A framework to identify ecological change and its ...

Katharina Fabricius & Glenn De’ath (2004) – Ecological Applications 14:1448-1465 – p1

A framework to identify ecological change and its causes: a case study on the effects of terrestrial run-off on coral reefs

Katharina E. Fabricius and Glenn De’ath

Australian Institute of Marine Science PMB No. 3, Townsville MC, Qld 4810, Australia. email: [email protected]

Key words: Bayesian analysis, biodiversity, bootstrap, causality, community structure, coral reef, detection of change, environmental impact, epidemiology, Great Barrier Reef, hypothesis tests, model averaging, model selection, pollution, terrestrial run-off, weight of evidence.

Abstract The successful management of ecosystems depends on early detection of change and identification of factors causing such change. Determination of change and causality in ecosystems is difficult, both philosophically and practically, and these difficulties increase with the scale and complexity of ecosystems. Management also depends on the communication of scientific results to the broader public, and this can fail if the evidence of change and causality is not synthesized in a transparent manner. We developed a framework to address these problems when assessing the effects of agricultural run-off on coral reefs of the Australian Great Barrier Reef (GBR). The framework is based on improved methods of statistical estimation (rejecting the use of statistical tests to detect change), and the use of epidemiological causal criteria that are both scientifically rigorous and understood by non-specialists. Many inshore reefs of the GBR are exposed to terrestrial run-off from agriculture. However, detecting change and attributing it to the increasing loads of nutrients, sediments and pesticides is complicated by the large spatial scale, presence of additional disturbances and lack of historic data. Three groups of ecological attributes, namely benthos cover, octocoral richness, and community structure, were used to discriminate between potential causes of change. Ecological surveys were conducted along water quality gradients in two regions; one that receives river flood plumes from agricultural areas, and one exposed to run-off from catchments with little or no agriculture. The surveys showed increasing macroalgal cover and decreasing octocoral biodiversity along the gradients within each of the regions, and low hard coral and octocoral cover in the region exposed to terrestrial run-off. Effects were strong and ecologically relevant, occurred independently in different populations, agreed with known biological facts of organism responses to pollution, and were consistent with pollution effects found in other parts of the world. The framework enabled us to maximize the information derived from observational data and other sources, weigh the evidence of changes across potential causes, make decisions in a coherent and transparent manner, and communicate information and conclusions to the broader public. The framework is applicable to a wide range of ecological assessments.

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Introduction Early detection of ecological change and identification of factors causing such change are essential for successful ecosystem management. Despite the availability of the best scientific data, interested parties often disagree about the existence of ecogical change and its causes. There are many reasons for this including: (1) the selective use of scientific data and other information by interested parties to support individual claims and objectives, (2) the misinterpretation and abuse of technical concepts such as probability and causality (Newman and Evans 2002), (3) the complexities of large-scale ecosystems which cannot be simply explained or reliably predicted, and (4) the exploitation of disagreement amongst scientists by stakeholders. The resulting lack of consensus can lead successively to conflict, confusion over policy development, government inaction and environmental degradation. To overcome these problems, we developed a framework based on: (1) the use of improved methods for determining 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 scientific and other data according to criteria that are both scientifically rigorous and widely accepted. Application of the framework is simple and transparent in order to effectively communicate scientific evidence to decision makers and the public. This enables the detection of change and judgments about causality to be made in a rigorous, structured and open manner, and thus the agreement among stakeholders, necessary for successful implementation of management strategies, can be obtained. An application of the framework follows in a case study on the effects of water pollution on coral reef benthos in the Great Barrier Reef, Australia.

Statistical issues in the detection of change “The statistical significance test does not tell us what we want to know, and we so much want to know what we want to know that, out of desperation, we nevertheless believe that it does!" (Cohen 1994). Most studies of environmental change (or impacts) adopt a falsification perspective; that is they assume no change has occurred and assess the level of evidence against this premise. If the evidence against “no change” is strong, then they accept change has occurred, whereas if the evidence against “no change” is weak, then the initial position is retained. Evidence against the null hypothesis is almost invariably based on a frequentist statistical significance test of a point (precise) null hypothesis. Since ecosystems are constantly changing through time and space such hypotheses are a priori false; i.e. they are not plausible (Berger and Sellke 1987). Despite the “no change” premise seeming indefensible, the majority of studies continue to adopt it as a starting point for investigations of temporal and spatial change. Why is this so? As a basis for management decisions, hypothesis tests are inadequate, and it can be argued that decision-making should not be a part of impact studies, which should inform, not decide (Stewart-Oaten 1996a). Even when a point null hypothesis is plausible, frequentist tests are problematic (Berger and Sellke 1987, Berger et al. 1997). They are remarkably uninformative (“reject” or “fail to reject”) and can be misleading when improperly interpreted; e.g. by describing a failure to reject as “evidence of no change” or by misinterpretation of Pvalues. Such misunderstandings and the repeated use of tests of low power can also lead to “false knowledge” as the null hypothesis becomes accepted as knowledge. Basing decisions on the result of tests also conflicts with the precautionary principle (Bodansky 1991) and can be hazardous; e.g. requiring a positive test as evidence of population decline for a rare species with high natural variation can lead to local extinction. The problem of lack of information as basis for accepting the null hypothesis has lead to the proposal for reversing the burden of proof (Dayton 1998) – e.g. it must be shown that proposed actions will not result in environmental damage, rather than allowing all actions which cannot be shown to result in damage. Frequentists have argued that power analysis (typically post-hoc) offers protection against this problem (e.g. Cohen 1988). However this approach has been criticised, and equivalence testing suggested as a formal alternative (Hoenig and Heisey 2001). Despite such shortcomings of frequentist hypothesis tests being repeatedly noted (Berger and Sellke 1987, Raftery 1995, Stewart-Oaten 1996a, Johnson 1999), the use of tests still prevails. Various alternatives have been suggested, primarily through the use of estimation of parameters with confidence [credibility] intervals (Stewart-Oaten 1996a, Burnham and Anderson 1998,

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Burnham et al. 2000) or Bayesian methods (Ellison 1996, Berger et al. 1997, Berger and Pericchi 2001). By abandoning hypothesis tests in studies of change immense benefit can be derived, irrespective of whether frequentist or Bayesian methods are used. The principal objective then becomes one of how to quantify (model) change and obtain accurate estimates of parameters representing the quantities of interest. When testing for significance or estimating the magnitude of change, typically one or more parameters of a chosen model represent the change process. It is often forgotten, or not realized, that all inference, be it hypothesis tests or parameter estimation, is conditional on the selected model. Thus, the validity of conclusions based on results of significance tests and parameter estimates are always contingent on the model being an accurate representation of reality, i.e. that the model is “true”, or is at least a good approximation. Often, many models are compared before one is chosen, but the uncertainty involved in model selection is seldom taken into account (Burnham and Anderson 1998), and this can result in biased and/or over-precise estimates, and probability values from hypothesis tests that are too small. In welldesigned experiments or surveys, the model is largely determined by the study design, and we can be relatively confident that it is a reasonable representation of reality. However, when studies are not as well controlled, or many variables are involved, then the choices of model can be vast. This further increases if we consider interactions, transformations of variables and alternative error structures. If all possible explanatory variables are included in a model, the power to detect change (equivalently, the precision of the estimate of change) may be severely reduced. Conversely, if important variables are omitted, then estimates of change are likely to be biased. The problems of model selection are particularly difficult for small sample sizes with weak relationships between the response(s) and predictors, and large data sets with many predictors. The use of hypothesis tests for model selection has received intense scrutiny in recent times, and based on simulations, the use of tests has been shown to be sub-optimal for identifying true models (Freedman 1983, Draper 1995, Burnham and Anderson 1998). The use of selection criteria such as Akaike’s information criteria (AIC; Sakamoto et al. 1986, Bozdogan 1987) and Bayes’ information criteria (BIC; Schwarz 1978, Madigan and Raftery 1994) have been advocated, particularly in the ecological literature (Burnham and Anderson 1998). These criteria outperform the use of hypothesis tests in determining true models, however no single criteria will universally find the true or best approximating model. Based on AIC or BIC, weights of evidence can be calculated for each of the competing models. These weights quantify the uncertainty of model selection and can be treated as relative probabilities of the models. They do not however represent the probability 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. If none of the models are true (or good approximation), then inferences based on the best model are also likely to be biased. One way to overcome the issue of choosing a single model is simply to avoid it by model-averaging. This also has the attractive property of generating more accurate estimates and predictions (Hastie et al. 2001). Model-averaging involves the fitting of several plausible models to the data and averaging the results (either parameter estimates or predictions) over all of the models. The averaging is usually weighted by some measure of the relative probability or predictive accuracy of each model, with more likely models receiving proportionally more weight (Raftery 1988, Burnham and Anderson 1998). In this way, poor models receive little weight and have a negligible influence on the final model. Criteria such as AIC (or variations thereof) and BIC are often used for this purpose. The use of BIC in this manner is an approximation to Bayesian model averaging, a process which is technically complex and computationally demanding, though the need to use approximations has declined through the use of increased computational power, and Monte Carlo simulations can give more accurate results compared to BIC. As is the case with selection of a single model, we need some (or at least one) of the models to be a good approximation to reality for inferences to be unbiased. The shift from hypothesis testing to estimation, and use of model averaging to better manage the uncertainty of model selection, does not negate the importance of good sampling design in environmental studies

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(Schmitt and Osenberg, 1996). Indeed, the additional uncertainty of model selection is incorporated into the estimation of effect sizes and thus these estimates will be less precise, but hopefully more honest, than those based on a single model.

Attribution of causality The concept of causality has a long and complex history, and it has many meanings. Its everyday usage is often straightforward, but as a philosophical or scientific concept, its definition and use are often contentious. At this level, at least three notions of causality are supported (Pearl 2000, Gillies 2001). First, some deny the existence of causality or view it as scientifically unnecessary. Second, some acknowledge causality as a useful concept, but do not give it a central role in their models. Causality is thus seen as a useful way of explaining some aspects of empirical laws. Third, some advocate causality as a fundamental construct. Cause is assumed rather than demonstrated, and in the formulation of mathematical models, causality takes precedence over probability. Of course, there is no conclusive argument giving universal support to any one of the three views, and different phenomena can be used to support differing views, e.g. our everyday experiences support the concept of causality as something fundamental, but few mathematical models require it. Statistics is widely accepted within ecology as a primary empirical methodology, yet statisticians are typically not strong advocates of causality as a fundamental construct, favoring probability models instead. There are statistical approaches that do promote cause over probability (Pearl 2000), but equally there are warnings against the causal interpretations (Speed 1990). The randomized experiment is often invoked as one method for unambiguously determining causality, but even that is questionable when outcomes are stochastic and we rely on statistical interpretation. Causal arguments are needed in ecosystem management in order to convince interested parties that management actions should be implemented and will be effective. These arguments need to balance scientific rigor with ease of communication to non-scientists. This situation is not novel, and we can borrow from epidemiology which deals with issues of comparable complexity to the ecological and environmental sciences, and also has similar requirements of scientific rigor and communication. Epidemiologists developed criteria to assess causality as part of the research into the link between cigarette smoking and lung cancer. This link was accepted by the Surgeon General after decades of research (US Dept. HEW, 1964) when evidence compiled from multiple sources of information and numerous studies fulfilled a set of criteria (Hill 1965), the main ones being: A. The relationship between the dose (the putative cause) and the response should be monotonic. B. The association between the dose and the response should be strong. C. The response should be specific to the cause. D. There should be a logical time sequence of events; i.e. the response should occur after the dose has been applied. E. There should be consistency both across populations within a study, and with results from other studies. F. The observations should agree with known biological facts. These criteria (or subsets, extended sets or re-defined sets of them) are routinely used in epidemiology to judge whether or not an association is causal. None of the criteria are taken as indicative by themselves, but equally, none are seen as absolutely necessary to evaluate causal significance of associations (Roth et al. 1982). The more criteria that are satisfied and the stronger the association, the more confidence we should have in our judgment that the association is causal. Similar criteria have been proposed for ecoepidemiological studies (Fox 1991) and impact assessment studies (Stewart-Oaten 1996b, Schroeter et al. 1993, USEPA 1998). The criteria may need to be adapted or interpreted for particular studies; e.g. the dose-response relationship may be non-monotonic due to toxicity, and the strength of the relationship could

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be expressed in many ways dependent on how dose and response are measured. In this work we formalize these procedures and extend its use to multiple possible causes. We have argued that a shift from the use of hypothesis tests to estimation of parameters, and adoption of better model selection processes or model averaging can lead to more informative analyses of the detection of change. We have also suggested that causal criteria can be used to rigorously yet transparently attribute causality. Finally, by selecting combinations of ecological attributes that are complementary with respect to possible causes of change, we can better discriminate between likely agents of change, and reduce the likelihood of confounding that may lead to spurious findings. The chosen attributes may be aspects of the physical-chemical environment (either measured directly or as proxies), abundances and biodiversity of key species groups, or ecological processes. These processes – improved statistical analysis, use of epidemiological causal criteria and selection of combinations of complementary ecological attributes – can lead to more effective ecological assessments, and we illustrate this in the following case study.

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Figure 1. Maps of the northern Great Barrier Reef and the study regions. The risk of exposure to agricultural run-off (a) is shown 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 locations and names of inshore target reefs (black circles) in PC (b) and WT (c), and locations of additional reef surveys across the continental shelf (gray circles), and estuaries of the main rivers affecting the regions.

The Case Study Increasing terrestrial run-off of nutrients, sediments and pesticides is a major management issue facing the Australian Great Barrier Reef (GBR), but the presence of measurable effects of run-off on inshore areas has been controversial (Bell 1991, van Woesik et al. 1999, Larcombe and Woolfe 1999, Haynes and MichalekWagner 2000, Brodie et al. 2001, Devlin et al. 2001, Furnas 2003, McCulloch et al. 2003). River discharges are the principal source of nutrients and sediments for the shallow continental shelf waters (Furnas 2003), and land clearing for agriculture, removal of soil-retaining wetlands, and intensive use of agricultural chemicals have increased nutrients and sediments in these discharges 3 to 11-fold since European settlement in 1850 (Furnas 2003). Discharges from the 423,000 km2 catchment area contained 11-14 million tonnes of sediment in 2002 compared with 1-4.4 million tonnes pre-1850, and trends of increasing soil erosion are recorded in coral cores (McCulloch et al. 2003). Further, 100,000 tonnes of nitrogen and 20,000 tonnes of phosphorus fertilizers are now applied to the catchments annually, though how much of it eventually enters the marine system is unknown (Furnas 2003). A region along the 200 km long wet-tropical coastline between Tully and Port Douglas containing 60 coral reefs within 20 km from the coast (latitude 18°00’ to 16°20’ S, longitude 146°10’ to 145°30’ E; Fig. 1) has been identified as the area of greatest risk from agricultural run-off (Devlin et al. 2002). However, causal links between pollution and reef degradation in

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this region have been difficult to demonstrate. This is due to factors such as a lack of historical data, the large spatial scale, the presence of natural cross-shelf gradients in community structure (Dinesen 1983) and suspended particulate matter (Furnas 2003), and the presence of other types of disturbance. As in many environmental impact studies, there is no spatial replication for impacted and non-impacted regions. Furthermore, the spatial and temporal variability of the flood-related episodic river discharges of several pollutants is high, and the fate of pollutants while undergoing dilution, biological uptake, sediment burial and repeated resuspension during the transport from river to reef is little understood. Finally, ecological responses (linear or threshold relationships, synergistic responses) vary greatly across the multitude of organisms that characterize the highly diverse coral reef ecosystem. Around well-defined point sources such as sewage outfalls or coastal developments, increased sediments and nutrients are known to cause local reduction in coral recruitment, increase mortality, and shift the dominance from hard corals to non-reef building organisms (Smith et al. 1981, Wittenberg and Hunte 1992, Hunter and Evans 1995). Results from laboratory and field experiments also demonstrate detrimental effects of sedimentation and pesticides on individual organism groups and life stages (Rogers 1990, Dubinsky and Stambler 1996, Philipps and Fabricius 2003, Jones et al. 2003). While enhanced concentrations of inorganic nutrients appear to have no direct effects on coral health (Szmant 2002), they can affect coral populations indirectly, e.g., by shifting competitive advantages towards otherwise nutrient-limited algae when grazing pressure is low (McCook 1999), or by the formation of marine snow (Fabricius et al. 2003). Thus, while causal links between pollution and reef degradation have been difficult to demonstrate at regional scales such as the inshore reefs of the GBR, pollution impacts are well documented and accepted at local scales and under controlled conditions. In this study, we assess possible associations between the state of some GBR inshore reefs that are exposed to terrestrial run-off, but have also been exposed to a number of other disturbances. This case study has a high political and environmental profile, both locally due to large economic interests in a healthy reef through tourism revenues, and globally since land-based pollution and coastal development put 22% and 30%, respectively, of coral reefs on earth at risk (Bryant et al. 1998).

Methods 1. Field Data The study is based on water quality analyses, and ecological surveys of benthic cover, biodiversity and octocoral community structure. Only summaries of the relevant water quality and ecological surveys are presented here; other laboratory and field studies have been or will be described in greater detail elsewhere (Phillips and Fabricius 2003, Fabricius et al. 2003, Diaz-Pulido and McCook in press, Fabricius et al., in prep.). Study Sites and Survey Methods The field research was carried out in two regions within the Great Barrier Reef (GBR), with one-off surveys characterizing 54 reef sites across the whole continental shelf, and targeted research on 10-14 inshore reefs (depending on the type of data collected) (Fig. 1). The Wet Tropics (WT) lies between Tully and Port Douglas, and inshore reefs experience local river plumes from agricultural catchments on an almost annual basis, and large plumes from the distant Burdekin River on a decadal basis (Furnas 2003). This region has the highest exposure to run-off from agricultural areas within the GBR (Fig. 1a; Devlin et al. 2002). The second region lies north of Princess Charlotte Bay (PC) and ~400 km north of WT, and the inshore reefs are exposed to run-off from sparsely populated catchments that have received little or no fertilizer and pesticides to date, but have low-density cattle grazing in some parts. Both regions contain turbid inshore reefs in similar geophysical settings, located within 20 km of the coast at 12-18 m depth of the surrounding sea floor, and protected by a barrier of mid- and outershelf reefs up to 40 km offshore (Figs. 1b and 1c). In WT most discharged material is eventually transported northward away from the reefs, whereas the large north-facing PC creates anticyclonic eddies which result in trapped and deposited sediment (Torgersen et al. 1983). The

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research on the inshore target reefs was conducted between 2000 and 2002. An additional 40 mid- and outer-shelf reef sites were visited within the two regions for cross-shelf one-off surveys of benthic cover and octocoral communities. The detailed disturbance histories of individual reefs are largely unknown. In the WT, some reefs experienced outbreaks of the coral-eating crown-of-thorns seastar Acanthaster planci in the late 1990s, tropical cyclones in 1986 and 1990, and mortality through coral bleaching (the expulsion of symbiotic algae from the coral tissue, primarily caused by high temperatures) in 1998, with bleaching estimated as ‘moderate’ to ‘extreme’ on most reefs (Berkelmans and Oliver 1999). In PC, no data exist for crown-of thorns seastar (high numbers were observed on a reef neighboring the inshore target reefs in 1991), but four tropical cyclones have passed through the region within the last two decades. It is likely that the PC inshore target reefs did not bleach in 1998 since satellite-based estimates of sea surface temperatures were near-normal, but some reefs did suffer severe bleaching mortality in early 2002 after the surveys were completed and during the coral settlement experiment. The data on crown-of-thorns, cyclone and bleaching disturbance history were insufficient to assess or attribute effects on the scale of individual reefs. In order to distinguish between the potential causes of change, a combination of ecological attributes with contrasting responses to bleaching, crown-of-thorns seastar predation, run-off and cyclones were chosen for the study. The chosen attributes were: 1. Benthic cover of hard corals, octocorals, and macroalgae. These are the main groups of organisms usually measured in the assessment of coral reefs, and were expected to respond to changing environmental conditions and disturbances in contrasting ways. 2. Taxonomic richness of zooxanthellate and azooxanthellate octocorals (Anthozoa, Octocorallia; commonly termed ‘soft corals’ and ‘sea fans’). This group contains genera with and without symbiotic algae (called zooxanthellae) in their tissue, the former group depending on water clarity and light for photosynthetic nutrition, whereas the latter group is independent of water clarity (Fabricius and De’ath 2001). Octocorals were chosen as indicators for ecological attributes because of their abundance, and because they respond more specifically to water quality than hard corals; azooxanthellate octocorals (which constitute about half of the genera) do not suffer from coral bleaching, while zooxanthellate octocorals respond strongly to turbidity, probably because of low photosynthetic efficiency (Fabricius and Klumpp 1995). Octocorals are also rarely eaten by crown-ofthorns seastar (De’ath and Moran 1998), 3. Community structure of octocorals on both the inshore target reefs and along the cross-shelf chlorophyll gradient. This measure was chosen because communities are known to respond more strongly to environmental conditions than abundances of the main groups (unpublished data). One-off rapid ecological assessment surveys (Fabricius and De’ath, 2001, Fabricius and Alderslade, 2001) were used to characterize the ecological condition of 54 reef sites across the continental shelf in both regions, and 13 inshore target reefs in both regions. Surveys were conducted on 2 sites per reef (windward and leeward sides) at 5 depth zones per site (0-18 m); each survey at each depth zone covered about ~500 m2 of reef area. Survey data were collected on percentage cover of the main benthos groups (hard coral, octocoral, macro algae, turf algae, coralline algae, sand and rubble) and taxonomic inventories and abundance estimates (rating 0-5) of all genera of octocorals. Water quality data Two sets of water quality data were available from the two regions. First, a 10-year data set of chlorophyll concentrations at sites across the continental shelf on the GBR in both regions (Brodie et al., in prep.). The chlorophyll measurements were sampled up to 12 times a year at each site. Second, water quality data were collected around the inshore target reefs. Concentrations of water quality parameters (suspended solids, particulate nitrogen and phosphorus, nitrate, nitrite and ammonium, phosphate, total dissolved nitrogen and total dissolved phosphorus, chlorophyll and phaeopigments, salinity and silicate) ) were determined from water samples taken at each of the inshore target reefs during 9 visits between December 2000 and April 2002. Water analyses followed standard procedures (Furnas and Mitchell 1996). The location of the inshore target reefs along the cross-shelf chlorophyll gradient is indicated in Fig. 3. Only a short section of the

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chlorophyll gradient is represented in the inshore water quality samples, as the innermost reefs in PC were avoided due to the likely presence of the saltwater crocodile Crocodylus porosus. All nutrient data except salinity were highly correlated and they were standardized (z-scores) and summed to form a ‘water quality index’ for each reef. Low index values correspond to water with low nutrients, chlorophyll and suspended particles.

2. Statistical methods The analytical methods of this study did not include the use of hypothesis tests for the reasons argued in the Introduction. Instead we estimated effect sizes and predicted values with interval coverages. Inferences were based on model averaging, cross-validated smoothing splines and bootstrap estimation. For each of these methods, the numerical results can be interpreted from either frequentist or Bayesian perspectives, but of course the interpretations differ in each case. All data analyses used S-Plus (Statistical Sciences, 1999). The relationships between chlorophyll concentrations and octocoral richness (zooxanthellate and azooxanthellate) were modeled for each region (WT and PC) as a function of relative cross-shelf distance (defined as the distance of a site from the coast divided by sum of distances from the coast and the edge of the outer continental shelf). The relationships of the responses with relative cross-shelf distance were nonlinear, and smoothing splines were used with the degree of smoothing estimated by cross-validation (Hastie and Tibshirani 1990). The water quality data and chlorophyll data were pre-analysed prior to inclusion in models relating the benthic variables to gradients. To investigate the relationships between the inshore water quality variables, a principal components analysis was used. A water quality index was then calculated as the sum of all standardized (z-scores) variables other than salinity, and scores on this index were used as measures of water quality for each reef in subsequent analyses. The ecological survey data and the long-term chlorophyll data were not recorded at identical sites, hence chlorophyll levels at the survey sites were estimated by the weighted mean of nearest neighbors from the chlorophyll sites. Three sets of ecological attributes, namely (1) benthic cover (hard corals, octocorals, and macroalgae), (2) taxonomic richness (zooxanthellate and azooxanthellate octocorals), and (3) community composition (all octocorals) were related to gradients (either chlorophyll or inshore water quality) and regional (WT v. PC) differences. For both the chlorophyll and water quality gradient analyses of benthic cover and richness, loglinear regression models with linear gradient effects and categorical regional effects were used since variation increased with the mean and the implicit log transformation helped linearize gradient effects. For each response, five models were fitted: (i) different slopes (gradient effects) within each region and different intercepts (region effects), (ii) same slope for both regions, but different intercepts (region and gradient effects), (iii) single gradient common to both regions, (iv) no gradient effect but region effects, and (v) no gradient or region effects. The regional effects were included to account for biological differences due to region, which were partly confounded with the gradients. The data sets were small with ~50 and 13-20 observational units for the chlorophyll or inshore water quality data respectively. Preliminary analyses indicated relatively weak associations between the responses and explanatory variables for the smaller data sets, and suggested that conclusions based on hypothesis tests may not adequately reflect seemingly consistent patterns across the chlorophyll and inshore water quality data (Fig. 4). Thus, in order to select an optimum form of analysis, we conducted simulations based on estimates of effect sizes and error obtained from preliminary analyses of benthic cover and richness gradient data (unpublished data). These simulations showed BIC (Schwarz 1978) to be marginally better than both AIC and AICc (Burnham and Anderson 1998) for model selection, and much better than hypothesis tests (see Appendix). However, none of these methods reliably selected the true model, and model averaging (Raftery 1988, 1995) gave slightly better predictions than single best models. Hence we have used model averaging for all gradient analyses of benthic cover and richness. Confidence (credibility) intervals were obtained by bootstrapping (Efron and Tibshirani 1993, Davison and Hinkley 1997).

Katharina Fabricius & Glenn De’ath (2004) – Ecological Applications 14:1448-1465 – p9

Redundancy analyses (RDA, Rao 1964, Jongman et al. 1995) were used to assess the dependence of octocoral communities on regional differences (WT and PC) and on both cross-shelf chlorophyll data and inshore water quality data. The abundances of octocorals were fourth-root transformed to downweight dominant taxa, and reef-averaged over depths and sites. For community analyses involving the chlorophyll gradient, relative distance across the shelf was also included. Ecological gradients were relatively short (moderate species turnover) thereby justifying the use of RDA, which requires linear changes of species along gradients. The strengths of regional and gradient effects were quantified by bootstrapping (Efron and Tibshirani 1993, Davison and Hinkley 1997) the pseudo-F statistic (ter Braak 1992).

3. Synthesis To successfully use the causal criteria, both the ecological attributes and the criteria have to be defined for each study individually. Our ecological attributes were cover of hard corals, octocorals, and macroalgae, species richness of zooxanthellate and azooxanthellate octocorals, and community structure, across the shelf along the chlorophyll gradient and on the inshore target reefs along the water quality gradient. The criteria were slightly modified from Hill’s initial list (Hill 1965) to best reflect the nature of the case study, using the following definitions: A. Dose–response relationship was satisfied if the probability of gradient effect was >0.99. This corresponds to a Bayes factor of 100:1 and can be taken as strong evidence for a relationship between dose and response (Raftery 1995). B. 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 extreme values) of the inshore water quality gradient within WT. C. Logical time sequence indicates that the change did not precede exposure to the disturbance. An assessment of this criterion relies on historic data from the study regions, which were sparse in our case (no data exist from the remote PC, and only a few observations exist from WT; Ayling and Ayling 2001, AIMS Long-Term Monitoring Program, unpublished data). D. Consistency across populations was defined as ‘consistency with other studies’ (i.e., responses recorded in our study were similar to those reported from other regions in independent published studies). E. Specificity referred to responses that were known (from published literature) to be caused by run-off and was unlikely to be caused by another disturbance type. F. Agreement with biological facts was scored by comparing the responses found in our field surveys with the results of studies where relationships were directly assessed through manipulative experiments. By applying each of Hill’s criteria to each of the ecological attributes, we created a matrix that was used to determine to which extent the observed types or changes in the inshore coral reefs might have been caused by exposure to run-off. Criteria A to C (dose-response relationship, strength of association, and logical time sequence) were assessed based on the relationships between the ecological attributes and the two water quality data sets. Criteria D to F (consistency with other studies, specificity of response, and agreement with biological facts) were scored by comparing the responses of the ecological attributes with results from other independent sets of published studies (laboratory experiments on pollution effects, and data from polluted locations in other regions). For ease of communication, we summarized all results in the matrix by scoring each cell in one of three possible ways (cells that could not be addressed due to the lack of data were marked with ‘na’): ‘agreement of the response of the attribute with the criterion’ (+1), ‘weak or inconclusive response’ due to inconsistent results or weakness in the study design (0), ‘responses that are in disagreement with the criterion’ (-1). The overall evidence for a causal association with water quality was then assessed as the sum over all cells for each of the ecological attributes, expressed as proportion of the total number of cells for each attribute for which data were available.

Katharina Fabricius & Glenn De’ath (2004) – Ecological Applications 14:1448-1465 – p10

Results 1. Field data a) Regional differences in water quality and ecological attributes on inshore target reefs Mean concentrations of suspended solids, particulate nitrogen and particulate phosphorus were 170 to 270% higher in the water around the WT reefs than in the water around PC reefs, and mean nitrate levels were 580% higher in WT than in PC (Table 1). Concentrations of other dissolved and particulate nutrients were also at least as high or higher in WT than in PC. All water quality variables except salinity were highly correlated, and most of the inshore target reefs in WT were exposed to higher nutrient and sediment concentrations than in PC (Fig. 2). The ecological attributes of WT also differed substantially from those of PC. On WT reefs 67.7% ± 5.6 (SE) of space was covered in algae (turf, coralline and macroalgae). In contrast, algae occupied 39.6% ± 5.4 of space on PC reefs. Coral cover was lower in WT than in PC (mean hard coral cover: 15.1% ± 2.4 v. 43.4% ± 1.0; octocorals: 2.8% ± 0.8 v. 5.2% ± 1.5). The richness of octocorals was also lower in WT than in PC (25.5 ± 2.4 v. 35.6 ± 2.3 genera per reef). In contrast, dead coral cover in WT was higher than in PC (17.2% ± 6.0 v. 3.8% ± 0.4). Salinity

Chl

NO2

PN Phae PP

Water Quality Index

SS NH4

PC WT

Dim 2 23.49 %

TDP

NO3

Sil TDN

Dim 1 55.07 %

Figure 2. Principal components biplot of water quality data (log-transformed and z-score transformed) at inshore target reefs in the two study regions (gray = Princess Charlotte Bay, black = Wet Tropics). Each symbol represents a target reef, for which data were averaged over within-reef locations and sampling times; the fill of the symbols represents octocoral richness. Reefs from the same region are surrounded by convex hulls. Vectors of the water quality variables and water quality index point at reefs with highest concentrations. Abbreviations of the water quality variables are listed in Table 1.

Katharina Fabricius & Glenn De’ath (2004) – Ecological Applications 14:1448-1465 – p11

Chlorophyll 1.0

Zooxanthellate genera 30

R2 = 37.4%

PC 0.6 0.4

(mg / m3 )

0.2 0.0 1.0

R2 = 66.6%

0.8 0.6

WT

0.4 0.2

Octocoral richness (genera / reef)

0.8

R2 = 23.4%

Azooxanthellate genera 30

25

25

20

20

15

15

10

10

5

5

0

0

30

30

25

25

20

20

15

15

10

10

5

0.0 0.2 0.4 0.6 0.8 1.0

R2 = 26.9%

5 2

R = 67.9%

0 0.0

R2 = 13%

0.0

0.2

0.4 0.6

0

0.8 1.0

0.0

0.2 0.4

0.6 0.8 1.0

Relative distance across the Reef

Figure 3. Cross-shelf gradients in mean chlorophyll concentrations, based on 10 years of chlorophyll monitoring data and taxonomic richness in octocorals with and without zooxanthellae (PC: Princess Charlotte Bay region, WT: Wet Tropics region). Thick solid lines indicate cross-validated smoothing splines, and thinner lines are 95% confidence intervals. Vertical gray bands indicate the locations of the inshore target reefs across the shelf.

Water Quality

PC

SE(PC)

WT

SE(WT)

Ratio

95% CI

P

Suspended solids (SS) [mg/L] Chlorophyll (Chl) [µg/L]

1.40

(0.13)

3.77

(0.84)

2.69

(1.49, 3.99)

>0.99

0.40

(0.04)

0.56

(0.09)

1.40

(0.93, 1.99)

0.93

Phaeopigments (phae) [µg/L]

0.19

(0.03)

0.32

(0.06)

1.68

(0.97, 2.73)

0.96

Particulate nitrogen (PN) [µmole/L]

1.41

(0.13)

2.55

(0.38)

1.81

(1.27, 2.49)

0.99

Nitrate (NO3) [µmole/L]

0.024

(0.01)

0.14

(0.07)

5.83

(0.71, 27.4)

0.88

Ammonium (NH4) [µmole/L]

0.16

(0.02)

0.24

(0.08)

1.50

(0.55, 2.67)

0.83

Nitrite (NO2) [µmole/L]

0.012

(0.002)

0.019

(0.003)

1.58

(0.99, 2.46)

0.96

Total dissolved nitrogen (TDN) [µmole/L]

8.46

(0.59)

8.38

(1.01)

0.99

(0.75, 1.34)

0.49

Particulate phosphorus (PP) [µmole/L]

0.096

(0.012)

0.16

(0.03)

1.67

(1.01, 2.55)

0.97

Total dissolved phosphorus (TDP) [µmole/L]

0.43

(0.06)

0.56

(0.06)

1.30

(0.95, 1.92)

0.93

Silicate (Sil) [µmole/L]

4.04

(1.19)

9.46

(2.59)

2.34

(1.08, 6.38)

0.99

Salinity (Sal) [g/L]

31.8

(0.48)

29.1

(1.54)

0.93

(0.82, 1.01)

0.02

Table 1. Comparison of water quality values around inshore reefs of the Wet Tropics (WT) and Princess Charlotte Bay (PC). 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 be treated as confidence intervals or credibility intervals with a non-informative prior.

Katharina Fabricius & Glenn De’ath (2004) – Ecological Applications 14:1448-1465 – p12

Variable

R*G

R+G

G

R

1

G

exp(G)

95% CI

P

(a) Chlorophyll MA % Cover

0.03

0.17

0.80*

0.00

2.48

(1.93, 3.18)

>0.99

0.00

0.02

0.10

0.14

0.00 0.73*

0.91

HC % Cover

-0.01

0.99

(0.92, 1.07)

0.61

OC % Cover

0.19

0.44

0.00

-1.49

0.23

(0.15, 0.34)

>0.99

0.10

0.15

0.37* 0.75*

0.00

OC Richness (zoox.)

0.00

-0.75

0.47

(0.38, 0.60)

>0.99

OC Richness (azoox.)

0.07

0.40

0.00

0.00 0.52*

0.01

0.29

1.34

(0.95, 1.95)

0.04

(b) Water Quality MA % Cover

0.39

0.21

0.24

1.12

3.06

(1.61, 6.71)

>0.99

0.11

0.00

0.00

0.03

1.03

(0.82, 1.32)

0.39

OC % Cover

0.24

0.27 0.28

0.06 0.62*

0.10*

HC % Cover

0.21

0.25*

0.02

-0.01

0.99

(0.60, 1.61)

0.51

OC Richness (zoox.)

0.05

0.19

0.72*

0.01

-0.34

0.71

(0.59, 0.87)

>0.99

OC Richness (azoox.)

0.09

0.29

0.26

0.02 0.36*

0.00

-0.24

0.79

(0.55, 1.13)

0.91

Table 2. Analyses of models relating ecological responses to (a) chlorophyll gradients and cross-shelf regions, and (b) inshore water quality gradients and regions. 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 are categorical. For each ecological response five models are compared: (R*G) = different gradient effects (slopes) within each region and different region effects (intercepts), (R+G) same slope but different region effects, (G) single slope common to both regions, (R) no gradient effect but regional effects, and (1) no gradient or regional effects. The best model (most likely according to BIC) is denoted by bold text, and the best model selected by backward elimination of non-significant terms (P>0.05) is indicated by an asterisk. For four of the 10 models the most likely model was not selected by backwards elimination. For four of the models, the relative probability for the most likely model is low (1 is shown, and 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.

Effect

Df

SS

MS

%SS

Pseudo-F

95% CI

P

Across

1

53.3

53.3

13.9

9.52

(6.77, 12.82)

>0.99

Chlorophyll

1

28.1

28.1

7.3

5.02

(3.47, 7.44)

>0.99

3.13

(1.24, 4.92)

0.99

(a)

Region

1

17.5

17.5

4.5

Residuals

51

285.5

5.6

74.3

1

16.969

16.9

23.1

3.39

(1.32, 6.66)

>0.99

1.25

(0.45, 5.56)

0.61

(b) Water Quality Index Region

1

6.285

6.3

8.6

Residuals

10

50.048

5.0

68.3

Table 3. Analysis of variance of community composition of octocoral genera showing the effects of (a) relative distance across the shelf, the chlorophyll cross-shelf gradient and regions (WT and PC), and (b) the inshore water quality gradient and regions. For (a) 66 genera and for (b) 57 genera were analyzed and the sequential sums of squares were summed over the responses to give analysis of variance tables. The pseudo-F (pF) statistic was bootstrapped and bias adjusted to give 95% intervals. P denotes the probability that the true value of pF was >1 (the expected value of pF given a variable has no effect) indicate evidence for an effect. For (A) there are strong effects of relative distance across the shelf and chlorophyll, and weaker regional differences. For (B) there was a strong effect of water quality and no effect of region. Reversing the order of inclusion of chlorophyll and region, and water quality and region, slightly increased the strength of the region effects (~30%) and correspondingly reduced the chlorophyll and water gradient effects.

Katharina Fabricius & Glenn De’ath (2004) – Ecological Applications 14:1448-1465 – p13

% MA cover

30 25 20 15 10 5 0

% HC cover

50 40 30 20 10 0

% SC cover

30 25 20 15 10 5

OC richness (azoox.)

OC richness (zoox.)

0 30 25 20 15 10 5 0 25 20 15 10 5 0 0.1

0.2

0.3

0.4

Chlorophyll

0.5

0.6

-10 -5

0

5

10

15

Water quality index

Figure 4. Relationship between ecological reef attributes and chlorophyll and water quality. Grey and black lines and circles indicate reefs within the PC and WT regions, respectively. Solid lines indicate the model-averaged predictions from log-linear model fits, and dashed lines indicate 95% confidence (credibility) intervals derived by boostrapping. The left column of plots shows variation of ecological attributes along the chlorophyll gradient across the continental shelf, and the right column similarly for the water quality index, calculated as the sum of the z-scores of all water quality variables excepting salinity of each reef at inshore target reefs (high index values: high nutrient concentrations, low values: cleaner water). Ecological attributes are: HC = hard corals, OC = octocorals, MA = macroalgae, zoox. = zooxanthellate, azoox. = azooxanthellate. Richness is defined as the number of genera of octocorals per reef encountered during swim surveys.

Katharina Fabricius & Glenn De’ath (2004) – Ecological Applications 14:1448-1465 – p14

PC

Across

Chlorophyll

Dim 2 29.92 %

WT

Dim 1 57.19 %

Figure 5. Redundancy analysis biplot of octocoral coral communities at reefs showing the dependence on relative distance across the continental shelf (Across) and region (gray = Princess Charlotte Bay, black = Wet Tropics). Each symbol represents a reef, and the fill of the symbol represents the generic richness of that reef. Chlorophyll and distance of the reefs across the shelf (Across) are represented as vectors and point in the directions of highest chlorophyll loads and offshore reefs, respectively.

Water Quality Index

PC

Dim 2 20.66 %

WT

Dim 1 79.34 %

Figure 6: Redundancy analysis biplot of octocoral coral communities at the inshore target reefs in PC (gray symbols) and WT (black symbols). Each symbol represents a reef and the fill of the symbol represents the taxonomic richness of that reef. The water quality index vector points in the direction of highest nutrient loads.

Katharina Fabricius & Glenn De’ath (2004) – Ecological Applications 14:1448-1465 – p15

Criteria

Benthic cover

Richness

Communities

HC

OC

MA

OC Z

OC A

OC C

OC I

A1. Biological gradient: Cross-shelf

–

+

+

+

–

+

+

B1. Effect size: Cross-shelf

–

+

+

+

–

+

+

A2. Biological gradient: Inshore

–

–

+

+

–

+

+

B2. Effect size: Inshore

–

–

+

+

0

+

+

C. Logical time sequence

+

na

na

na

na

na

na

D. Consistency with other studies

0

+

+

+

+

na

na

E. Specificity of response

–

0

+

+

+

na

na

F. Agreement with biological facts

+

+

+

+

+

+

+

-3/8

2/7

7/7

7/7

0/8

5/5

5/5

Score

Table 4. Synthesis matrix defined by causality criteria and ecological attributes, summarizing ecological evidence of terrestrial run-off effects on reefs within the Wet Tropical region of the Great Barrier Reef (Fig. 1). Data from this region are compared with those from reefs at low risk from run-off in the Princess Charlotte Bay. Criteria A and B address responses observed in this study (Figs 4-6, Tables 2-3). Criteria C to F address the agreement between responses in this study and results from other runoff-exposed regions and from laboratory studies on pollution effects. Symbols in the matrix indicate the following: Agreement with the criterion (+), inconclusive (0: inconsistent or weak), disagreement (–) , and not addressed due to the lack of data (na). HC = hard coral, OC = octocorals, Z = zooxanthellate, A = azooxanthellate. C = cross-shelf, I = inshore. The final row is the sum of scores as proportion of all scores for each attribute, scoring ‘+’ as +1, ‘–’ as –1, and ignoring ‘na’.

b) Changes in ecological attributes along the chlorophyll gradient across the continental shelf The gradients in mean water column chlorophyll concentrations across the continental shelf differed between PC and WT (Fig. 3). In WT, chlorophyll increased steeply towards the coast, and was up to 3 times higher in the innermost 20 km of the region than offshore or in PC. In PC, chlorophyll remained constant and relatively low across the shelf. Changes in the taxonomic richness of octocorals across the continental shelf also showed clear patterns. Richness of zooxanthellate octocorals (genera that contain symbiotic algae in their tissue and depend on water clarity and light for their nutrition) declined by 30% in the WT inshore region within the innermost 20 km of the shelf where chlorophyll was high (Fig. 3). In contrast, no clear cross-shelf changes in richness of the zooxanthellate genera were apparent in PC where chlorophyll was stable across the shelf. The richness of octocorals without zooxanthellae (which do not require light for their nutrition) varied little across the continental shelf in WT and PC and appeared unrelated to the chlorophyll gradient. Some of the ecological attributes varied systematically along the chlorophyll gradient in both regions (Fig. 4, Table 2). Macroalgal cover increased with increasing chlorophyll concentrations in both regions. While hard coral cover was highly variable and unrelated to chlorophyll and region, octocoral cover declined with chlorophyll, reaching lowest cover at a chlorophyll concentration of >0.3 µg L-1. The richness of zooxanthellate octocorals decreased with increasing chlorophyll, whereas the richness of azooxanthellate octocorals was unrelated to chlorophyll. Octocoral communities showed strong cross-shelf (Pseudo-F = 9.52), chlorophyll (5.02) and regional effects (3.13) (Fig. 5, Table 3). Community composition varied most along the chlorophyll gradient within the WT region, with highest chlorophyll levels associated with lowest richness. Few genera occurred in areas of high chlorophyll concentrations, and these conditions were only found on some of the WT inshore reefs. While mid-shelf reefs had highest richness, some wave-exposed outer-shelf reefs also had relatively low richness, but were characterized by a very different suite of species than the low-diversity high-chlorophyll near-shore reefs. c) Changes in ecological attributes along the water quality gradients on the inshore target reefs Changes in ecological attributes along the inshore water quality gradient were weaker than those along the chlorophyll gradient across the shelf, most likely because of the relative shortness of the inshore gradient

Katharina Fabricius & Glenn De’ath (2004) – Ecological Applications 14:1448-1465 – p16

and the lower number of inshore reefs (13-20). Nonetheless, three of the ecological attributes varied along the water quality gradient in similar ways to the cross-shelf variation along the chlorophyll gradient (Fig. 4, Table 2). Only one of the three effects would have been detected had frequentist tests been used for the analyses (Table 2). Macroalgal cover increased along the water quality gradient in WT around three-fold, but was variable in PC. Hard coral cover and the richness of azooxanthellate octocorals both showed strong regional differences, but were unrelated to the water quality gradient. Octocoral cover was similar in both regions and also appeared unrelated to water quality. In contrast, the richness of zooxanthellate octocorals declined with increasing nutrients along the water quality gradient in both regions. The octocoral communities varied most strongly along the water quality gradient (Pseudo-F = 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 numbers in WT water of low water quality (Fig. 6). Within each region, reefs associated with the highest nutrient and sediment levels were those with lowest richness and vice versa. Only two octocorals species (the encrusting Briareum sp. and Clavularia koellikeri) were associated with high nutrients, whereas a large proportion of genera were strongly associated with the lownutrient PC reefs.

2. Synthesis We combined the results of our field study with results from other regions and from laboratory experiments, and used Hill’s causality criteria to evaluate the potential link between water quality and the condition of the inshore coral reefs. The results were expressed concisely in matrix format with the rows of the matrix (Table 4) defined by the criteria, and the columns by the ecological attributes, namely, cover of the main benthos groups, richness of zooxanthellate and azooxanthellate octocorals, and community structure. A. Dose-Response Relationship There was strong evidence for dose-response relationships for 3 of the 5 ecological attributes both along the cross-shelf chlorophyll gradient and along the water quality gradient on the inshore target reefs (Fig. 4, Tables 2 and 3). Dose-response relationships were established for macroalgal cover, octocoral cover, generic richness of zooxanthellate octocorals, and octocoral community structure. In contrast, hard coral cover, although much lower in WT than in PC, was unrelated to the chlorophyll and inshore water quality gradients, possibly due to the effect of other disturbances. The richness of azooxanthellate octocorals was also unrelated to water quality, 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 expected from existing biological knowledge. B. Strength of Association Some of the effects along the water quality gradient across the shelf and within the WT inshore region were large and ecologically significant (Fig. 4, Table 2). Macroalgal cover was 3.1 times higher on the reefs with highest nutrient and particle loads compared with those in clearest water in WT. The number of octocoral genera was 2.4 times higher on WT reefs with clearest water compared with those in the least clear water. Associations between community structure and the water quality and chlorophyll gradients were stronger than the differences between the regions (Table 3), with depauperate communities recorded in the reefs with highest nutrient and particle loads. C. Logical Time Sequence The assessment of this criterion is limited by the scarcity of historic data. Few data exist to compare the ecological attributes on any of the inshore target reefs with those from the past, and some differences between the two regions in biodiversity are likely to have always existed due to the natural decline in biodiversity with increasing latitudes. The only historic data available are for one reef in the WT region where hard coral cover decreased from ~80% in 1989 to the present state due to a number of disturbances (Ayling and Ayling 2002). The logical time sequence criterion of water quality effects on coral reefs has however

Katharina Fabricius & Glenn De’ath (2004) – Ecological Applications 14:1448-1465 – p17

been met in other places, e.g. in Hawaii, where coral cover increased and algal cover decreased following offshore diversion of a coastal sewage outfall site (Smith 1982, Hunter and Evans 1995). D. Consistency across different studies The findings of increased macroalgal cover and low hard coral cover are consistent with previous work in other coral reef regions exposed to terrestrial run-off (e.g., Tomascik et al. 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 richness in response to disturbance have been reported for other groups of reef organisms (Edinger et al. 1998). The response of zooxanthellate octocorals, and the absence of a response in azooxanthellate octocorals are consistent with a study on larger scale on the relationship between octocoral richness and water clarity (Fabricius and De’ath 2001). E. The response is specific for the cause, thus an association should be stronger if there are few rather than many causal factors Some, but not all of the responses were specific to water quality. In particular, total hard coral cover is a nonspecific response, as it may be low after a bleaching or crown-of-thorns disturbance, or it may be high even in chronically adverse conditions due to the asexual spread of a few resistant species. Macroalgal growth increases with nutrients, and unlike corals is not known to be affected by periods of high temperatures. Macroalgal cover remains low despite high nutrient levels at high grazing pressure (McCook 1999), however macroalgal cover is rarely high in low-nutrient environments, and macroalgae have established dominance on reefs in some areas of eutrophication (Smith et al. 1984). Octocoral cover is more specific than hard coral cover, as octocorals are rarely eaten by the crown-of-thorns seastar (De’ath and Moran 1998), or indeed any other major group of predators, and azooxanthellate octocorals are unaffected by coral bleaching. A few octocorals of the families Alcyoniidae, Clavu lariidae and Briareidae may increase in cover in areas of high nutrient loads, if other physical environmental conditions such as currents and light are favorable. However, bleaching-susceptible zooxanthellate genera tend to be more abundant than azooxanthellate genera, and thus octocoral cover may be reduced by bleaching. The decrease in octocoral richness appears to be specific to water quality. This conclusion is supported by the rarity of species with low tolerance of poor water quality, the abundance of some more tolerant taxa (e.g. Briareum sp.) and the lack of response in azooxanthellate taxa in WT. Similarly, the gradients in octocoral community structure, with communities being progressively depauperate with increasing nutrients and sediments, could not be explained by other major disturbances (cyclones, bleaching and crown-of-thorns). F. Association Agrees with Known Biological Facts Increased supply of limiting nutrients is known to increase macroalgal growth rates in the absence of other limiting factors (Schaffelke and Klumpp 1998). Sedimentation affects recruitment and increases adult mortality in hard and octocorals, thus potentially reducing coral cover (Riegl and Branch 1995, Philipps and Fabricius 2003, Fabricius et al. 2003). A reduction in biodiversity is likely due to variable tolerances of species to sedimentation, turbidity, and bleaching. Octocoral richness is known to decline by one genus per meter reduction in visibility on the Great Barrier Reef in areas where visibility is 0.05), AIC, AICc and BIC. Columns 7-9 show posterior probabilities for each of the 5 models averaged over the 1000 simulations based on AIC, AICc and BIC. Sample size (N)

12

50

Selected model

PTest

AIC

AICc

BIC

P-AIC

P-AICc

P-BIC

1

52

147

45

106

0.26

0.08

0.22

2

130

347

225

344

0.34

0.30

0.36

3

742

454

632

492

0.33

0.50

0.34

4

70

52

97

78

0.07

0.12

0.08

5

6

0

1

0

0.00

0.00

0.00

1

38

150

109

36

0.34

0.28

0.16

2

725

748

771

786

0.54

0.55

0.61

3

237

102

120

178

0.12

0.17

0.23

4

0

0

0

0

0.00

0.00

0.00

5

0

0

0

0

0.00

0.00

0.00

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