IDENTIFYING ECOLOGICAL CHANGE AND ITS CAUSES: A CASE STUDY ON CORAL REEFS

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Ecological Applications, 14(5), 2004, pp. 14481465q 2004 by the Ecological Society of America

IDENTIFYING ECOLOGICAL CHANGE AND ITS CAUSES:A CASE STUDY ON CORAL REEFS

KATHARINA E. FABRICIUS1 AND GLENN DEATHAustralian Institute of Marine Science, PMB No. 3, Townsville MC, Qld 4810, Australia

Abstract. The successful management of ecosystems depends on early detection ofchange and identification of factors causing such change. Determination of change andcausality in ecosystems is difficult, both philosophically and practically, and these diffi-culties increase with the scale and complexity of ecosystems. Management also dependson the communication of scientific results to the broader public, and this can fail if theevidence of change and causality is not synthesized in a transparent manner. We developeda framework to address these problems when assessing the effects of agricultural runoffon coral reefs of the Australian Great Barrier Reef (GBR). The framework is based onimproved methods of statistical estimation (rejecting the use of statistical tests to detectchange), and the use of epidemiological causal criteria that are both scientifically rigorousand understood by nonspecialists. Many inshore reefs of the GBR are exposed to terrestrialrunoff from agriculture. However, detecting change and attributing it to the increasing loadsof nutrients, sediments, and pesticides is complicated by the large spatial scale, presenceof additional disturbances, and lack of historical data. Three groups of ecological attributes,namely, benthos cover, octocoral richness, and community structure, were used to discrim-inate between potential causes of change. Ecological surveys were conducted along waterquality gradients in two regions: one that receives river flood plumes from agriculturalareas and one exposed to runoff from catchments with little or no agriculture. The surveysshowed increasing macroalgal cover and decreasing octocoral biodiversity along the gra-dients within each of the regions, and low hard coral and octocoral cover in the regionexposed to terrestrial runoff. Effects were strong and ecologically relevant, occurred in-dependently in different populations, agreed with known biological facts of organism re-sponses to pollution, and were consistent with pollution effects found in other parts of theworld. The framework enabled us to maximize the information derived from observationaldata and other sources, weigh the evidence of changes across potential causes, make de-cisions in a coherent and transparent manner, and communicate information and conclusionsto the broader public. The framework is applicable to a wide range of ecological assessments.

Key words: Bayesian analysis; biodiversity; bootstrap; causality; community structure; environ-mental impact; epidemiology; Great Barrier Reef; model averaging; model selection; pollution; ter-restrial runoff.

INTRODUCTION

Early detection of ecological change and identifi-cation of factors causing such change are essential forsuccessful ecosystem management. Despite the avail-ability of the best scientific data, interested parties oftendisagree about the existence of ecological change andits causes. There are many reasons for this, including(1) the selective use of scientific data and other infor-mation by interested parties to support individualclaims and objectives, (2) the misinterpretation andabuse of technical concepts such as probability andcausality (Newman and Evans 2002), (3) the complex-ities of large-scale ecosystems which cannot be simplyexplained or reliably predicted, and (4) the exploitationof disagreement amongst scientists by stakeholders.The resulting lack of consensus can lead successively

Manuscript received 15 October 2003; accepted 23 February2004. Corresponding Editor: P. K. Dayton.

1 E-mail: [email protected]

to conflict, confusion over policy development, gov-ernment inaction and environmental degradation.

To overcome these problems, we developed a frame-work based on (1) the use of improved methods fordetermining change through estimation of effect sizes,as opposed to the usual use of hypothesis tests(McCullagh and Nelder 1989, Nelder 1999), and (2)the use of epidemiological criteria to attribute causality.The framework can synthesize and evaluate scientificand other data according to criteria that are both sci-entifically rigorous and widely accepted. Applicationof the framework is simple and transparent in order toeffectively communicate scientific evidence to decisionmakers and the public. This enables the detection ofchange and judgments about causality to be made in arigorous, structured, and open manner, and thus theagreement among stakeholders, necessary for success-ful implementation of management strategies, can beobtained. An application of the framework follows ina case study on the effects of water pollution on coralreef benthos in the Great Barrier Reef, Australia.

October 2004 1449IDENTIFYING ECOLOGICAL CHANGE AND CAUSE

Statistical issues in the detection of change

The statistical significance test does not tell us whatwe want to know, and we so much want to knowwhat we want to know that, out of desperation, wenevertheless believe that it does!

Cohen (1994)Most studies of environmental change (or impacts)

adopt a falsification perspective; that is, they assumeno change has occurred and assess the level of evidenceagainst this premise. If the evidence against nochange is strong, then they accept change has oc-curred, whereas if the evidence against no changeis weak, then the initial position is retained. Evidenceagainst the null hypothesis is almost invariably basedon a frequentist statistical significance test of a point(precise) null hypothesis. Since ecosystems are con-stantly changing through time and space such hypoth-eses are a priori false; i.e., they are not plausible (Ber-ger and Sellke 1987). Despite the no change premiseseeming indefensible, the majority of studies continueto adopt it as a starting point for investigations of tem-poral and spatial change. Why is this so? As a basisfor management decisions, hypothesis tests are inad-equate, and it can be argued that decision-makingshould not be a part of impact studies, which shouldinform, not decide (Stewart-Oaten 1996a). Even whena point null hypothesis is plausible, frequentist testsare problematic (Berger and Sellke 1987, Berger et al.1997). They are remarkably uninformative (rejector fail to reject) and can be misleading when im-properly interpreted, e.g., by describing a failure toreject as evidence of no change or by misinterpre-tation of P values. Such misunderstandings and therepeated use of tests of low power can also lead tofalse knowledge as the null hypothesis becomes ac-cepted as knowledge. Basing decisions on the result oftests also conflicts with the precautionary principle(Bodansky 1991) and can be hazardous, e.g., requiringa positive test as evidence of population decline for arare species with high natural variation can lead to localextinction. The problem of lack of information as basisfor accepting the null hypothesis has led to the proposalfor reversing the burden of proof (Dayton 1998), e.g.,it must be shown that proposed actions will not resultin environmental damage, rather than allowing all ac-tions which cannot be shown to result in damage. Fre-quentists have argued that power analysis (typicallypost hoc) offers protection against this problem (e.g.,Cohen 1988). However, this approach has been criti-cized, and equivalence testing suggested as a formalalternative (Hoenig and Heisey 2001). Despite suchshortcomings of frequentist hypothesis tests being re-peatedly noted (Berger and Sellke 1987, Raftery 1995,Stewart-Oaten 1996a, Johnson 1999), the use of testsstill prevails. Various alternatives have been suggested,primarily through the use of estimation of parameters

with confidence [credibility] intervals (Stewart-Oaten1996a, Burnham and Anderson 1998, Burnham et al.2000) or Bayesian methods (Ellison 1996, Berger etal. 1997, Berger and Pericchi 2001). By abandoninghypothesis tests in studies of change, immense benefitcan be derived, irrespective of whether frequentist orBayesian methods are used. The principal objectivethen becomes one of how to quantify (model) changeand obtain accurate estimates of parameters represent-ing the quantities of interest.

When testing for significance or estimating the mag-nitude of change, typically one or more parameters ofa chosen model represent the change process. It is oftenforgotten, or not realized, that all inference, be it hy-pothesis tests or parameter estimation, is conditionalon the selected model. Thus, the validity of conclusionsbased on results of significance tests and parameterestimates are always contingent on the model being anaccurate representation of reality, i.e., that the modelis true, or is at least a good approximation. Often,many models are compared before one is chosen, butthe uncertainty involved in model selection is seldomtaken into account (Burnham and Anderson 1998), andthis can result in biased and/or over-precise estimates,and probability values from hypothesis tests that aretoo small. In well-designed experiments or surveys, themodel is largely determined by the study design, andwe can be relatively confident that it is a reasonablerepresentation of reality. However, when studies arenot as well controlled, or many variables are involved,then the choices of model can be vast. This furtherincreases if we consider interactions, transformationsof variables, and alternative error structures. If all pos-sible explanatory variables are included in a model, thepower to detect change (equivalently, the precision ofthe estimate of change) may be severely reduced. Con-versely, if important variables are omitted, then esti-mates of change are likely to be biased. The problemsof model selection are particularly difficult for smallsample sizes with weak relationships between the re-sponse(s) and predictors, and large data sets with manypredictors.

The use of hypothesis tests for model selection hasreceived intense scrutiny in recent times, and based onsimulations, the use of tests has been shown to be sub-optimal for identifying true models (Freedman 1983,Draper 1995, Burnham and Anderson 1998). The useof selection criteria such as Akaikes information cri-teria (AIC; Sakamoto et al. 1986, Bozdogan 1987) andBayes information criteria (BIC; Schwarz 1978, Ma-digan and Raftery 1994) have been advocated, partic-ularly in the ecological literature (Burnham and An-derson 1998). These criteria outperform the use of hy-pothesis tests in determining true models, however nosingle criteria will universally find the true or best ap-proximating model. Based on AIC or BIC, weights ofevidence can be calculated for each of the competingmodels. These weights quantify the uncertainty of mod-

1450 KATHARINA E. FABRICIUS AND GLENN DEATH Ecological ApplicationsVol. 14, No. 5

el selection and can be treated as relative probabilitiesof the models. They do not, however, represent theprobability that any particular model is the true model,or best approximating model of all possible models,since all of the proposed models may be deficient. Ifnone of the models are true (or good approximations),then inferences based on the best model are also likelyto be biased.

One way to overcome the issue of choosing a singlemodel is simply to avoid it by model averaging. Thisalso has the attractive property of generating more ac-curate estimates and predictions (Hastie et al. 2001).Model averaging involves the fitting of several plau-sible models to the data and averaging the results (ei-ther parameter estimates or predictions) over all of themodels. The averaging is usually weighted by somemeasure of the relative probability or predictive ac-curacy of each model, with more likely models re-ceiving proportionally more weight (Raftery 1988,Burnham and Anderson 1998). In this way, poor mod-els receive little weight and have a negligible influenceon the final model. Criteria such as AIC (or variationsthereof) and BIC are often used for this purpose. Theuse of BIC in this manner is an approximation to Bayes-ian model averaging, a process which is technicallycomplex and computationally demanding, though theneed to use approximations has declined through theuse of increased computational power, and Monte Carlosimulations can give more accurate results comparedto BIC. As is the case with selection of a single model,we need some (or at least one) of the models to be agood approximation to reality for inferences to be un-biased.

The shift from hypothesis testing to estimation, anduse of model averaging to better manage the uncer-tainty of model selection, does not negate the impor-tance of good sampling design in environmental studies(Schmitt and Osenberg 1996). Indeed, the additionaluncertainty of model selection is incorporated into theestimation of effect sizes and thus these estimates willbe less precise, but hopefully more honest, than thosebased on a single model.

Attribution of causalityThe concept of causality has a long and complex

history and it has many meanings. Its everyday usageis often straightforward, but as a philosophical or sci-entific concept, its definition and use are often conten-tious. At this level, at least three notions of causalityare supported (Pearl 2000, Gillies 2001). First, somedeny the existence of causality or view it as scientifi-cally unnecessary. Second, some acknowledge causal-ity as a useful concept, but do not give it a central rolein their models. Causality is thus seen as a useful wayof explaining some aspects of empirical laws. Third,some advocate causality as a fundamental construct.Cause is assumed rather than demonstrated, and in theformulation of mathematical models, causality takes

precedence over probability. Of course, there is no con-clusive argument giving universal support to any oneof the three views, and different phenomena can beused to support differing views, e.g., our everyday ex-periences support the concept of causality as somethingfundamental, but few mathematical models require it.

Statistics is widely accepted within ecology as a pri-mary empirical methodology, yet statisticians are typ-ically not strong advocates of causality as a funda-mental construct, favoring probability models instead.There are statistical approaches that do promote causeover probability (Pearl 2000), but equally there arewarnings against the causal interpretations (Speed1990). The randomized experiment is often invoked asone method for unambiguously determining causality,but even that is questionable when outcomes are sto-chastic and we rely on statistical interpretation.

Causal arguments are needed in ecosystem manage-ment in order to convince interested parties that man-agement actions should be implemented and will beeffective. These arguments need to balance scientificrigor with ease of communication to nonscientists. Thissituation is not novel and we can borrow from epide-miology, which deals with issues of comparable com-plexity to the ecological and environmental sciencesand also has similar requirements of scientific rigor andcommunication. Epidemiologists developed criteria toassess causality as part of the research into the linkbetween cigarette smoking and lung cancer. This linkwas accepted by the Surgeon General after decades ofresearch (U.S. Department of Health, Education, andWelfare 1964) when evidence compiled from multiplesources of information and numerous studies fulfilleda set of criteria (Hill 1965), the main ones being:

1) The relationship between the dose (the putativecause) and the response should be monotonic.

2) The association between the dose and the responseshould be strong.

3) The response should be specific to the cause.4) There should be a logical time sequence of events;

i.e., the response should occur after the dose has beenapplied.

5) There should be consistency both across popu-lations within a study, and with results from other stud-ies.

6) The observations should agree with known bio-logical facts.

These criteria (or subsets, extended sets, or redefinedsets of them) are routinely used in epidemiology tojudge whether or not an association is causal. None ofthe criteria are taken as indicative by themselves, butequally, none are seen as absolutely necessary to eval-uate causal significance of associations (Roth et al.1982). The more criteria that are satisfied and the stron-ger the association, the more confidence we shouldhave in our judgment that the association is causal.Similar criteria have been proposed for ecoepidemiol-ogical studies (Fox 1991) and impact assessment stud-


FIG. 1. Maps of the northern Great Barrier Reef and the study regions. (a) The risk of exposure to agricultural runoff isshown in four shades of gray indicating (from dark to light) high, moderate, small, and minimal risk (from Devlin et al.2002). Boxes surround the two study regions (PC, Princess Charlotte Bay; WT, wet tropics). Also shown are the locationsand names of inshore target reefs (black circles) in (b) PC and (c) WT, and locations of additional reef surveys across thecontinental shelf (smaller gray circles), and estuaries of the main rivers affecting the regions.

ies (Schroeter et al. 1993, Stewart-Oaten 1996b, U.S.Environmental Protection Agency 1998). The criteriamay need to be adapted or interpreted for particularstudies; e.g., the doseresponse relationship may benon-monotonic due to toxicity, and the strength of therelationship could be expressed in many ways depen-dent on how dose and response are measured. In thiswork, we formalize these procedures and extend its useto multiple possible causes.

We have argued that a shift from the use of hypoth-esis tests to estimation of parameters, and adoption ofbetter model selection processes or model averagingcan lead to more informative analyses of the detectionof change. We have also suggested that causal criteriacan be used to rigorously yet transparently attributecausality. Finally, by selecting combinations of eco-logical attributes that are complementary with respectto possible causes of change, we can better discriminatebetween likely agents of change, and reduce the like-lihood of confounding that may lead to spurious find-ings. The chosen attributes may be aspects of the phys-ical-chemical environment (either measured directly oras proxies), abundances and biodiversity of key speciesgroups, or ecological processes. These processesim-proved statistical analysis, use of epidemiological caus-al criteria, and selection of combinations of comple-mentary ecological attributescan lead to more effec-tive ecological assessments, and we illustrate this inthe following case study.

THE CASE STUDYIncreasing terrestrial runoff of nutrients, sediments

and pesticides is a major management issue facing theAustralian Great Barrier Reef (GBR), but the presenceof measurable effects of runoff on inshore areas has

been controversial (Bell 1991, Larcombe and Woolfe1999, van Woesik et al. 1999, Haynes and Michalek-Wagner 2000, Brodie et al. 2001, Devlin et al. 2001,Furnas 2003, McCulloch et al. 2003). River dischargesare the principal source of nutrients and sediments forthe shallow continental shelf waters (Furnas and Mitch-ell 2001), and land clearing for agriculture, removal ofsoil-retaining wetlands, and intensive use of agricul-tural chemicals have increased nutrients and sedimentsin these discharges three- to 11-fold since Europeansettlement in 1850 (Furnas 2003). Discharges from the423 000-km2 catchment area contained 1114 3 106 Mgof sediment in 2002 compared with 14.4 3 106 Mgbefore 1850, and trends of increasing soil erosion arerecorded in coral cores (McCulloch et al. 2003). Fur-ther, 100 000 Mg of nitrogen and 20 000 Mg of phos-phorus fertilizers are now applied to the catchmentsannually, though how much of it eventually enters themarine system is unknown (Furnas 2003). A regionalong the 200 km long wet-tropical coastline betweenTully and Port Douglas containing 60 coral reefs within20 km from the coast (latitude 188009 to 168209 S,longitude 1468109 to 1458309 E; Fig. 1) has been iden-tified as the area of greatest risk from agricultural run-off (Devlin et al. 2002). However, causal links betweenpollution and reef degradation in this region have beendifficult to demonstrate. This is due to factors such asa lack of historical data, the large spatial scale, thepresence of natural cross-shelf gradients in communitystructure (Dinesen 1983) and suspended particulatematter (Furnas 2003), and the presence of other typesof disturbance. As in many environmental impact stud-ies, there is no spatial replication for impacted andnonimpacted regions. Furthermore, the spatial and tem-poral variability of the flood-related episodic river dis-


PLATE 1. A naturally turbid but highly di-verse inshore reef in Princess Charlotte Bay.Photo credit: K. Fabricius.

charges of several pollutants is high, and the fate ofpollutants while undergoing dilution, biological up-take, sediment burial, and repeated resuspension duringthe transport from river to reef is little understood.Finally, ecological responses (linear or threshold re-lationships, synergistic responses) vary greatly acrossthe multitude of organisms that characterize the highlydiverse coral reef ecosystem.

Around well-defined point sources such as sewageoutfalls or coastal developments, increased sedimentsand nutrients are known to cause local reduction incoral recruitment, increase mortality, and shift the dom-inance from hard corals to non-reef building organisms(Smith et al. 1981, Wittenberg and Hunte 1992, Hunterand Evans 1995). Results from laboratory and fieldexperiments also demonstrate detrimental effects ofsedimentation and pesticides on individual organismgroups and life stages (Rogers 1990, Dubinsky andStambler 1996, Jones et al. 2003, Philipps and Fabri-cius 2003). While enhanced concentrations of inorgan-ic nutrients appear to have no direct effects on coralhealth (Szmant 2002), they can affect coral populationsindirectly, e.g., by shifting competitive advantages to-ward otherwise nutrient-limited algae when grazingpressure is low (McCook 1999), or by the formationof marine snow (Fabricius et al. 2003). Thus, whilecausal links between pollution and reef degradationhave been difficult to demonstrate at regional scalessuch as the inshore reefs of the GBR, pollution impactsare well documented and accepted at local scales andunder controlled conditions.

In this study, we assess possible associations be-tween the state of some GBR inshore reefs that areexposed to terrestrial runoff, but have also been ex-posed to a number of other disturbances. This casestudy has a high political and environmental profile,both locally due to large economic interests in a healthyreef through tourism revenues, and globally since land-based pollution and coastal development put 22% and30%, respectively, of coral reefs on Earth at risk (Bry-ant et al. 1998).

METHODS

Field data

The study is based on water quality analyses, andecological surveys of benthic cover, biodiversity, andoctocoral community structure. Only summaries of therelevant water quality and ecological surveys are pre-sented here; other laboratory and field studies havebeen or will be described in greater detail elsewhere(Fabricius et al. 2003, Phillips and Fabricius 2003,Diaz-Pulido and McCook 2003; K. E. Fabricius, G.Death, E. Turak, and D. Williams, unpublished man-uscript).

Study sites and survey methods.The field researchwas carried out in two regions within the Great BarrierReef (GBR), with one-off surveys characterizing 54reef sites across the whole continental shelf, and tar-geted research on 13 inshore reefs (Fig. 1). The wettropics (WT) lies between Tully and Port Douglas, andinshore reefs experience local river plumes from ag-ricultural catchments on an almost annual basis, andlarge plumes from the distant Burdekin River on a de-cadal basis (Furnas 2003). This region has the highestexposure to runoff from agricultural areas within theGBR (Fig. 1a; Devlin et al. 2002). The second regionlies north of Princess Charlotte Bay (PC) and ;400km north of WT, and the inshore reefs are exposed torunoff from sparsely populated catchments that havereceived little or no fertilizer and pesticides to date,but have low-density cattle grazing in some parts (seePlate 1). Both regions contain turbid inshore reefs insimilar geophysical settings, located within 20 km ofthe coast at 1218 m depth of the surrounding sea floor,and protected by a barrier of mid- and outer-shelf reefsup to 40 km offshore (Fig. 1b and 1c). In WT, mostdischarged material is eventually transported north-ward away from the reefs, whereas the large north-facing PC creates anticyclonic eddies which result intrapped and deposited sediment (Torgersen et al. 1983).The research on the inshore target reefs was conductedbetween 2000 and 2002. An additional 40 mid- and


outer-shelf reef sites were visited within the two re-gions for cross-shelf one-off surveys of benthic coverand octocoral communities.

The detailed disturbance histories of individual reefsare largely unknown. In the WT, some reefs experi-enced outbreaks of the coral-eating crown-of-thornsseastar Acanthaster planci in the late 1990s, tropicalcyclones in 1986 and 1990, and mortality through coralbleaching (the expulsion of symbiotic algae from thecoral tissue, primarily caused by high temperatures) in1998, with bleaching estimated as moderate to ex-treme on most reefs (Berkelmans and Oliver 1999).In PC, no data exist for crown-of-thorns seastar (highnumbers were observed on a reef neighboring the in-shore target reefs in 1991), but four tropical cycloneshave passed through the region within the last two de-cades. It is likely that the PC inshore target reefs didnot bleach in 1998 since satellite-based estimates ofsea surface temperatures were near-normal, but somereefs did suffer severe bleaching mortality in early2002 after the surveys were completed and during thecoral settlement experiment. The data on crown-of-thorns, cyclone, and bleaching disturbance historywere insufficient to assess or attribute effects on thescale of individual reefs. In order to distinguish be-tween the potential causes of change, a combination ofecological attributes with contrasting responses tobleaching, crown-of-thorns seastar predation, runoff,and cyclones were chosen for the study. The chosenattributes were:

1) Benthic cover of hard corals, octocorals, and ma-croalgae. These are the main groups of organisms usu-ally measured in the assessment of coral reefs, and wereexpected to respond to changing environmental con-ditions and disturbances in contrasting ways.

2) Taxonomic richness of zooxanthellate and azoox-anthellate octocorals (Anthozoa, Octocorallia; com-monly termed soft corals and sea fans). Thisgroup contains genera with and without symbiotic al-gae (called zooxanthellae) in their tissue, the formergroup depending on water clarity and light for pho-tosynthetic nutrition, whereas the latter group is in-dependent of water clarity (Fabricius and Death 2001).Octocorals were chosen as indicators for ecologicalattributes because of their abundance, and because theyrespond more specifically to water quality than hardcorals; azooxanthellate octocorals (which constituteabout half of the genera) do not suffer from coralbleaching, while zooxanthellate octocorals respondstrongly to turbidity, probably because of low photo-synthetic efficiency (Fabricius and Klumpp 1995). Oc-tocorals are also rarely eaten by crown-of-thorns seas-tar (Death and Moran 1998).

3) Community structure of octocorals on both theinshore target reefs and along the cross-shelf chloro-phyll gradient. This measure was chosen because com-munities are known to respond more strongly to en-

vironmental conditions than abundances of the maingroups (K. E. Fabricius, unpublished data).

One-off rapid ecological assessment surveys (Fa-bricius and Death 2001, Fabricius and Alderslade2001) were used to characterize the ecological condi-tion of 54 reef sites across the continental shelf in bothregions, and 13 inshore target reefs in both regions.Surveys were conducted on two sites per reef (wind-ward and leeward sides) at five depth zones per site(018 m); each survey at each depth zone covered;500 m2 of reef area. Survey data were collected onpercentage cover of the main benthos groups (hard cor-al, octocoral, macro algae, turf algae, coralline algae,sand and rubble) and taxonomic inventories and abun-dance estimates (rating 05) of all genera of octocorals.

Water quality data.Two sets of water quality datawere available from the two regions. First, a 10-yr dataset of chlorophyll concentrations at sites across thecontinental shelf on the GBR in both regions (J. Brodie,G. Death, M. Skuza, and M. Furnas, unpublished man-uscript). The chlorophyll measurements were sampledup to 12 times a year at each site. Second, water qualitydata were collected around the inshore target reefs.Concentrations of water quality parameters (suspendedsolids, particulate nitrogen and phosphorus, nitrate, ni-trite and ammonium, phosphate, total dissolved nitro-gen and total dissolved phosphorus, chlorophyll andphaeopigments, salinity and silicate) were determinedfrom water samples taken at each of the inshore targetreefs during nine visits between December 2000 andApril 2002. Water analyses followed standard proce-dures (Furnas and Mitchell 1996). Only a short sectionof the chlorophyll gradient is represented in the inshorewater quality samples, as the innermost reefs in PCwere avoided due to the likely presence of saltwatercrocodiles (Crocodylus porosus). All nutrient data ex-cept salinity were highly correlated and they were stan-dardized (z scores) and summed to form a water qual-ity index for each reef. Low index values correspondto water with low nutrients, chlorophyll, and suspendedparticles.

Statistical methodsThe analytical methods of this study did not include

the use of hypothesis tests for the reasons argued inthe Introduction. Instead, we estimated effect sizes andpredicted values with interval coverages. Inferenceswere based on model averaging, cross-validatedsmoothing splines and bootstrap estimation. For eachof these methods, the numerical results can be inter-preted from either frequentist or Bayesian perspectives,but of course the interpretations differ in each case.All data analyses used S-Plus (Statistical Sciences1999).

The relationships between chlorophyll concentra-tions and octocoral richness (zooxanthellate and azoo-xanthellate) were modeled for each region (WT andPC) as a function of relative cross-shelf distance (de-


fined as the distance of a site from the coast dividedby sum of distances from the coast and the edge of theouter continental shelf). The relationships of the re-sponses with relative cross-shelf distance were nonlin-ear, and smoothing splines were used with the degreeof smoothing estimated by cross-validation (Hastie andTibshirani 1990).

The water quality data and chlorophyll data were pre-analyzed prior to inclusion in models relating the ben-thic variables to gradients. To investigate the relation-ships between the inshore water quality variables, aprincipal components analysis was used. A water qual-ity index was then calculated as the sum of all stan-dardized (z scores) variables other than salinity, andscores on this index were used as measures of waterquality for each reef in subsequent analyses. The eco-logical survey data and the long-term chlorophyll datawere not recorded at identical sites, hence chlorophylllevels at the survey sites were estimated by the weight-ed mean of nearest neighbors from the chlorophyllsites.

Three sets of ecological attributes, namely (1) ben-thic cover (hard corals, octocorals, and macroalgae),(2) taxonomic richness (zooxanthellate and azooxan-thellate octocorals), and (3) community composition(all octocorals) were related to gradients (either chlo-rophyll or inshore water quality) and regional (WT vs.PC) differences. For both the chlorophyll and waterquality gradient analyses of benthic cover and richness,log-linear regression models with linear gradient ef-fects and categorical regional effects were used sincevariation increased with the mean and the implicit logtransformation helped linearize gradient effects. Foreach response, five models were fitted (1) differentslopes (gradient effects) within each region and dif-ferent intercepts (region effects), (2) same slope forboth regions, but different intercepts (region and gra-dient effects), (3) single gradient common to both re-gions, (4) no gradient effect but region effects, and (5)no gradient or region effects. The regional effects wereincluded to account for biological differences due toregion, which were partly confounded with the gradi-ents. The data sets were small with ;50 and 1320observational units for the chlorophyll or inshore waterquality data respectively.

Preliminary analyses indicated relatively weak as-sociations between the responses and explanatory var-iables for the smaller data sets, and suggested that con-clusions based on hypothesis tests may not adequatelyreflect seemingly consistent patterns across the chlo-rophyll and inshore water quality data. Thus, in orderto select an optimum form of analysis, we conductedsimulations based on estimates of effect sizes and errorobtained from preliminary analyses of benthic coverand richness gradient data. These simulations showedBIC (Schwarz 1978) to be marginally better than bothAIC and AICc (Burnham and Anderson 1998) for mod-el selection, and much better than hypothesis tests (see

Appendix). However, none of these methods reliablyselected the true model, and model averaging (Raftery1988, 1995) gave slightly better predictions than singlebest models. Hence, we have used model averaging forall gradient analyses of benthic cover and richness.Confidence (credibility) intervals were obtained bybootstrapping (Efron and Tibshirani 1993, Davison andHinkley 1997).

Redundancy analyses (RDA; Rao 1964, Jongman etal. 1995) were used to assess the dependence of oc-tocoral communities on regional differences (WT andPC) and on both cross-shelf chlorophyll data and in-shore water quality data. The abundances of octocoralswere fourth-root transformed to downweight dominanttaxa, and reef averaged over depths and sites. For com-munity analyses involving the chlorophyll gradient,relative distance across the shelf was also included.Ecological gradients were relatively short (moderatespecies turnover) thereby justifying the use of RDA,which requires linear changes of species along gradi-ents. The strengths of regional and gradient effectswere quantified by bootstrapping (Efron and Tibshirani1993, Davison and Hinkley 1997) the pseudo-F statistic(ter Braak 1992).

Synthesis

To successfully use the causal criteria, both the eco-logical attributes and the criteria have to be defined foreach study individually. Our ecological attributes werecover of hard corals, octocorals, and macroalgae, spe-cies richness of zooxanthellate and azooxanthellate oc-tocorals, and community structure, across the shelfalong the chlorophyll gradient and on the inshore targetreefs along the water quality gradient. The criteria wereslightly modified from Hills initial list (Hill 1965) tobest reflect the nature of the case study, using the fol-lowing definitions:

1) Doseresponse relationship was satisfied if theprobability of gradient effect was .0.99. This corre-sponds to a Bayes factor of 100:1 and can be taken asstrong evidence for a relationship between dose andresponse (Raftery 1995).

2) Strength of association was defined by effect size.A strong effect was defined as .100% increase (or 50%decrease) along 80% of the length (to exclude extremevalues) of the inshore water quality gradient within WT.

3) Logical time sequence indicates that the changedid not precede exposure to the disturbance. An as-sessment of this criterion relies on historic data fromthe study regions, which were sparse in our case (nodata exist from the remote PC, and only a few obser-vations exist from WT; Ayling and Ayling 2002; AIMSLong-Term Monitoring Program, unpublished data).

4) Consistency across populations was defined asconsistency with other studies (i.e., responses re-corded in our study were similar to those reported fromother regions in independent published studies).


TABLE 1. Comparison of water quality values around inshore reefs of the wet tropics (WT) and Princess Charlotte Bay(PC).

Inshore water qualityPC

Mean SE

WTMean SE Ratio 95% CI P

Suspended solids (SS; mg/L)Chlorophyll (Chl; mg/L)Phaeopigments (phae; mg/L)Particulate nitrogen (PN; mmol/L)Nitrate (NO3; mmol/L)Ammonium (NH4; mmol/L)

1.400.400.191.410.0240.16

0.130.040.030.130.010.02

3.770.560.322.550.140.24

0.840.090.060.380.070.08

2.691.401.681.815.831.50

1.49, 3.990.93, 1.990.97, 2.731.27, 2.490.71, 27.40.55, 2.67

.0.990.930.960.990.880.83

Nitrite (NO2; mmol/L)Total dissolved nitrogen (TDN; mmol/L)Particulate phosphorus (PP; mmol/L)Total dissolved phosphorus (TDP; mmol/L)Silicate (Sil; mmol/L)Salinity (Sal; g/L)

0.0128.460.0960.434.04

31.8

0.0020.590.0120.061.190.48

0.0198.380.160.569.46

29.1

0.0031.010.030.062.591.54

1.580.991.671.302.340.93

0.99, 2.460.75, 1.341.01, 2.550.95, 1.921.08, 6.380.82, 1.01

0.960.490.970.930.990.02

Notes: The table lists means and standard errors for each region together with the ratio for WT/PC, 95% confidence(credibility) intervals based on bootstrap resampling, and the probability that the WT/PC ratio is .1. The intervals can betreated as confidence intervals or credibility intervals with a non-informative prior.

5) Specificity referred to responses that were known(from published literature) to be caused by runoff andwas unlikely to be caused by another disturbance type.

6) Agreement with biological facts was scored bycomparing the responses found in our field surveyswith the results of studies where relationships weredirectly assessed through manipulative experiments.

By applying each of Hills criteria to each of theecological attributes, we created a matrix that was usedto determine to which extent the observed types orchanges in the inshore coral reefs might have beencaused by exposure to runoff. Criteria 13 (dosere-sponse relationship, strength of association, and logicaltime sequence) were assessed based on the relation-ships between the ecological attributes and the twowater quality data sets. Criteria 46 (consistency withother studies, specificity of response, and agreementwith biological facts) were scored by comparing theresponses of the ecological attributes with results fromother independent sets of published studies (laboratoryexperiments on pollution effects, and data from pol-luted locations in other regions). For ease of commu-nication, we summarized all results in the matrix byscoring each cell in one of three possible ways (cellsthat could not be addressed due to the lack of data weremarked with na): agreement of the response of theattribute with the criterion (11), weak or inconclu-sive response due to inconsistent results or weaknessin the study design (0), responses that are in dis-agreement with the criterion (21). The overall evi-dence for a causal association with water quality wasthen assessed as the sum over all cells for each of theecological attributes, expressed as proportion of thetotal number of cells for each attribute for which datawere available.

RESULTSField data

Regional differences in water quality and ecologicalattributes on inshore target reefs.Mean concentra-

tions of suspended solids, particulate nitrogen, and par-ticulate phosphorus were 170270% higher in the wateraround the WT reefs than in the water around PC reefs,and mean nitrate levels were 580% higher in WT thanin PC (Table 1). Concentrations of other dissolved andparticulate nutrients were also at least as high or higherin WT than in PC. All water quality variables exceptsalinity were highly correlated, and most of the inshoretarget reefs in WT were exposed to higher nutrient andsediment concentrations than in PC (Fig. 2).

The ecological attributes of WT also differed sub-stantially from those of PC. On WT reefs 67.7% 65.6% (mean 6 1 SE) of space was covered in algae(turf, coralline, and macroalgae). In contrast, algae oc-cupied 39.6% 6 5.4% of space on PC reefs. Coral coverwas lower in WT than in PC (mean hard coral cover,15.1% 6 2.4% vs. 43.4% 6 1.0%; octocorals, 2.8% 60.8% vs. 5.2% 6 1.5%). The richness of octocoralswas also lower in WT than in PC (25.5 6 2.4 vs. 35.66 2.3 genera per reef). In contrast, dead coral coverin WT was higher than in PC (17.2% 6 6.0% vs. 3.8%6 0.4%).

Changes in ecological attributes along the chloro-phyll gradient across the continental shelf.The gra-dients in mean water column chlorophyll concentra-tions across the continental shelf differed between PCand WT (Fig. 3). In WT, chlorophyll increased steeplytowards the coast, and was up to three times higher inthe innermost 20 km of the region than offshore or inPC. In PC, chlorophyll remained constant and rela-tively low across the shelf. Changes in the taxonomicrichness of octocorals across the continental shelf alsoshowed clear patterns. Richness of zooxanthellate oc-tocorals (genera that contain symbiotic algae in theirtissue and depend on water clarity and light for theirnutrition) declined by 30% in the WT inshore regionwithin the innermost 20 km of the shelf where chlo-rophyll was high (Fig. 3). In contrast, no clear cross-shelf changes in richness of the zooxanthellate genera


FIG. 2. Principal components biplot of water quality data(log-transformed and z-score-transformed) at inshore targetreefs in the two study regions (gray, Princess Charlotte Bay;black, wet tropics). The first two components accounted for78.6% of water quality variation. Each symbol represents atarget reef, for which data were averaged over within-reeflocations and sampling times; the extent of fill of the symbolsrepresents octocoral richness, with lowest and highest rich-ness displayed as open and completely filled symbols, re-spectively. Reefs from the same region are surrounded by apolygon. Vectors of the water quality variables and water qual-ity index point at reefs with highest concentrations. Abbre-viations of the water quality variables are listed in Table 1.

were apparent in PC where chlorophyll was stableacross the shelf. The richness of octocorals withoutzooxanthellae (which do not require light for their nu-trition) varied little across the continental shelf in WTand PC and appeared unrelated to the chlorophyll gra-dient.

Some of the ecological attributes varied systemati-cally along the chlorophyll gradient in both regions(Fig. 4, Table 2). Macroalgal cover increased with in-creasing chlorophyll concentrations in both regions.While hard coral cover was highly variable and unre-lated to chlorophyll and region, octocoral cover de-clined with chlorophyll, reaching lowest cover at achlorophyll concentration of .0.3 mg/L. The richnessof zooxanthellate octocorals decreased with increasingchlorophyll, whereas the richness of azooxanthellateoctocorals was unrelated to chlorophyll.

Octocoral communities showed strong cross-shelf(pseudo-F 5 9.52), chlorophyll (pseudo-F 5 5.02), andregional effects (pseudo-F 5 3.13; Fig. 5, Table 3).Community composition varied most along the chlo-

rophyll gradient within the WT region, with highestchlorophyll levels associated with lowest richness. Fewgenera occurred in areas of high chlorophyll concen-trations, and these conditions were only found on someof the WT inshore reefs. While mid-shelf reefs hadhighest richness, some wave-exposed outer-shelf reefsalso had relatively low richness, but were characterizedby a very different suite of species than the low-di-versity high-chlorophyll near-shore reefs.

Changes in ecological attributes along the waterquality gradients on the inshore target reefs.Changesin ecological attributes along the inshore water qualitygradient were weaker than those along the chlorophyllgradient across the shelf, most likely because of therelative shortness of the inshore gradient and the lowernumber of inshore reefs (1320). Nonetheless, three ofthe ecological attributes varied along the water qualitygradient in similar ways to the cross-shelf variationalong the chlorophyll gradient (Fig. 4, Table 2). Onlyone of the three effects would have been detected hadfrequentist tests been used for the analyses (Table 2).Macroalgal cover increased along the water quality gra-dient in WT around threefold, but was variable in PC.Hard coral cover and the richness of azooxanthellateoctocorals both showed strong regional differences, butwere unrelated to the water quality gradient. Octocoralcover was similar in both regions and also appearedunrelated to water quality. In contrast, the richness ofzooxanthellate octocorals declined with increasing nu-trients along the water quality gradient in both regions.

The octocoral communities varied most stronglyalong the water quality gradient (pseudo-F 5 3.39,Table 3) and to a lesser degree between regions (1.25).Generic richness was highest in clear water PC reefs,and most genera were absent or occurred in low num-bers in WT water of low water quality (Fig. 6). Withineach region, reefs associated with the highest nutrientand sediment levels were those with lowest richnessand vice versa. Only two octocorals species (the en-crusting Briareum sp. and Clavularia koellikeri) wereassociated with high nutrients, whereas a large pro-portion of genera were strongly associated with thelow-nutrient PC reefs.

SynthesisWe combined the results of our field study with re-

sults from other regions and from laboratory experi-ments, and used Hills causality criteria to evaluate thepotential link between water quality and the conditionof the inshore coral reefs. The results were expressedconcisely in matrix format with the rows of the matrix(Table 4) defined by the criteria, and the columns bythe ecological attributes, namely, cover of the mainbenthos groups, richness of zooxanthellate and azoo-xanthellate octocorals, and community structure.

Doseresponse relationship.There was strong ev-idence for doseresponse relationships for three of thefive ecological attributes both along the cross-shelf


FIG. 3. Cross-shelf gradients in mean chlorophyll concentrations, based on 10 years of chlorophyll monitoring data andtaxonomic richness in octocorals with and without zooxanthellae (PC, Princess Charlotte Bay; WT, wet tropics). Thick solidlines indicate cross-validated smoothing splines; thinner lines are 95% confidence intervals. Vertical light gray bands indicatethe locations of the inshore target reefs across the shelf.

chlorophyll gradient and along the water quality gra-dient on the inshore target reefs (Fig. 4, Tables 2 and3). Doseresponse relationships were established formacroalgal cover, octocoral cover, generic richness ofzooxanthellate octocorals, and octocoral communitystructure. In contrast, hard coral cover, although muchlower in WT than in PC, was unrelated to the chlo-rophyll and inshore water quality gradients, possiblydue to the effect of other disturbances. The richness ofazooxanthellate octocorals was also unrelated to waterquality, as expected from their biological requirements.In all of the relationships, the directions of change (i.e.,increase or decrease along the water quality gradient,or differences between regions) agreed with those ex-pected from existing biological knowledge.

Strength of association.Some of the effects alongthe water quality gradient across the shelf and withinthe WT inshore region were large and ecologically sig-nificant (Fig. 4, Table 2). Macroalgal cover was 3.1times higher on the reefs with highest nutrient and par-ticle loads compared with those in clearest water inWT. The number of octocoral genera was 2.4 timeshigher on WT reefs with clearest water compared withthose in the least clear water. Associations betweencommunity structure and the water quality and chlo-rophyll gradients were stronger than the differencesbetween the regions (Table 3), with depauperate com-munities recorded in the reefs with highest nutrient andparticle loads.

Logical time sequence.The assessment of this cri-terion is limited by the scarcity of historic data. Fewdata exist to compare the ecological attributes on anyof the inshore target reefs with those from the past, andsome differences between the two regions in biodiver-sity are likely to have always existed due to the naturaldecline in biodiversity with increasing latitudes. Theonly historic data available are for one reef in the WTregion where hard coral cover decreased from ;80%in 1989 to the present state due to a number of dis-turbances (Ayling and Ayling 2002). The logical timesequence criterion of water quality effects on coralreefs has however been met in other places, e.g., inHawaii, where coral cover increased and algal coverdecreased following offshore diversion of a coastalsewage outfall site (Smith et al. 1981, Hunter and Evans1995).

Consistency across different studies.The findingsof increased macroalgal cover and low hard coral coverare consistent with previous work in other coral reefregions exposed to terrestrial runoff (e.g., Tomascik etal. 1993, Edinger et al. 2000, Hodgson and Yau 1997,van Woesik et al. 1999, West and Van Woesik 2001).Few studies have included octocorals, but species rich-ness in response to disturbance have been reported forother groups of reef organisms (Edinger et al. 1998).The response of zooxanthellate octocorals, and the ab-sence of a response in azooxanthellate octocorals areconsistent with a study on larger scale on the relation-


FIG. 4. Relationship between ecological reef attributes and chlorophyll and water quality. Gray and black lines and circlesindicate reefs within the PC and WT regions, respectively. Solid lines indicate the model-averaged predictions from log-linear model fits, dashed lines indicate 95% confidence (credibility) intervals derived by bootsrapping, and the (pseudo-) R2are the explained deviance of the model-averaged fit divided by the deviance of the null model. The left column of plotsshows variation of ecological attributes along the chlorophyll gradient across the continental shelf, and the right columnsimilarly for the water quality index, calculated as the sum of the z scores of all water quality variables excepting salinityof each reef at inshore target reefs (high index values, high nutrient concentrations; low values, cleaner water). Ecologicalattributes are: HC, hard corals; OC, octocorals; MA, macroalgae; zoox, zooxanthellate; azoox, azooxanthellate. Richness isdefined as the number of genera of octocorals per reef encountered during swim surveys.


TABLE 2. Analyses of models relating ecological responses to (a) chlorophyll gradients and cross-shelf regions and (b)inshore water quality gradients and regions.

Variable R 3 G R 1 G G R 1 G exp(G ) 95% CI Pa) Cross-shelf (chlorophyll)

MA cover (%)HC cover (%)OC cover (%)OC richness (zoox)OC richness (azoox)

0.030.000.190.100.07

0.170.020.440.150.40

0.80*0.100.37*0.75*0.00

0.000.140.000.000.52*

0.000.73*0.000.000.01

0.9120.0121.4920.75

0.29

2.480.990.230.471.34

1.93, 3.180.92, 1.070.15, 0.340.38, 0.600.95, 1.95

.0.990.61

.0.99

.0.990.04

b) Inshore (water quality index)MA cover (%)HC cover (%)OC cover (%)OC richness (zoox)OC richness (azoox)

0.390.110.240.050.09

0.210.270.280.190.29

0.240.000.210.72*0.26

0.060.62*0.25*0.020.36*

0.10*0.000.020.010.00

1.120.03

20.0120.3420.24

3.061.030.990.710.79

1.61, 6.710.82, 1.320.60, 1.610.59, 0.870.55, 1.13

.0.990.390.51

.0.990.91

Notes: Abbreviations: MA, macroalgae; HC, hard coral; OC, octocoral; zoox, zooxanthellate; azoox, azooxanthellate. Log-linear models were used for all analyses. In the models, the gradient (G) effects are linear, and the region (R) effects arecategorical. For each ecological response, five models are compared: R 3 G, different gradient effects (slopes) within eachregion and different region effects (intercepts); R 1 G, same slope but different region effects; G, single slope common toboth regions; R, no gradient effect but regional effects; and 1, no gradient or regional effects. The best model (most likelyaccording to Bayes information criteria [BIC]) is denoted by boldface text, and the best model selected by backward eliminationof nonsignificant terms (P . 0.05) is indicated by an asterisk. For four of the 10 models, the most likely model was notselected by backward elimination. For four of the models, the relative probability for the most likely model is low (,0.5).Models were averaged, weighted by the relative probabilities, and the estimated gradient (G ), proportional change (exp(G )),and 95% confidence (credibility) interval (CI) were estimated. For macroalgal cover (expected to increase along the gradient),the probability (P) that proportional change was .1 is shown; for other responses, the probabilities of decline are shown.For each response, the predicted values weighted by the relative probabilities and averaged across models are shown in Fig. 3.

FIG. 5. Redundancy analysis biplot of oc-tocoral coral communities, showing the depen-dence on chlorophyll, relative distance acrossthe continental shelf (across) and region(gray, Princess Charlotte Bay; black, wet trop-ics). The first two components shown in thebiplot accounted for 78.6% of the explained var-iation in octocoral abundances. Each symbolrepresents a reef, and the fill of the symbol rep-resents the generic richness in octocorals of thatreef. Chlorophyll and distance of the reefsacross the shelf are represented as vectors andpoint in the directions of highest chlorophyllloads and offshore reefs, respectively.

ship between octocoral richness and water clarity (Fa-bricius and Death 2001).

The response is specific for the cause, thus an as-sociation should be stronger if there are few ratherthan many causal factors.Some, but not all of theresponses were specific to water quality. In particular,total hard coral cover is a nonspecific response, as itmay be low after a bleaching or crown-of-thorns dis-turbance, or it may be high even in chronically adverseconditions due to the asexual spread of a few resistantspecies. Macroalgal growth increases with nutrients,and unlike corals is not known to be affected by periodsof high temperatures. Macroalgal cover remains lowdespite high nutrient levels at high grazing pressure(McCook 1999), however macroalgal cover is rarelyhigh in low-nutrient environments, and macroalgaehave established dominance on reefs in some areas of

eutrophication (Smith et al. 1981). Octocoral cover ismore specific than hard coral cover, as octocorals arerarely eaten by the crown-of-thorns seastar (Death andMoran 1998), or indeed any other major group of pred-ators, and azooxanthellate octocorals are unaffected bycoral bleaching. A few octocorals of the families Al-cyoniidae, Clavulariidae, and Briareidae may increasein cover in areas of high nutrient loads, if other physicalenvironmental conditions such as currents and light arefavorable. However, bleaching-susceptible zooxanthel-late genera tend to be more abundant than azooxan-thellate genera, and thus octocoral cover may be re-duced by bleaching. The decrease in octocoral richnessappears to be specific to water quality. This conclusionis supported by the rarity of species with low toleranceof poor water quality, the abundance of some moretolerant taxa (e.g., Briareum sp.) and the lack of re-


TABLE 3. ANOVA of community composition of octocoral genera showing the effects of (a)relative distance across the shelf, the chlorophyll cross-shelf gradient and regions (WT andPC), and (b) the inshore water quality gradient and regions.

Effect df SS MS SS (%) Pseudo-F 95% CI Pa) Cross-shelf

AcrossChlorophyllRegionResiduals

111

51

53.328.117.5

285.5

53.328.117.5

5.6

13.97.34.5

74.3

9.525.023.13

6.77, 12.823.47, 7.441.24, 4.92

.0.99

.0.990.99

b) InshoreWater quality indexRegionResiduals

11

10

16.9696.285

50.048

16.96.35.0

23.18.6

68.3

3.391.25

1.32, 6.660.45, 5.56

.0.990.61

Notes: For panel (a) 66 genera were analyzed, and for panel (b) 57 genera were analyzed;the sequential sums of squares were summed over the responses to give ANOVA tables. Thepseudo-F statistic was bootstrapped and bias-adjusted to give 95% intervals. P denotes theprobability that the true value of pseudo-F was .1 (the expected value of pseudo-F, giventhat a variable has no effect). For (a) there were strong effects of relative distance across theshelf and chlorophyll, and weaker regional differences. For (b) there was a strong effect ofwater quality and no effect of region. Reversing the order of inclusion of chlorophyll andregion, and water quality and region, slightly increased the strength of the region effects (;30%)and correspondingly reduced the chlorophyll and water gradient effects.

FIG. 6. Redundancy analysis biplot of octocoral coral communities at the inshore target reefs in PC (gray symbols) andWT (black symbols). The first two components shown in the biplot accounted for all of the explained variation in octocoralabundances. Each symbol represents a reef, and the fill of the symbol represents the generic richness of octocorals at thatreef. The water quality index vector points in the direction of highest nutrient loads.

sponse in azooxanthellate taxa in WT. Similarly, thegradients in octocoral community structure, with com-munities being progressively depauperate with increas-ing nutrients and sediments, could not be explained byother major disturbances (cyclones, bleaching, andcrown-of-thorns).

Association agrees with known biological facts.Increased supply of limiting nutrients is known to in-crease macroalgal growth rates in the absence of otherlimiting factors (Schaffelke and Klumpp 1998). Sedi-mentation affects recruitment and increases adult mor-tality in hard and octocorals, thus potentially reducingcoral cover (Riegl and Branch 1995, Fabricius et al.2003, Philipps and Fabricius 2003). A reduction in bio-diversity is likely due to variable tolerances of speciesto sedimentation, turbidity, and bleaching. Octocoralrichness is known to decline by one genus per meterreduction in visibility on the Great Barrier Reef in areaswhere visibility is ,10 m (Fabricius and Death 2001),a finding that matches the decline in richness along thewater quality and chlorophyll gradients in this study.

DISCUSSION

Identifying anthropogenic causes of ecologicalchange and distinguishing such change from naturaldynamics is a nontrivial task. However causal attri-bution profoundly enhances the ability of scientists tocontribute to environmental management, and increas-es the effectiveness of management action. For ex-ample, in coral reef ecology the cause(s) of outbreaksof the crown-of-thorns seastar Acanthaster planci arestill being debated by scientists 40 years after the firstobservations of outbreaks. Costly local eradication pro-grams are now in place to protect some tourism sites;funding that might have been spent on preventativemeasures if causes had been identified with a reason-able level of certainty. A contrasting example is thathigh sea surface temperatures are now accepted bymost scientists as the major cause of coral bleaching(Strong et al. 1997), and agreement on the cause ofpredicted massive ecosystem changes by coral bleach-ing is adding momentum to the call for political action


TABLE 4. Synthesis matrix defined by causality criteria and ecological attributes, summarizingecological evidence of terrestrial runoff effects on reefs within the wet tropical region of theGreat Barrier Reef (Fig. 1).

CriteriaBenthic cover

HC OC MARichness

OC Z OC ACommunitiesOC C OC I

A1) Biological gradient: cross-shelfB1) Effect size: cross-shelfA2) Biological gradient: inshoreB2) Effect size: inshore

2222

1122

1111

1111

2220

1111

1111

C) Logical time sequenceD) Consistency with other studiesE) Specificity of responseF) Agreement with biological facts

1021

NA101

NA111

NA111

NA111

NANANA1

NANANA1

Score 23/8 2/7 7/7 7/7 0/8 5/5 5/5Notes: Data from this region are compared with those from reefs at low risk from runoff in

the Princess Charlotte Bay. Criteria A and B address responses observed in this study (Figs.46, Tables 23). Criteria CF address the agreement between responses in this study andresults from other runoff-exposed regions and from laboratory studies on pollution effects.Symbols in the matrix indicate the following: agreement with the criterion (1), inconclusive(0, inconsistent or weak), disagreement (2), and not addressed due to the lack of data (NA).Abbreviations: HC, hard coral; OC, octocorals; Z, zooxanthellate; A, azooxanthellate; C, cross-shelf; I, inshore. The final row is the sum of scores as the proportion of all scores for eachattribute, scoring 1 as 11, 2 as 21, and ignoring NA.

to combat greenhouse gas emission. While enormousinternational commitments are required to halt or re-verse global climate change, in our case study the re-duction of soil and fertilizer loss through terrestrialrunoff is achievable through integrated river basin man-agement and long-term government support on regionalscales (Brodie 2003).

The use of a framework based on the estimation ofeffect sizes to determine change, an established set ofcriteria to attribute causality, and a rigorous choice ofecological attributes with contrasting environmental re-sponses, represents a shift in philosophy and practicefrom some of the existing ecological techniques. Forsimple ecological analyses, methodologies such as con-trol-impact studies and ecological indicators can ef-fectively detect change, and meta-analysis of multiplestudies can help relate change to potential causes on abroader scale (Schmitt and Osenberg 1996). However,for large or complex ecosystems, existing methods areoften insufficient to detect change and attribute cau-sality (Stewart-Oaten 1996a, b). Many forms of evi-dence of varying strengths are to be considered, e.g.,field studies with notoriously imperfect controls andlaboratory experiments that oversimplify natural sys-tems. It is therefore not surprising that simple hypoth-esis tests are unable to resolve such complex questions.After all, it took decades of extensive and expensiveresearch for epidemiologists to assemble sufficient ev-idence to identify cigarette smoking as a cause of lungcancera link that is obvious in hindsight. Hills meth-od of causal attribution has been recommended by theEnvironmental Protection Agency of the United States(U.S. Environmental Protection Agency 1998), but ap-plications, both on single-factor single-cause analyses,or on ecosystem analyses, have so far rarely been at-tempted in the ecological literature.

In our study, we found the inshore reefs in the WTregion to be in a disturbed state. The use of Hills causalcriteria helped us to synthesize and assess a body ofdata that quantified the ecological attributes and theenvironmental conditions in the two regions, and alongwater quality gradients within the regions. The twobiological gradients, the large effect sizes, the consis-tency across populations and with other studies, andthe agreement with biological facts, all contributed toenhancing the belief that water quality had an adverseeffect on aspects of the ecological state of these reefs.These data indicated the existence of causal links be-tween high nutrient loads and high macroalgal coveras well as low octocoral biodiversity. Hard coral coveralso differed more than two-fold between the two re-gions. Since the two regions contrasted not only inwater quality but also in exposure to other disturbances(coral bleaching and coral predation by crown-of-thorns seastar), these strong regional differences couldnot be attributed exclusively to water quality, althoughresponses to these multiple disturbances including wa-ter quality may have been cumulative or synergistic.We found the matrix to be helpful in synthesizing thedata, attributing causality to some attributes while ex-cluding others, and communicating the results to otherscientists and the public.

The scientific understanding of coastal marine sys-tems in the tropics is far less developed than in tem-perate systems such as Chesapeake Bay or the Northand Baltic Seas, where considerable effort has beeninvested into understanding the effects of nutrient en-richment and toxic chemicals. In our case study, theavailable data are comparatively sparse. Due to the highbiological diversity of the Great Barrier Reef, we fo-cused on responses of relatively broad ecologicalgroups rather than of individual key species. It became


apparent that community structure responded more sen-sitively to water quality than summary parameters suchas benthic cover, indicating that substantial informationis contained in the responses of individual species thatwill require follow-up laboratory exposure-effects ex-periments. It also became apparent that there was littlespecies replacement in octocorals from clean to turbidwater, rather species increasingly dropped out towardshigh nutrient conditions, and only two species appearedto grow well in the waters with the least clean water.Although octocorals do not contribute to the formationof coral reefs, they are the second-most abundant groupof macrobenthos in coral reefs. The disappearance ofa large number of octocoral genera in areas of highloads of nutrients and suspended particles may be takenas an indicator for severe deterioration of environmen-tal conditions, with potential consequences for otherorganism groups that were not included in the study.

What remained unresolved in our study is the originof the high nutrient levels in the water around the in-shore target reefs, and thus the direct link betweenterrestrial runoff and nutrient enrichment. It may beargued that nutrient, chlorophyll, and sediment levelsin the marine inshore region of the WT may have al-ways been high. Rivers discharging from catchmentswith heavily fertilized sugar crops into the wet tropicalmarine system carry levels of nutrients, sediments andpesticides that are several times higher than estimatedlevels in pre-European times and than rivers discharg-ing into Princess Charlotte Bay (Furnas 2003). Therelatively high levels of nitrate measured around theWT reefs (Table 1) indicate fresh supply of this nutri-ent, as concentrations of dissolved inorganic nitrogenare rapidly taken up by nutrient-limited algae and bac-teria in tropical marine systems. This supply is likelyto have come from the rivers, which are the largestsource of new nutrients in the GBR lagoon away fromupwelling areas (Furnas et al. 1997, Mitchell et al.2001). Combined with flood-plume dispersal models(Devlin et al. 2002), the limited existing data thereforesuggest that modern agrochemicals do reach the targetinshore reefs of the WT where they may contribute tothe measurably higher concentrations of water nutri-ents. Similarly, it may be argued that coral cover andbiodiversity on the WT reefs may have always beenlow. However, the widths of the reef flats around theislands in both regions indicate positive reef accretionover extended periods in earlier times (Hopley et al.1983). Observations on one WT reef group (the Rus-sell-Normanby Islands) show declining coral coversince 1990 due to a series of disturbances by bleaching,cyclones, and a crown-of thorns seastar outbreak (Ayl-ing and Ayling 2002), and it appears that the reefs havefailed to recover from these disturbances. Coral coverhas also been monotonously declining on a number ofother WT inshore reefs since 1985 to ;10% cover in2002 (AIMS Long-Term Monitoring Program, unpub-lished data). Coral cover can increase by 10% per year

on some inshore reefs after disturbance (Ayling andAyling 2002), and anecdotal reports indicated that coralcover on the Russell-Normanby Islands did recover af-ter tropical cyclones in 1975 and 1977, and reachedpre-cyclone levels in 1990 after a severe tropical cy-clone in 1986 (Devantier 1994). This suggests that mul-tiple disturbances have reduced coral cover on WTreefs, while exposure to terrestrial runoff appears toretarding coral recovery after the recent disturbances.In the absence of new disturbances, coral cover mayrecover within one to two decades despite continuingexposure to high levels of nutrients and sediments(probably through the re-establishment of fast-growingMontipora and Acropora), whereas the processes andtime frames leading to restoration of coral richness andpresence of more sensitive or slower-growing speciesare unknown. Importantly, although changes betweenpast and present conditions both in water quality andin the ecological properties of the WT inshore reefscan not be established with certainty, the ecologicalgradients along the water quality gradient (as demon-strated in this study) can serve to predict future changesin macroalgal cover and octocoral biodiversity, shouldthe water quality deteriorate or improve as land usepractices change.

To conclude, using the matrix in combination withmodern statistical analyses, we have aimed at over-coming the problems that often prevent the early de-tection of ecological change and its causes. We havesynthesized multiple and complex sources of infor-mation to avoid any misleading selective use of sci-entific data, and used modern statistical methods toovercome abuse of technical concepts such as proba-bility and causality. As ecologists, we need to synthe-size multiple and complex sources of information,weigh the evidence, quantify effect sizes, and predictthe ecological consequences and socioeconomic costsof alternative actions. The matrix framework can helpdoing all this. By applying stringent, scientifically ro-bust, yet simple criteria to well-chosen properties ofthe ecosystem, we can greatly improve our capacity todetermine change and attribute causality. This shouldlead to higher performance benchmarks for complexecological studies, and a greater scientific contributionto sustainable ecosystem management. It is then up toa better-informed society to decide how much ecolog-ical change is acceptable.

ACKNOWLEDGMENTSWe gratefully acknowledge the contributions of Jon Brodie

for providing the long-term chlorophyll monitoring data ofthe Great Barrier Reef Marine Park Authority, and of MichelleSkuza, Jane WuWon, and Margaret Wright for analyzing thewater quality samples. We thank the Subject Editor Paul Day-ton, Simon Thrush, and an anonymous reviewer for very help-ful reviews and comments. Thanks also to Miles Furnas, JonBrodie, David Williams, Britta Schaffelke, Lyndon Devantier,Janice Lough, and Malcom McCulloch for comments on ear-lier versions of the manuscript. A number of volunteers andthe crews of the AIMS Research Vessels were a great help


in the field studies. The research was funded by the Coop-erative Research Centre for the Great Barrier Reef WorldHeritage Area (CRC Reef), and the Australian Institute ofMarine Science (AIMS).

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IDENTIFYING ECOLOGICAL CHANGE AND ITS CAUSES: A CASE STUDY ON CORAL REEFS