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Ecological patterns arise from the interplay of many different processes, and yet the emergence of consistent phenomena across a diverse range of ecological systems suggests that many patterns may in part be determined by statistical or numerical constraints. Differentiating the extent to which patterns in a given system are determined statistically, and where it requires explicit ecological processes, has been difficult. We tackled this challenge by directly comparing models from a constraint-based theory, the Maximum Entropy Theory of Ecology (METE) and models from a process-based theory, the size-structured neutral theory (SSNT). Models from both theories were capable of characterizing the distribution of individuals among species and the distribution of body size among individuals across 76 forest communities. However, the SSNT models consistently yielded higher overall likelihood, as well as more realistic characterizations of the relationship between species abundance and average body size of conspecific individuals. This suggests that the details of the biological processes contain additional information for understanding community structure that are not fully captured by the METE constraints in these systems. Our approach provides a first step towards differentiating between process- and constraint-based models of ecological systems and a general methodology for comparing ecological models that make predictions for multiple patterns.

Transient species occur infrequently in a community over time and do not maintain viable local populations. Because transient species interact differently than non-transients with their biotic and abiotic environment, it is important to characterize the prevalence of these species and how they impact our understanding of ecological systems. We quantified the prevalence and impact of transient species in communities using data on over 19,000 community time series spanning an array of ecosystems, taxonomic groups, and spatial scales. We found that transient species are a general feature of communities regardless of taxa or ecosystem. The proportion of these species decreases with increasing spatial scale leading to a need to control for scale in comparative work. Removing transient species from analyses influences the form of a suite of commonly studied ecological patterns including species-abundance distributions, species-energy relationships, species-area relationships, and temporal turnover. Careful consideration should be given to whether transient species are included in analyses depending on the theoretical and practical relevance of these species for the question being studied.

Macroecological patterns such as the species-area relationship (SAR), the species-abundance distribution (SAD), and the species-time relationship (STR) exhibit regular behavior across ecosystems and taxa. However, determinants of these patterns remain poorly understood. Emerging theoretical frameworks for macroecology attempt to understand this regularity by ignoring detailed ecological interactions and focusing on the influence of a small number of community-level state variables, such as species richness and total abundance, on these patterns. We present results from a 15-year rodent removal experiment evaluating the response of three different macroecological patterns in two distinct annual plant communities (summer and winter) to two levels of manipulated seed predation. Seed predator manipulations significantly impacted species composition on all treatments in both communities, but did not significantly impact richness, community abundance, or macroecological patterns in most cases. How'ever, winter community abundance and richness responded significantly to the removal of all rodents. Changes in richness and abundance were coupled with significant shifts in macroecological patterns (SADs, SARs, and STRs). Because altering species interactions only impacted macroecological patterns when the state variables of abundance and richness also changed, we suggest that, in this system, local-scale processes primarily act indirectly through these properties to determine macroecological patterns.

Power-law frequency distributions characterize a wide array of natural phenomena. In ecology, biology, and many physical and social sciences, the exponents of these power laws are estimated to draw inference about the processes underlying the phenomenon, to test theoretical models, and to scale up from local observations to global patterns. Therefore, it is essential that these exponents be estimated accurately. Unfortunately, the binning-based methods traditionally used in ecology and other disciplines perform quite poorly. Here we discuss more sophisticated methods for fitting these exponents based on cumulative distribution functions and maximum likelihood estimation. We illustrate their superior performance at estimating known exponents and provide details on how and when ecologists should use them. Our results confirm that maximum likelihood estimation outperforms other methods in both accuracy and precision. Because of the use of biased statistical methods for estimating the exponent, the conclusions of several recently published papers should be revisited.

Evidence from numerous studies suggests that species richness is an emergent property of local communities. The maintenance of species richness, despite changes in species composition and environmental conditions, requires compensatory colonization and extinction events with species coming from a regional pool. Using long-term data from a rodent community in the Chihuahuan Desert, we use randomization methods to test the null hypothesis that changes in species richness occur randomly. We find that the dynamics of species richness differ significantly from a random process, and that these nonrandom dynamics occur largely within the most speciose guild. Finally, we propose a general framework for assessing the importance of species compensation in maintaining biodiversity within local communities. Our results highlight the importance of niche complementarity and compensation in maintaining relatively constant species richness over time.

The species-area relationship (SAR) plays a central role in biodiversity re- search, and recent work has increased awareness of its temporal analogue, the species- time relationship (STR). Here we provide evidence for a general species-time-area rela- tionship (STAR), in which species number is a function of the area and time span of sampling, as well as their interaction. For eight assemblages, ranging from lake zooplankton to desert rodents, this model outperformed a sampling-based model and two simpler models in which area and time had independent effects. In every case, the interaction term was negative, meaning that rates of species accumulation in space decreased with the time span of sampling, while species accumulation rates in time decreased with area sampled. Al- though questions remain about its precise functional form, the STAR provides a tool for scaling species richness across time and space, for comparing the relative rates of species turnover in space and time at different scales of sampling, and for rigorous testing of mechanisms proposed to drive community dynamics. Our results show that the SAR and STR are not separate relationships but two dimensions of one unified pattern.

Evidence from numerous studies suggests that species richness is an emergent property of local communities. The maintenance of species richness, despite changes in species composition and environmental conditions, requires compensatory colonization and extinction events with species coming from a regional pool. Using long-term data from a rodent community in the Chihuahuan Desert, we use randomization methods to test the null hypothesis that changes in species richness occur randomly. We find that the dynamics of species richness differ significantly from a random process, and that these nonrandom dynamics occur largely within the most speciose guild. Finally, we propose a general framework for assessing the importance of species compensation in maintaining biodiversity within local communities. Our results highlight the importance of niche complementarity and compensation in maintaining relatively constant species richness over time.

The rank-abundance distribution (RAD) represents the manner in which spe- cies divide resources. Community-specific division rules that determine resource allocation among species, and thereby the shape of the RAD, have been hypothesized to account for observed stability of local species richness over time. While the shape of the RAD has been well studied, the temporal dynamics of this distribution have received much less attention. Here we assess changes in the shape of the RAD through time in a desert rodent community in Arizona (USA). Because energy use may be more appropriate for studying resource division than abundance, we also evaluate an energetic equivalent of the RAD. Significant, directional trends in the shapes of both distributions are present in this com- munity. These changes are driven by trends in the relative abundances (or energy use) of Ranks 2, 3, and 4, and are significantly correlated with variation in total energy use by the community and with compositional change. Our results suggest that (1) rank-abundance and rank-energy distributions are not static and can change directionally through time, (2) species richness and rank distributions are not necessarily as intimately connected as early studies assumed, and (3) rank-abundance and rank-energy distributions are influenced by both the amount of energy available to the community and species-specific interactions.

The species abundance distribution (SAD) is one of themost studied patterns in ecology due to its potential insights into commonness and rarity, community assembly, and patterns of biodiversity. It is well established that communities are composed of a few common and many rare species, and numerous theoretical models have been proposed to explain this pattern. However, no attempt has been made to determine how well these theoretical characterizations capture observed taxonomic and global-scale spatial variation in the general form of the distribution. Here, using data of a scope unprecedented in community ecology, we show that a simple maximum entropy model produces a truncated log-series distribution that can predict between 83% and 93% of the observed variation in the rank abundance of species across 15 848 globally distributed communities including birds, mammals, plants, and butterflies. This model requires knowledge of only the species richness and total abundance of the community to predict the full abundance distribution, which suggests that these factors are sufficient to understand the distribution for most purposes. Since geographic patterns in richness and abundance can often be successfully modeled, this approach should allow the distribution of commonness and rarity to be characterized, even in locations where empirical data are unavailable.

Power-law relationships are among the most well-studied functional relationships in biology. Recently the common practice of fitting power laws using linear regression (LR) on log-transformed data has been criticized, calling into question the conclusions of hundreds of studies. It has been suggested that nonlinear regression (NLR) is preferable, but no rigorous comparison of these two methods has been conducted. Using Monte Carlo simulations, we demonstrate that the error distribution determines which method performs better, with NLR better characterizing data with additive, homoscedastic, normal error and LR better characterizing data with multiplicative, heteroscedastic, lognormal error. Analysis of 471 biological power laws shows that both forms of error occur in nature. While previous analyses based on log-transformation appear to be generally valid, future analyses should choose methods based on a combination of biological plausibility and analysis of the error distribution. We provide detailed guidelines and associated computer code for doing so, including a model averaging approach for cases where the error structure is uncertain.

Ecologists have long sought to understand the mechanisms underlying the assembly and structure of communities. Such understanding is relevant to both basic science and conservation-related issues. The macroecological approach to this problem involves asking scientific questions using a large number of communities in order to elucidate generalities in pattern and process. Such analyses are typically conducted using a substantial amount of data from a particular taxonomic group across a diversity of systems. Large community databases are available for a number of taxa, but no publicly available database exists for mammals. Given the logistical challenges of collecting such data de novo, compiling existing information from the literature provides the best avenue for acquiring the necessary data. Here, we provide a data set that includes species lists for 1000 mammal communities, excluding bats, with species-level abundances available for 940 of these communities. All communities found in the literature that included complete, site-specific sampling data, composed of species lists with or without associated abundances, were included in the data set. Most, but not all, sites are limited to species groups that are sampled using a single technique (e.g., small mammals sampled with Sherman traps). The data set consists of 7977 records from 1000 georeferenced sites encompassing a variety of habitats throughout the world, and it includes data on 660 mammal species with sizes ranging from 2 g to >500 kg. The complete data sets corresponding to abstracts published in the Data Papers section of the journal are published electronically in Ecological Archives at 〈http://esapubs.org/archive〉. (The accession number for each Data Paper is given directly beneath the title.)