different datasets collected in different areas may lead to different results regarding the relative importance of common versus rare species, and such effects should be expected on both broad and fine spatial scales. Despite these effects, we suggest that infrequent species will tend to be more strongly correlated to species richness at local scales than at broader scales as a result of more right‐skewed species‐occupancy distributions.     

The importance of local and landscape-scale processes to the occupancy of wetlands by pond-breeding amphibians

Variation in the distribution and abundance of species across landscapes has traditionally been attributed to processes operating at fine spatial scales (i.e., environmental conditions at the scale of the sampling unit), but processes that operate across larger spatial scales such as seasonal migration or dispersal are also important. To determine the relative importance of these processes, we evaluated hypothesized relationships between the probability of occupancy in wetlands by two amphibians [wood frogs (Lithobates sylvaticus) and boreal chorus frogs (Pseudacris maculata)] and attributes of the landscape measured at three spatial scales in Rocky Mountain National Park, Colorado. We used cost-based buffers and least-cost distances to derive estimates of landscape attributes that may affect occupancy patterns from the broader spatial scales. The most highly ranked models provide strong support for a positive relationship between occupancy by breeding wood frogs and the amount of streamside habitat adjacent to a wetland. The model selection results for boreal chorus frogs are highly uncertain, though several of the most highly ranked models indicate a positive association between occupancy and the number of neighboring, occupied wetlands. We found little evidence that occupancy of either species was correlated with local-scale attributes measured at the scale of individual wetlands, suggesting that processes operating at broader scales may be more important in influencing occupancy patterns in amphibian populations.     

Why dispersal should be maximized at intermediate scales of heterogeneity

Dispersal is a fundamental biological process that results in the redistribution of organisms due to the interplay between the mode of dispersal, the range of scales over which movement occurs, and the scale of spatial heterogeneity, in which patchiness may occur across a broad range of scales. Despite the diversity of dispersal mechanisms and dispersal length scales in nature, we posit that a fundamental scaling relationship should exist between dispersal and spatial heterogeneity. We present both a conceptual model and mathematical formalization of this expected relationship between the scale of dispersal and the scale of patchiness, which predicts that the magnitude of dispersal (number of individuals) among patches should be maximized when the scale of spatial heterogeneity (defined in terms of patch size and isolation) is neither too fine nor too coarse relative to the gap-crossing abilities of a species. We call this the “dispersal scaling hypothesis” (DSH). We demonstrate congruence in the functional form of this relationship under fundamentally different dispersal assumptions, using well-documented isotropic dispersal kernels and empirically derived dispersal parameters from diverse species, in order to explore the generality of this finding. The DSH generates testable hypotheses as to when and under what landscape scenarios dispersal is most likely to be successful. This provides insights into what management scenarios might be necessary to either restore landscape connectivity, as in certain conservation applications, or disrupt connectivity, as when attempting to manage landscapes to impede the spread of an invasive species, pest, or pathogen.     

Occupancy versus colonization–extinction models for projecting population trends at different spatial scales

Understanding spatiotemporal population trends and their drivers is a key aim in population ecology. We further need to be able to predict how the dynamics and sizes of populations are affected in the long term by changing landscapes and climate. However, predictions of future population trends are sensitive to a range of modeling assumptions. Deadwood‐dependent fungi are an excellent system for testing the performance of different predictive models of sessile species as these species have different rarity and spatial population dynamics, the populations are structured at different spatial scales, and they utilize distinct substrates. We tested how the projected large‐scale occupancies of species with differing landscape‐scale occupancies are affected over the coming century by different modeling assumptions. We compared projections based on occupancy models against colonization–extinction models, conducting the modeling at alternative spatial scales and using fine‐ or coarse‐resolution deadwood data. We also tested effects of key explanatory variables on species occurrence and colonization–extinction dynamics. The hierarchical Bayesian models applied were fitted to an extensive repeated survey of deadwood and fungi at 174 patches. We projected higher occurrence probabilities and more positive trends using the occupancy models compared to the colonization–extinction models, with greater difference for the species with lower occupancy, colonization rate, and colonization:extinction ratio than for the species with higher estimates of these statistics. The magnitude of future increase in occupancy depended strongly on the spatial modeling scale and resource resolution. We encourage using colonization–extinction models over occupancy models, modeling the process at the finest resource‐unit resolution that is utilizable by the species, and conducting projections for the same spatial scale and resource resolution at which the model fitting is conducted. Further, the models applied should include key variables driving the metapopulation dynamics, such as the availability of suitable resource units, habitat quality, and spatial connectivity.     

Plants on small islands revisited: the effects of spatial scale and habitat quality on the species–area relationship

Understanding how species diversity is related to sampling area and spatial scale is central to ecology and biogeography. Small islands and small sampling units support fewer species than larger ones. However, the factors influencing species richness may not be consistent across scales. Richness at local scales is primarily affected by small‐scale environmental factors, stochasticity and the richness at the island scale. Richness at whole‐island scale, however, is usually strongly related to island area, isolation and habitat diversity. Despite these contrasting drivers at local and island scales, island species–area relationships (SARs) are often constructed based on richness sampled at the local scale. Whether local scale samples adequately predict richness at the island scale and how local scale samples influence the island SAR remains poorly understood. We investigated the effects of different sampling scales on the SAR of trees on 60 small islands in the Raja Ampat archipelago (Indonesia) using standardised transects and a hierarchically nested sampling design. We compared species richness at different grain sizes ranging from single (sub)transects to whole islands and tested whether the shape of the SAR changed with sampling scale. We then determined the importance of island area, isolation, shape and habitat quality at each scale on species richness. We found strong support for scale dependency of the SAR. The SAR changed from exponential shape at local sampling scales to sigmoidal shape at the island scale indicating variation of species richness independent of area for small islands and hence the presence of a small‐island effect. Island area was the most important variable explaining species richness at all scales, but habitat quality was also important at local scales. We conclude that the SAR and drivers of species richness are influenced by sampling scale, and that the sampling design for assessing the island SARs therefore requires careful consideration.     

Modeling species distributions by breaking the assumption of self-similarity

Species distributions are commonly measured as the number of sites, or geographic grid cells occupied. These data may then be used to model species distributions and to examine patterns in both intraspecific and interspecific distributions. Harte et al. (1999) used a model based on a bisection rule and assuming self-similarity in species distributions to do so. However, this approach has also been criticized for several reasons. Here we show that the self-similarity in species distributions breaks down according to a power relationship with spatial scales, and we therefore adopt a power-scaling assumption for modeling species occupancy distributions. The outcomes of models based on these two assumptions (self-similar and power-scaling) have not previously been compared. Based on Harte's bisection method and an occupancy probability transition model under these two assumptions (self-similar and power-scaling), we compared the scaling pattern of occupancy (also known as the area-of-occupancy) and the spatial distribution of species. The two assumptions of species distribution lead to a relatively similar interspecific occupancy frequency distribution pattern, although the spatial distribution of individual species and the scaling pattern of occupancy differ significantly. The bimodality in occupancy frequency distributions that is common in species communities, is confirmed to a result for certain mathematical and statistical properties of the probability distribution of occupancy. The results thus demonstrate that the use of the bisection method in combination with a power-scaling assumption is more appropriate for modeling species distributions than the use of a self-similarity assumption, particularly at fine scales.     

At what spatial scale(s) do mammals respond to urbanization?

Spatial scale is fundamental in understanding species–landscape relationships because species’ responses to landscape characteristics typically vary across scales. Nonetheless, such scales are often unidentified or unreliably predicted by theory. Many landscapes worldwide are urbanizing, yet the spatial scaling of species’ responses to urbanization is poorly understood. We investigated the spatial scaling of urbanization effects on a community of 15 mammal species using ~60 000 wildlife detections collected from a constellation of 207 camera traps across an extensive urban park system. We embedded a bivariate Gaussian kernel in hierarchical multi-species models to determine two scales of effect (a scale of maximal effect and a broader scale of cumulative landscape effect) for two biological responses (occupancy and site visit frequency) across two seasons (winter and summer) for each species. We then assessed whether scales of effect varied according to theoretical predictions associated with biological responses and species traits (body size and mobility). Scales of effect ranged from < 50 m to > 9000 m and varied among species, but not as predicted by theory. Species’ occupancy generally showed a weak response to urbanization and the scale of this effect was both highly uncertain and consistent across species. We did not detect any relationship between scales of effect and species’ body size or mobility, nor was there any evident pattern of scaling across biological response or seasons. These results imply that 1) urbanization effects on mammals manifest across a very broad spectrum of spatial scales, and 2) current theories that a priori predict the scale at which urbanization affects mammals may be of limited use within a given system. Overall, this study suggests that developing general theory regarding the scaling of species–landscape relationships requires additional empirical work conducted across multiple species, systems and timescales.     

Scaling coexistence and assemblage patterns of desert small mammals

Scaling biodiversity patterns has been recognized lately as a very important issue in the search of global processes; however coexistence and assemblage patterns are typically approached at a single spatial scale. Here, we examined coexistence and co-occurrence patterns of desert small mammal communities across different spatial scales in the search of general community patterns. We sampled small mammals in Monte desert (Argentina, South America) for small spatial scales and reviewed published papers from other worldwide deserts for large spatial scale analyses. We used classic community estimators (Shannon, Richness), rank abundance curves and fitting distributions to analyze species coexistence and co-occurrence patterns. Assemblage patterns were analyzed evaluating nestedness across spatial scales and among deserts. Worldwide desert small mammal assemblages are characterized mainly by low species richness and high variation in species composition. The central Monte desert of Argentina showed a consistent assemblage pattern across spatial scales, with a generalist species being the most abundant and widely distributed, accompanied by other subordinate and more narrowly distributed species. All Monte desert communities were significantly nested, with nestedness increasing with scale from patch to regional. Assemblage and coexistence patterns were similar when comparing worldwide deserts despite differences in total richness and faunal singularity. The degree of nestedness varied among worldwide deserts; however all of them showed a consistent nested pattern. Differences in the degree of nestedness could be a result of different regulating factors depending on the desert and scale. These results highlight the importance of including multiscale approaches when dealing with processes that structure desert communities.     

Patterns of spatial and temporal variation of macrofauna under boulders in a sheltered boulder field

Abstract Spatial and temporal patterns of abundance of animals and plants must be quantified before models to explain distributions can be developed. These patterns also provide essential data for measuring potential effects of environmental disturbances. Studies in many different habitats have shown that most organisms, particularly invertebrates, have highly variable and interactive patterns of abundance, with much variability at the smallest temporal and spatial scales. Intertidal boulder fields in New South Wales, Australia, support a diverse fauna, many species of which are relatively rare. These habitats are commonly found near rock‐platforms and in sheltered estuaries and are subjected to many human disturbances. Although there have been a few studies on the fauna in boulder fields, none has documented variability of the assemblage using multivariate and univariate techniques and most studies have not incorporated different spatial and temporal scales. This study quantifies spatial variation at three scales (metres, tens of metres alongshore and tens of metres upshore) and temporal variation at two scales (3 months and 2 years) of the assemblage of molluscs and echinoderms in a sheltered boulder field subjected to little natural or human disturbance. Multivariate analyses revealed that each site contained a distinct assemblage, mainly due to the relative abundances of a few species. Most species, those generally only found under boulders and common, widespread species, had considerable spatial variability in abundances, with more than 90% measured at the smallest scale, that is metre to metre within a site. Changes in abundances over 3 months or 2 years varied among species and sites in unpredictable ways. These data show that sampling designs to measure impacts on these fauna will need to be complex and must incorporate a number of spatial and temporal scales if they are to be able to detect impact against such a variable background.