Search

Temperature imposes a constraint on the rates and outcomes of ecological processes that determine community-and ecosystem-level patterns. The application of metabolic scaling theory has advanced our understanding of the influence of temperature on pattern and process in marine communities. Metabolic scaling theory uses the fundamental and ubiquitous patterns of temperature-dependent metabolism to predict how environmental temperature influences patterns and processes at higher levels of biological organization. Here, we outline some of these predictions to review recent advances and illustrate how scaling theory might be applied to new challenges. For example, warming can alter species interactions and food-web structure and can also reduce total animal biomass supportable by a given amount of primary production by increasing animal metabolism and energetic demand. Additionally, within a species, larval development is faster in warmer water, potentially influencing dispersal and other demographic processes like population connectivity and gene flow. These predictions can be extended further to address major questions in marine ecology, and present an opportunity for conceptual unification of marine ecological research across levels of biological organization. Drawing on work by ecologists and oceanographers over the last century, a metabolic scaling approach represents a promising way forward for applying ecological understanding to basic questions as well as conservation challenges. [ABSTRACT FROM AUTHOR]

Abstract: Plant stoichiometry, the relative concentration of elements, is a key regulator of ecosystem functioning and is also being altered by human activities. In this paper we sought to understand the global drivers of plant stoichiometry and compare the relative contribution of climatic vs. anthropogenic effects. We addressed this goal by measuring plant elemental (C, N, P and K) responses to eutrophication and vertebrate herbivore exclusion at eighteen sites on six continents. Across sites, climate and atmospheric N deposition emerged as strong predictors of plot‐level tissue nutrients, mediated by biomass and plant chemistry. Within sites, fertilization increased total plant nutrient pools, but results were contingent on soil fertility and the proportion of grass biomass relative to other functional types. Total plant nutrient pools diverged strongly in response to herbivore exclusion when fertilized; responses were largest in ungrazed plots at low rainfall, whereas herbivore grazing dampened the plant community nutrient responses to fertilization. Our study highlights (1) the importance of climate in determining plant nutrient concentrations mediated through effects on plant biomass, (2) that eutrophication affects grassland nutrient pools via both soil and atmospheric pathways and (3) that interactions among soils, herbivores and eutrophication drive plant nutrient responses at small scales, especially at water‐limited sites. [ABSTRACT FROM AUTHOR]

Butterflies and moths, collectively Lepidoptera, are integral components of ecosystems, providing key services such as pollination and a prey resource for vertebrate and invertebrate predators. Lepidoptera are a relatively well studied group of invertebrates. In Great Britain and Ireland numerous citizen science projects provide data on changes in distribution and abundance. The availability of high‐quality monitoring and recording data, combined with the rapid response of Lepidoptera to environmental change, makes them ideal candidates for traits‐based ecological studies. Recently, there has been an increase in the number of studies documenting traits‐based responses of Lepidoptera, highlighting the demand for a standardized and referenced traits database. There is a wide range of primary and secondary literature sources available regarding the ecology of British and Irish Lepidoptera to support such studies. Currently these sources have not been collated into one central repository that would facilitate and enhance future research. Here, we present a comprehensive traits database for the butterflies and macro‐moths of Great Britain and Ireland. The database covers 968 species in 21 families. Ecological traits fall into four main categories: life cycle ecology and phenology, host plant specificity and characteristics, breeding habitat, and morphological characteristics. The database also contains data regarding species distribution, conservation status, and temporal trends for abundance and occupancy. This database can be used for a wide array of purposes including further fundamental research on species and community responses to environmental change, conservation and management studies, and evolutionary biology. There are no copyright restrictions, and this paper must be cited if data are used in publications. [ABSTRACT FROM AUTHOR]

Since the publication of the unified neutral theory in 2001, there has been much discussion of the theory, pro and con. The hypothesis of ecological equivalence is the fundamental yet controversial idea behind neutral theory. Assuming trophically similar species are demographically alike (symmetric) on a per capita basis is only an approximation, but it is equivalent to asking: How many of the patterns of ecological communities are the result of species similarities, rather than of species differences? The strategy behind neutral theory is to see how far one can get with the simplification of assuming ecological equivalence before introducing more complexity. In another paper, I review the empirical evidence that led me to hypothesize ecological equivalence among many of the tree species in the species-rich tropical forest on Barro Colorado Island (BCI). In this paper, I develop a simple model for the evolution of ecological equivalence or niche convergence, using as an example evolution of the suite of life history traits characteristic of shade tolerant tropical tree species. Although the model is simple, the conclusions from it seem likely to be robust. I conclude that ecological equivalence for resource use are likely to evolve easily and often, especially in species-rich communities that are dispersal and recruitment limited. In the case of the BCI forest, tree species are strongly dispersal- and recruitment-limited, not only because of restricted seed dispersal, but also because of low recruitment success due to heavy losses of the seedling stages to predators and pathogens and other abiotic stresses such as drought. These factors and the high species richness of the community strongly reduce the potential for competitive exclusion of functionally equivalent or nearly equivalent species. [ABSTRACT FROM AUTHOR]

Replies to criticism on the author's paper on paired intervention analysis. Meaning of the term paierd intervention analysis used by the author in his research paper; Popular usage of the term paired intervention in ecological literature; Significance of ecosystem level studies.

The role of theory within ecology has changed dramatically in recent decades. Once primarily a source of qualitative conceptual framing, ecological theories and models are now often used to develop quantitative explanations of empirical patterns and to project future dynamics of specific ecological systems. In this essay, I recount my own experience of this transformation, in which accelerating computing power and the widespread incorporation of stochastic processes into ecological theory combined to create some novel integration of mathematical and statistical models. This stronger integration drives theory towards incorporating more biological realism, and I explore ways in which we can grapple with that realism to generate new general theoretical insights. This enhanced realism, in turn, may lead to frameworks for projecting ecological responses to anthropogenic change, which is, arguably, the central challenge for 21st-century ecology. In an era of big data and synthesis, ecologists are increasingly seeking to infer causality from observational data; but conventional biometry provides few tools for this project. This is a realm where theorists can and should play an important role, and I close by pointing towards some analytical and philosophical approaches developed in our sister discipline of economics that address this very problem. While I make no grand prognostications about the likely discoveries of ecological theory over the coming century, you will find in this essay a scattering of more or less far-fetched ideas that I, at least, think are interesting and (possibly) fruitful directions for our field. [ABSTRACT FROM AUTHOR]

The article comments on the paper "Riparian Zones Increase Regional Species Richness by Harboring Different, Not More, Species," by John L. Salbo and Candan U. Soykan. The authors of the paper found no general pattern of higher species richness in riparian compared to upland areas for either local or cumulative diversity. However, they cannot state anything at all regarding the level of the contribution of species from the riparian areas to the regional species pool.

Issues of residual spatial autocorrelation (RSA) and spatial scale are critical to the study of species-environment relationships, because RSA invalidates many statistical procedures, while the scale of analysis affects the quantification of these relationships. Although these issues independently are.widely covered in the literature, only sparse attention is giver to their integration. This paper focuses on the interplay between RSA and the spatial scaling of species-environment relationships. Using a hypothetical species in an artificial landscape, we show that a misipatch between the scale of analysis and the scale of a species' response to its environment leads to a decrease in the portion of variation explained by environmental predictors. Moreover, it results in RSA and biased regression coefficients. This bias stems from error-predictor dependencies due to the scale mismatch, the magnitude of which depends on the interaction between the scale of landscape heterogeneity and the scale of a species' response to this heterogeneity. We show that explicitly considering scale effects on RSA can reveal the characteristic scale of a species' response to its environment. This is important, because the estimation of species-environment relationships using spatial regression methods proves to be erroneous in case of a scale mismatch, leading to spurious conclusions when scaling issues are not explicitly considered. The findings presented here highlight the importance of examining the appropriateness of the spatial scales used in analyses, since scale mismatches affect the rigor of statistical analyses and thereby the ability to understand the processes underlying spatial patterning in ecological phenomena. [ABSTRACT FROM AUTHOR]

An improved understanding of the rapid nitrogen fluxes that occur in plant rhizospheres has not been adequately incorporated into the study of how soil N availability and plant N uptake control a number of important ecological processes. One reason for this is that current methods that measure N availability do not account for the rapid exchange of resources between roots and their closely associated microbial communities. In this paper, we review the tight interactions between roots and microbes and discuss why ignoring the significance of these interactions has led to unrepresentative estimates of N availability in intact plant communities and an incomplete understanding of the environmental factors that control plant-available N. We also explain why current standard methods to measure soil N availability do not account for important rhizospheric processes. Finally, we issue a challenge to develop new methods that will estimate soil N transformations in intact plant communities and offer some potential approaches that may help catalyze this effort. [ABSTRACT FROM AUTHOR]

Data are often collected for a single species within an ecological community, so quantitative tools for drawing inferences about the unobserved portions of the community from single-species data are valuable. In this paper, we present and examine a method for estimating community dimension (the number of strongly interacting species or groups) from time series data on a single species. The dynamics of one species can be strongly affected by environmental stochasticity acting not only on itself, but also on other species with which it interacts. By fully accounting for the effects of stochasticity on populations embedded in a community, our approach gives better estimates of community dimension than commonly used methods. Using a combination of time series data and simulations, we show that failing to properly account for stochasticity when attempting to relate population dynamics to attributes of the community can give misleading information about community dimension. [ABSTRACT FROM AUTHOR]

Bayesian statistical modeling has several benefits within an ecological context. In particular, when observed data are limited in sample size or representativeness, then the Bayesian framework provides a mechanism to combine observed data with other "'prior" information. Prior information may be obtained from earlier studies, or in their absence, from expert knowledge. This use of the Bayesian framework reflects the scientific "learning cycle." where prior or initial estimates are updated when new data become available. In this paper we outline a framework for statistical design of expert elicitation processes for quantifying such expert knowledge, in a form suitable for input as prior information into Bayesian models. We identify six key elements: determining the purpose and motivation for using prior information; specifying the relevant expert knowledge available: formulating the statistical model; designing effective and efficient numerical encoding; managing uncertainty; and designing a practiced elicitation protocol. We demonstrate this framework applies to a variety of situations, with two examples from the ecological literature and three from our experience. Analysis of these examples reveals several recurring important issues affecting practical design of elicitation in ecological problems. [ABSTRACT FROM AUTHOR]

Functional ecology aims at determining the relationships between species traits and environmental variables in order to better understand biological processes in ecosystems. From a methodological point of view, this biological objective calls for a method linking three data matrix tables: a table L with abundance or presence-absence values for species at a series of sites, a table R with variables describing the environmental conditions of the sites, and a table Q containing traits (e.g., morphological or behavioral attributes) of the species. Ten years ago, the fourth-corner method was proposed to measure and test the relationships between species traits and environmental variables using tables R, L, and Q simultaneously. In practice, this method is rarely used. The major reasons for this lack of interest are the restriction of the original method and program to presence-absence data in L and to the analysis of a single trait and a single environmental variable at a time. Moreover, ecologists often have problems in choosing a permutation model among the four originally proposed. In this paper, we revisit the fourth-corner method and propose improvements to the original approach. First, we present an extension to measure the link between species traits and environmental variables when the ecological community is described by abundance data. A new multivariate fourth-corner statistic is also proposed. Then, using numerical simulations, we discuss and evaluate the existing testing procedures. A new two-step testing procedure is presented. We hope that these elements will help ecologists use the best possible methodology to analyze this type of ecological problem. [ABSTRACT FROM AUTHOR]

The North Atlantic intertidal community provides a rich set of organismal and environmental material for the study of ecological genetics. Clearly defined environmental gradients exist at multiple spatial scales: there are broad latitudinal trends in temperature, meso-scale changes in salinity along estuaries, and smaller scale gradients in desiccation and temperature spanning the intertidal range. The geology and geography of the American and European coasts provide natural replication of these gradients, allowing for population genetic analyses of parallel adaptation to environmental stress and heterogeneity. Statistical methods have been developed that provide genomic neutrality tests of population differentiation and aid in the process of candidate gene identification. In this paper, we review studies of marine organisms that illustrate associations between an environmental gradient and specific genetic markers. Such highly differentiated markers become candidate genes for adaptation to the environmental factors in question, but the functional significance of genetic variants must be comprehensively evaluated. We present a set of predictions about locus-specific selection across latitudinal, estuarine, and intertidal gradients that are likely to exist in the North Atlantic. We further present new data and analyses that support and contradict these simple selection models. Some taxa show pronounced clinal variation at certain loci against a background of mild clinal variation at many loci. These cases illustrate the procedures necessary for distinguishing selection driven by internal genomic vs. external environmental factors. We suggest that the North Atlantic intertidal community provides a model system for identifying genes that matter in ecology due to the clarity of the environmental stresses and an extensive experimental literature on ecological function. While these organisms are typically poor genetic and genomic models, advances in comparative genomics have provided access to molecular tools that can now be applied to taxa with well-defined ecologies. As many of the organisms we discuss have tight physiological limits driven by climatic factors, this synthesis of molecular population genetics with marine ecology could provide a sensitive means of assessing evolutionary responses to climate change. [ABSTRACT FROM AUTHOR]

This paper proposes a new way of using forward selection of explanatory variables in regression or canonical redundancy analysis. The classical forward selection method presents two problems: a highly inflated Type I error and an overestimation of the amount of explained variance. Correcting these problems will greatly improve the performance of this very useful method in ecological modeling. To prevent the first problem, we propose a two-step procedure. First, a global test using all explanatory variables is carried out. If, and only if, the global test is significant, one can proceed with forward selection. To prevent overestimation of the explained variance, the forward selection has to be carried out with two stopping criteria: (1) the usual alpha significance level and (2) the adjusted coefficient of multiple determination (Ra²) calculated using all explanatory variables. When forward selection identifies a variable that brings one or the other criterion over the fixed threshold, that variable is rejected, and the procedure is stopped. This improved method is validated by simulations involving univariate and multivariate response data. An ecological example is presented using data from the Bryce Canyon National Park, Utah, USA. [ABSTRACT FROM AUTHOR]

In the three decades since its introduction, resource selection analysis (RSA) has become a widespread method for analyzing spatial patterns of animal relocations obtained from telemetry studies. Recently, mechanistic home range models have been proposed as an alternative framework for studying patterns of animal space-use. In contrast to RSA models, mechanistic home range models are derived from underlying mechanistic descriptions of individual movement behavior and yield spatially explicit predictions for patterns of animal space-use. In addition, their mechanistic underpinning means that, unlike RSA, mechanistic home range models can also be used to predict changes in space-use following perturbation. In this paper, we develop a formal reconciliation between these two methods of home range analysis, showing how differences in the habitat preferences of individuals give rise to spatially explicit patterns of space-use. The resulting unified framework combines the simplicity of resource selection analysis with the spatially explicit and predictive capabilities of mechanistic home range models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Over the last several decades, there has been a growing appreciation of the importance of nonequilibrial phenomena and transient dynamics in explaining the structure of ecological communities. This paper provides an overview of theoretical themes related to resource pulses. Theoretical models suggest short-term responses to a single pulse can qualitatively differ from longer-term responses. Recurrent resource pulses can alter community structure, permitting coexistence that otherwise would not occur, or hamper coexistence mechanisms effective in stable environments. For a given resource input, system responses can be more dramatic with short pulses. Resource pulses can cause transitions between alternative states. Dispersal permits species to exploit locally sporadic-resource pulses and persist in environments that on average are unsuitable. All these issues are ripe for further theoretical explorations. [ABSTRACT FROM AUTHOR]

Scaling relationships between mean body masses and abundances of species in multitrophic communities continue to be a subject of intense research and debate. The top-down mechanism explored in this paper explains the frequently observed inverse linear relationship between body mass and abundance (i.e., constant biomass) in terms of a balancing of resource biomasses by behaviorally and evolutionarily adapting foragers, and the evolutionary response of resources to this foraging pressure. The mechanism is tested using an allometric, multitrophic community model with a complex food web structure. It is a statistical model describing the evolutionary and population dynamics of tens to hundreds of species in a uniform way. Particularities of the model are the detailed representation of the evolution and interaction of trophic traits to reproduce topological food web patterns, prey switching behavior modeled after experimental observations, and the evolutionary adaptation of attack rates. Model structure and design are discussed. For model states comparable to natural communities, we find that (I) the body-mass- abundance scaling does not depend on the allometric scaling exponent of physiological rates in the form expected from the energetic equivalence rule or other bottom-up theories; (2) the scaling exponent of abundance as a function of body mass is approximately -1, independent of the allometric exponent for physiological rates assumed; (3) removal of top-down control destroys this pattern, and energetic equivalence is recovered. We conclude that the top-down mechanism is active in the model, and that it is a viable alternative to bottom-up mechanisms for controlling body-mass-abundance relations in natural communities. [ABSTRACT FROM AUTHOR]

The recognition that predators play important roles in ecosystems has prompted research to resolve how combinations of predator species influence ecosystem functions. Interactions among predator species and their prey can lead to a host of linear and nonlinear effects. Understanding the conditions causing these effects is critical for assigning predator species to functional groups in ways that lead to predictive theory of predator diversity effects on trophic interactions. To this end, I provide a synthesis of experiments examining multiple-predator-species effects on mortality of single shared prey. I show how experimental design and experimental venue can determine the conclusion about the importance of predator diversity on trophic interactions. In addition, I link natural history insights on predator species habitat and hunting behavior with linear and nonlinear multiple-predator effects to derive a new concept of predator diversity effects on trophic interactions. This concept holds that the nature of predator diversity effects is contingent upon predator species hunting mode plus predator and prey species habitat domain (defined as the spatial extent to which a micro habitat is used by a species). This concept allows the classification of multiple-predator effects into four broad functional categories: substitutable, nonlinear due to predator species interference, nonlinear due to intra guild predation, and nonlinear due to predator species synergism. Experimental evidence so far provides ample and comparatively equal support for substitutable, interference, and intra guild effects, and equivocal support for nonlinear synergisms. The paper closes by discussing ways to further a research program aimed at using the building blocks presented here to understand predator functional diversity and trophic interactions in complex ecological systems. [ABSTRACT FROM AUTHOR]

Soil microbial communities have the metabolic and genetic capability to adapt to changing environmental conditions on very short time scales. In this paper we combine biogeochemical and molecular approaches to reveal this potential, showing that microbial biomass can turn over on time scales of days to months in soil, resulting in a succession of microbial communities over the course of a year. This new understanding of the year-round turnover and succession of microbial communities allows us for the first time to propose a temporally explicit N cycle that provides mechanistic hypotheses to explain both the loss and retention of dissolved organic N (DON) and inorganic N (DIN) throughout the year in terrestrial ecosystems. In addition, our results strongly support the hypothesis that turnover of the microbial community is the largest source of DON and DIN for plant uptake during the plant growing season. While this model of microbial biogeochemistry is derived from observed dynamics in the alpine, we present several examples from other ecosystems to indicate that the general ideas of biogeochemical fluxes being linked to turnover and succession of microbial communities are applicable to a wide range of terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

Two oft-cited drawbacks of kernel density estimators (KDEs) of home range are their sensitivity to the choice of smoothing parameter(s) and their need for independent data. Several simulation studies have been conducted to compare the performance of objective, data-based methods of choosing optimal smoothing parameters in the context of home range and utilization distribution (UD) estimation. Lost in this discussion of choice of smoothing parameters is the general role of smoothing in data analysis, namely, that smoothing serves to increase precision at the cost of increased bias. A primary goal of this paper is to illustrate this bias-variance trade-off by applying KDEs to sampled locations from simulated movement paths. These simulations will also be used to explore the role of autocorrelation in estimating UDs. Autocorrelation can be reduced (1) by increasing study duration (for a fixed sample size) or (2) by decreasing the sampling rate. While the first option will often be reasonable, for a fixed study duration higher sampling rates should always result in improved estimates of space use. Further, KDEs with typical data-based methods of choosing smoothing parameters should provide competitive estimates of space use for fixed study periods unless autocorrelation substantially alters the optimal level of smoothing. [ABSTRACT FROM AUTHOR]

After House Finches were introduced from the western to the eastern United States and rapidly increased in numbers, House Sparrows declined, leading to suggestions that the decline was caused by interspecific competition. However, other potential causes were not excluded. The rapid decline in House Finches following the emergence of a new disease (mycoplasmal conjunctivitis) caused by a novel strain of Mycoplasma gallisepticum (MG) in 1994 has provided a natural experiment and an opportunity to revisit the hypothesis that interspecific competition from House Finches drives population changes in House Sparrows. If true, the recent decline in House Finches should lead to an increase in House Sparrows. In this paper we test the hypothesis that House Sparrow and House Finch numbers in the northeastern United States vary inversely by examining data from three independent volunteer programs that monitor bird species' abundance and distribution (Christmas Bird Count, Project FeederWatch, and Breeding Bird Survey). In the first analysis we found that House Sparrow and House Finch numbers varied inversely during a time interval when House Finches were increasing and a time interval when House Finches were decreasing. In the second analysis, we found that the rates of geometric change in House Sparrow abundance (In[HOSPt+1/HOSPt]) were negatively correlated with initial House Finch (HOFIt) and sparrow (HOSPt) abundances at individual sites, irrespective of the time period. Given that finch range expansion and subsequent declines in abundance are the result of two very different phenomena, it would be very unlikely for apparent competition or spurious correlations to cause the observed concomitant changes in House Sparrow abundance. We conclude that interspecific competition exists between these two species. [ABSTRACT FROM AUTHOR]

The aim of this paper is to tackle the problem that arises from asymmetrical data cubes formed by two crossed factors fixed by the experimenter (factor A and factor B, e.g., sites and dates) and a factor which is not controlled for (the species). The entries of this cube are densities in species. We approach this kind of data by the comparison of patterns, that is to say by analyzing first. the effect of factor B on the species-factor A pattern, and second the effect of factor A on the species-factor B pattern. The analysis of patterns instead of individual responses requires a correspondence analysis. We use a method we call Foucart's correspondence analysis to coordinate the correspondence analyses of several independent matrices of species × factor A (respectively B) type, corresponding to each modality of factor B (respectively A). Such coordination makes it possible to evaluate the effect of factor B (respectively A) on the species-factor A (respectively B) pattern. The results obtained by such a procedure are much more insightful than those resulting from a classical single correspondence analysis applied to the global matrix that is obtained by simply unrolling the data cube, juxtaposing for example the individual species × factor A matrices through modalities of factor B. This is because a single global correspondence analysis combines three effects of factors in a way that cannot be determined from factorial maps (factor A, factor B, and factor A × factor B interaction) whereas the applications of Foucart's correspondence analysis clearly discriminate two different issues. Using two data sets, we illustrate that this technique proves to be particularly powerful in the analyses of ecological convergence which include several distinct data sets and in the analyses of spatiotemporal variations of species distributions. [ABSTRACT FROM AUTHOR]

The article presents a response on the paper "Plant Species Invasions Along the Latitudinal Gradient in the United States." The authors stress the two major findings of the research that include explanation about the variation of the latitudinal gradients in native species as well as the richness of the native plant species as the predictors of nonnative species richness. However, researchers emphasize that these criticisms are not applicable to the data and derive from the peremptory specification of the species area functional form, since the data set are the results of the spurious patterns of covaration. Researchers agreed on the survey that claims completeness to affect inferences from species-area data.

Understanding how organisms selectively use resources is essential for designing wildlife management strategies. The probability that an individual uses a given resource, as characterized by environmental factors, can be quantified in terms of the resource selection probability function (RSPF). The present literature on the topic has claimed that, except when both used and unused sites are known, the RSPF is non-estimable and that only a function proportional to RSPF, namely, the resource selection function (RSF) can be estimated. This paper describes a close connection between the estimation of the RSPF and the estimation of the weight function in the theory of weighted distributions. This connection can be used to obtain fully efficient, maximum likelihood estimators of the resource selection probability function under commonly used survey designs in wildlife management. The method is illustrated using GPS collar data for mountain goats (Oreamnos americanus de Blainville 1816) in northwest British Columbia, Canada. [ABSTRACT FROM AUTHOR]

The development of methods to analyze habitat selection when resources are defined by several categories (e.g., vegetation types) is a topical issue in radio-tracking studies. The White and Garrott statistic, an extension of the widely used test of Neu et al., can be used to determine whether habitat selection is significant. As well, Manly's selection ratio, a particularly useful measure of resource selectivity by resource users, allows detection of the most strongly selected habitat types. However, when both the number of animals and types of habitat are large, the biologist often has to deal with an excessively large number of measures. In this paper we present a new method, the eigenanalysis of selection ratios, that generalizes these two common methods within the framework of eigenanalyses. This method undertakes an additive linear partitioning of the White and Garrott statistic, so that the difference between habitat use and availability is maximized on the first factorial axes. The eigenanalysis of selection ratios is therefore optimal in habitat selection studies. Although we primarily consider the case where the habitat availability is the same for all animals (design II), we also extend this analysis to the case where the habitat availability varies from one animal to another (design III). An application of this method is provided using radio-tracking data collected on 17 squirrels in five habitat types. The results indicate variability in habitat selection, with two groups of animals displaying two patterns of preference. This difference between the two groups is explained by the patch structure of the study area. Because this method is mainly exploratory, and therefore does not rely on any distributional assumption, we recommend its use in studies of habitat selection. [ABSTRACT FROM AUTHOR]

The provisioning of sustaining goods and services that we obtain from natural ecosystems is a strong economic justification for the conservation of biological diversity. Understanding the relationship between these goods and services and changes in the size, arrangement, and quality of natural habitats is a fundamental challenge of natural resource management. In this paper, we describe a new approach to assessing the implications of habitat loss for loss of ecosystem services by examining how the provision of different ecosystem services is dominated by species from different trophic levels. We then develop a mathematical model that illustrates how declines in habitat quality and quantity lead to sequential losses of trophic diversity. The model suggests that declines in the provisioning of services will initially be slow but will then accelerate as species from higher trophic levels are lost at faster rates. Comparison of these patterns with empirical examples of ecosystem collapse (and assembly) suggest similar patterns occur in natural systems impacted by anthropogenic change. In general, ecosystem goods and services provided by species in the upper trophic levels will be lost before those provided by species lower in the food chain. The decrease in terrestrial food chain length predicted by the model parallels that observed in the oceans following overexploitation. The large area requirements of higher trophic levels make them as susceptible to extinction as they are in marine systems where they are systematically exploited. Whereas the traditional species area curve suggests that 50% of species are driven extinct by an order-of-magnitude decline in habitat abundance, this magnitude of loss may represent the loss of an entire trophic level and all the ecosystem services performed by the species on this trophic level. [ABSTRACT FROM AUTHOR]

Stage-based demographic data are now available on many species of plants and some animals, and they often display temporal and spatial variability. We provide exact formulas to compute age-specific life expectancy and survivorship from stage-based data for three models of temporal variability: cycles, serially independent random variation, and a Markov chain. These models provide a comprehensive description of patterns of temporal variation. Our formulas describe the effects of cohort (birth) environmental condition on mortality at all ages, and of the effects on survivorship of environmental variability experienced over the course of life. This paper complements existing methods for time-invariant stage-based data, and adds to the information on population growth and dynamics available from stochastic demography. [ABSTRACT FROM AUTHOR]

Plants return a wide range of carbon (C) substrates to the soil system. The decomposition rate of these substrates is determined by their chemical nature, yet few studies have examined the relative ecological role of specific substrates (i.e., substrate identity) or mixtures of substrates. Carbon substrate identity and diversity may alter soil chemistry and soil community composition, resulting in changes in belowground ecosystem functions such as decomposition and nutrient transfer, creating feedbacks that may affect plant growth and the aboveground community. A laboratory experiment was set up in which eight C substrates of varying chemical complexity were added to a base soil singly, in pairs, fours, or with all eight together every four days over a 92-day period. After 92 days these soils were analyzed for changes in chemistry, microbial community structure, and components of ecosystem functioning. The identity of the added C substrates significantly affected soil chemistry, microbial basal and substrate-induced respiration, and soil microbial community structure measured by either the catabolic response profile (CRP) technique or phospholipid fatty acid composition. These belowground changes strongly affected the ability of the soil microflora to decompose cellulose paper, probably because of differential effects of the C substrates on soil energy supplies and enzyme activities. The addition of C substrates to soils also reduced plant growth compared to the unamended control soil, but less so in soils amended with a tannin than those amended with other substrates. Carbon substrate diversity effects saturated at low diversity levels, tended to have neutral or negative effects on ecosystem functions, and depended strongly on which C substrates were added. It increased CRP compound use but had little effect on other measures of the soil microbial community. Overall, results showed that the chemical nature of C substrates added to soil, and sometimes their diversity, can affect the soil microbial community and soil chemistry, which subsequently affect other ecosystem processes such as decomposition and plant growth. The identity and diversity of substrates that plants add to soil may therefore have important consequences for both above- and belowground ecosystem functions. [ABSTRACT FROM AUTHOR]

It is well known that sampling variability, if not properly taken into account, affects various ecologically important analyses. Statistical inference for stochastic population dynamics models is difficult when, in addition to the process error, there is also sampling error. The standard maximum-likelihood approach suffers from large computational burden. In this paper, I discuss an application of the composite-likelihood method for estimation of the parameters of the Gompertz model in the presence of sampling variability. The main advantage of the method of composite likelihood is that it reduces the computational burden substantially with little loss of statistical efficiency. Missing observations are a common problem with many ecological time series. The method of composite likelihood can accommodate missing observations in a straightforward fashion. Environmental conditions also affect the parameters of stochastic population dynamics models. This method is shown to handle such nonstationary population dynamics processes as well. Many ecological time series are short, and statistical inferences based on such short time series tend to be less precise. However, spatial replications of short time series provide an opportunity to increase the effective sample size. Application of likelihood-based methods for spatial time-series data for population dynamics models is computationally prohibitive. The method of composite likelihood is shown to have significantly less computational burden, making it possible to analyze large spatial time-series data. After discussing the methodology in general terms, I illustrate its use by analyzing a time series of counts of American Redstart (Setophaga ruticilla) from the Breeding Bird Survey data, San Joaquin kit fox (Vulpes macrotis mutica) population abundance data, and spatial time series of Bull trout (Salvelinus confluentus) redds count data. [ABSTRACT FROM AUTHOR]

Field studies have shown that elevated CO2 can cause increased forest growth over the short term (<6 years) even in the face of N limitation. This is facilitated to some degree by greater biomass production per unit N uptake (lower tissue N concentrations), but more often than not, N uptake is increased with elevated CO2 as well. Some studies also show that N sequestration in the forest floor is increased with elevated CO2. These findings raise the questions of where the ‘extra’ N comes from and how long such growth increases can continue without being truncated by progressive N limitation (PNL). This paper reviews some of the early nutrient cycling literature that describes PNL during forest stand development and attempts to use this information, along with recent developments in soil N research, to put the issue of PNL with elevated CO2 into perspective. Some of the early studies indicated that trees can effectively ‘mine’ N from soils over the long term, and more recent developments in soil N cycling research suggest mechanisms by which this might have occurred. However, both the early nutrient cycling literature and more recent simulation modeling suggest that PNL will at some point truncate the observed increases in growth and nutrient uptake with elevated CO2, unless external inputs of N are increased by either N fixation or atmospheric deposition. [ABSTRACT FROM AUTHOR]

The capability of terrestrial ecosystems to sequester carbon (C) plays a critical role in regulating future climatic change yet depends on nitrogen (N) availability. To predict long-term ecosystem C storage, it is essential to examine whether soil N becomes progressively limiting as C and N are sequestered in long-lived plant biomass and soil organic matter. A critical parameter to indicate the long-term progressive N limitation (PNL) is net change in ecosystem N content in association with C accumulation in plant and soil pools under elevated CO2. We compiled data from 104 published papers that study C and N dynamics at ambient and elevated CO2. The compiled database contains C contents, N contents, and C:N ratio in various plant and soil pools, and root:shoot ratio. Averaged C and N pool sizes in plant and soil all significantly increase at elevated CO2 in comparison to those at ambient CO2, ranging from a 5% increase in shoot N content to a 32% increase in root C content. The C and N contents in litter pools are consistently higher in elevated than ambient CO2 among all the surveyed studies whereas C and N contents in the other pools increase in some studies and decrease in other studies. The high variability in CO2-induced changes in C and N pool sizes results from diverse responses of various C and N processes to elevated CO2. Averaged C:N ratios are higher by 3% in litter and soil pools and 11% in root and shoot pools at elevated relative to ambient CO2. Elevated CO2 slightly increases root:shoot ratio. The net N accumulation in plant and soil pools at least helps prevent complete down-regulation of, and likely supports, long-term CO2 stimulation of C sequestration. The concomitant C and N accumulations in response to rising atmospheric CO2 may reflect intrinsic nature of ecosystem development as revealed before by studies of succession over hundreds to millions of years. [ABSTRACT FROM AUTHOR]

Species patchiness implies that nearby observations of species abundance tend to be similar or that individual conspecific organisms are more closely spaced than by random chance. This can be caused either by the positive spatial autocorrelation among the locations of individual organisms due to ecological spatial processes (e.g., species dispersal, competition for space and resources) or by spatial dependence due to (positive or negative) species responses to underlying environmental conditions. Both forms of spatial structure pose problems for statistical analysis, as spatial autocorrelation in the residuals violates the assumption of independent observations, while environmental heterogeneity restricts the comparability of replicates, in this paper, we discuss how spatial structure due to ecological spatial processes and spatial dependence affects spatial statistics, landscape metrics, and statistical modeling of the species-environment correlation. For instance, while spatial statistics can quantify spatial pattern due to an endogeneous spatial process, these methods are severely affected by landscape environmental heterogeneity. Therefore, statistical models of species response to the environment not only need to accommodate spatial structure, but need to distinguish between components due to exogeneous and endogeneous processes rather than discarding all spatial variance. To discriminate between different components of spatial structure, we suggest using (multivariate) spatial analysis of residuals or delineating the spatial realms of a stationary spatial process using boundary detection algorithms. We end by identifying conceptual and statistical challenges that need to be addressed for adequate spatial analysis of landscapes. [ABSTRACT FROM AUTHOR]

Secondary succession reflects, at least in part, community assembly-the sequences of colonizations and extinctions. These processes in turn are expected to be sensitive to the size of the site undergoing assembly and its location relative to source pools. In this paper we describe patterns of succession over 18 years in an experimentally fragmented landscape created in eastern Kansas, USA, in 1984. The design of the experiment permits one to assess the influence of patch size and landscape position on successional dynamics. The general trajectory of succession follows that typical of succession in much of the eastern United States. In the initial years of the study, there was relatively little effect of patch size or distance to sources. Here we show that spatial effects in this system have become increasingly evident with time, as gauged both by repeated-measures ANOVA and ordination techniques. Woody plants have colonized more rapidly (per unit area) on large and nearby patches. Species richness at a local (within-quadrat) scale in general has increased, with slightly greater richness in large than in small patches later in the study. Temporal stability in community composition has generally been greater in large patches. Spatial heterogeneity in community composition has increased during succession, but with different patterns in large and small patches. This long-term experiment suggests that landscape structure influences many aspects of community structure and dynamics during succession, and that stich effects become more pronounced with the passage of time. [ABSTRACT FROM AUTHOR]

Most species abundance data show that a small number of species contribute the vast majority of individuals to a community. Thus, most taxa in a community are uncommon or rare. Yet such species will frequently be of ecological, conservation, or management interest. Data for uncommon or rare species will be presence/absence data or counts of abundance that contain a greater number of zero observations than would be predicted using standard, unimodal statistical distributions. Such data are generally referred to as zero-inflated data and require specialized methods for statistical analysis. Statistical approaches to modeling zero-inflated data include nonstandard mixture models; two-part, conditional models; and birth process models. In this paper, we briefly summarize two of these methods and illustrate the two-part, conditional approach to the problem of modeling count data with extra zeros. An advantage of this approach includes separate fits and separate interpretations of both components of count data; that is to say, the presence/absence component and the abundance component (given presence) can be analyzed separately. This can be valuable not only for simplicity,, but also such a two-step method may assist ecological understanding in cases where the basis for species presence might be separated from the underlying reasons affecting the population size of that species at those sites where it is present. We present two case studies of the application of the two-part conditional model for modeling count data with extra zeros. One deals with modeling relationships between counts of the rare and endangered arboreal marsupial, Leadbeater's possum (Gymnobelideus lead- beaten) and habitat variables in the wet eucalypt forests of southeastern Australia. The other is an analysis of data obtained from a monitoring study of seabird nesting from the Coral Sea off northeastern Australia. Finally, we briefly discuss some inferential and practical issues in developing designs and models for presence/absence data (which is the first component in the two-part conditional approach) when observed occurrences are low (e.g., <5%). [ABSTRACT FROM AUTHOR]

The potential advantage of extreme value theory in modeling ecological disturbances is the central theme of this paper. The statistics of extremes have played only a very limited role in ecological modeling, despite the disproportionate influence of unusual disturbances on ecosystems. An overview of this theory is provided, with emphasis on recent developments that both make more efficient use of the available data on extremes and enable applications that are more ecologically realistic. Consistent with the emphasis on scale in ecology, scaling properties of extremes are emphasized. It is argued that the existence of distributions whose extreme upper tail is "heavy" (i.e., decreases at a relatively slow rate) implies that ecological disturbances are sometimes regarded as more "surprising" than they ought to be. The application focuses on modeling disturbances in paleoecology. Two examples are considered: the first, a sediment yield time series for Nicolay Lake in the high Arctic,. reflects only the influence of hydrologic disturbances; the second, a sediment rate time series in the Chesapeake Bay, includes both climatic and anthropogenic influences. Strong evidence supports a heavy-tailed distribution for the Nicolay Lake sediment yield, but not necessarily for the Chesapeake Bay sediment rates. For each example, it is demonstrated how the statistics of extremes can readily incorporate information about covariates, such as large-scale atmospheric-oceanic circulation patterns or land use. [ABSTRACT FROM AUTHOR]

Although the mid-domain effect (MDE) model for species richness in bounded geographical domains has proved controversial, several studies have revealed its explanatory potential for patterns of species richness. This paper investigates unexplored aspects of one-dimensional MDE, based on a data set of latitudinal distributions of New World birds (3706 species) on a 1° scale. Two previously published data sets for other taxa are also considered. We adjusted band sums (number of species per latitudinal band) for longitudinal area by constructing species-area curves for each band. Area-corrected richness patterns differed substantially from raw band sums, although both confirmed a strong, mid- tropical peak in richness. An MDE model accounted for 47% of the adjusted pattern, whereas area alone explained 13% of variation. Area-adjusted band sum data proved preferable to coastal transect data from the same data set. MDE was relatively more important in smaller latitudinal domains and/or for taxa with relatively large ranges. On fundamental grounds, we concluded that MDE randomizations based on empirical ranges are more appropriate than those based on theoretical range size distributions. Models that, like MDE, produce quantitative richness predictions should be evaluated statistically against the null hypothesis of equality, not simply correlation, of empirical vs. predicted richness values. [ABSTRACT FROM AUTHOR]

The decline of corals on tropical reefs is usually ascribed to a combination of natural and anthropogenic factors, but the relative importance of these causes remains unclear. In this paper, we attempt to quantify the contribution of hurricanes to Caribbean coral cover decline over the past two decades using meta-analyses. Our study included published and unpublished data from 286 coral reef sites monitored for variable lengths of time between 1980 and 2001. Of these, 177 sites had experienced hurricane impacts during their period of survey. Across the Caribbean, coral cover is reduced by ∼17%, on average, in the year following a hurricane impact. The magnitude of this immediate loss increases with hurricane intensity and with the time elapsed since the last impact. In the following year, no further loss is discernible, but the decline in cover then resumes on impacted sites at a rate similar to the regional background rate of decline for nonimpacted sites. There is no evidence of recovery to a pre-storm state for at least eight years after impact. Overall, coral cover at sites impacted by a hurricane has declined at a significantly faster rate (6% per annum) than nonimpacted sites (2% per annum), due almost exclusively to higher rates of loss in the year after impact in the 1980s. While hurricanes, through their immediate impacts, appear to have contributed to changing coral cover on many Caribbean reefs in the 1980s, the similar decline in coral cover at impacted and nonimpacted sites in the 1990s suggests that other stressors are now relatively more important in driving the overall pattern of change in coral cover in this region. The overall lack of post-hurricane recovery points to a general impairment of the regeneration potential of Caribbean coral reefs. [ABSTRACT FROM AUTHOR]

There is a wealth of literature documenting a directional change of body size in heavily harvested populations. Most of this work concentrates on aquatic systems, but terrestrial populations are equally at risk. This paper explores the capacity of harvest refuges to counteract potential effects of size-selective harvesting on the allele frequency of populations. We constructed a stochastic, individual-based model parameterized with data on red kangaroos. Because we do not know which part of individual growth would change in the course of natural selection, we explored the effects of two alternative models of individual growth in which alleles affect either the growth rate or the maximum size. The model results show that size-selective harvesting can result in significantly smaller kangaroos for a given age when the entire population is subject to harvesting. In contrast, in scenarios that include dispersal from harvest refuges, the initial allele frequency remains virtually unchanged. [ABSTRACT FROM AUTHOR]

Spatial structures may not only result from ecological interactions, they may also play an essential functional role in organizing the interactions. Modeling spatial patterns at multiple spatial and temporal scales is thus a crucial step to understand the functioning of ecological communities. PCNM (principal coordinates of neighbor matrices) analysis achieves a spectral decomposition of the spatial relationships among the sampling sites, creating variables that correspond to all the spatial scales that can be perceived in a given data set. The analysis then finds the scales to which a data table of interest responds. The significant PCNM variables can be directly interpreted in terms of spatial scales, or included in a procedure of variation decomposition with respect to spatial and environmental components. This paper presents four applications of PCNM analysis t ecological data representing combinations of: transect or surface data, regular or irregular sampling schemes, univariate or multivariate data. The data sets include Amazonian ferns, tropical marine zooplankton, chlorophyll in a marine lagoon, and oribatid mites in a peat bog. In each case, new ecological knowledge was obtained through PCNM analysis. [ABSTRACT FROM AUTHOR]

There is now quite an extensive literature based on analysis of multivariate abundances, which have often been collected according to a MANOVA design. Many test statistics have been proposed specifically for this case, yet remarkably the power of these methods has not previously been compared. In this paper, the power of distance-based statistics (e.g., Mantel, analysis of similarities) is compared to variable-based statistics (e.g., redundancy analysis, the sum of ANOVA F statistics), when using permutation tests to assess significance of all statistics. Different choice of transformation, standardization, and distance measure were considered. For 19 data sets taken from the literature, P values for the. different statistics were compared. Power simulations were then conducted, where data were generated to mimic the properties of each of the 19 data sets. For transformed data, using different distance measures (Euclidean, Manhattan, Bray- Curtis) and different distance-based statistics had little effect on power. Overall, statistics based on multivariate analysis of variance (MANOVA) were at least as powerful as others, although particular data sets gave different results. The distance-based statistics most commonly used in the literature do not standardize abundances, so these were more powerful when effects are present in taxa that are more variable (on the transformed scale), and less powerful otherwise. There are several reasons to prefer a statistic based on MANOVA to others (e.g., interpretability, generalization to more complex designs), and so we generally recommend that the MANOVA-based statistics used here be preferred to distance-based statistics. [ABSTRACT FROM AUTHOR]

Gradient analysis uses ordination methods to study the structure of biotic communities caused by biotic processes operating in a heterogeneous environment. This structure has two spatial components: spatial processes within the community create autocorrelation,. and the spatial structure of environmental factors creates spatial dependence. Ordination methods, however, do not make use of spatial Information. Spatial alternatives are available in multivariate geostatistics, but are not compatible with important ordination methods used in gradient analysis, correspondence analysis and canonical correspondence analysis (CA, CCA). This paper shows how CA and CCA can be partitioned by distance (indirect and direct multi-scale ordination) and integrated with geostatistics. A diagnostic tool enables ecologists to partition ordination results by distance, to distinguish between components of spatial dependence and of spatial autocorrelation, and to check assumptions of independent residuals, stationarity, and scale-invariant correlation. The application is illustrated with a well-known data set of oribatid mites. Empirical chi-square variograms of individual species, their pair-wise cross variograms, and the variogram of the total inertia are defined and summarized in a variogram matrix, which leads to a spatial partitioning of the eigenvalues. The empirical variogram matrix provides a link to coregionalization analysis that may be used to simultaneously model spatial dependence and spatial autocorrelation. This will be useful for answering questions about the organism-specific scale of response to the environment, the optimal spacing of sampling units, or the scale-dependent effect of environmental factors. [ABSTRACT FROM AUTHOR]

In this paper, we present a statistical method called STATICO that can be used to analyze series of pairs of ecological tables. The objective of this method is to find the stable part in the dynamics of the relationships between the species and their environment. The treated data are a sequence of pairs of ecological tables. Each pair is made of a species abundance table (species in columns) and an environmental variables table (variables in columns). The sampling sites (in rows) must be the same for the two tables of one pair, but they may be different among the pairs. The environmental variables must be the same in all the environmental tables, and the list of species must be the same in all the species tables too, although some species may be absent from some tables (the corresponding columns will contain all zeros). From a statistical point of view, STATICO is a multitable analysis (partial triadic analysis) performed on the series of cross-tables resulting from the co-inertia analysis of each pair of tables. A small ecological example data set is analyzed and the results are discussed to show how this method can be used to extract the stable part of species-environment relationships. All computations and graphical displays can be performed with free software available on Internet. [ABSTRACT FROM AUTHOR]

In experimental population ecology, there is often a gap between realistic models used to hypothesize about population dynamics and statistical models used to analyze data. Ecologists routinely conduct experiments where the data from each replicate are short time series of estimated population abundances structured by stage, species, and/or other information, and the, conventional test for treatment effects uses a general linear model (GLM) such as analysis of variance (ANOVA). However, GLMs do not incorporate demographic relationships between abundances, through time. An alternative is to use population-dynamics models as frameworks for statistical hypothesis testing. This approach requires general methods for fitting structured population models that can incorporate both process noise (stochastic dynamics) and observation error (inaccurate data). This paper presents such methods and compares them to GLMs for testing population-dynamics hypotheses from experiments. The methods are Monte Carlo state-space likelihood methods, including a basic Monte Carlo integration method and a recently developed Monte Carlo kernel likelihood method. Three simulated examples of population-dynamics experiments were used to compare analysis with a population model to ANOVA, analysis of covariance (ANCOVA), and repeated-measures ANOVA. The examples considered manipulations of host-plant growth conditions, causing decreased survival and increased fecundity; predator addition to investigate a behaviorally mediated change in prey demography; and changed host-plant growth conditions with a more complex model for herbivore dynamics than the one used for analysis. For the first example, a population model gave much higher statistical power than any of the ANOVA methods and provides greater biological insight. For the second example, ANOVA models are not suited to test for the behavioral effect, but a population model detected it with high statistical power. [ABSTRACT FROM AUTHOR]

Experiments in ecology are usually designed to provide tests of hypotheses on the influence of the mean intensity of causal processes, whereas the variance around mean effects has been largely overlooked as a causal force in biological assemblages, Repetition of experiments in space and time provides an estimate of this variability at specific scares, but does not explain how changes in variance generate structure in assemblages and the extent to which variance and mean intensity interact. This paper seeks to identify suitable procedures for empirical analyses on the influence of variance and mean intensity of predictor ecological variables on spatial and temporal patterns in natural populations. A survey of the ecological literature indicates that Temporal variability in studies of disturbance and in analyses of consumer-resource interactions is generally expressed in terms of frequency of events. This is inappropriate, as frequency confounds the variance with the mean effect size of a process. A possible solution to the problem involves experimental designs in which levels of intensity and those of variability are chosen independently over explicit spatial or temporal scales and treated as fixed, orthogonal factors. Examples are offered for various scenarios of consumer-resource interactions along with indications for statistical tests of hypotheses. Such novel approaches have important ramifications for understanding variability in a wide range of ecological contexts ann for predicting the response of assemblages to increased environmental fluctuations, including those expected under modified climate conditions. [ABSTRACT FROM AUTHOR]

Understanding the nature and causes of global gradients in species richness is a perennial ecological problem, and recent work has highlighted the need to assess these gradients relative to an appropriate statistical expectation. This paper examines latitudinal and longitudinal gradients in species richnesses of corals and reef fishes in the Indo-Pacific domain and compares them with gradients predicted by a mid-domain model in which geographic domains are located at random between the latitudinal and longitudinal boundaries of this region. We test for significant differences between observed and predicted species-richness patterns, and we identify regions that are enriched or depauperate in species, relative to expectation. In addition, we move beyond previous mid-domain analyses by directly comparing observed spatial distributions of geographic ranges with those predicted by a mid-domain model. This comparison indicates precisely how species-richness anomalies are produced by nonrandomness in the distribution of species ranges. For both corals and fishes, large and statistically significant differences exist between observed latitudinal and longitudinal species-richness gradients and those predicted by mid-domain models. Longitudinally, species richness is markedly higher than predicted along the African coast and, to a lesser extent, within the Indo-Australian Archipelago (IAA), and it is markedly lower than expected in the eastern Pacific. Latitudinally, species richness becomes increasingly higher than predicted as one moves from the equator to the tropical margins; then it becomes sharply lower than predicted beyond the tropics. Unexpectedly, differences between observed and predicted spatial distributions of range endpoints and midpoints reveal a pattern of nonrandomness that is highly congruent with the hypothesis that gyres in the Indian and Pacific Oceans, with the IAA forming a porous boundary between them, have a major influence on Indo-Pacific species-richness patterns. Our analyses indicate that the perspective offered by a focus on explaining nonrandomness in the location of geographic ranges (rather than explaining why species numbers vary in space) is likely to dramatically alter our assessments of alternative explanations for global species-richness gradients. [ABSTRACT FROM AUTHOR]

It has been argued that nonstandard mechanisms of dispersal are often responsible for long-distance dispersal in plants. For example, plant seeds that appear to be adapted for wind dispersal may occasionally be dispersed long distances by birds, or vice versa. In this paper, we explore whether existing data on dispersal distances, colonization rates, and migration rates support the idea that dispersal processes suggested by the morphology of the dispersal unit are responsible for long distance dispersal. We conclude that the relationship between morphologically defined dispersal syndrome and long-distance dispersal is poor. This relationship is poor because the relationship between the morphology of dispersal units and the multiple processes that move seeds are often complex. We argue that understanding gleaned from the often anecdotal literature on nonstandard and standard means of long distance dispersal is the foundation for both statistical and mechanistic models of long-distance dispersal. Such models hold exciting promise for the development of a quantitative ecology of long-distance dispersal. [ABSTRACT FROM AUTHOR]

Ecologists have traditionally neglected demographic stochasticity in describing the spread of an invading species. However, the region most critical in determining wave speed is often the leading edge, precisely the point where demographic stochasticity is most pronounced. In this paper, I analyze a common class of one-dimensional, single-species invasion models and find that, for very general conditions, demographic stochasticity slows biological invasions. Nonetheless, the slowing is not large enough to be noticeable in most ecological time series. I also briefly discuss the role of transient dynamics and rare, long-distance dispersal. [ABSTRACT FROM AUTHOR]

Rhizobial bacteria, endomycorrhizal fungi (also known as arbuscular mycorrhizas), and pseudomonad bacteria associated with plant roots can provide substantial benefits to the plants by fixing nitrogen, supplying phosphorus, or controlling root pathogens, respectively. A significant fraction of plant photosynthetic carbon may be used by these associated microorganisms, both to support their beneficial activities and for microbial growth and reproduction. Because many microbial individuals are associated with each individual plant, the individual benefit to a microbe that allocates more resources to its own reproduction (thereby allocating less to fixing N[sub 2], supplying P, or producing antifungal metabolites) would exceed its individual loss from any resulting reduction in collective benefits (mainly plant carbon substrates). An initially rare "free rider" mutant strain might therefore be expected to displace its more cooperative parental strain. Yet, the mycorrhizal and legume-rhizobium mutualisms have persisted (often coexisting with "cheating") for millions of years. This paper discusses the importance of microbial cooperation (with plants and with other microbes) and possible reasons for its evolutionary persistence in the rhizosphere. In undisturbed soils, spatial structure can favor kin selection, but this may be counterbalanced by the increased likelihood that future competitors will be among the beneficiaries of current cooperation. In loose associations, direct fitness benefits to microorganisms may explain the evolutionary persistence of activities (e.g., production of antifungal compounds) that can benefit plants as a side effect. In closer, more symbiotic, relationships, host sanctions against individuals or clones that fail to perform their symbiotic function may be more important. New molecular methods and other research tools are facilitating research on this topic, and some of these conclusions soon may be revised. [ABSTRACT FROM AUTHOR]