OCEANOGRAPHY and MARINE BIOLOGY: AN ANNUAL REVIEW (Volume 45) - Chapter 4 pptx

Use of diversity measures inbenthic ecology has largely parallelled studies in other ecosystems with an emphasis upon measuresthat are informative when applied to large amounts of data w

OF SEDIMENTARY BENTHIC COMMUNITIES

ROBERT S CARNEY

Department of Oceanography and Coastal Sciences, Louisiana State University,

Baton Rouge, Louisiana, U.S.A 70803

E-mail: rcarne1@lsu.edu

Abstract The soft-bottom benthos covers most of the sea floor Measurement and analysis of thespecies richness of these habitats are increasingly needed for studies of community regulation andfor providing scientific criteria for the conservation of the ocean bottom at all depths Diversitymeasures provide an evolving suite of tools that allow benthic ecologists to meet both basic andapplied needs While species diversity is now considered a fundamental aspect of communities andecosystems, the measurement of benthic diversity did not become commonplace until the late 1960s.Prior to that communities were characterised by representative species with the implicit assumptionthat minor species components did not warrant detailed analysis Use of diversity measures inbenthic ecology has largely parallelled studies in other ecosystems with an emphasis upon measuresthat are informative when applied to large amounts of data with high species numbers Non-parametricindices such as Simpson’s and Shannon’s are widely used along with simple species richness Log-series and log-normal distributions have been advocated as general neutral models but receive lessuse Current research is especially focused upon extrapolation of unsampled species richness anddiversity relationships across spatial scales Major contributions from benthic ecology include therarefaction of samples to a uniform size, the development of indices that include phylogeneticrelationships in diversity estimation and the extrapolation of full species richness from observedvalues In meeting scientific and societal needs, benthic ecologists must apply methods that areinsightful yet can be simply explained within the resource-policy arena

Introduction

Justification

Estimation of diversity has become an integral part of benthic ecology There is so much recentliterature and software available that review may seem unneeded Benthic ecology is, however,now experiencing a change in the ways that species data are accessed and analytical results usedthat is both scientific and societal in origin Both origins require that concepts and estimation ofdiversity be reconsidered The greatest scientific change is the increasing accessibility of surveydata through open Internet databases This allows the search for geographic and temporal patternsnot anticipated in the original study designs and a search across multiple studies by experts inanalysis and theory who may be largely unfamiliar with benthic ecology and the taxonomy ofbenthic organisms The second change is societal in the sense that international regulatory policiesincreasingly mandate the preservation of biological diversity in both marine and terrestrial systems.Benthic ecologists must provide regulators with estimates of diversity that can be explained anddefended if these estimates are to serve agencies as the basis for conservation decisions Thus, theintent of this review is to provide users of databases an explanation of what benthic ecologists have

found and provide benthic ecologists a guide to the changes associated with the shift in terminologyfrom diversity to biodiversity.

Contraction of the term biological diversity to biodiversity seems to have originated within theU.S government environmental management structure and was then progressively used by thoseecologists especially interested in conservation biology (Harper & Hawksworth 1994) Along withdevelopment of conservation biology, biodiversity began to encompass a much broader conceptthan species diversity alone and now may be considered a distinct concept or suite of concepts(Hamilton 2005) One marine definition of biodiversity included the variety of genomes, speciesand ecosystems occurring in a defined region (National Research Council 1995) and followed asimilar combination of genetic and ecological perspectives used by Norse and his colleagues (Norse

et al 1986)

The official definition of biodiversity as contained in Article 2 of the Convention on BiologicalDiversity included “variability among living organisms from all sources … within species, betweenspecies, and of ecosystems” (United Nations Conference on Environment and Development, 1992).The view adopted in this review is that biodiversity is largely a policy term rather than scientificand its use should be avoided Efforts to better define biodiversity from a scientific standpoint areneeded and reflect conservation biologists’ duty to provide objective tools to managers faced withmandates to preserve biodiversity in marine as well as terrestrial systems (Lubchenco et al 2003).Presently, however, policy usage of ‘biodiversity’ carries with it many assumptions that have notbeen proven scientifically such as a link between diversity of ecosystem health (Norse 1993) andecosystem stability

Notable efforts in ecology to provide management tools were the adoption in benthic ecology

of taxonomic indices that weight diversity by phylogenetic differences (Warwick & Clarke 2001)and the search for indicator species to be used in place of more comprehensive diversity assessment

As discussed in the historical review, selection of indicator species bears a strong similarity to theselection of characteristic species during the decades of benthic ecology research prior to anyinterest in the diversity of bottom communities

‘Biodiversity informatics’ is the term applied to the growing development and use of databasesfor diversity studies and is very broadly defined to include biogeography and certain aspects ofsystematics Progress and challenges for systems that will provide marine data have been outlined

by Costello & Berghe (2006) There is already progress for deep-sea studies starting with datacompiled by many French cruises (Fabri et al 2006) and by many studies conducted in shallowEuropean seas (Costello et al 2006) Initially, these marine databases can most confidently be usedfor determining geographic and bathymetric ranges of individual species As problems of incon-sistent and incorrect taxonomy are solved, however, the datasets will be extremely useful forestimating benthic diversity over a wide range of scales

Structure of the review

This review takes a broad historical perspective to examine how benthic ecology has treated diversityfrom approximately 1870 until the present time with special attention to soft bottoms Benthicecologists carried out surveys as early as the 1900s that were similar to the projects of today, butlacked both the modern concepts of diversity and the computational tools to compute indices.However, there are strong similarities between the struggle of early benthic ecologists to simplifydiscussion of species-rich systems and the search of contemporary conservation biologists forindicator taxa that can be used in the estimation of overall community diversity (Pearson 1994).The mathematics of diversity estimation are treated herein only in sufficient detail to indicatewhat benthic ecologists do and have done with respect to concepts and data analysis Only thoseapproaches widely used in or originating in benthic ecology are considered Texts by Hayek &

Buzas (1997) and Magurran (2004) do an excellent job of focusing upon the major concepts andmethods The former text has somewhat greater mathematical detail, while the latter text providesmore information about concept development Even these recent books are quickly outdated.Methods, concepts and large-scale patterns of diversity with respect to mud bottoms have beenconsidered in highly informative reviews of Gray (2000, 2001, 2002) The information presentedherein is intended to compliment these works by taking a broader historical perspective and tracingthe use of analytical tools more than by discussing details of many individual results Unfortunately,all reviews must choose to omit something The two serious omissions here are (1) the use ofevenness measures to compliment diversity and (2) the effect of pollution stress on benthic diversity.Both topics warrant separate treatment in the future In concluding, recommendations are made as

to a future course in benthic ecology that will allow both a better understanding of diversity and

an ability to provide managers with useful information

Basics

To avoid contributing to additional confusion, it is necessary to state the concept of diversity used

in this review According to a simple view of systems ecology, there are three types of informationabout a benthic community (Figure 1) First, an ‘inventory’ is a list of all species and theirabundance Second, a set of interactions among the component species is often represented by amatrix Third, a set of relationships exists between the fauna and the physical environment Sam-pling, identification and enumeration produce the inventory Determination of fauna-environmentrelationships can be made through sampling designs that capture variation in sediment type, salinity,temperature, and so on Assessment of species interactions is the most difficult information toobtain Certainly, soft-bottom communities are impractical locations to determine the populationinteraction parameters required by theoretical community matrices (Levins 1968) In some cases,however, associations such as dependence on biogenic structure are obvious and a variety of toolscan be used to determine at least a trophic position The assumption is that the abundance of eachspecies in the inventory can be explained to some extent by the interactions among species and theinteractions with the environment

Of these three sets of information, diversity is an attribute of the inventory (Peet 1974) Whengiven a mathematical definition, diversity should afford a parsimonious means of comparing theinventories of different systems The underlying assumption is that differences in diversity reflectdifferences in species interactions Common questions in benthic ecology have been directed towhether ubiquitous gradients of diversity exist with depth, with latitude and with anthropogenicstress In each case, diversity is a convenient indicator of ecosystem differences

Terminology varies greatly in the larger ecological literature, but most authors take the positionadvocated by Hill (1973) and Hurlbert (1971) Measures of species diversity (the variety of theinventory) are based on two simple attributes: the number of species (species richness) and the pro-portional abundances An effective means of describing the variability of proportional abundance isevenness (i.e.,departure from equal proportions) Using these two attributes, indices can be calcu-lated and used as an overall measure of heterogeneity (Magurran 2004)

A somewhat unsettling aspect about species diversity is that all species are treated equally,making no use of additional knowledge about biotic or abiotic interactions and life histories Failure

to treat some species as more important would seem to make a traditional species diversity measurepoorly suited to be used for conservation decisions about which communities should be affordedspecial protections A partial solution is seen in a recent development in benthic ecology, use ofindices of taxonomic distinctness (Warwick & Clarke 2001) Still an attribute of the inventory,these indices make use of additional information about taxonomic position of the componentspecies The adoption of these indices marks a major change in benthic community analysis

From Forbes zones to Petersen communities

When benthic studies from the late 1800s through the mid-1900s are reviewed a peculiar situationemerges about use of species diversity Early hints of interest in diversity existed prior to the advent

Figure 1 Basic nature of soft-bottom benthic survey data Ecology theory takes the position that population

levels of individual species in a community are influenced by interactions with the environment, including resource utilisation, and pairwise relationships among species In application, benthic surveys produce quan- titative species-by-sample data according to designs that nest replicates with stations within larger ocean areas Interactions of species with the environment are often expressed as correlation coefficients and are limited to the few factors included in the sampling design An actual matrix of the relationships among pairs of species

is rarely known, but statistical associations are sometimes developed as substitutes from the species-sample data Traditionally, species diversity has been seen as a property of the species-by-sample data alone, ignoring the other two types of data.

of community ecology, but then there was surprisingly little interest during early formative years ofcommunity ecology Finally tremendous new interest began in the 1950s as niche theory and easycomputation facilitated inquiry Certainly, benthic surveys produced inventories in which a fewspecies were common and many more rare, but comments as to this fact are largely absent fromabout 1900 to 1960 With so much emphasis upon diversity today, it is informative to consider ahistorical period of very active benthic surveying when the concept seems to have been missing orunimportant.

Estimation of species diversity is now associated with quantitative benthic sampling Towardthe end of the 1800s, seafloor studies began the transition from the description of faunal zonesbased upon qualitative trawl and dredge sampling (Forbes 1859, Mills 1978, Carney 2005) to morequantitative grab and core surveys Interest in species diversity during qualitative sampling can beseen from the criticism of the CHALLENGER Expedition (1872–1876) by Anton Stuxburg (1883).Stuxburg complained about the lack of synthesis in the largely taxonomic works and specificallysuggested that the number of species and the proportions of each be presented trawl by trawl.Possibly accepting these suggestions, the summary of the expedition issued 12 yr later carefullynoted that deep samples contained a greater variety of megafauna species that showed lowernumerical dominance than shallow samples in spite of the numerically smaller catch (Murray 1895)

No explanation of this higher deep diversity was presented, and the observation was largely forgotten,possibly due to the much greater emphasis upon quantitative shallow water studies that soon followed.Contemporary surveys of soft bottom benthic communities are distinguished by a strongemphasis on numerical analysis of truly quantitative samples of the fauna in a known volume ofsediment lying under a similarly known area of the sea floor The origin of this type of surveying

is generally attributed to the work of pioneering fisheries ecologist, C.G.J Petersen (Petersen 1918),The method was developed during the course of ecologically comprehensive fish stock assessmentbegun in the late 1880s

Petersen-type surveys producing species inventories were widely adopted Local surveys wereconducted around Great Britain at such locations as in the vicinity of the Plymouth MarineLaboratory (Ford 1923, Smith 1932) and Scotland (Stephen 1928, 1934, Clark & Milne 1955).Numerous surveys took place along other west European coasts such as off Iceland and in theMediterranean By the 1900s larger scale surveys were conducted in the English Channel (Holme1966) In North America, Allee (1923) surveyed the benthos in the vicinity of Woods Hole Possiblymost influential were benthic surveys in Puget Sound on the Pacific coast by Shelford (1935) whowas a strong proponent of the super-organism view of community structure and function Similarsurveys were spread across the Arctic from the 1920s onward, and were summarised in English

by Zenkevitch (1963) The techniques were also adopted along the Japanese coast in the 1930sand 1940s by Miyada (cited by Thorson 1957)

These many Petersen-type surveys were all quite similar although sampling gear and sedimentprocessing evolved over the course of the studies (Spärck 1935, Thorson 1955) The general trendwas towards larger areas of sampling and more reliable penetration of the bottom Statisticalanalyses were minimal, and results were often presented as a map of both faunal assemblages andoceanographic conditions Assemblages were inventoried in detail, then described and named onthe basis of the two characteristic species Graphics were used to portray the relative abundance

of dominant species

Diversity, as an aspect of the species inventories, was neither discussed nor analyzed in studiesinto the 1960s This was despite the availability of useful indices since the 1940s, and theirwidespread use terrestrially for both plant and insect surveys In addition these early workersconsidered themselves to be studying communities as interacting systems However, hints exist thatquestions about species diversity were beginning to be formulated In the survey by Smith (1932)

of the Eddystone grounds species richness was presented with singletons and more abundant species

carefully noted Possibly reflecting growing ideas and better calculators, more sophisticated analysesbegan to appear such as the dispersion of species across samples (Clarke & Milne 1955) By thetime of the English Channel survey (Holme 1966), the Petersen tradition of naming assemblagesafter two characteristic species had been dropped due to the finding that species composition variedgreatly within such assemblages.

The surprisingly little interest in species diversity or in any related characterisation of speciesinventories probably had several causes The three most likely are a lack of practical utility, a lack

of relevant concepts, and a lack of computational tools With respect to utility, many of these benthicsurveys were associated with fisheries studies making community productivity the parameter ofinterest The apparent lack of ideas about species diversity may be related to the immaturity of thecommunity concept In the early 1900s, mapping of communities and characterisation of theircomponent species was the major activity, and not a careful investigation of community structureand function that might be implied from the species inventory

Jones (1950) reviewed the status of benthic studies in the context of community theory andconcluded that many workers accepted the idea that they were studying integrated systems in whichbiological interactions were important Few, however, seemed to fully embrace the idea that benthiccommunities were superorganisms passing through biologically controlled successive states until

a certain climax was reached Indeed, the distribution of benthic assemblages was always explained

in terms of control by physical conditions such as depth, sediment type, salinity, etc One notableexception was Shelford, who was one of the framers of the climax community and biome concepts(Clements & Shelford 1939) He divided the oceans into a series of biomes largely associated withdepth and geographic position without reference to species richness Another ecology pioneer wasAllee (1934), a strong proponent of benthic communities functioning as superorganisms, tracingthe idea back to Verrill

At the end of Petersen era

In 1957, the state of knowledge about benthic ecology was compiled by international experts in atwenty nine-chapter memoir and published by the Committee on Marine Ecology and Paleoecology

of the Geological Society of America (Hedgepeth 1957) Of particular relevance to the concept ofdiversity was the paper on bottom communities by Thorson (1957) This paper clearly marks atransition from the era of naming communities to one of discussing diversity patterns The levelmud bottom was correctly seen as one of the largest, and apparently homogenous, environments

on Earth Due to the strong dependence upon oceanographic conditions, bottom communities withsimilar taxonomic structure should be found over very large areas These parallel communitieswere viewed as having relatively minor differences around the world

More importantly, Thorson compiled species richness data on selected taxa and found anincrease from pole to tropics for epifauna and no gradient for infauna Strongly influenced byphysiological explanations, the increase was attributed to greater thermal stability in the tropics Adifferent view of benthic community stability emerged based upon ‘Thorson’s Rule’, a generalisa-tion about increased occurrence of pelagic larvae in the tropics seen as having many exceptionsbut some general validity (Laptikhovsky 2006) It was then suggested that the tropical benthoswould show greater spatial and temporal variation in species composition because of a largevariation in survival to settlement in the plankton Higher latitudes should have a more stablecommunity structure due to the prevalence of direct development

The strong emphasis on parsimoniously characterising multispecies communities in a mannersuitable for mapping without actual mathematical analyses lead early benthic ecologists to depend

on nomenclature, or the naming of communities A reading of the very detailed “ideal rules” ofThorson (1957) indicates how subjective the process actually was Recommendations on how to

select characteristic species would be of only historical interest if a similar need did not exist today

to simply describe benthic communities for conservation planning Later in this review it will beshown that naming Petersen communities is similar to picking indicator species and assigninggreater importance to some species than others

The primary task of naming communities was to identify within the collected fauna thosespecies that are ‘characteristic’ of the community The five rules of Thorson paraphrased here were.First, more than one such species should be selected Second, short life-span species should beavoided because their numbers fluctuate too much to be consistently characteristic Third, highlymobile animals and predators should be avoided as being be too transient Fourth, characteristicspecies should be big enough and abundant enough to be immediately conspicuous and have goodidentification traits without consultation with a specialist Fifth, biomass and/or density can be used

an indicator of abundance as long as they are not misleading due to large brood sizes or very largespecimens

Even within the mundane task of picking names for communities, an interest in diversity can

be seen Thorson divided the species inventory into four categories or orders based on abundanceand fidelity of association with a particular community A first-order characteristic species should

be conspicuous, found throughout the range of the community in at least 50% of the samples, and

at least 5% of the biomass and restricted to that community A second-order characteristic speciesshould have a similar frequency of occurrence and biomass dominance, but limited to only portions

of the range A third-order species would be found in other communities as well as in at least 70%

of the units and at least 10% biomass A fourth-order of ‘associated animals or influents’ would

be in at least 25% of the units and as much as 2% of the biomass but of little diagnostic value due

to a wide distribution crossing other communities

Beginning of a new era

While formative elements of modern ecological theory may be found in many lines of earlypopulation research, ecological questions about niche filling, resource utilisation, and competitiveexclusion were first expressed by G.E Hutchinson and his students and colleagues in the 1960s(Maurer 1999) The “diversity of a species inventory” was modelled as a balance achieved throughcompetition, resource specialisation, habitat complexity, resource availability, and history (Mac-Arthur 1972) The details of community structure and function were being examined with mathe-matical tools, and species diversity was a parameter of great interest

The transition to the new view is most evident in a series of benthic studies begun in shallowestuaries (Sanders 1960) and then extended to abyssal depths (Sanders et al 1965) Initially, com-

munities were still named on the basis of characteristic species such as the Nephthys incisa – Nucula proxima community, and diversity indices were not calculated (Sanders 1960) By 1965, descriptive

habitat names were used in place of characteristic species, and new diversity tools were proposed.There was obvious interest in species richness and proportions, the large number of rarer species,and the quantitative analysis of recurrent groups using trellis diagrams Sanders’ benchmark com-parative study of marine benthic diversity (Sanders 1968) marked the beginning of an adoption ofniche theory and analytical methods by benthic ecologists worldwide that persists to this day.This comprehensive paper by Sanders made four major contributions First, it objectivelyexamined the use of several diversity measures, and found that the information-based Shannon’sindex was adequate, but species richness was preferred Secondly, rarefaction, a procedure forestimating species richness in computationally reduced samples was presented to reduce the effect

of sample size Third, Thorson’s infauna versus epifauna latitude gradients were challenged andregional oceanographic conditions considered to be of greater importance in controlling diversityhighs and lows Fourth, the high diversity of deep-sea macrofauna was noted for the first time since

the CHALLENGER Expedition and proposed as a general ocean feature A stability-time hypothesiswas proposed as a general model for all benthic environments In this explanation physical instabilitywas predicted to cause low diversity and biological accommodation would cause high diversity wherephysical conditions were stable.

Sanders was extremely careful about making a distinction between measurements of diversitythat are reflective of species number (species diversity) and those reflective of proportional abun-dance (dominance diversity) Although categorising several indices as being of one or the othercategory, Sanders employed his own method of using species number per sample size for speciesdiversity His method of calculating dominance diversity was to first plot a species accumulationcurve for each sample He then compared that curve at reduced sample sizes (arrived at byrarefaction) with a baseline curve representing maximum equitability with all species having thesame proportional abundance Unfortunately, full details of the method were omitted

Sanders proceeded to examine the behaviour of species diversity versus dominance diversity

in eight benthic habitats reducing the sample size artificially through rarefaction A graphical meanswas employed to track changes in rank of diversity as samples were rarefied The ranks determined

by species number were found to be fairly consistent upon rarefaction, while ranks determined bydominance were very inconsistent He concluded that species number was the more conservativemeasure of diversity while dominance was more variable due to the physical environment

Influx of indices

The 1960s and 1970s saw a rapid adoption of diversity measures and multivariate approaches tothe analysis of benthic data This adoption was due to a more fully developed niche theory, a betteraccess to computers, and a dissatisfaction with the subjectivity of Petersen-like community descrip-tion (Lie personal communication) The origins of the indices, however, preceded adoption bybenthic ecologists by a decade or more

The inventories, lists and counts of species, found in benthic or any other type of surveysampling are categorical data in which individual specimens are assigned to a species category.Linguists also deal with categorical data, and pioneers like Zipf (1935) and Yule (1944) developedquantitative methods of comparing texts They counted the frequency of words in various texts,ordered those frequencies by rank and noted recurrent curves reflecting the fact that a few wordswere very common and many rare At roughly the same time period, R.A Fisher (Fisher et al.1943) proposed the use of a logarithmic series for examination of species categorical data Influ-enced by the linguistic indices, Simpson (1949) proposed use of a ‘concentration’ index, andShannon (1948) developed Information Theory that would be embraced by ecologists following asuggestion by Margalef (1958)

The literature on how diversity should be measured continues to grow rapidly Works in generalecology published in the 1960s through 1980s tend to fall into a either a category dealing withniche-theory models or a more practical category trying to improve the utility of indices Benthicstudies of diversity fit into both categories, but place emphasis on practical aspects The emphasis

on practical aspects stems from the increased number of surveys required to address environmentalproblems Both theoretical and practical works are now on an upsurge Increased theoretical interesthas been generated by the proposal by Hubbell (2001) of the “unified theory of biodiversity andbiogeography” and by multinational interest in the preservation of the European coastal seas The

‘unified theory’ has inspired considerable controversy (Whitfield 2002) and renewed examination

of diversity models (Pueyo 2006) Preservation of the coastal seas of many European nationsrequires standardised measures of diversity that are both scientifically meaningful and useful forpolicy and management decisions

Compared with terrestrial studies, the use of diversity measures by benthic ecologists has beenrelatively conservative in terms of restricting the types of indices proposed and applied This can

be attributed to the nature of benthic survey data, that is, a collection of many thousands ofindividuals and several hundred species The taxonomy for many of the benthic groups is poorlydeveloped and often in need of revision Many species are rare Compounding these problems,attempts at larger-scale syntheses are hindered by inconsistent sampling methods and great naturalvariation in sample size Therefore, benthic ecologists have always needed measures that wererobust when data were not ideal and which simplified the task of interpretation Most studies havemade use of just a few diversity measures based either upon fitting abundance distribution models

or calculating an index Most of these measures were well described by Gray (1981a) in benthicterms In the context of this review, use of a distribution means fitting and calculation of theparameters that generate the distribution Use of an index means the combining of two or morecharacteristics of species-abundance distributions to produce a single value on a scale that allowscomparison among communities Indices make no assumptions about the underlying distribution,but carry with them implicit definitions of diversity Use of distributions always allows for signif-icance testing For all common indices statistical properties have been developed and formal testing

is also possible

Traditional approaches

Diversity measures are so widely applied and improved measures are so actively sought that adivision into traditional versus newer approaches is somewhat artificial Old approaches are con-stantly being reconsidered That acknowledged, there are some approaches that have been in use

a long time and have been quite extensively discussed These shall be presented first Then some

of the more recent developments are considered

Log-series and log-normal abundance distributions

From a statistical perspective the most parsimonious means of describing diversity and conductingrigorous comparisons among communities is to first identify the underlying species abundancedistribution, and fit the model and estimate the parameters that characterise the distribution Severalsuch distributions have been used in diversity studies (Hayek & Buzas 1997, Magurran 2004), butthe two oldest have had the greatest usage in benthic ecology These are the log-series (Fisher et al.1943) and log-normal (Preston 1948) distributions The finding that either one or the other of thesedistributions fitted a wide variety of terrestrial and marine data was once considered to reflectprofound aspects about ecosystem structure (Odum et al 1960), and that studies of pattern alonecould definitively identify the causative processes It has now been realised, however, that suchdistributions may simply reflect the outcome of many complex processes, especially when thereare a large number of species and individuals are present (May 1975, Pueyo 2006) Indeed,information on species abundance alone is insufficient to select among alternate ecological theories

of causation (McGill 2003) Many different processes can generate the same distribution.Explanation of distributions, the process of fitting, and the determination of parameters issubstantially more complex than a discussion of diversity indices Hayek & Buzas (1997) provide

an excellent detailed account, but these authors are strong advocates for the wide application ofthe log-series The log-series can be characterised using only a single parameter Fisher’s α

Computing α requires an interactive computation When data actually fit the log-series, α is

approximately the number of species represented by a single specimen (singletons)

An especially successful use of the log-series in benthic systems was an application to archivedforaminiferan data from five coastal regions ranging from the Arctic into the Caribbean (Buzas &Culver 1989) Fisher’s α provided a highly useful measure of diversity and indicated a strong

geographic trend with the highest diversities in the tropical Caribbean and lowest in the Arctic Anunusual aspect of that analysis was that log-series rarefaction was used (Hayek & Buzas 1997) toproduce equivalency, and that occurrence among samples was used as a measure of abundancerather than counts within a sample

The log-normal refers to abundances that are normally distributed about a mean once the datahave been log transformed As for any normal distribution, it is characterised by two parameters —mean and variance, which can be used as indicators of diversity The log-normal has a rich history

of usage in ecology since first recognised as a widespread pattern (Preston 1948) An earlyapplication in benthic ecology was the re-examination by Gage & Tett (1973) of benthic data fromtwo lochs that had been previously analyzed using rarefacted species richness (Gage 1972) Thelog-normal distribution was fitted, and resulting means and variances used to search for patternsassociated with the salinity differences of two lochs, salinity gradient within each loch, and sedimenttype In the authors’ opinion, the two log-normal parameters provided a more informative picturethan rarefacted species richness The actual goodness of fit, however, can be questioned since single-tons were excluded before analysis The complete data may have been better fitted with the log-series.The most extensive use of the log-normal distribution in benthic ecology can be found in thestudies of John Gray and his colleagues Gray (1981a) noted that benthic assemblages containingmany singletons generally fit the log-series distribution, but the common assemblage in which mostspecies were represented by a few individuals fit the log-normal The log-normal distribution hasproven useful in identifying pollution impacts on benthic diversity (Gray 1981b, 1983, 1985) Thelog-normal has been proposed as a neutral model for soft bottom macrofauna assemblages in thesense that it is the expected outcome of certain ubiquitous processes of immigration, emigration,and resources partitioning (Ugland & Gray 1982, 1983) In a renewed discussion about the genera-tion of species abundance patterns by neutral models, the appropriateness of the log-normal hasbeen criticised (Williamson & Gaston 2005) Grey et al (2006a), however, considered both aterrestrial and a marine system, and argue that many systems may be effectively modelled ascompound log-normals in which two or more distributions are mixed Ecologically, it seems quitefeasible that benthic samples will include several suites of species for which the abundances reflectseparate and distinct histories Additional investigation is required

Species richness and its rarefaction

Species richness is defined as the number of species in the samples of interest Those samples mayrepresent replicates from a single location or from larger spatial scales The notation and nomen-clature of Gray (2000) serves to avoid confusion with other symbols and ambiguity as to scale

‘SR’ denotes species richness with subscripts applied to indicate spatial extent It is the most easilyexplained of all measures of diversity, and for a large segment of the concerned community it issynonymous with biodiversity In his classification of indices (Hill 1973), “SR” is viewed as givingequal weight to species of any abundance since it ignores those abundances completely Recognisingthat SR is a function of sample size N, SR is often normalised through division by N or areasampled Additionally, relationships of SR with sample size and abundance can be examined throughregression with the slope of a regression serving as a index of diversity These approaches are wellcovered by Hayek & Buzas (1997) and Magurran (2004) Species richness is often plotted againstsampling effort represented by counts, number of samples, or area sampled as an indication of thecompleteness of the species inventory In the case of a complete inventory, the curve becomesasymptotic

An important advancement in examination of species-effort and species-area curves was thegeneration of multiple plots of subsets of the data selected at random by computer (Colwell 2005).This produced both means and variances rather than single points along a curve Renewed interest

in such relationships is based upon the potential to extrapolate species richness beyond the actuallevel of sampling to be discussed below (Colwell & Coddington 1994, Ugland et al 2003) Ana-lytical approaches have, however, replaced randomisation

The best application of species richness as a diversity measure is in a situation where the biotahas been fully inventoried with all species collected and recognised This seldom if ever occurs inbenthic ecology Rare species go unsampled due to insufficient sample size, and fine distinctionsbetween similar-appearing species can be easily overlooked The problem of taxonomic error isquite hard to overcome, but an adjustment can be made for differences in SR arising from unequalsample size Rarefaction originated in benthic studies (Sanders 1968) and has been widely adoptedthroughout ecology Its purpose is to reduce multiple samples to a common N, and then estimatethe number of species that should be present Sanders also noted that the curves generated byrarefaction proceeding through a range of N’s could also be used to rank samples by diversity.Sanders use of rarefaction was not intended as a rigorous exercise in probabilities, and is bestconsidered as an instruction set for reducing sample size Hurlbert (1971) and Simberloff (1972)recognised the estimation of SR as a problem that could be solved by making use of the hyper-geometric distribution and introduced the term expected species E(Sn) where Sn denotes speciesrichness at the reduced sample size Rarefaction is no longer limited just to estimating SR, but toother diversity measures as well using the hypergeometric and other distributions Hayek & Buzas(1997) compared four rarefaction methods using tree survey data The hypergeometric producedthe best results, but Sander’s methods still proved both useful and simple

Simpson’s λ

Simpson’s λ is an index based upon the probabilities encountered when comparing any two

individuals in a set of species These probabilities are estimated from the proportional abundance

of each species in an assemblage When two individuals are drawn, they may either be the same

or a different species All possible outcomes can be displayed as a square matrix (Figure 2) Thediagonal of the matrix contains the probability of all possible ways in which the individuals drawnare in the same species The values above and below the diagonal are all the possible ways thatdissimilar species could be drawn Since the order in which the species is found is unimportant,the probabilities above and below the diagonal are equal The sum of all terms in the matrix areequal to one since no other combinations for two individuals exist Simpson’s λ is the sum of all

the elements on the diagonal where S equals the number of species (Equation 1)

Simpson’s λ was proposed (Simpson 1949) as a measure of the concentration of the classification

of individuals into species The index has great conceptual appeal since it is the likelihood that twoindividuals drawn at random without replacement from a community or sample of a communitybelong to the same species Terminology varies somewhat among users with Simpson’s D usuallyrefering to the form 1 – λ which has the preferred property of increasing with greater diversity

The index can also be expressed expressed as 1/ λ, 1 – λ, and ln(λ) (Magurran 2004) The form

1 – λ is the probability of drawing two individuals that are not the same species (Equation 2) The

double summation indicates that summing of the elements excludes the diagonal Only half the matrix

λ =

=

i 1 S

is summed according to this notation, requiring that the result be multiplied by two to get the actualprobability It has come into renewed application as a component of taxonomic distinctivenessdiscussed below The index is sometimes referred to as the Gini-Simpson index in recognition ofdevelopment of the same function by the economist C Gini in 1912 (Gorelick 2006).

(2)

In his classification of indices (Hill 1973), λ gives greatest weight to abundant species This

behaviour has reduced its popularity in benthic ecology and other fields that commonly encounternumerous species with low abundances For example, the index goes unmentioned in Gray (1981a).The emphasis on abundant species is a property of squaring the proportions Proportions are alwaysequal to or less than one When proportions are squared the product is an even smaller fraction

Figure 2 Distributions or calculation of indices Two common means of plotting species abundance, rank of

abundance versus proportion of sample and number of species versus number of individuals have led to the suggestion that either the log-series or log-normal distribution could parsimoniously describe the data Alter- nately indices can be calculated, most often using the proportion of abundance Proportions provide an estimate

of the probabilities that pairs of individuals drawn from the data will be the same (values on the diagonal) or different (off the diagonal) species.

Species × species probability of all pairs

Species abundance plots

Log-series?

Log-normal?

Proportional abundances for index calculation

j S

Thus, if the most dominant species in a sample has a p = 0.30, p 2 = 0.090 A species with half that

abundance, p = 0.15 will contribute p 2 = 0.023 to the summed index, or only a fourth as much.The positive side of λ’s insensitivity to rare species is that it is minimally influenced by sample

size because abundant species are usually sampled with low effort Therefore, λ produces relatively

consistent rankings of the least to the most diverse assemblages Lande et al (2000) It has alsobeen effectively used to show latitude gradients in intertidal mudflats (Attrill et al 2001), andwarrants greater consideration for similar comparisons across multiple studies The abundant speciesthat most influence λ are most likely to be the best surveyed and most consistently and correctly

identified

Information theory and Shannon’s H′

Shannon’s index is the summation of plog(p) for all S species (Equations 3a,b).

(3b)

Unlike the conceptually simple Simpson’s λ, Shannon’s H′ is based on the more abstract field of

information theory and systems entropy (Shannon 1948) The formula appeared much earlier inBoltzman’s 1872 work in entropy (Gorelick 2006) and simultaneously in the cybernetics work ofWeiner (1948) The index is sometimes termed the Shannon-Weiner index or incorrectly Shannon-Weaver due to citation confusion (Magurran 2004) In spite of unclear conceptual relevance toecology, it continues in widespread use due to its mathematical properties and history of application.Information theory provides a means to quantify the complexity of information that can be used

in the design of communication systems (Shannon 1948) It originated during World War II as atool for assuring the successful transmission and reception of encoded messages through noisyradio channels Its use in systems ecology for the quantification of diversity was first advocated byMargalef (1958) on the basis of an analogy between transmission systems and temporal changes

in ecosystems Very simplified, temporal changes are like a noisy channel between the structure of

an ecosystem at one time and another time Pielou (1966) was very influential in the adoption ofinformation diversity measures, but specifically rejected the underlying analogy (Pielou 1969).Margalef (1995) continued to advocate the utility of the analogy

H′ is a fundamentally different way of envisioning diversity, and is related to the complexity

of the task of sorting the specimens into correct species groups through a series of decisions.Compared to other measures of diversity, information has two very important distinguishing features

associated with the summed term plog(p), most often calculated as the natural logarithm pln(p) First, pln(p) is modal reaching a maximum of 0.3679 for a proportion of p = 0.3678 Higher and

lower proportions contribute less to the summed index Illustrating this point with an unlikelyassemblage of two species with proportions of 0.999994 and 0.000006, both the very common andthe very rare would contribute roughly equally to H′, approximately 0.000006 for both Second,

H′ increases linearly with geometric increase of species richness under conditions of full evenness

For example, if there are three assemblages with 10, 20 and 40 equally abundant species, the

p iln( )p i

respective H′ will be 2.303, 2.996 and 3.689 The increment in H′ is a consistent 0.693 even though

the species richness doubles Depending upon one’s concept of diversity, these are either good orbad properties

Use of information theory for diversity quantification in benthic studies had been initiated bythe late 1960s (Lie and Kelly 1970, Lie 1974) Its popularity in benthic studies can be seen from thefact that it is the only diversity index presented in Gray’s (1981a) succinct text on benthic ecology.This popularity continues to the present (Gobin and Warwick 2006, Warwick et al 2006) A variety

of diversity specialists have found the properties of H′ poorly suited for specific tasks (May 1975,

Lande 1996), and Magurran (2004) attributes its continued use largely to tradition H′ does, however,

have properties very useful in diversity analysis Specifically, it supports additive formulations ofdiversity across scales from sample to large area (Lande 1996, Veech et al 2002), and identification

of the underlying distribution of proportions can be made through examining the changes in speciesrichness, Equitability, and H′ during subsampling of data (SHE analysis, Hayek & Buzas 1997)

Newer developments

New developments in the measure of benthic diversity still fall into both theoretical and practicalcategories, although there is greater merger of the two than previously When sedimentary habitatsare sampled, the process of developing high quality species count data is far more time and effortconsuming than parallel activities such as chemical and granulometric analyses Once the benthicdata are available, confusion can exist in explaining the data analyses applied In the real situationwhen both time and money are critical, there is a great emphasis upon doing things more expedientlyand providing more informative results The use of surrogates to estimate diversity is an approachseeking to reduce effort The use of new taxonomic diversities is an effort to improve results Abit closer to theory are attempts to extrapolate from small samples to larger areas, and to gainknowledge over larger spatial scales by compiling local studies

Surrogates

The intent of the surrogate approach is to replace the hard and expensive task of compiling amultispecies inventory with an easier and less costly survey of indicator species, coarser taxonomiclevel, or restricted size class Proof that any of these surrogates are useful rests in demonstratingthat they allow for an accurate estimation of the diversity of unsampled species Weaker proof isthat the surrogate produces a similar diversity ranking of assemblages as that obtained by morecomprehensive methods Benthic ecologists are largely accepting that such approaches might work

if proven, since surrogacy is almost always applied to some extent Benthic systems like mostothers are complex, and benthic ecologists have traditionally met the need to adopt a practical focus

by dealing with a restricted size range or taxonomic category

The concept of an indicator or surrogate for full diversity measurement has been widelyexamined for terrestrial systems (Gaston & Williams 1993, Williams & Gaston 1994, Anderson

1995, Andelman & Fagan 2000) Unfortunately, approaches from the use of single species to moreinclusive groupings have shown little utility for reflecting diversity of the unsurveyed species(Eduardo & Grelle 2002, MacNally et al 2002, Su et al 2004)

When the criteria for indicator species developed by Pearson and associates (Pearson 1994)for conservation biology are critically examined, they seem intended to produce simple descriptors

of a community rather than to serve as a surrogate for diversity Indeed, they are similar to rulesfor identifying characteristic species in Petersen-type communities (Thorson 1957) Indicator cri-teria can be rephrased as:

1 The taxonomy should be well known, stable, and suitable for correct and consistentidentification by a non-specialist;

2 The biology and general life history should be well understood so as to make ecologicalroles known and sources of variation understood;

3 The populations should be readily surveyed and manipulated;

4 Higher taxa (i.e., genera, family) of the indicators should occupy a breadth of habitatsand a broad geographic range so that wide application is possible;

5 At lower taxonomic levels (populations, subspecies, species, etc.), there should be narrowhabitat specialisation so that the ability to detect small geographic differences is provided;

6 The patterns observed in the indicator should actually be an indicator of similar patterns

in other related or unrelated taxa; and

7 A species with potential economic impact may be especially useful for policy purposeseven though it fails to meet other criteria

While the possibility exists that some indicator species might reliably replace more hensive species in special cases, the wider application of simple species surrogates seems unlikely.Taxonomic surrogacy or taxonomic sufficiency (Ellis 1985, Quijón & Snelgrove 2006) is a analternative Taxonomic surrogacy has been effectively treated from a taxonomic perspective byBertrand et al (2006) Irish Sea polychaete data (Mackie et al 1995) were re-examined at differenttaxonomic resolutions employing three equally acceptable phylogenies ranging from splitter influ-enced to lumper influenced Good regressions between species richness and family richness existedfor each phylogeny, but slopes were dramatically different Therefore, the phylogeny used greatlyinfluences species richness estimates For most benthic marine fauna, phylogenies are not welldeveloped

compre-Field results also suggest caution in the adoption of taxonomic surrogates Only in the case ofhydrothermal vent fauna have genus, family, and order all been well correlated with species patterns(Doerries &Van Dover 2003) In deep sediments family-species correlations were poor (Naraya-naswamy et al 2003) Quijón & Snelgrove (2006) examined taxonomic surrogacy in a reexamina-tion of seafloor predator exclusion and found that the family level was effective only when familiescontained three or fewer species They concluded, as with many others, that species-level investi-gation should be the norm Following methods used in terrestrial systems (Su et al 2004), Kar-akassis et al (2006) compared similarity analyses of benthic samples in the eastern Mediterraneanwith community diversity measured by a broad range of indices The indicator taxa were multi-species groups of macrofauna collected by grab, ciliates collected similarly, and megafauna andfish colleted by trawl The measures of diversity based on the different indicator groups were poorlycorrelated

Most studies examining taxonomic surrogacy in marine systems have been primarily concerned

with the use of similarity analysis to detect differences rather than estimation of diversity per se.

Warwick (1988) re-examined macrobenthic data from five sites at a coarser resolution, and foundthat the family level provided adequate results Similar sufficiency at the family level has beenfound in impacted benthic systems (Olsgard et al 1998a,b) with the caveat that the level ofresolution should be limited to impacted systems containing steep gradients of impact Additionally,family-level studies should only be used following development of a species-level baseline.The question as to whether one size class can be used to determine diversity trends in another

is especially relevant in benthic ecology due to the traditional separation of macrofauna andmeiofauna studies Warwick et al (2006) carried out a carefully designed study across both sizegroups with interesting results Sieve-size fractions of the benthos showed similar diversities whensampled over a set range of spatial scales The Shannon Index and Expected Species at a sample

size of 50 were used as diversity measures Diversity of the 63, 125 and 250 µm fractions were

quite similar Diversities of the 500 and 1000 µm sizes were lower by a factor of about two, but

were similar to one another No one size fraction could be used as a surrogate for the whole, butthe diversity pattern in the larger and the smaller could possibly be studied at only two sieve sizes

Taxonomic diversity

The incorporation of taxonomic information into a diversity-like index represents a truly noveldevelopment Indeed, when the indices that form the taxonomic distinctness approach are examined,they both stretch and then depart from the traditional view that diversity combines species richnessand proportional abundance Initially viewed as a need in conservation biology (May 1990, Crozier1997), the approach has been extensively developed in benthic studies (Clark & Warwick 1998,

1999, 2001, Warwick & Clarke 1998, 2001) Although in use a relatively short time, the approach

is gaining wider acceptance It has already been reviewed in this journal (Warwick & Clarke 2001),and is widely available through the PRIMER-5 package of computer analysis routines

Combination of species diversity measures and numerical taxonomy into a more informativeindex was proposed in passing by Sneath & Sokal (1973), but the idea seems to have gone largelyunexplored until conservation biologists sought a means of better assessing diversity (Faith 1992,Posadas et al 2001, Mace et al 2003) In addition to the utility in conservation planning, the concept

is also ecologically appealing as nicely presented by Purvis & Hector (2000) When developing aoperational definition of diversity, three factors rather than two should be included In addition tospecies richness and proportional abundance, we should consider the inherent differences amongthe taxa present Giving a benthic example, we might judge that an assemblage of vermiformanimals consisting solely of polychaetes was in some way less diverse than an assemblage consisting

of burrowing anemones, phoronids, sipunculids, echiurans, holothuroids, and a few polychaetes

At this time, five descriptors for taxonomic distinctness have been developed (Clark & Warwick2001; Warwick & Clarke 2001): Taxonomic Diversity ∆, Taxonomic Distinctness ∆*, AverageTaxonomic Distinctness for presence/absence data ∆+, Variation in Taxonomic Distinctness Λ+, andTotal Taxonomic Distinctness s∆+ The first two can be considered three-component diversity indicescombining species richness, proportional abundance, and taxonomic information The latter threeomit a consideration of abundance These importance differences are best seen through an exam-ination of how the measures are calculated

As introduced in the discussion of Simpson’s λ, the relationship between all pairs of species

can be represented by a symmetrical square matrix (Figure 3) The heart of taxonomic distinctness

is such a matrix of taxonomic distinctness values ωij between each pair The matrix of distinctness

values is effectively similar to a dendrogram or cladogram Ideally, ωij values should be based oncarefully developed phylogenies (e.g., Bertrand et al 2006), but Warwick & Clarke (2001) haveeffectively made the case for starting with the Linnaean hierarchy until better values are available.Unlike phylogenies, the Linnaean hierarchy has fixed ranks Two individuals in the same species

(i = j) would have a ωij of zero Two individuals from separate congeneric species (i ≠ j) would

have a ωij of one If the pair were in confamilial genera, ωij would be two and so on These

increments can be rescaled to allow for taxonomies with many additional subdivisions such astribes, superfamilies, subclasses, etc (Warwick & Clarke 2001)

The calculation of Taxonomic Diversity and Distinctness combine the values of taxonomicdistinctness with abundance (Equation 4a) For these calculations, each element in the taxonomic

distinctness matrix is weighted by the product of the abundances of each pair of species (xixj) The

somewhat more familiar form of ∆ can be made by converting x i xj values to the probability of encountering the species pair (pij) simply by dividing each element by N2 (Equation 4b) The

relationship with 1 – λ (Equation 2) explained by Warwick & Clarke (1998) is more obvious in

this presentation It can also be noted that as N becomes large its effect on the calculated value

quickly becomes small Seen as an extension of Simpson’s λ, ∆ is the expected or average taxonomic

difference between any pair of specimens drawn from the assemblage on the condition that theyare not the same species

Figure 3 Taxonomic distinctness measures The taxonomic distinctness suite of indices is based upon

deter-mining distinctness between all pairs of species collected by sampling As an initial approximation of phylogenetic relationships, distinctness weight ( ω) is half the path length linking a species pair in the taxonomic

hierarchy The properties of the resulting distinctness matrix can be analyzed and expressed as a purely taxonomic-distinctness index like ∆* When combined with a matrix of probabilities of drawing species pairs,

an index of taxonomic diversity ( ∆) can be obtained that combines species richness, relative abundance and

interspecies evolutionary relationships This is a major extension of the species diversity concept.