Effects of species partition on explanatory variables in direct gradient analysis - a case study from Senegal

Abstract. Species-environment data from Senegal, West Africa, are used to study the effects of partition of a large species data set into subsets corresponding to rare and common species respectively. The original data set contains 129 woody plant species from 909 plots and 60 explanatory variables. By applying Canonical Correspondence Analysis to data subsets, marked differences in the forward-selected variables were detected. The highest resemblance was found between the complete species set and the common species subset. Only one of eight selected variables was common to all species and the rare species groups. These findings were tested with partial ordination, applying the selected variables from the original species group (Vb), as variables and covariables to the analyses of common and rare species. For the common species this application resulted in a constrained ordination with higher eigenvalues as compared to the set of variables selected with reference to the common species group. Using the rare species group, the application of Vb gave a much lower sum of eigenvalues than did the ordination with selected variables based on the rare species group only. Evidently, the set of variables selected on the basis of the rare species data were more significant. Hence, the resulting gradients depend on the frequency of the species. Gradient analysis is apparently only valid for groups of species with closely resembling characteristics. This implies that different functional types of species, with different distributions and abundances, respond individually to environmental variation. Extrapolating deduced gradients from one species group to another maybe risky, particularly when used in vegetation modelling.     

Null model tests of clustering of species, negative co-occurrence patterns and nestedness in meta-communities

We propose tests for patterns in meta-community structure. The tests for clustering and nestedness of the occurrences of species and negative co-occurrence patterns provide four important innovations. Firstly, they are not restricted to the analysis of communities along one-dimensional gradients or to the main axis of variation. Secondly, abundance data can also be considered in the null model whereas most previous approaches could consider only presence/absence data. And thirdly, habitat suitability and spatial autocorrelation can be incorporated in the null model so that patterns that might be caused by biotic interactions can be distinguished from patterns which are the result of differences in the suitability or accessibility of sites for the examined organisms. Finally, the test for nestedness is also appropriate if there is more than one set of nested subsets. A re-analysis of 35 data sets with these tests showed the importance of considering the autocorrelation of the occurrences of species in analyses of meta-community structure and demonstrated the advantage of abundance data for tests of clustering of species. With abundance data it could be shown that there is a significant clustering of species, i.e. there are positive associations of species in most meta-communities, even if an environmentally or spatially constrained null model is used for the test. Co-occurrence patterns that might indicate interspecific competition were found in many of the analysed presence/absence data sets. Surprisingly the analysis of abundance data sets provides less evidence for interspecific competition. A hierarchical organization of communities, i.e. nestedness, turned out to be a rare pattern, if the autocorrelation of the occurrences of species is considered.     

A test for assessment of saproxylic beetle biodiversity using subsets of “monitoring species”

In European forests, large scale biodiversity monitoring networks need to be implemented – networks which include components such as taxonomical groups that are at risk and that depend directly on forest stand structure. In this context, monitoring the species-rich group of saproxylic beetles is challenging. In the absence of sufficient resources to comprehensively survey a particular group, surrogates of species richness can be meaningful tools in biodiversity evaluations. In search of restricted subsets of species to use as surrogates of saproxylic beetle richness, we led a case study in Western Europe.Beetle data were compiled from 67 biodiversity surveys and ecological studies carried out from 1999 to 2010 with standardised trapping methods in France and Belgium. This large-scale dataset contains 642 forest plots, 1521 traps and 856 species. Twenty-two simplified species subsets were identified as potential surrogates, as well as the number of genera, a higher taxonomic level, taking into account, for each surrogate, the effort required for species identification, the practical monitoring experience necessary, the species conservation potential or the frequency of species occurrence. The performance of each surrogate was analyzed based on the following parameters: overall surrogacy (correlation between subset richness and total species richness), surrogacy vs. identification cost balance, surrogacy variation over a wide range of ecological conditions (forest type, altitude, latitude and bio-geographical area) and consistency with spatial scale. Ecological representativeness and ability to monitor rare species were supplementary criteria used to assess surrogate performance.The subsets consisting of the identifiable (or only easy-to-identify species) could easily be applied in practice and appear to be the best performing subsets, from a global point of view.The number of genera showed good prediction at the trap level and its surrogacy did not vary across wide environmental gradients. However, the subset of easy-to-identify species and the genus number were highly sensitive to spatial scale, which limits their use in large-scale studies. The number of rare species or the species richness of single beetle families (even the best single-family subset, the Cerambycidae) was very weak surrogates for total species richness. Conversely, the German list of monitoring species had high surrogacy, low identification costs and was not strongly influenced by the main geographical parameters, even with our French and Belgian data.In European-wide monitoring networks, such internationally validated subsets could be very useful with regard to the timing and cost-efficiency of field inventories.     

Evaluating the stability of the classification of community data

We propose a method for a posteriori evaluation of classification stability which compares the classification of sites in the original data set (a matrix of species by sites) with classifications of subsets of its sites created by without‐replacement bootstrap resampling. Site assignments to clusters of the original classification and to clusters of the classification of each subset are compared using Goodman‐Kruskal's lambda index. Many resampled subsets are classified and the mean of lambda values calculated for the classifications of these subsets is used as an estimation of classification stability. Furthermore, the mean of the lambda values based on different resampled subsets, calculated for each site of the data set separately, can be used as a measure of the influence of particular sites on classification stability. This method was tested on several artificial data sets classified by commonly used clustering methods and on a real data set of forest vegetation plots. Its strength lies in the ability to distinguish classifications which reflect robust patterns of community differentiation from unstable classifications of more continuous patterns. In addition, it can identify sites within each cluster which have a transitional species composition with respect to other clusters.     

Comparison of different treatments of rare species in canonical correspondence analysis

运用吕梁山南段植物群落及其环境调查数据,比较研究不同稀有种处理(剔除稀有种、稀有种不做处理与降低稀有种权重3种方法处理)对典范对应分析(CCA)排序结果的影响,并用Spearman秩相关系数检验对应排序轴的相关性。结果表明3种方法的分析效果基本一致,但它们对环境因子的解释趋势有差异。基于环境数据、物种数据和样方数据的排序轴相关分析结果显示:未处理稀有种的CCA与降低稀有种权重的CCA吻合度高于剔除稀有种的CCA与降低稀有种权重的CCA的吻合度,未处理稀有种的CCA与降低稀有种权重的CCA的前4轴呈极显著的一一对应关系;剔除稀有种的CCA和降低稀有种权重的CCA仅在基于环境数据和样方数据分析时前3轴呈极显著的一一对应关系,而在基于物种数据的相关分析时前4轴的对应相关性不显著。从物种-环境关系的解释量上来看,降低稀有种权重的CCA最优,剔除稀有种的CCA和未处理稀有种的CCA次之。结合对应排序轴的相关性分析和物种-环境关系累计解释量来看,这3种稀有种处理方法在准确地揭示物种与环境关系时的顺序依次为:降低稀有种权重>对稀有种不做处理>剔除稀有种。     

Are there differences in structure between marine and terrestrial assemblages?

Recently, interest in species abundance (SAD) distributions has been revived by introduction of a new model, the zero-sum multinomial (ZSM). Yet detailed statistical analyses show that the model does not differ from the lognormal distribution proposed in the 1940s. These analyses were based on data from tropical trees where all individuals in a defined area were identified to species. For many ecological data sets it is not possible to identify and count all individuals in a given area. Here we compare data on marine benthos and fish assemblages with data on terrestrial microfauna and ants. We show that these assemblages show similar SAD patterns and that the SADs are best described by a two-group lognormal model. Whereas the 2-group model fitted all data sets the single group model fitted all except the tropical rainforest ants. However, tests comparing the fits to the 2-group versus the single lognormal model showed that the 2-group model was a significantly better fit to the fish and insect data. The two groups are of rare and common species and the rare group dominates in all four data sets. We suggest that the reason for this is that rare species are continuously immigrating from outside the sampled area. Data on tropical tree assemblages where complete accounts were made do not show such high dominance of rare species where the sampled area is large. We conclude that SAD patterns are similar in marine and terrestrial systems that are open to immigration and that the lognormal distribution is still a valid model for SADs.     

Coherence, species turnover, and boundary clumping: elements of meta-community structure

Ecologists have identified several kinds of pattern in the distribution of species among sites, including a) nested subsets, b) checkerboards, c) Clementsian gradients, d) Gleasonian gradients, and e) evenly spaced gradients. Most past efforts to diagnose such patterns have focused on only one at a time, often contrasted with a sixth type of pattern, f) "randomness". While there are statistical tests to distinguish each of the first five patterns from randomness, there are currently no established methods for discriminating among these first five patterns in a given data set. Here we propose a method that will identify which of these possibilities is most prevalent in a site-by-species incidence matrix based on three basic aspects of meta-community structure. Our method is based on first ordinating the incidence matrix to identify the dominant axis of variation and identifying three aspects variation along this dominant axis. The first aspect, "coherence", is the degree to which pattern can be collapsed into a single dimension. The second, "species turnover", describes the number of species replacements along this dimension. The third aspect, "boundary clumping", has to do with how the edges of species boundaries are distributed along this dimension. We present methods for analyzing these three aspects of meta-community structure, use them to identify the six different patterns, and illustrate them with a representative set of cases drawn from previously published data.     

Scale‐dependent frequency distributions of plant species in dune slacks: Dispersal and niche limitation

Question: How do distribution patterns change with increasing scale level, and can this shift be attributed to dispersal and/or niche limitation? Location: Dune slacks at the Belgian and North French coast. Method: Frequency distribution patterns of species were tested over different scale levels (ranging from 0.008 to 45 km²). Analyses were executed for the total species pool and for subsets of species with high and low dispersal rates and habitat generalist and specialist species. Results: Species distributions for the total species pool on scale levels larger than 1.5 km² were unimodal, with an overrepresentation of rare species. With decreasing scale level, the proportion of common species increased, leading to weak bimodality. Distributions of subsets of slowly dispersing and habitat specialist species are on all scale levels characterised by a strongly unimodal pattern. The subset of species with high dispersal rates and habitat generalist species has a higher proportion of common species, leading to a significant core peak. Conclusions: On all scale levels both dispersal and niche limitation are hampering the wider distribution of rare species. However, since isolation and habitat heterogeneity are limited on the smaller scale levels, a larger proportion of well dispersing and habitat generalist species is able to occupy a high number of patches, resulting in a significant core peak for the total species pool.     

Cryptic diversity in the Mexican highlands: Thousands of UCE loci help illuminate phylogenetic relationships,species limits and divergence times of montane rattlesnakes (Viperidae: Crotalus)

With the continued adoption of genome‐scale data in evolutionary biology comes the challenge of adequately harnessing the information to make accurate phylogenetic inferences. Coalescent‐based methods of species tree inference have become common, and concatenation has been shown in simulation to perform well, particularly when levels of incomplete lineage sorting are low. However, simulation conditions are often overly simplistic, leaving empiricists with uncertainty regarding analytical tools. We use a large ultraconserved element data set (>3,000 loci) from rattlesnakes of the Crotalus triseriatus group to delimit lineages and estimate species trees using concatenation and several coalescent‐based methods. Unpartitioned and partitioned maximum likelihood and Bayesian analysis of the concatenated matrix yield a topology identical to coalescent analysis of a subset of the data in bpp . ASTRAL analysis on a subset of the more variable loci also results in a tree consistent with concatenation and bpp , whereas the SVDquartets phylogeny differs at additional nodes. The size of the concatenated matrix has a strong effect on species tree inference using SVDquartets , warranting additional investigation on optimal data characteristics for this method. Species delimitation analyses suggest up to 16 unique lineages may be present within the C. triseriatus group, with divergences occurring during the Neogene and Quaternary. Network analyses suggest hybridization within the group is relatively rare. Altogether, our results reaffirm the Mexican highlands as a biodiversity hotspot and suggest that coalescent‐based species tree inference on data subsets can provide a strongly supported species tree consistent with concatenation of all loci with a large amount of missing data.     

What is Meant by “95% of Species”? An Argument for the Inclusion of Rapid Tolerance Testing

It is increasingly common for water quality guidelines and risk assessments to consider the proportion of species at risk from a particular toxicant, based on the species sensitivity distribution (SSD) for that toxicant. There is a premise that the sensitivity data from species included in the SSD are sufficient to predict the effect on species for which there are no data. We discuss and review assumptions that follow this premise and find that for most toxicant SSDs include too few species, and that component species are biased toward particular taxonomic groups, common species and species from North America and western Europe. Consequently, protecting a given percentage, for example, 95%, of species in an SSD will likely protect more or less than 95% of species in nature, by an unknown amount. For the assumptions of SSDs to be better met, there is a need for tolerance data on more species, from more taxonomic and other groups, including rare species and those from widespread localities. In order to achieve this, we argue for the inclusion of rapid tests, which we define as toxicity tests designed to require less effort to conduct, relative to traditional tests, so sensitivity can be quickly and approximately determine in many species. Their use will allow for more species, more representative of natural communities, to be tested and therefore allow the construction of less biased SSDs and thus more accurate guidelines and assessments of risk.     

Evaluating the costs and benefits of systematic data acquisition for conservation assessments

Effective decision‐making in conservation often is constrained by data quality. Uncertainties associated with poor quality or sparse data can lead to the misuse of limited resources and potentially the failure of conservation practice. Data acquisition, which can help improve decision‐making, is constrained by limited budgets and time. This is especially concerning for rare species, the most in need of conservation, but the most difficult to accurately represent in conservation plans. Here we test the suitability of three different sampling design strategies (two systematic vs random) designed to improve the quality of information available for conservation planning involving rare species. We modelled the spatial distribution of freshwater fish species in a data rich area in northern Australia using a large dataset (representing the best attainable data or true distribution) and simulate increasing subsets of data acquired through the three alternative sampling designs. We then evaluated omission and commission errors in conservation planning outcomes, efficiency and return on investment of data acquisition for conservation planning outcomes obtained from the different data availability × sampling design strategies. Even though we were able to find new species more effectively through systematic sampling designs, this did not 1) translate into reduced errors in conservation planning outcomes for rare species and 2) meet our goal of enhancing cost‐effectiveness of conservation planning. Our results suggest that collecting more biodiversity data, irrespective of the sampling design used, does not necessarily reduce data uncertainty issues and could lead to the misuse of the limited resources and ultimately the failure of conservation practice.     

Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data

Although whole-genome association studies using tagSNPs are a powerful approach for detecting common variants, they are underpowered for detecting associations with rare variants. Recent studies have demonstrated that common diseases can be due to functional variants with a wide spectrum of allele frequencies, ranging from rare to common. An effective way to identify rare variants is through direct sequencing. The development of cost-effective sequencing technologies enables association studies to use sequence data from candidate genes and, in the future, from the entire genome. Although methods used for analysis of common variants are applicable to sequence data, their performance might not be optimal. In this study, it is shown that the collapsing method, which involves collapsing genotypes across variants and applying a univariate test, is powerful for analyzing rare variants, whereas multivariate analysis is robust against inclusion of noncausal variants. Both methods are superior to analyzing each variant individually with univariate tests. In order to unify the advantages of both collapsing and multiple-marker tests, we developed the Combined Multivariate and Collapsing (CMC) method and demonstrated that the CMC method is both powerful and robust. The CMC method can be applied to either candidate-gene or whole-genome sequence data.     

Heterogeneity‐constrained random resampling of phytosociological databases

Aim: Phytosociological databases often contain unbalanced samples of real vegetation, which should be carefully resampled before any analyses. We propose a new resampling method based on species composition, called heterogeneity‐constrained random (HCR) resampling. Method: Many subsets of the source vegetation database are selected randomly. These subsets are sorted by decreasing mean dissimilarity between pairs of the vegetation plots, and then sorted again by increasing variance of these dissimilarities. Ranks from both sortings are summed for each subset, and the subset with the lowest summed rank is considered as the most representative. The performance of this method was tested using simulated point patterns that represented different levels of aggregation of vegetation plots within a database. The distributions of points in the subsets resulting from different resampling methods, both with and without database stratification, were compared using Ripley's K function. The mean of random selections from an unbiased sample was used as a reference in these comparisons. The efficiency of the method was also demonstrated with real phytosociological data. Results: Both stratified and HCR resampling yielded selection patterns more similar to the reference than resampling without these tools. Outcomes from the resampling that combined these two methods were the most similar to the reference. The efficiency of the HCR resampling method varied with different levels of aggregation in the database. Conclusions: This new method is efficient for resampling phytosociological databases. As it only uses information on species occurrences/abundances, it does not require the definition of strata, thereby avoiding the effect of subjective decisions on the selection outcome. Nevertheless, this method can also be applied to stratified databases.     

Determinants of species rarity: population growth rates of species sharing the same habitat

Determining differences between common and rare species is commonly used to identify factors responsible for rarity. Existing studies, however, suffer from two important drawbacks. First, studies compare species that are closely related phylogenetically but occupy different habitats. Second, these studies concentrate on single life history traits, with unknown relevance for population growth rates. Complete life cycles of one rare and one common Cirsium species sharing the same habitat were compared. Population growth rate was slightly lower in the rare species, translating into a large difference in local extinction probability. Seed predation intensity did not differ between species. However, it can be demonstrated that in connection with the data on complete demography, seed predation is the key factor causing a lower population growth rate in the rare species. These results are the first estimation of factors responsible for commonness or rarity of plants in terms of population growth rate without confounding differences in ecology. They demonstrate that conclusions based on single traits may be misleading and that only a comparison based on a complete life cycle can provide unequivocal evidence for concluding which factors are really those responsible for species commonness or rarity.     

Book reviews

Editing of community data matrices is complementary to analyzing data by multivariate techniques of classification and ordination in the overall task of data analysis. A computer program, DATAEDIT, is described that can perform numerous editing functions, including data transformation, deletion of certain species or samples, deletion of rare species, deletion of outliers, separation of disjunet sample groups, reordering of the species or samples of a data matrix, and the formation of composite samples or of sample subsets. DATAEDIT can use the information in a nonhierarchical or hierarchical classification, and includes its own internal routine for reciprocal averaging ordination.We appreciate valuable suggestions from the late Robert H. Whittaker, and from Philip Dixon, David Hieks, Laura Huenneke, Linda Olsvig-whittaker, and Mark Wilson. Mark O. Hill kindly supplied a fast subroutine for reciprocal averaging.     

南亚热带森林常见种和偶见种的功能多样性特征

群落特征可能取决于物种在功能实体功能特征的独特组合（FE）中的分布。了解这些分布是保护生物多样性和维持生态系统功能的基础。鼎湖山南亚热带常绿阔叶林群落物种丰富,根据物种的多度来区分常见种（每hm²超过一个个体）和偶见种（每hm²少于一个个体）。基于功能实体,分别计算功能实体数量、功能冗余度、功能脆弱度和功能过度冗余度来描述每个样方中常见种和偶见种的功能多样性。采用Wilcoxon检验检测常见种和偶见种功能性状的差异,通过线性回归分析常见种和偶见种的功能多样性与物种数量的关系,采用冗余分析（RDA）影响常见种和偶见种的功能多样性的环境因子。研究结果表明：（1）鼎湖山南亚热带常绿阔叶林群落中功能性状在常见种和偶见种间差异显著。（2）群落中数量较低的偶见种保持着较高比例的功能实体数量。功能实体数量、功能冗余度与常见种和偶见种数量显著正相关,功能过度冗余度与偶见种数量显著正相关,功能脆弱度与常见种和偶见种的物种数量呈显著负相关。（3）海拔是影响常见种功能多样性最关键的地形因子,凹凸度是影响偶见种功能多样性最关键的地形因子。土壤含水量是影响常见种功能多样性最关键的土壤因子,速效钾是影响偶见种功能多样性最关键的环境因子。研究结果表明,尽管物种丰富的热带森林具有较高的功能冗余,但其提供的保险效应不能抵消生态系统的功能脆弱性。减少具有独特功能实体和偶见种的损失是避免热带森林生态系统功能损失的有效和必要的方法。     

The predictive skill of species distribution models for plankton in a changing climate

Statistical species distribution models (SDMs) are increasingly used to project spatial relocations of marine taxa under future climate change scenarios. However, tests of their predictive skill in the real‐world are rare. Here, we use data from the Continuous Plankton Recorder program, one of the longest running and most extensive marine biological monitoring programs, to investigate the reliability of predicted plankton distributions. We apply three commonly used SDMs to 20 representative plankton species, including copepods, diatoms, and dinoflagellates, all found in the North Atlantic and adjacent seas. We fit the models to decadal subsets of the full (1958–2012) dataset, and then use them to predict both forward and backward in time, comparing the model predictions against the corresponding observations. The probability of correctly predicting presence was low, peaking at 0.5 for copepods, and model skill typically did not outperform a null model assuming distributions to be constant in time. The predicted prevalence increasingly differed from the observed prevalence for predictions with more distance in time from their training dataset. More detailed investigations based on four focal species revealed that strong spatial variations in skill exist, with the least skill at the edges of the distributions, where prevalence is lowest. Furthermore, the scores of traditional single‐value model performance metrics were contrasting and some implied overoptimistic conclusions about model skill. Plankton may be particularly challenging to model, due to its short life span and the dispersive effects of constant water movements on all spatial scales, however there are few other studies against which to compare these results. We conclude that rigorous model validation, including comparison against null models, is essential to assess the robustness of projections of marine planktonic species under climate change.     

Reconsidering association testing methods using single-variant test statistics as alternatives to pooling tests for sequence data with rare variants

Association tests that pool minor alleles into a measure of burden at a locus have been proposed for case-control studies using sequence data containing rare variants. However, such pooling tests are not robust to the inclusion of neutral and protective variants, which can mask the association signal from risk variants. Early studies proposing pooling tests dismissed methods for locus-wide inference using nonnegative single-variant test statistics based on unrealistic comparisons. However, such methods are robust to the inclusion of neutral and protective variants and therefore may be more useful than previously appreciated. In fact, some recently proposed methods derived within different frameworks are equivalent to performing inference on weighted sums of squared single-variant score statistics. In this study, we compared two existing methods for locus-wide inference using nonnegative single-variant test statistics to two widely cited pooling tests under more realistic conditions. We established analytic results for a simple model with one rare risk and one rare neutral variant, which demonstrated that pooling tests were less powerful than even Bonferroni-corrected single-variant tests in most realistic situations. We also performed simulations using variants with realistic minor allele frequency and linkage disequilibrium spectra, disease models with multiple rare risk variants and extensive neutral variation, and varying rates of missing genotypes. In all scenarios considered, existing methods using nonnegative single-variant test statistics had power comparable to or greater than two widely cited pooling tests. Moreover, in disease models with only rare risk variants, an existing method based on the maximum single-variant Cochran-Armitage trend chi-square statistic in the locus had power comparable to or greater than another existing method closely related to some recently proposed methods. We conclude that efficient locus-wide inference using single-variant test statistics should be reconsidered as a useful framework for devising powerful association tests in sequence data with rare variants.     

Spatial distribution of rare species: statistical approach

Many species are locally rare (have low occurrences in a given region). The method is proposed for the analysis of spatial distribution of such species. The procedure is based on the comparison of actual mean distances between records with Monte-Carlo-simulated mean distances expected for random occurrence pattern. The method was applied to two datasets: benthic macrofauna from the Barents and Kara Seas (Kucheruk, Kotov, 2002) and the Black Sea interstitial ciliates (original data). For both datasets, the number of rare species positively correlates with number of common species per site and with the total number of species. Furthermore (and irrespectively of total diversity pattern), rare species usually were locally clumped in their distribution: each of the species was mostly found at sites that were close together. This tendency, however, was significant for the macrofauna species but not for ciliates.     

Identifying hotspots for biodiversity management using rank abundance distributions

Aim Identification of biodiversity hotspots has typically relied on species richness. We extend this approach to include prediction to regional scales of other attributes of biodiversity based on the prediction of Rank Abundance Distributions (RADs). This allows us to identify areas that have high numbers of rare species and areas that have a rare assemblage structure. Location Continental slope and shelf of south‐western Australia, between 20.5 and 30° S and depths of 100–1500 m. Methods We use a recently developed method to analyse RADs from biological surveys and predict attributes of RADs to regional scales from spatially abundant physical data for demersal fish and invertebrates. Predictions were made for total abundance (N), species richness (S) and relative evenness at 147,996 unsampled locations using data from two spatially limited surveys. The predictions for S and relative evenness were then independently split into categories, creating a bivariate distribution. The RAD categories are mapped spatially between 20.5 and 30° S to depths of 1500 m to allow identification of areas with rare species and assemblage structure across this region. Results Rank abundance distributions for demersal fish vary with large scale oceanographic patterns. Peaks in abundance and unevenness are found on the shelf break. The bivariate distributions for richness and evenness for both fish and invertebrates show that all assemblage structures are not equally likely. The RAD categories identify regions that have high numbers of rare species and areas with unique assemblage structure. Main conclusions Predicted RADs over large regions can be used to identify biodiversity hotspots in more detail than richness alone. Areas of rare species and rare assemblage structure identified from fish and invertebrates largely overlap, despite the underlying data coming from two different data sets with two different collection methods. This approach allows us to target conservation management at species that would otherwise be missed.