Heavy-weighting rare species in dissimilarity indices improve recovery of multivariate groups

Dissimilarity indices differ in the relative weight given to rare species. Heavy-weighting of rare species may be justified in terms of sampling. An index may erroneously estimate high dissimilarity between two identical communities if they are composed of many rare species and the sampling effort is insufficient to observe most of them in both samples. Heavy-weighting of rare species is thought to compensate for this negative bias. I evaluated two quantitative indices that heavy-weight rare species, NNESS (New Normalized Expected Species Shared) and Goodall, and two probability versions of the Sørensen index, one that takes into account shared unseen rare species and the other that does not. They were compared against the widely used Bray-Curtis (or Sørensen quantitative) and the Morisita-Horn. Indices were computed using raw abundance data or coded data that heavy-weight rare species (frequency in sample units, log-transformation and standardization by the maximum abundance within species). Indices were evaluated for their ability to distinguish, using distance-based MANOVA, season-defined (summer, winter) groups of samples of stream macroinvertebrates and groups of samples obtained by simulation. Sørensen corrected for unseen shared species performed poorly in the empirical study and intermediate in the simulations. NNESS was good in the empirical study and intermediate in the simulations. Goodall scored inversely as NNESS, being intermediate in the empirical assessment and very good in the simulations. The Sørensen uncorrected for unseen shared species, Bray-Curtis and the Morisita-Horn presented poor or intermediate results using raw abundance data. Their performance, however, improved consistently using coded data that heavy-weight rare species and made them good or very good. I conclude that heavy-weighting rare species improves the ability to detect multivariate groups. Heavy-weighting of rare species may be achieved either by using specific formulae (NNESS, Goodall) or using coded data.     

A statistical approach to estimate soil ciliate diversity and distribution based on data from five continents

A total of 359 soil samples collected from five continents (Africa, Asia, Australia, Europe and South America) were investigated for the presence/absence of soil ciliate species. Merging records by species identity, we have compiled a master data list (species by sample matrix). In the list, a total of 964 soil ciliate species (644 described and 320 undescribed) are recorded. The species distributions within the 359 samples and across the five continents are examined. The frequency distribution of the species over samples is used for global diversity estimation. A statistical ACE (abundance‐based coverage estimation) model which links observed data to unseen species is applied to assess regional and global soil ciliate species richness. The model, whose reliability was tested by its power to predict the number of new species in additional samples from Africa, may resolve the controversial issue on global species diversity of soil ciliates. Although an accurate point estimate is not feasible due to severe undersampling, the statistical model enables us to obtain a minimum regional diversity and global species diversity. A consistent finding over all five continents is that at least half of the species diversity is still undiscovered. Our model also yields a global soil ciliate diversity of at least 1900 species, that is, doubles the number of currently known species, and thus diversity is relatively high. This is consistent with the finding of Foissner, who used a probability‐based method. Soil ciliate distributions between continent pairs are analyzed by adjusted abundance‐based similarity/overlap indices. These new indices account for the effect of unseen species and also reduce the bias generated by undersampling. The adjusted abundance‐based Jaccard (or Sørensen) index shows that there is about 30% (18% for Sørensen) dis‐similarity between any two continents, supporting the moderate endemicity model. The results are discussed with respect to protist species distribution, that is, whether they are cosmopolitan or of restricted distribution.     

Estimating similarity of communities: a parametric approach to spatio‐temporal analysis of species diversity

Several stochastic models with environmental noise generate spatio‐temporal Gaussian fields of log densities for the species in a community. Combinations of such models for many species often lead to lognormal species abundance distributions. In spatio‐temporal analysis it is often realistic to assume that the same species are expected to occur at different times and/or locations because extinctions are rare events. Spatial and temporal β‐diversity can then be analyzed by studying pairs of communities at different times or locations defined by a bivariate lognormal species abundance model in which a single correlation occurs. This correlation, which is a measure of similarity between two communities, can be estimated from samples even if the sampling intensities vary and are unknown, using the bivariate Poisson lognormal distribution. The estimators are approximately unbiased, although each specific correlation may be rather uncertain when the sampling effort is low with only a small fraction of the species represented in the samples. An important characteristic of this community correlation is that it relates to the classical Jaccard‐ or the Sørensen‐indices of similarity based on the number of species present or absent in two communities. However, these indices calculated from samples of species in a community do not necessarily reflect similarity of the communities because the observed number of species depends strongly on the sampling intensities. Thus, we propose that our community correlation should be considered as an alternative to these indices when comparing similarity of communities. We illustrate the application of the correlation method by computing the similarity between temperate bird communities.     

Subsampling Herbarium Collections to Assess Geographic Diversity Gradients: A Case Study with Endemic Orchidaceae and Rubiaceae in Cameroon

We compiled herbarium specimen data to provide an improved characterization of geographic patterns of diversity using indices of species diversity and floristic similarity based on rarefaction principles. A dataset of 3650 georeferenced plant specimens belonging to Orchidaceae and Rubiaceae endemic to Atlantic Central Africa was assembled to assess species composition per half‐degree or one‐degree grid cells. Local diversity was measured by the expected number of species (S_k) per grid cell found in subsamples of increasing size and compared with raw species richness (S_R). A nearly unbiased estimator of the effective number of species per grid cell was also used, allowing quantification of ratios of ‘true diversity’ between grid cells. Species turnover was measured using a presence/absence‐based similarity index (Sørensen) and an abundance‐based index that corrects for sampling bias (NNESS). Our results confirm that the coastal region of Cameroon is more diverse in endemic species than those more inland. The southern part of this coastal forest is, however, as diverse as the more intensively inventoried northern part, and should also be recognized as an important center of endemism. A strong congruence between Sørensen and NNESS similarity matrices lead to similar delimitations of floristic units. Hence, heterogeneous sampling seems to confer more bias when measuring patterns of local diversity using raw species richness than species turnover using Sørensen index. Overall, we argue that subsampling methods represent a useful way to assess diversity gradients using herbarium specimens while correcting for heterogeneous sampling effort. Abstract in French is available in the online version of this article.     

Multisite and multispecies measures of overlap,co‐occurrence,and co‐diversity

Several indices measure the association or segregation between two species and the similarity or differentiation between two sets of species. These indices are based on the overlap in the distribution of species (measured with the number of co‐occurrences) or on the overlap in species composition of sites (measured with the number of species that are shared between two sites). This paper shows that when evaluating more than two species the number of overlaps and the number of pairwise co‐occurrences are not equal, as it is the case for two species. Equivalently, when comparing more than two species assemblages, the number of overlaps differ from the number of instances of species sharing by pairs of sites (the ‘co‐diversities’). Considering this distinction, two different types of multispecies and multisite indices can be derived: indices of general overlap and indices of co‐occurrence or co‐diversity. Here I present a complete series of the two types of indices that correspond to the popular Jaccard, Sørensen, and Simpson two‐species or two‐site indices. Indices of general overlap are defined by three parameters (the total number of species, the total number of sites, and the total number of occurrences), whereas indices of co‐occurrence or co‐diversity depend on those parameters plus an additional one that is defined by the values of species richness or range size. Consequently, the two types of indices respond differently to null models, depending on the parameters that are fixed or randomized. All indices correlate well with the mean of the traditional indices calculated pair by pair, and the correspondence is extremely close for the new indices of co‐occurrence and co‐diversity. These properties should be useful in clarifying some of the confusion that exists in the current discussion about the advantages and disadvantages of pairwise vs community‐wide approaches in the analysis of diversity.     

A two-stage probabilistic approach to multiple-community similarity indices

SUMMARY: A traditional approach for assessing similarity among N (N > 2) communities is to use multiple pairwise comparisons. However, pairwise similarity indices do not completely characterize multiple-community similarity because the information shared by at least three communities is ignored. We propose a new and intuitive two-stage probabilistic approach, which leads to a general framework to simultaneously compare multiple communities based on abundance data. The approach is specifically used to extend the commonly used Morisita index and NESS (normalized expected species shared) index to the case of N communities. For comparing N communities, a profile of N- 1 indices is proposed to characterize similarity of species composition across communities. Based on sample abundance data, nearly unbiased estimators of the proposed indices and their variances are obtained. These generalized NESS and Morisita indices are applied to comparison of three size classes of plant data (seedling, saplings, and trees) within old-growth and secondary rain forest plots in Costa Rica.     

Cost‐efficiency of Subsampling Protocols to Evaluate Oribatid‐Mite Communities in an Amazonian Savanna

Sampling oribatid mites in large areas using conventional methods is expensive, time‐consuming, and this constrains their use in environmental monitoring programs. We used samples collected in 38 plots of 3.75 ha spread over 30,000 ha in an Amazonian savanna to evaluate the reduction in costs and person‐hours in sampling and sorting and to elaborate cost‐effective protocols. Ten samples per plot were collected and extracted using a Berlese‐Tullgren apparatus. In the laboratory, samples were reduced to 50, 25, 12.5, and 6.25 percent of the initial content. Field‐effort reduction was estimated by reducing the number of subsamples per plot. Dissimilarity matrices were generated using Bray–Curtis, Sørensen, and Chao–Sørensen indices. Correlations between each reduced‐effort dissimilarity matrix and 100 or 50 percent sorting were used as an index of how much information was retained in reduced‐effort sampling, and could still be used in multivariate analyses. The effects of most predictor variables on mite composition were detected in data based on every level of sample reduction. The intensive sampling was insufficient to reveal the full oribatid‐mite fauna in the savanna; as more plots were sampled, more species were recorded. Our data indicate subsampling protocols for biodiversity assessment of oribatid mites in savanna that increase field and laboratory efficiency, and optimize both taxonomic and ecological aspects of the investigation.     

Nonparametric Lower Bounds for Species Richness and Shared Species Richness under Sampling without Replacement

Summary A number of species richness estimators have been developed under the model that individuals (or sampling units) are sampled with replacement. However, if sampling is done without replacement so that no sampled unit can be repeatedly observed, then the traditional estimators for sampling with replacement tend to overestimate richness for relatively high-sampling fractions (ratio of sample size to the total number of sampling units) and do not converge to the true species richness when the sampling fraction approaches one. Based on abundance data or replicated incidence data, we propose a nonparametric lower bound for species richness in a single community and also a lower bound for the number of species shared by multiple communities. Our proposed lower bounds are derived under very general sampling models. They are universally valid for all types of species abundance distributions and species detection probabilities. For abundance data, individuals' detectabilities are allowed to be heterogeneous among species. For replicated incidence data, the selected sampling units (e.g., quadrats) need not be fully censused and species can be spatially aggregated. All bounds converge correctly to the true parameters when the sampling fraction approaches one. Real data sets are used for illustration. We also test the proposed bounds by using subsamples generated from large real surveys or censuses, and their performance is compared with that of some previous estimators.     

Effects of habitat age and disturbance intensity on the biodiversity of three trophic levels in Central Kenya

In recent decades, the extent of primary forest in tropical regions has decreased drastically, with concurrent increases in the extent of tropical secondary forest. This has important implications for conservation management. We present novel data on species diversity and composition for three taxa (bats, geometrid moths and plants) in forests at two stages of secondary growth located in the Aberdare Mountains in Central Kenya. We found no significant differences in alpha diversity for any of the sampled groups between forest types. However, we found disturbance‐driven differences of tree and herb community compositions and correlations between tree and moth – and tree and shrub community compositions. Changes in community compositions were more pronounced using an abundance‐based indicator (Bray–Curtis) in comparison with an incidence based (Sørensen). Our results demonstrate that solely working with alpha diversity values can be misleading in conservation planning as they might not reflect compositional changes between habitats. Furthermore, abundance‐based compositional measures appear to be superior to incidence‐based measures for detecting subtle effects of disturbance on biodiversity.     

Abundance-based similarity indices and their estimation when there are unseen species in samples

A wide variety of similarity indices for comparing two assemblages based on species incidence (i.e., presence/absence) data have been proposed in the literature. These indices are generally based on three simple incidence counts: the number of species shared by two assemblages and the number of species unique to each of them. We provide a new probabilistic derivation for any incidence-based index that is symmetric (i.e., the index is not affected by the identity ordering of the two assemblages) and homogeneous (i.e., the index is unchanged if all counts are multiplied by a constant). The probabilistic approach is further extended to formulate abundance-based indices. Thus any symmetric and homogeneous incidence index can be easily modified to an abundance-type version. Applying the Laplace approximation formulas, we propose estimators that adjust for the effect of unseen shared species on our abundance-based indices. Simulation results show that the adjusted estimators significantly reduce the biases of the corresponding unadjusted ones when a substantial fraction of species is missing from samples. Data on successional vegetation in six tropical forests are used for illustration. Advantages and disadvantages of some commonly applied indices are briefly discussed.     

Seedling regeneration,environment and management in a semi‐deciduous African tropical rain forest

Questions: How is seedling regeneration of woody species of semi‐deciduous rain forests affected by (a) historical management for combinations of logging, arboricide treatment or no treatment, (b) forest community type and (c) environmental gradients of topography, light and soil nutrients? Location: Budongo Forest Reserve, Uganda. Methods: Seedling regeneration patterns of trees and shrubs in relation to environmental factors and historical management types were studied using 32 0.5‐ha plots laid out in transects along a topographic gradient. We compared seedling species diversity, composition and distribution patterns along topographic gradients and within types of historical management regimes and forest communities to test whether environmental factors contributed to differences in species composition of seedlings. Results: A total of 85 624 woody seedlings representing 237 species and 46 families were recorded in this rain forest. Cynometra alexandri C.H. Wright and Lasiodiscus mildbraedii Engl. had high seedling densities and were widely distributed throughout the plots. The most species‐rich families were Euphorbiaceae, Fabaceae, Rubiaceae, Meliaceae, Moraceae and Rutaceae. Only total seedling density was significantly different between sites with different historical management, with densities highest in logged, intermediate in logged/arboricided and lowest in the nature reserve. Forest communities differed significantly in terms of seedling diversity and density. Seedling composition differed significantly between transects and forest communities, but not between topographic positions or historical management types. Both Chao‐Jaccard and Chao‐Sørensen abundance‐based similarity estimators were relatively high in the plot, forest community and in terms of historical management levels, corroborating the lack of significant differences in species richness within these groups. The measured environmental variables explained 59.4% of variance in seedling species distributions, with the three most important being soil organic matter, total soil titanium and leaf area index (LAI). Total seedling density was positively correlated with LAI. Differences in diversity of >2.0 cm dbh plants (juveniles and adults) also explained variations in seedling species diversity. Conclusions: The seedling bank is the major route for regeneration in this semi‐deciduous tropical rain forest, with the wide distribution of many species suggesting that these species regenerate continuously. Seedling diversity, density and distribution are largely a function of adult diversity, historical management type and environmental gradients in factors such as soil nutrient content and LAI. The species richness of seedlings was higher in soils both rich in titanium and with low exchangeable cations, as well as in logged areas that were more open and had a low LAI.     

Modified TWINSPAN classification in which the hierarchy respects cluster heterogeneity

Aim: To propose a modification of the TWINSPAN algorithm that enables production of divisive classifications that better respect the structure of the data. Methods: The proposed modification combines the classical TWINSPAN algorithm with analysis of heterogeneity of the clusters prior to each division. Four different heterogeneity measures are involved: Whittaker's beta, total inertia, average Sørensen dissimilarity and average Jaccard dissimilarity. Their performance was evaluated using empirical vegetation datasets with different numbers of plots and different levels of heterogeneity. Results: While the classical TWINSPAN algorithm divides each cluster coming from the previous division step, the modified algorithm divides only the most heterogeneous cluster in each step. The four tested heterogeneity measures may produce identical or very similar results. However, average Jaccard and Sørensen dissimilarities may reach extreme values in clusters of small size and may produce classifications with a highly unbalanced cluster size. Conclusions: The proposed modification does not alter the logic of the TWINSPAN classification, but it may change the hierarchy of divisions in the final classification. Thus, unsubstantiated divisions of homogeneous clusters are prevented, and classifications with any number of terminal clusters can be created, which increases the flexibility of TWINSPAN.     

Optimizing performance of nonparametric species richness estimators under constrained sampling

Understanding the functional relationship between the sample size and the performance of species richness estimators is necessary to optimize limited sampling resources against estimation error. Nonparametric estimators such as Chao and Jackknife demonstrate strong performances, but consensus is lacking as to which estimator performs better under constrained sampling. We explore a method to improve the estimators under such scenario. The method we propose involves randomly splitting species‐abundance data from a single sample into two equally sized samples, and using an appropriate incidence‐based estimator to estimate richness. To test this method, we assume a lognormal species‐abundance distribution (SAD) with varying coefficients of variation (CV), generate samples using MCMC simulations, and use the expected mean‐squared error as the performance criterion of the estimators. We test this method for Chao, Jackknife, ICE, and ACE estimators. Between abundance‐based estimators with the single sample, and incidence‐based estimators with the split‐in‐two samples, Chao2 performed the best when CV < 0.65, and incidence‐based Jackknife performed the best when CV > 0.65, given that the ratio of sample size to observed species richness is greater than a critical value given by a power function of CV with respect to abundance of the sampled population. The proposed method increases the performance of the estimators substantially and is more effective when more rare species are in an assemblage. We also show that the splitting method works qualitatively similarly well when the SADs are log series, geometric series, and negative binomial. We demonstrate an application of the proposed method by estimating richness of zooplankton communities in samples of ballast water. The proposed splitting method is an alternative to sampling a large number of individuals to increase the accuracy of richness estimations; therefore, it is appropriate for a wide range of resource‐limited sampling scenarios in ecology.     

Choosing the best similarity index when performing fuzzy set ordination on binary data

Abstract. Fuzzy set ordination (FSO) may be used with either abundance data or binary (presence/absence) data. FSO requires a similarity index that returns values between 0 and 1. Many indices will do so, but their suitability for FSO has not been tested. Nine binary indices were evaluated in this study. Simulated plant community data sets were generated with COMPAS; they contained five levels of β‐diversity, two levels of qualitative noise, and two sampling arrangements (regular or random) along one gradient. Indices were evaluated with rank and linear correlations between the apparent ecological gradient positions generated by FSO and actual gradient positions; the abilities of the best‐performing indices to minimize the curlover effect were also compared. All indices performed best at intermediate levels of β‐diversity and with regular sampling. Five indices had consistently higher rank and linear correlations (Baroni‐Urbani & Buser, Jaccard, Kulczynski, Ochiai and Sørensen), whereas four were consistently lower (Faith, Russell & Rao, Rogers & Tanimoto and Simple Matching). There were no significant differences in curlover among the five best indices. A step‐across algorithm, a flexible shortest path adjustment, improved correlations and reduced curlover for the five best indices at higher β‐diversity levels. We recommend that one of the five best‐performing similarity indices be used with FSO on binary data; a flexible shortest path adjustment should also be employed at higher β‐diversities when possible.     

Measuring fractions of beta diversity and their relationships to nestedness: a theoretical and empirical comparison of novel approaches

Beta diversity and nestedness are central concepts of ecology and biogeography and evaluation of their relationships is in the focus of contemporary ecological and conservation research. Beta diversity patterns are originated from two distinct processes: the replacement (or turnover) of species and the loss (or gain) of species leading to richness differences. Nested distributional patterns are generally thought to have a component deriving from beta diversity which is independent of replacement processes. Quantification of these phenomena is often made by calculating a measure of beta diversity, and the resulting value being subsequently partitioned into a contribution by species replacement plus a fraction shared by beta diversity and nestedness. Three methods have been recently proposed for such partitioning, all of them based on pairwise comparisons of sites. In this paper, the performance of these methods was evaluated on theoretical grounds and tested by a simulation study in which different gradients of dissimilarity, with known degrees of species replacement and species loss, were created. Performance was also tested using empirical data addressing land‐use induced changes in endemic arthropod communities of the Terceira Island in the Azores. We found that the partitioning of β_cc (dissimilarity in terms of the Jaccard index) into two additive fractions, β_‐3 (dissimilarity due to species replacement) plus β_rich (dissimilarity due to richness differences) reflects the species replacement and species loss processes across the simulated gradients in an ecologically and mathematically meaningful way, whilst the other two methods lack mathematical consistency and prove conceptually self‐contradictory. Moreover, the first method identified a selective local extinction process for endemic arthropods, triggered by land‐use changes, while the latter two methods overweighted the replacement component and led to false conclusions. Their basic flaw derives from the fact that the proposed replacement and nestedness components (deemed to account for species loss) are not scaled in the same way as the measure that accounts for the total dissimilarity (Sørensen and Jaccard indices). We therefore recommend the use of β_cc=β_‐3+β_rich, since its components are scaled in the same units and their responses are proportional to the replacement and the gain/loss of species.     

Diversity,floristic and structural patterns of cerrado vegetation in Central Brazil

The cerrado has been identified as one of the richest and most threatened biomes of the world, but few phytogeographical studies have been undertaken in the region. A total of 70 land systems based on climate, landscape and soils have been identified in the region, but it remains to be seen if the distribution and structure of the plant communities support these divisions. The aim of this work was to compare the floristic and structural similarity of cerrado sensu stricto within and between three physiographic units, named Pratinha, Veadeiros and São Francisco, which contain six land systems in central Brazil and cover 10 degrees of latitude and five degrees of longitude. The woody vegetation of 15 selected sites of the cerrado sensu stricto physiognomy was surveyed under a standardized methodology. The number of species per site varied from 55 to 97, with most sites having around 60 to 70 species, and Shannon´s diversity indices ranged from 3.44 to 3.73, with most sites around 3.5 suggesting high alpha diversity. Sørensen´s floristic similarity index was high, with all Figures above 0.5 between the sites in the same land system in each physiographic unit but low between sites in different land systems in the Veadeiros. Czekanowski similarity indices were lower than Sørensen’s in the comparisons due to a high structural differentiation between the sites. There is a large overlap in species occurrence in the sites but the size of their populations is very different at each site. Therefore, the high beta diversity is mostly due to differences in abundance of species between sites. The sites were separated by physiographic units, considering the first three divisions of TWINSPAN classification. The first axis of DCA ordination showed a gradient going from the cerrado on deep soils in Pratinha, through to those on sandy soils in São Francisco and ending on the shallower soils of the Veadeiros. Land systems conformed well with the floristic and structural variations of the vegetation, indicating their potential use in designing a network of conservation areas in the cerrado region and as a basis for decision-making on management.     

Diatoms as indicators: The influences of experimental nitrogen enrichment on diatom assemblages in sub-Arctic streams

Indices using diatoms are widely used to assess water quality, but are usually constructed from field correlations and not tested through rigorous experimentation. We tested experimentally the performance of the Sørensen and the Shannon indices, and the trophic diatom index (TDI). Nitrogen was naturally limiting in the eight remote sub-Arctic streams used and we measured the effects of experimental nitrogen enrichment on diatom assemblages. Diatom densities increased significantly in the enriched reaches but there was no significant difference in invertebrate density between control and treatment reaches. Grazing effects were thus controlled for. Diversity within streams (Shannon index) was significantly reduced by nutrient addition but the Sørensen index did not change. The trophic diatom index (TDI), which is presumed to reflect nutrient concentration, was not influenced by nutrient addition and generally the values were low in both control and treatment reaches. Densities of the diatom genera Achnanthes and Gomphonema increased significantly with enrichment while those of Nitzschia and Fragilaria decreased significantly. Less abundant diatom species, which collectively constituted around 40% in relative abundance in the control reaches, were around 15–18% in treatment reaches. Growth forms were altered by the nutrients. Diatoms attached by mucilage pads were more abundant in treated reaches compared with control reaches. Motile diatoms became scarcer. The size of diatom species was unaffected by nutrient enrichment. This study showed that it is important to test experimentally indices that are developed for particular habitats before using them elsewhere.     

Limits to the estimation of species richness: The use of relative abundance distributions

The present study demonstrates the possibility of estimating species numbers of animal or plant communities from samples using relative abundance distributions. We use log‐abundance–species‐rank order plots and derive two new estimators that are based on log‐series and lognormal distributions. At small to moderate sample sizes these estimators appear to be more precise than previous parametric and nonparametric estimators. We test our estimators using samples from 171 published medium‐sized to large animal and plant communities taken from the literature. By this we show that our new estimators define also limits of precision.     

Proportional mixture of two rarefaction/extrapolation curves to forecast biodiversity changes under landscape transformation

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.     

Does phylogenetic distance aid in detecting environmental gradients related to species composition?

Questions: How should we evaluate the success of new distance measures combining community abundance and phylogenetic information? How do we interpret ordinations using these metrics? Methods: We generated synthetic data along a known environmental gradient with two hypothetical underlying phylogenetic structures: niche phylogenetically conserved or dispersed along a gradient. We also examined tree species composition associated with gradients in elevation and longitude in Oregon, USA. NMS ordinations of plots in species space from phylogenetic (PD) and Sørensen distance (SD) matrices allowed comparison of the use of PD in different scenarios. Results: PD caused plots to cluster based on the clades that they contained, reducing stress with the synthetic data but not with the real example. Phylogenetic distance highlighted clades related to gradients when these were associated. When phylogeny was not conserved along a gradient, that gradient was less strong. Regardless of phylogenetic conservation, NMS using SD consistently extracted the strongest gradients in species composition. Conclusions: The success of PD should be evaluated on how well it extracts gradients in species composition and allows community ecologists to determine which gradients are partially explained by phylogeny and not based on its ability to reduce ordination stress. PD ordinations can help community ecologists interpret niche conservation but may obscure gradients related to species composition when niches are not conserved along the gradient of interest at the scale of the study.