Nicholas J. Gotelli

SPECIES CO-OCCURRENCE: A META-ANALYSIS OF J. M. DIAMOND'S ASSEMBLY RULES MODEL

J. M. Diamonds assembly rules model predicts that competitive interactions between species lead to nonrandom co-occurrence patterns. We conducted a meta-analysis of 96 published presence–absence matrices and used a realistic “null model” to generate patterns expected in the absence of species interactions. Published matrices were highly nonrandom and matched the predictions of Diamonds model: there were fewer species combinations, more checkerboard species pairs, and less co-occurrence in real matrices than expected by chance. Moreover, nonrandom structure was greater in homeotherm vs. poikilotherm matrices. Although these analyses do not confirm the mechanisms of Diamonds controversial assembly rules model, they do establish that observed co-occurrence in most natural communities is usually less than expected by chance. These results contrast with previous analyses of species co-occurrence patterns and bridge the apparent gap between experimental and correlative studies in community ecology.

Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies

Quantifying and assessing changes in biological diversity are central aspects of many ecological studies, yet accurate methods of estimating biological diversity from sampling data have been elusive. Hill numbers, or the effective number of species, are increasingly used to characterize the taxonomic, phylogenetic, or functional diversity of an assemblage. However, empirical estimates of Hill numbers, including species richness, tend to be an increasing function of sampling effort and, thus, tend to increase with sample completeness. Integrated curves based on sampling theory that smoothly link rarefaction (interpolation) and prediction (extrapolation) standardize samples on the basis of sample size or sample completeness and facilitate the comparison of biodiversity data. Here we extended previous rarefaction and extrapolation models for species richness (Hill number q D, where q ¼ 0) to measures of taxon diversity incorporating relative abundance (i.e., for any Hill number q D, q . 0) and present a unified approach for both individual-based (abundance) data and sample- based (incidence) data. Using this unified sampling framework, we derive both theoretical formulas and analytic estimators for seamless rarefaction and extrapolation based on Hill numbers. Detailed examples are provided for the first three Hill numbers: q ¼ 0 (species richness), q ¼ 1 (the exponential of Shannons entropy index), and q ¼ 2 (the inverse of Simpsons concentration index). We developed a bootstrap method for constructing confidence intervals around Hill numbers, facilitating the comparison of multiple assemblages of both rarefied and extrapolated samples. The proposed estimators are accurate for both rarefaction and short-range extrapolation. For long-range extrapolation, the performance of the estimators depends on both the value of q and on the extrapolation range. We tested our methods on simulated data generated from species abundance models and on data from large species inventories. We also illustrate the formulas and estimators using empirical data sets from biodiversity surveys of temperate forest spiders and tropical ants.

Plant species richness and ecosystem multifunctionality in global drylands

Global Ecosystem Analysis The relationship between species richness and the functional properties of their ecosystems has often been studied at small scales in experimental plots. Maestre et al. (p. 214; see the Perspective by Midgley) performed field measurements at 224 dryland sites from six continents and assessed 14 ecosystem functions related to carbon, nitrogen, and phosphorus cycling. Positive relationships were observed between perennial plant species richness and ecosystem functionality. The relative importance of biodiversity was found to be as large as, or larger than, many key abiotic variables. Thus, preservation of plant biodiversity is important to buffer negative effects of climate change and desertification in drylands, which collectively cover 41% of Earths land surface and support over 38% of the human population. Plant species richness is positively related to ecosystem multifunctionality in drylands at a global scale. Experiments suggest that biodiversity enhances the ability of ecosystems to maintain multiple functions, such as carbon storage, productivity, and the buildup of nutrient pools (multifunctionality). However, the relationship between biodiversity and multifunctionality has never been assessed globally in natural ecosystems. We report here on a global empirical study relating plant species richness and abiotic factors to multifunctionality in drylands, which collectively cover 41% of Earth’s land surface and support over 38% of the human population. Multifunctionality was positively and significantly related to species richness. The best-fitting models accounted for over 55% of the variation in multifunctionality and always included species richness as a predictor variable. Our results suggest that the preservation of plant biodiversity is crucial to buffer negative effects of climate change and desertification in drylands.

Community disassembly by an invasive species

Invasive species pose serious threats to community structure and ecosystem function worldwide. The impacts of invasive species can be more pervasive than simple reduction of species numbers. By using 7 years of data in a biological preserve in northern California, we documented the disassembly of native ant communities during an invasion by the Argentine ant. In sites without the Argentine ant, native ant communities exhibit significant species segregation, consistent with competitive dynamics. In sites with the Argentine ant, native ant communities appear random or weakly aggregated in species co-occurrence. Comparisons of the same sites before and after invasion indicate that the shift from a structured to a random community is rapid and occurs within a year of invasion. Our results show that invasive species not only reduce biodiversity but rapidly disassemble communities and, as a result, alter community organization among the species that persist.

Assemblage time series reveal biodiversity change but not systematic loss

Changing Assemblages Although the rate of species extinction has increased markedly as a result of human activity across the biosphere, conservation has focused on endangered species rather than on shifts in assemblages. Dornelas et al. (p. 296; see the Perspective by Pandolfi and Lovelock), using an extensive set of biodiversity time series of species occurrences in both marine and terrestrial habitats from the past 150 years, find species turnover above expected but do not find evidence of systematic biodiversity loss. This result could be caused by homogenization of species assemblages by invasive species, shifting distributions induced by climate change, and asynchronous change across the planet. All of which indicates that it is time to review conservation priorities. Ecological communities are experiencing changes in species composition rather than unidirectional loss. [Also see Perspective by Pandolfi and Lovelock] The extent to which biodiversity change in local assemblages contributes to global biodiversity loss is poorly understood. We analyzed 100 time series from biomes across Earth to ask how diversity within assemblages is changing through time. We quantified patterns of temporal α diversity, measured as change in local diversity, and temporal β diversity, measured as change in community composition. Contrary to our expectations, we did not detect systematic loss of α diversity. However, community composition changed systematically through time, in excess of predictions from null models. Heterogeneous rates of environmental change, species range shifts associated with climate change, and biotic homogenization may explain the different patterns of temporal α and β diversity. Monitoring and understanding change in species composition should be a conservation priority.

NULL MODEL ANALYSIS OF SPECIES NESTEDNESS PATTERNS

Nestedness is a common biogeographic pattern in which small communities form proper subsets of large communities. However, the detection of nestedness in binary presence-absence matrices will be affected by both the metric used to quantify nestedness and the reference null distribution. In this study, we assessed the statistical performance of eight nestedness metrics and six null model algorithms. The metrics and algorithms were tested against a benchmark set of 200 random matrices and 200 nested matrices that were created by passive sampling. Many algorithms that have been used in nestedness studies are vulnerable to type I errors (falsely rejecting a true null hypothesis). The best-performing algorithm maintains fixed row and fixed column totals, but it is conservative and may not always detect nestedness when it is present. Among the eight indices, the popular matrix temperature metric did not have good statistical properties. Instead, the Brualdi and Sanderson discrepancy index and Cutlers index of unexpected presences performed best. When used with the fixed-fixed algorithm, these indices provide a conservative test for nestedness. Although previous studies have revealed a high frequency of nestedness, a reanalysis of 288 empirical matrices suggests that the true frequency of nested matrices is between 10% and 40%.

Sufficient sampling for asymptotic minimum species richness estimators

Biodiversity sampling is labor intensive, and a substantial fraction of a biota is often represented by species of very low abundance, which typically remain undetected by biodiversity surveys. Statistical methods are widely used to estimate the asymptotic number of species present, including species not yet detected. Additional sampling is required to detect and identify these species, but richness estimators do not indicate how much sampling effort (additional individuals or samples) would be necessary to reach the asymptote of the species accumulation curve. Here we develop the first statistically rigorous nonparametric method for estimating the minimum number of additional individuals, samples, or sampling area required to detect any arbitrary proportion (including 100%) of the estimated asymptotic species richness. The method uses the Chao1 and Chao2 nonparametric estimators of asymptotic richness, which are based on the frequencies of rare species in the original sampling data. To evaluate the performance of the proposed method, we randomly subsampled individuals or quadrats from two large biodiversity inventories (light trap captures of Lepidoptera in Great Britain and censuses of woody plants on Barro Colorado Island [BCI], Panama). The simulation results suggest that the method performs well but is slightly conservative for small sample sizes. Analyses of the BCI results suggest that the method is robust to nonindependence arising from small-scale spatial aggregation of species occurrences. When the method was applied to seven published biodiversity data sets, the additional sampling effort necessary to capture all the estimated species ranged from 1.05 to 10.67 times the original sample (median approximately equal to 2.23). Substantially less effort is needed to detect 90% of the species (0.33-1.10 times the original effort; median approximately equal to 0.80). An Excel spreadsheet tool is provided for calculating necessary sampling effort for either abundance data or replicated incidence data.

Effects of disturbance frequency, intensity, and area on assemblages of stream macroinvertebrates

Abstract Disturbance frequency, intensity, and areal extent may influence the effects of disturbance on biological communities. Furthermore, these three factors may have interacting effects on biological diversity. We manipulated the frequency, intensity, and area of disturbance in a full-factorial design on artificial substrates and measured responses of benthic macroinvertebrates in a northern Vermont stream. Macroinvertebrate abundance was lower in all disturbance treatments than in the undisturbed control. As in most other studies in streams, species density (number of species/sample) was lower in disturbed treatments than in undisturbed controls. However, species density is very sensitive to total abundance of a sample, which is usually reduced by disturbance. We used a rarefaction method to compare species richness based on an equivalent number of individuals. In rarefied samples, species richness was higher in all eight disturbed treatments than in the undisturbed control, with significant increases in species richness for larger areas and greater intensities of disturbance. Increases in species richness in response to disturbance were consistent within patches, among patches with similar disturbance histories, and among patches with differing disturbance histories. These results provide some support for Huston’s dynamic-equilibrium model but do not support the intermediate-disturbance hypothesis. Our analyses demonstrate that species richness and species density can generate opposite patterns of community response to disturbance. The interplay of abundance, species richness, and species density has been neglected in previous tests of disturbance models.

Predicting continental-scale patterns of bird species richness with spatially explicit models

The causes of global variation in species richness have been debated for nearly two centuries with no clear resolution in sight. Competing hypotheses have typically been evaluated with correlative models that do not explicitly incorporate the mechanisms responsible for biotic diversity gradients. Here, we employ a fundamentally different approach that uses spatially explicit Monte Carlo models of the placement of cohesive geographical ranges in an environmentally heterogeneous landscape. These models predict species richness of endemic South American birds (2248 species) measured at a continental scale. We demonstrate that the principal single-factor and composite (species-energy, water-energy and temperature-kinetics) models proposed thus far fail to predict (r2⩽0.05) the richness of species with small to moderately large geographical ranges (first three range-size quartiles). These species constitute the bulk of the avifauna and are primary targets for conservation. Climate-driven models performed reasonably well only for species with the largest geographical ranges (fourth quartile) when range cohesion was enforced. Our analyses suggest that present models inadequately explain the extraordinary diversity of avian species in the montane tropics, the most species-rich region on Earth. Our findings imply that correlative climatic models substantially underestimate the importance of historical factors and small-scale niche-driven assembly processes in shaping contemporary species-richness patterns.

Metapopulation Models: The Rescue Effect, the Propagule Rain, and the Core-Satellite Hypothesis

Metapopulation models are important ools for understanding distribution and abundance of organisms on large spatial scales (Levins 1969a; Hanski 1989). These models integrate local population dynamics with immigration and extinction events occurring between population sites (Levins 1969a, 1970; den Boer 1981; Hanski 1982, 1989). In this sense, they form a bridge between the traditionally separate domains of population ecology (local abundance) and biogeography (regional occurrence) (Andrewartha and Birch 1954; Hanski 1982). Metapopulation models provide a useful framework for understanding both correlative (Gill 1978; Hanski and Ranta 1983; Harrison et al. 1988) and experimental (Bengtsson 1989) data on distribution and abundance of natural populations. Extensions to the optimal design of subdivided nature reserves are also promising (Quinn and Hastings 1987). Levins (1969a, 1970) introduced an important class of metapopulation models of the following form: