In Situ Speciation of Dispersal‐Limited Taxa Leads to Strong Phylogenetic Community Clustering - M. L. Araujo; M. T. P. Coelho; N. J. Gotelli; R. K. Colwell; T. F. Rangel; C. S. Dambros (Journal of Biogeography, 2025)
Aim Phylogenetic community structures have traditionally been interpreted through the lens of niche processes, such as environmental filtering and interspecific competition. However, these structures have been continuously shaped over evolutionary time by biogeographical dynamics, a process that is often overlooked. Here, we disentangled the influence of speciation and dispersal on spatial phylogenetic structures using neutral simulation models, while turning off niche‐based processes. Location South America. Taxon Simulated clades. Methods We simulated biogeographical dynamics of individuals evolving under random speciation, dispersal and demographic processes. Each model incorporated different combinations of speciation and dispersal rates, and the phylogenetic structures of its communities were assessed using standardised effect sizes of Mean Phylogenetic Distances (SES MPD ). We evaluated the SES MPD in response to varying degrees of speciation and dispersal rates, and its relationship with community isolation, using Ordinary Least Square (OLS) models. Results Increased speciation rates and reduced rates both contribute to higher phylogenetic clustering, leading to a higher co‐occurrence of closely related species. While both variables independently influence phylogenetic structure, the extent of phylogenetic clustering is primarily determined by the increased ratio between speciation and dispersal rates. This trend toward phylogenetic clustering persisted even at smaller scales, albeit with reduced intensity. Clustering was more pronounced in isolated communities, with the effects intensifying based on the combination of speciation and dispersal rates. Main Conclusions Our study demonstrates that biogeographical dynamics can drive strong phylogenetic structures even in the absence of niche processes. Specifically, in situ speciation rates can enhance phylogenetic clustering by continuously adding species to community pools, such as through the exploitation of novel ecological opportunities. However, the extent of this clustering largely depends on the degree of geographical isolation and dispersal limitations experienced by the radiating lineages, which influence biotic interchange and phylogenetic homogenisation among community compositions.

Springtime upwelling conditions influence microbial communities and dissolved thiamin compounds in the California Current Ecosystem - Kelly C. Shannon; Gillian St. John; Robin Gould; Christopher Hartzell; Hailey Matthews; Elizabeth J. Brennan; Luis M. Bolaños; Steven T. Lindley; John C. Field; Nate Mantua; Rachel Johnson; Carson Jeffres; et al. (Limnology and Oceanography, 2025)
Understanding dissolved concentrations of the essential coenzyme thiamin (vitamin B 1 ) can provide insights into the biological controls on highly productive upwelling systems such as the California Current Ecosystem. To connect thiamin availability with microbial communities in the California Current Ecosystem, we measured concentrations of dissolved thiamin and its biochemically related moieties (thiamin congeners) and 16S rRNA gene‐based microbial communities during the spring. We found that strong upwelling caused a depletion of dissolved thiamin precursor compounds and abiotic degradation products relative to periods of weak upwelling. Specific microbial taxa, including species of SAR11 ecotypes, Candidatus Nitrosopumilus, and SUP05 cluster, were also significantly enriched with strong upwelling. Our data provide evidence that alterations to microbial communities in the mixed layer that occur as a result of upwelling could constrain the availability of dissolved thiamin and its chemical congeners in the California Current Ecosystem.

Corrigendum to “Lizards in the wind: The impact of wind on the thermoregulation of the common wall lizard” [J. Therm. Biol. 121 (2024) 103855] - Sierra Spears; Ciara Pettit; Sophie Berkowitz; Simone Collier; Cece Colwell; Ethan H. Livingston; Wyatt McQueen; Princeton L. Vaughn; Brooke L. Bodensteiner; Vianey Leos-Barajas; Eric J. Gangloff (Journal of Thermal Biology, 2024)

Lizards in the wind: The impact of wind on the thermoregulation of the common wall lizard - Sierra Spears; Ciara Pettit; Sophie Berkowitz; Simone Collier; Cece Colwell; Ethan H. Livingston; Wyatt McQueen; Princeton L. Vaughn; Brooke L. Bodensteiner; Vianey Leos-Barajas; Eric J. Gangloff (Journal of Thermal Biology, 2024)

The 16th Workshop on Antarctic Meteorology and Climate and 6th Year of Polar Prediction in the Southern Hemisphere Meeting - David H. Bromwich; Matthew A. Lazzara; Arthur M. Cayette; Jordan G. Powers; Kirstin Werner; John J. Cassano; Steven R. Colwell; Scott Carpentier; Xun Zou (Advances in Atmospheric Sciences, 2022)

The 13th and 14th Workshops on Antarctic Meteorology and Climate - Matthew A. Lazzara; Sophie A. Orendorf; Taylor P. Norton; Jordan G. Powers; David H. Bromwich; Scott Carpentier; John J. Cassano; Steven R. Colwell; Arthur M. Cayette; Kirstin Werner (Advances in Atmospheric Sciences, 2020)

Antarctic temperature variability and change from station data - John Turner; Gareth J. Marshall; Kyle Clem; Steve Colwell; Tony Phillips; Hua Lu (International Journal of Climatology, 2020)
Variability and change in near‐surface air temperature at 17 Antarctic stations is examined using data from the SCAR READER database. We consider the relationships between temperature, and atmospheric circulation, sea ice concentration and forcing by the tropical oceans. All 17 stations have their largest inter‐annual temperature variability during the winter and the annual mean temperature anomalies are dominated by winter temperatures. The large inter‐annual temperature variability on the western Antarctic Peninsula has decreased over the instrumental period as sea ice has declined. Variability in the phase of the SAM exerts the greatest control of temperatures, although tropical Pacific forcing has also played a large part, along with local atmospheric circulation variability at some locations. The relationship of positive (negative) SAM and high (low) Peninsula and low (high) East Antarctic temperatures was not present before the mid‐1970s. Thirteen of the 17 stations have experienced a positive trend in their annual mean temperature over the full length of their record, with the largest being at Vernadsky (formerly Faraday) (0.46° ± 0.15°C·dec −1 ) on the western side of the Antarctic Peninsula. The deepening of the Amundsen Sea low as a result of the more positive SAM and changes in the IPO and PDO have contributed to the warming of the Peninsula. Beyond the Antarctic Peninsula there has been little significant change in temperature. The two plateau stations had a small cooling from the late 1970s to the late 1990s consistent with the SAM becoming positive, but have subsequently warmed. During spring there has been an Antarctic‐wide warming, with all but one station having experienced an increase in temperature, although the only trends that were significant were at Vostok, Scott base, Vernadsky and Amundsen‐Scott. In this season, much of the Peninsula/West Antarctic warming can be attributed to tropical Pacific forcing through the IPO/PDO.

Transcutaneous electrical nerve stimulation improves fatigue performance of the treated and contralateral knee extensors - D. G. Behm; E. M. Colwell; G. M. J. Power; H. Ahmadi; A. S. M. Behm; A. Bishop; C. Murph; J. Pike; B. McAssey; K. Fraser; S. Kearley; M. Ryan (European Journal of Applied Physiology, 2019)

Proportional mixture of two rarefaction/extrapolation curves to forecast biodiversity changes under landscape transformation - Anne Chao; Robert K. Colwell; Nicholas J. Gotelli; Simon Thorn (Ecology Letters, 2019)
Progressive habitat transformation causes global changes in landscape biodiversity patterns, but can be hard to quantify. Rarefaction/extrapolation approaches can quantify within‐habitat biodiversity, but may not be useful for cases in which one habitat type is progressively transformed into another habitat type. To quantify biodiversity patterns in such transformed landscapes, we use Hill numbers to analyse individual‐based species abundance data or replicated, sample‐based incidence data. Given biodiversity data from two distinct habitat types, when a specified proportion of original habitat is transformed, our approach utilises a proportional mixture of two within‐habitat rarefaction/extrapolation curves to analytically predict biodiversity changes, with bootstrap confidence intervals to assess sampling uncertainty. We also derive analytic formulas for assessing species composition (i.e. the numbers of shared and unique species) for any mixture of the two habitat types. Our analytical and numerical analyses revealed that species unique to each habitat type are the most important determinants of landscape biodiversity patterns.

Deciphering the enigma of undetected species, phylogenetic, and functional diversity based on Good‐Turing theory - Anne Chao; Chun‐Huo Chiu; Robert K. Colwell; Luiz Fernando S. Magnago; Robin L. Chazdon; Nicholas J. Gotelli (Ecology, 2017)
Estimating the species, phylogenetic, and functional diversity of a community is challenging because rare species are often undetected, even with intensive sampling. The Good‐Turing frequency formula, originally developed for cryptography, estimates in an ecological context the true frequencies of rare species in a single assemblage based on an incomplete sample of individuals. Until now, this formula has never been used to estimate undetected species, phylogenetic, and functional diversity. Here, we first generalize the Good‐Turing formula to incomplete sampling of two assemblages. The original formula and its two‐assemblage generalization provide a novel and unified approach to notation, terminology, and estimation of undetected biological diversity. For species richness, the Good‐Turing framework offers an intuitive way to derive the non‐parametric estimators of the undetected species richness in a single assemblage, and of the undetected species shared between two assemblages. For phylogenetic diversity, the unified approach leads to an estimator of the undetected Faith's phylogenetic diversity ( PD , the total length of undetected branches of a phylogenetic tree connecting all species), as well as a new estimator of undetected PD shared between two phylogenetic trees. For functional diversity based on species traits, the unified approach yields a new estimator of undetected Walker et al.'s functional attribute diversity ( FAD , the total species‐pairwise functional distance) in a single assemblage, as well as a new estimator of undetected FAD shared between two assemblages. Although some of the resulting estimators have been previously published (but derived with traditional mathematical inequalities), all taxonomic, phylogenetic, and functional diversity estimators are now derived under the same framework. All the derived estimators are theoretically lower bounds of the corresponding undetected diversities; our approach reveals the sufficient conditions under which the estimators are nearly unbiased, thus offering new insights. Simulation results are reported to numerically verify the performance of the derived estimators. We illustrate all estimators and assess their sampling uncertainty with an empirical dataset for Brazilian rain forest trees. These estimators should be widely applicable to many current problems in ecology, such as the effects of climate change on spatial and temporal beta diversity and the contribution of trait diversity to ecosystem multi‐functionality.

Midpoint attractors and species richness: Modelling the interaction between environmental drivers and geometric constraints - Robert K. Colwell; Nicholas J. Gotelli; Louise A. Ashton; Jan Beck; Gunnar Brehm; Tom M. Fayle; Konrad Fiedler; Matthew L. Forister; Michael Kessler; Roger L. Kitching; Petr Klimes; Jürgen Kluge; et al. (Ecology Letters, 2016)
We introduce a novel framework for conceptualising, quantifying and unifying discordant patterns of species richness along geographical gradients. While not itself explicitly mechanistic, this approach offers a path towards understanding mechanisms. In this study, we focused on the diverse patterns of species richness on mountainsides. We conjectured that elevational range midpoints of species may be drawn towards a single midpoint attractor – a unimodal gradient of environmental favourability. The midpoint attractor interacts with geometric constraints imposed by sea level and the mountaintop to produce taxon‐specific patterns of species richness. We developed a Bayesian simulation model to estimate the location and strength of the midpoint attractor from species occurrence data sampled along mountainsides. We also constructed midpoint predictor models to test whether environmental variables could directly account for the observed patterns of species range midpoints. We challenged these models with 16 elevational data sets, comprising 4500 species of insects, vertebrates and plants. The midpoint predictor models generally failed to predict the pattern of species midpoints. In contrast, the midpoint attractor model closely reproduced empirical spatial patterns of species richness and range midpoints. Gradients of environmental favourability, subject to geometric constraints, may parsimoniously account for elevational and other patterns of species richness.

The 10th Antarctic meteorological observation, modeling, and forecasting workshop - Steve R. Colwell; Arthur M. Cayette; Matthew A. Lazzara; Jordan G. Powers; David H. Bromwich; John J. Cassano; Scott Carpentier (Advances in Atmospheric Sciences, 2016)

Unveiling the species‐rank abundance distribution by generalizing the Good‐Turing sample coverage theory - Anne Chao; T. C. Hsieh; Robin L. Chazdon; Robert K. Colwell; Nicholas J. Gotelli (Ecology, 2015)
Based on a sample of individuals, we focus on inferring the vector of species relative abundance of an entire assemblage and propose a novel estimator of the complete species‐rank abundance distribution (RAD). Nearly all previous estimators of the RAD use the conventional “plug‐in” estimator p̂ i (sample relative abundance) of the true relative abundance p i of species i . Because most biodiversity samples are incomplete, the plug‐in estimators are applied only to the subset of species that are detected in the sample. Using the concept of sample coverage and its generalization, we propose a new statistical framework to estimate the complete RAD by separately adjusting the sample relative abundances for the set of species detected in the sample and estimating the relative abundances for the set of species undetected in the sample but inferred to be present in the assemblage. We first show that p̂ i is a positively biased estimator of p i for species detected in the sample, and that the degree of bias increases with increasing relative rarity of each species. We next derive a method to adjust the sample relative abundance to reduce the positive bias inherent in p̂ i . The adjustment method provides a nonparametric resolution to the longstanding challenge of characterizing the relationship between the true relative abundance in the entire assemblage and the observed relative abundance in a sample. Finally, we propose a method to estimate the true relative abundances of the undetected species based on a lower bound of the number of undetected species. We then combine the adjusted RAD for the detected species and the estimated RAD for the undetected species to obtain the complete RAD estimator. Simulation results show that the proposed RAD curve can unveil the true RAD and is more accurate than the empirical RAD. We also extend our method to incidence data. Our formulas and estimators are illustrated using empirical data sets from surveys of forest spiders (for abundance data) and soil ciliates (for incidence data). The proposed RAD estimator is also applicable to estimating various diversity measures and should be widely useful to analyses of biodiversity and community structure.

Specimen‐Based Modeling, Stopping Rules, and the Extinction of the Ivory‐Billed Woodpecker - NICHOLAS J. GOTELLI; ANNE CHAO; ROBERT K. COLWELL; WEN‐HAN HWANG; GARY R. GRAVES (Conservation Biology, 2012)
Assessing species survival status is an essential component of conservation programs. We devised a new statistical method for estimating the probability of species persistence from the temporal sequence of collection dates of museum specimens. To complement this approach, we developed quantitative stopping rules for terminating the search for missing or allegedly extinct species. These stopping rules are based on survey data for counts of co‐occurring species that are encountered in the search for a target species. We illustrate both these methods with a case study of the Ivory‐billed Woodpecker ( Campephilus principalis ), long assumed to have become extinct in the United States in the 1950s, but reportedly rediscovered in 2004. We analyzed the temporal pattern of the collection dates of 239 geo‐referenced museum specimens collected throughout the southeastern United States from 1853 to 1932 and estimated the probability of persistence in 2011 as −5 , with a probable extinction date no later than 1980. From an analysis of avian census data (counts of individuals) at 4 sites where searches for the woodpecker were conducted since 2004, we estimated that at most 1–3 undetected species may remain in 3 sites (one each in Louisiana, Mississippi, Florida). At a fourth site on the Congaree River (South Carolina), no singletons (species represented by one observation) remained after 15,500 counts of individual birds, indicating that the number of species already recorded (56) is unlikely to increase with additional survey effort. Collectively, these results suggest there is virtually no chance the Ivory‐billed Woodpecker is currently extant within its historical range in the southeastern United States. The results also suggest conservation resources devoted to its rediscovery and recovery could be better allocated to other species. The methods we describe for estimating species extinction dates and the probability of persistence are generally applicable to other species for which sufficient museum collections and field census results are available.

Proteomic and targeted qPCR analyses of subsurface microbial communities for presence of methane monooxygenase - Andrzej J. Paszczynski; Ravindra Paidisetti; Andrew K. Johnson; Ronald L. Crawford; Frederick S. Colwell; Tonia Green; Mark Delwiche; Hope Lee; Deborah Newby; Eoin L. Brodie; Mark Conrad (Biodegradation, 2011)

A novel statistical method for classifying habitat generalists and specialists - Robin L. Chazdon; Anne Chao; Robert K. Colwell; Shang-Yi Lin; Natalia Norden; Susan G. Letcher; David B. Clark; Bryan Finegan; J. Pablo Arroyo (Ecology, 2011)
We develop a novel statistical approach for classifying generalists and specialists in two distinct habitats. Using a multinomial model based on estimated species relative abundance in two habitats, our method minimizes bias due to differences in sampling intensities between two habitat types as well as bias due to insufficient sampling within each habitat. The method permits a robust statistical classification of habitat specialists and generalists, without excluding rare species a priori. Based on a user‐defined specialization threshold, the model classifies species into one of four groups: (1) generalist; (2) habitat A specialist; (3) habitat B specialist; and (4) too rare to classify with confidence. We illustrate our multinomial classification method using two contrasting data sets: (1) bird abundance in woodland and heath habitats in southeastern Australia and (2) tree abundance in second‐growth (SG) and old‐growth (OG) rain forests in the Caribbean lowlands of northeastern Costa Rica. We evaluate the multinomial model in detail for the tree data set. Our results for birds were highly concordant with a previous nonstatistical classification, but our method classified a higher fraction (57.7%) of bird species with statistical confidence. Based on a conservative specialization threshold and adjustment for multiple comparisons, 64.4% of tree species in the full sample were too rare to classify with confidence. Among the species classified, OG specialists constituted the largest class (40.6%), followed by generalist tree species (36.7%) and SG specialists (22.7%). The multinomial model was more sensitive than indicator value analysis or abundance‐based phi coefficient indices in detecting habitat specialists and also detects generalists statistically. Classification of specialists and generalists based on rarefied subsamples was highly consistent with classification based on the full sample, even for sampling percentages as low as 20%. Major advantages of the new method are (1) its ability to distinguish habitat generalists (species with no significant habitat affinity) from species that are simply too rare to classify and (2) applicability to a single representative sample or a single pooled set of representative samples from each of two habitat types. The method as currently developed can be applied to no more than two habitats at a time.

Ion microprobe (SHRIMP) U–Pb dating of Upper Cretaceous volcanics from the northern Lord Howe Rise, Tasman Sea - K. Higgins; T. Hashimoto; G. Fraser; N. Rollet; J. Colwell (Australian Journal of Earth Sciences, 2011)

Patterns and causes of species richness: a general simulation model for macroecology - Nicholas J. Gotelli; Marti J. Anderson; Hector T. Arita; Anne Chao; Robert K. Colwell; Sean R. Connolly; David J. Currie; Robert R. Dunn; Gary R. Graves; Jessica L. Green; John‐Arvid Grytnes; Yi‐Huei Jiang; et al. (Ecology Letters, 2009)
Understanding the causes of spatial variation in species richness is a major research focus of biogeography and macroecology. Gridded environmental data and species richness maps have been used in increasingly sophisticated curve‐fitting analyses, but these methods have not brought us much closer to a mechanistic understanding of the patterns. During the past two decades, macroecologists have successfully addressed technical problems posed by spatial autocorrelation, intercorrelation of predictor variables and non‐linearity. However, curve‐fitting approaches are problematic because most theoretical models in macroecology do not make quantitative predictions, and they do not incorporate interactions among multiple forces. As an alternative, we propose a mechanistic modelling approach. We describe computer simulation models of the stochastic origin, spread, and extinction of species’ geographical ranges in an environmentally heterogeneous, gridded domain and describe progress to date regarding their implementation. The output from such a general simulation model (GSM) would, at a minimum, consist of the simulated distribution of species ranges on a map, yielding the predicted number of species in each grid cell of the domain. In contrast to curve‐fitting analysis, simulation modelling explicitly incorporates the processes believed to be affecting the geographical ranges of species and generates a number of quantitative predictions that can be compared to empirical patterns. We describe three of the ‘control knobs’ for a GSM that specify simple rules for dispersal, evolutionary origins and environmental gradients. Binary combinations of different knob settings correspond to eight distinct simulation models, five of which are already represented in the literature of macroecology. The output from such a GSM will include the predicted species richness per grid cell, the range size frequency distribution, the simulated phylogeny and simulated geographical ranges of the component species, all of which can be compared to empirical patterns. Challenges to the development of the GSM include the measurement of goodness of fit (GOF) between observed data and model predictions, as well as the estimation, optimization and interpretation of the model parameters. The simulation approach offers new insights into the origin and maintenance of species richness patterns, and may provide a common framework for investigating the effects of contemporary climate, evolutionary history and geometric constraints on global biodiversity gradients. With further development, the GSM has the potential to provide a conceptual bridge between macroecology and historical biogeography.

Sufficient sampling for asymptotic minimum species richness estimators - Anne Chao; Robert K. Colwell; Chih-Wei Lin; Nicholas J. Gotelli (Ecology, 2009)
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 ≈ 2.23). Substantially less effort is needed to detect 90% of the species (0.33–1.10 times the original effort; median ≈ 0.80). An Excel spreadsheet tool is provided for calculating necessary sampling effort for either abundance data or replicated incidence data.

Architecture of volcanic rifted margins: new insights from the Exmouth – Gascoyne margin, Western Australia - N. G. Direen; H. M. J. Stagg; P. A. Symonds; J. B. Colwell (Australian Journal of Earth Sciences, 2008)

Molecular assessment of the genetic integrity, distinctiveness and phylogeographic context of the Saltwater crocodile (Crocodylus porosus) on Palau - M. A. Russello; P. Brazaitis; J. Gratten; G. J. Watkins-Colwell; A. Caccone (Conservation Genetics, 2007)

Antarctic climate change during the last 50 years - John Turner; Steve R. Colwell; Gareth J. Marshall; Tom A. Lachlan-Cope; Andrew M. Carleton; Phil D. Jones; Victor Lagun; Phil A. Reid; Svetlana Iagovkina (International Journal of Climatology, 2005)

The near‐surface wind field over the Antarctic continent - N. P. M. van Lipzig; J. Turner; S. R. Colwell; M. R. van Den Broeke (International Journal of Climatology, 2004)
A 14 year integration with a regional atmospheric model has been used to determine the near‐surface climatological wind field over the Antarctic ice sheet at a horizontal grid spacing of 55 km. Previous maps of the near‐surface wind field were generally based on models ignoring the large‐scale pressure‐gradient forcing term in the momentum equation. Presently, state‐of‐the‐art atmospheric models include all pressure‐gradient forcing terms. Evaluation of our model output against in situ data shows that the model is able to represent realistically the observed increase in wind speed going from the interior to the coast, as well as the observed wind direction at South Pole and Dumont d'Urville and the bimodal wind distribution at Halley. Copyright © 2004 Royal Meteorological Society

Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness - Nicholas J. Gotelli; Robert K. Colwell (Ecology Letters, 2001)
Species richness is a fundamental measurement of community and regional diversity, and it underlies many ecological models and conservation strategies. In spite of its importance, ecologists have not always appreciated the effects of abundance and sampling effort on richness measures and comparisons. We survey a series of common pitfalls in quantifying and comparing taxon richness. These pitfalls can be largely avoided by using accumulation and rarefaction curves, which may be based on either individuals or samples. These taxon sampling curves contain the basic information for valid richness comparisons, including category–subcategory ratios (species‐to‐genus and species‐to‐individual ratios). Rarefaction methods – both sample‐based and individual‐based – allow for meaningful standardization and comparison of datasets. Standardizing data sets by area or sampling effort may produce very different results compared to standardizing by number of individuals collected, and it is not always clear which measure of diversity is more appropriate. Asymptotic richness estimators provide lower‐bound estimates for taxon‐rich groups such as tropical arthropods, in which observed richness rarely reaches an asymptote, despite intensive sampling. Recent examples of diversity studies of tropical trees, stream invertebrates, and herbaceous plants emphasize the importance of carefully quantifying species richness using taxon sampling curves.

Plant Finding and Acceptance Behaviors of Anasa tristis (DeGeer) - Colwell A. Cook; Jonathan J. Neal (Journal of Insect Behavior, 1999)

Benthic invertebrates at foraging locations of nine waterbird species in managed wetlands of the northern San Joaquin Valley, California - Rebecca J. Safran; Craig R. Isola; Mark A. Colwell; Oriane E. Williams (Wetlands, 1997)

Factors regulating annual mating success and reproductive success in spotted sandpipers (Actitis macularia) - Lewis W. Oring; J. Michael Reed; Mark A. Colwell; David B. Lank; Stephen J. Maxson (Behavioral Ecology and Sociobiology, 1991)

Lifetime reproductive success in the spotted sandpiper (Actitis macularia) : sex differences and variance components - Lewis W. Oring; Mark A. Colwell; J. Michael Reed (Behavioral Ecology and Sociobiology, 1991)