Decadal trends in a coral community and evidence of changed disturbance regime
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A 23 year data set (1981–2003 inclusive) and the spatially explicit individual-based model “Compete©” were used to investigate the implications of changing disturbance frequency on cover and taxonomic composition of a shallow coral community at Lizard Island, Australia. Near-vertical in situ stereo-photography was used to estimate rates of coral growth, mortality, recruitment and outcomes of pair-wise competitive interactions for 17 physiognomic groups of hard and soft corals. These data were used to parameterise the model, and to quantify impacts of three acute disturbance events that caused significant coral mortality: 1982—a combination of coral bleaching and Crown-of-Thorns starfish; 1990—cyclone waves; and 1996—Crown-of-Thorns starfish. Predicted coral community trajectories were not sensitive to the outcomes of competitive interactions (probably because average coral cover was only 32% and there was strong vertical separation among established corals) or to major changes in recruitment rates. The model trajectory of coral cover matched the observed trajectory accurately until the 1996 disturbance, but only if all coral mortality was confined to the 3 years of acute disturbance. Beyond that date (1997–2003), when the observed community failed to recover, it was necessary to introduce annual chronic background mortality to obtain a good match between modelled and observed coral cover. This qualitative switch in the model may reflect actual loss of resilience in the real community. Simulated over a century, an 8 year disturbance frequency most closely reproduced the mean community composition observed in the field prior to major disturbance events. Shorter intervals between disturbances led to reduced presence of the dominant hard coral groups, and a gradual increase in the slow growing, more resilient soft corals, while longer intervals (up to 16 years) resulted in monopolization by the fastest growing table coral, Acropora hyacinthus.
KeywordsResilience Chronic disturbance Community structure Climate change Diversity
Coral reefs are among the world’s ecosystems most at risk from global change, both climate related and from direct pressures of human over-exploitation and pollution (Kinzie and Buddemeier 1996; Hoegh-Guldberg 1999; Kleypas et al. 1999; Hughes et al. 2003). Events that kill extensive areas of coral reefs are increasing in frequency and intensity and include coral bleaching (Hoegh-Guldberg 1999; Kleypas et al. 1999), outbreaks of coral-eating predators (Seymour and Bradbury 1999) and disease (Harvell et al. 2002; Rosenberg and Ben-Haim 2002). In addition, deteriorating coastal water quality can also threaten reef health (Wilkinson 1999; McCulloch et al. 2003).
Coral reefs have evolved within this spectrum of disturbances, with variation in the type, scale, intensity and frequency of disturbance all playing a major role in determining community dynamics and structure (Connell 1978; Huston 1985). Much research has focussed on the response of reefs to disturbance, and in particular whether hard corals recover to approximate the communities that existed prior to disturbance (Pearson 1981; Connell 1997) or whether there is a switch to alternative states dominated by macro-algae, soft corals, or “barren” grounds characterised by coralline and filamentous algae (Done 1992; Hughes 1994; Knowlton 1992; Wooldridge et al. 2005). Scientists predict a continued increase in disturbance frequency with dire consequences for coral reefs (Hoegh-Guldberg 1999; Kleypas et al. 1999; Hughes et al. 2003). It is predicted that most coral reef systems will be experiencing near-annual bleaching events that will exceed the extent of the 1998 bleaching event by the year 2040 (Hoegh-Guldberg 1999). However, a detailed understanding of how coral reefs might respond to changing patterns of coral mortality is unclear.
An indication of likely future changes in coral reefs under altered environmental conditions and disturbance regimes may be gained by investigating changes in the recent past. In particular, it would be valuable to understand past variability in the trajectories of coral cover and composition, and to understand the relative roles of key ecological processes, such as recruitment, growth, survivorship and competition, in driving that variability. In this study, a coupled-lattice model of an area of reef was built and tuned to closely match the observed trajectory of the study area in the period 1981–2003. This provided a basis to understand the role of the various ecological and disturbance processes in structuring the community, and to investigate possible futures for this area in coming decades.
Throughout the lives of corals, there can be significant physical contact and competition for space on the reef. Outcomes of competition can be important in determining the survival of individual corals (Tanner et al. 1994; Sheppard 1985). Dunstan and Johnson (2005) successfully modelled marine epibenthic community trajectories based on the probability distributions describing outcomes of pair-wise competitive interactions, recruitment, mortality and growth rates. This study explored how these same processes have played out (photographic monitoring 1981–2003), and might shape coral community dynamics in the future (2003–2103), in a 32 m2 area. Processes that play a key role in structuring communities at one time may become less important once disturbance becomes more frequent, leading to lower benthic cover and reduced likelihood of interaction among established colonies than in more crowded sites, and greater availability of space for growth and new colonisation. The study area experienced three major disturbance events in the period 1981–2003. In 1982 there was significant coral mortality from a combination of coral bleaching and crown-of-thorns starfish (Harriot 1985), in 1990 corals were damaged or removed entirely by waves from Cyclone Ivor (van Woesik et al. 1991) while in 1996 corals were significantly affected by predation from crown-of-thorns starfish (Sweatman et al. 1998; Pratchett 2005).
The particular aims of the study were to determine (1) whether local ecological processes affecting corals can be modelled to produce a community trajectory similar to the observed community over 23 years; (2) which processes are most important in determining the trajectory; and (3) the range of variability in predicted trajectories of the modelled community under various disturbance intervals over 100 years.
Materials and methods
The benthic community within the study site was monitored most years using near-vertical stereo photography (see Done 1981). Photographs were taken on Kodachrome 64 slide film from ∼2 m above the substratum at 1 m intervals along tape measures stretched between steel pegs within the site (Fig. 1b) using two Nikonos cameras fitted with 28 mm lenses and mounted 300 mm apart on a rigid aluminium frame. A 1.9 m rod with scale object and spirit levels fitted at right angles was used to acquire overlapping stereo pairs of the entire site in 16 of the 23 years between 1981 and 2003 (Fig. 2a).
The photographic slides were digitised (file size ∼27 MB) and analysed using two methods: (1) a point sampling method to map and quantify changes in community structure based on empirical data, and (2) stereo observation and photogrammetry to quantify rates of growth, mortality, recruitment and outcomes of inter-specific interactions to paramaterise the models. In the point sampling, each image pair (32 pairs per year) was viewed stereoscopically and the benthos underlying a 10 × 10 grid of evenly spaced points was identified to the highest possible taxonomic level before assigning it to one of 17 physiognomic groups. This procedure provided an estimate of percentage cover of each physiognomic group through time (the “observed” trajectories) and also a base map for the spatial model (1981 only). For the base map, the observed benthic attribute at each point was assumed to fully occupy a 10 × 10 cm square (Fig. 2b). Stereo observation and photogrammetry were then used to provide estimates of growth, mortality, recruitment and outcomes of inter-specific interactions for each physiognomic group. Subsets of images (the same 14 pairs on each trip, see Fig. 1b) were displayed on a flat monitor using photogrammetry software (Intergraph ImageStation© Stereo Softcopy Kit, see http://www.intergraph.com), viewed through liquid crystal shutter eyewear (Stereographics CE-3), and mapped on-screen using a mouse-driven cursor (Fig. 2c). The time-series of colony sizes and status (live, dead, injured) were then used to compute parameter rates for recruitment, growth, mortality and pair-wise interaction outcomes to be used in the spatial model “Compete©” (available at http://www.zoo.utas.edu.au/CJPblist/PublistCJohnson2.htm).
Modelling the community trajectory
The 8 m × 4 m base map of the 1981 coral community was represented on a spatial landscape of 320 × 160 cells, with each cell representing a real area of 25 × 25 mm on the reef. The “Compete©” model was then run on the landscape for either 23 or 100 years at time steps of 1 month. Compete© uses a probabilistic (stochastic) cellular automaton as its kernel, with growth, mortality, recruitment and disturbance expressed as conditional probabilities of cell occupancy at each time-step. The terms “site” and “model” are used below to distinguish between the empirical data (from photographs) and the modelled outputs.
At the site, measurements for growth were derived for each physiognomic group from annual change in colony area (until the colony died or reached 2003). Growth was converted from change in area to a rate of linear extension (based on change in equivalent circle diameter) to a monthly probability of growth into an empty cell. Average and variable growth were trialled in the model. Compete© models variable growth rate by assigning new recruits a growth rate selected randomly from a given range of growth rates. This range was defined as the average growth ±1 standard deviation.
Mortality in a given physiognomic group was defined in the model by (i) the probability of mortality for that group, which applied to every cell of that group in each time-step; and (ii) the size of the mortality event, which defined the maximum number of contiguous cells of the same group that were removed in a single mortality event. At the site, the probability of mortality (i) was derived from the number of whole colonies dying per year for each group. The size of the mortality event (ii) was set to the average size of colonies that died across all groups (the model does not allow for different size mortality events to be set for each group). Therefore, to account for differences in the size of the mortality events among groups, a correction factor was applied to mortality probability. For example, if the size of the mortality event for a given group was only half as big as the set mortality size, its mortality probability was halved.
Recruitment occurred only in empty cells on the landscape and was set to “open” whereby recruitment did not depend on the relative abundance of physiognomic groups on the current landscape. Instead, a group was selected at random from any that had existed in the system, and it recruited to the empty cell with a probability defined by its recruitment rate. At the site, recruitment rate for each group was estimated by converting the average number of recruits/year/area to the monthly probability (0–1) that a group would recruit to an unoccupied cell on the model landscape.
Outcome of interactions between each pair of physiognomic groups was assessed at the site and converted to a probability of win, loss or stand-off. In the model, if a central cell was occupied by a group Y, then a neighbouring cell X was chosen at random, and group X overgrew group Y (the central cell) with probability PrXY*GXY where PrXY was the probability of X winning an encounter with Y, and GXYwas the growth rate of X over Y. All of the 136 possible pair-wise combinations among the 17 groups at the site were considered in deriving probabilities of win, loss or stand-off. However, reasonable data (defined arbitrarily as n ≥ 8 independent observations) were available for only 43 interactions. All other interactions were assigned equal probabilities of win, loss and standoff.
1982: coral bleaching and crown-of-thorns starfish (acute disturbance),
1983–1989: inter-disturbance period 1,
1990: cyclone damage (acute disturbance),
1991–1995: inter-disturbance period 2,
1996: crown-of-thorns starfish (acute disturbance),
1997–2003: inter-disturbance period 3.
Effects of recruitment and interactions
For recruitment, the following scenarios were examined: (1) failed recruitment (all recruitment set to zero); (2) the observed mean recruitment rate across all physiognomic groups; (3) double the observed mean recruitment rate of each physiognomic group; and (4) tenfold the observed recruitment rate of each physiognomic group. For competitive interactions, the observed interaction outcomes were compared with three arbitrary scenarios, namely, (1) equal probability of win, loss or standoff (outcomes as 33:33:34%), (2) 100% standoff (outcomes as 0:0:100%), and (3) randomly determined probabilities of win and loss, with no standoffs allowed (outcome as [win] + [loss] = 100%).
Effects of changed disturbance frequencies projected over 100 years
Effects of various disturbance intervals on the modelled community were simulated over a 100 year period, with disturbance fixed at regular intervals of 2, 4, 6, 8 and 16 years. Each disturbance was defined using the average of parameter rates determined for the three disturbance years (1982, 1990, 1996). All inter-disturbance periods were defined by the parameters averaged over all other years, except for background mortality, which was set to zero (see “Results”). The 1981 base map provided the starting spatial configuration, and the first disturbance occurred at year 1 in all cases followed by the requisite number of inter-disturbance years, i.e., for a 16 year disturbance frequency, 1 year with disturbance followed by 15 years without disturbance, repeated until reaching 100 years. Immediately prior to each disturbance the model community composition was recorded and compared to pre-disturbance communities from the observed trajectory (January 1981, 1989, 1994, 1995 and 2002).
Analyses to compare observed and model trajectories
Absolute percent cover data was generated for the observed (based on point-sampling empirical data) and model trajectories for each physiognomic group. These data were square-root transformed to down-weigh the importance of the highly abundant species, and a Bray–Curtis similarity matrix was generated among years. Changes in composition through time were followed using Kruskal’s non-metric multidimensional scaling (NMDS) of the pre-disturbance abundances of each group. A Procrustes rotation analysis was used to assess the degree of concordance between the modelled trajectories and the observed trajectory. Procrustes analysis works by scaling, rotating and dilating one ordination solution and then superimposing it on the second, maximising the fit between corresponding observations. The least-square criterion that minimizes the sum of the squared residuals (m2) between two configurations was used; low values for m2 indicate strong concordance (Gower 1971). “ProTest”, which assesses the statistical significance of the Procrustean fit (Peres-Neto and Jackson 2001), was used with 9999 permutations to assess the significance of pair-wise comparisons of the trajectory for each disturbance interval against the observed trajectory.
The NMDS analysis was carried out using the package MASS in R version 2.4.1 (R Development Core Team 2005, see http://www.r-project.org/). The R package Vegan was used to calculate the Bray-Curtis distance measure, and to undertake the Procrustes and ProTest procedures. Primer 5 (Clarke and Warwick 2001) was used to generate plots of the 100 year NMDS trajectories.
Observed changes in the study site
The 17 physiognomic groups showing percent cover for the period 1981–2003
Physiognomic group by family
Average % cover
Alcyonidae (Soft corals)
Soft coral cover was only about one third that of hard coral and fluctuated independently of acute disturbance (Fig. 3b), apart perhaps from physical damage caused by cyclone-generated waves in 1990. Two of the three soft corals, Sinularia sp. and Sarcophyton sp., maintained a remarkably uniform cover and population size throughout the study. They had relatively little turnover, moderate to low growth and moderate to high recruitment (Fig. 4a–c). Sinularia sp. had the highest average cover through time (6.9%) of all physiognomic groups.
Mean growth rates ranged from negative values up to 50 mm increases in equivalent circular diameter per year (Fig. 4a). Five physiognomic groups had negative growth (due to partial mortality exceeding overall growth), and seven (including all soft corals) had faster mean growth during acute disturbances than inter-disturbance years. Seven of the eight fastest growing physiognomic groups were in the genus Acropora, and all of the most rapidly growing ten groups were either Acropora or Pocillopora. The massive and encrusting hard corals and the soft corals were the slowest growing groups.
Massive Porites had the highest recruitment rate and this occurred during acute disturbance years (Fig. 4b). In some cases, new Porites colonies only became visible in the images after upper storey Acropora species had died, making it hard to determine exactly when Porites recruitment had occurred. The five most abundant physiognomic groups had the five highest recruitment rates except for Sinularia sp. which was ninth highest. Recruitment for physiognomic groups ranged from <1 to 11 recruits per year over the study area (i.e., 32 m2). The average total number of recruits (all groups combined) was 27 per year (which is equivalent to <0.02 m2 or about 0.05% of the area of the site).
During acute disturbance years, most physiognomic groups (16 out of 17) had higher mortality rates than in inter-disturbance years (Fig. 4c). During acute disturbances the fast growing and abundant Acropora species had the highest mortality with slower growing hard and soft corals having lower mortality. During inter-disturbance years, mortality was low and relatively even across all physiognomic groups.
Physiognomic groups rated by relative percentage
Tuning the model
Results of the procrustes rotation analyses and ProTest permutation test on the non-metric multidimensoinal scaling (NMDS): observed versus model trajectories
Procrustes sum of squares
(a) Model selection
Observed trajectory versus
Average growth, background mortality ON
Variable growth, background mortality ON
Variable growth, background mortality OFF
Variable growth, background mortality on 1997–2003
Observed trajectory versus
Model [Best fit model selected from (a) above]
Standoff (outcomes as 0:0:100%),
Equal (outcomes as 33:33:34%)
Random (randomly determined probabilities of win and loss, with no standoffs allowed)
Observed trajectory versus
Model [Best fit model selected from (a) above]
Mean recruitment rate across all physiognomic groups
Double the mean recruitment rate of each physiognomic group
Tenfold the recruitment rate of each physiognomic group
(d) 100 year models
Observed trajectory versus
Model [Best fit model selected from (a) above]
Disturbance every 16 years
Disturbance every 8 years
Disturbance every 6 years
Disturbance every 4 years
Disturbance every 2 years
Effects of interaction and recruitment
Predictions from 100 year models for changed disturbance frequency
Crown-of-thorns starfish, coral bleaching and cyclones are large scale disturbances that can exert considerable impact on mid-shelf reefs of the northern and central Great Barrier Reef (Endean and Cameron 1990; Done 1992; van Woesik et al. 1991; Berkelmans et al. 2004). In this study the key parameters driving a local reef community were measured and it was possible to approximately replicate the dynamics of this community using the individual-based model Compete©. Various methods were investigated to construct a model that was reasonably faithful in its representation of the community over the 23 year study period. A model based on variable growth rates performed better (closer match to observed) than one based on fixed average growth rates. This allows both exceptional years and size-dependent growth rates to affect community trajectory in a way average growth alone cannot (c.f. Done et al. 2007).
Up until 1996, the 23 year model would only closely reproduce the community trajectory if all modelled mortality was confined to acute disturbance years only. Beyond that date (1997–2003) it was necessary to introduce background mortality into the model to adequately replicate the lack of post-1996 recovery of hard corals. Up until 1996, the study site had been characterised by inter-disturbance periods in which hard coral cover either regained or exceeded previous levels and there was low mortality. It is proposed that this community has entered a new phase in which inter-disturbance “recovery” periods are characterised by chronic disturbance, the model’s “background mortality”, caused by predation, bleaching and disease. The study community was still recovering from the major 1996 COTS outbreak when region-wide mass bleaching occurred in 1998 and 2002 (Berkelmans et al. 2004), and there were also small numbers of COTS in and near the site at those times (Sweatman et al. 1998; Pratchett 2005; personal observations). Although a number of corals at the site partially bleached during both bleaching events there was not a marked increase in coral mortality (i.e., complete colony death). However, bleaching can reduce coral growth rates, lower fecundity and increase the incidence of disease (Baird and Marshall 2002; Douglas 2003; Harvell et al. 2002), all of which could slow recovery. It is proposed that since 1996 there has not been a typical inter-disturbance period free of disturbance in which coral can recover. Instead, various disturbances including residual COTS, low-level bleaching and secondary disease, have impacted the community and kept coral cover stable at low post 1996 disturbance levels.
Neither recruitment rate or interaction outcome had much impact on the model trajectory. Failed recruitment did not cause a significant drop in coral cover, and a tenfold increase in recruitment did not cause a significant rise, over the ∼20 year time frame modelled. Lack of sensitivity to an order of magnitude variability in recruitment reflects very low absolute densities of recruits recorded at the site. (The combined average totalled across all groups was only 27 recruits per year, which, based on a size of 25 × 25 mm per model recruit, would cover about 0.05% of the whole area). Moreover, the 7–8 year interval of recruit growth between disturbances would not be sufficient time for such a low starting percentage, even multiplied ten times, to make a significant contribution to cover compared to the growth of large established colonies. Estimates of recruitment at the site may have been low due to overtopping by A. hyacinthus. It cannot be assumed that this site is generally representative of this habitat in respect of recruitment rates, but this study does show that recruitment cannot be taken for granted as a major driver of observed changes over 2–3 decades; it can take a distant third place to survival and growth of established colonies. For interactions, the conventional wisdom is that they play a major role in shaping coral community structure and dynamics (Lang 1973; but c.f. Bradbury and Young 1983). The present study, however, indicated that interactions were unimportant because frequent disturbances created a surfeit of free space and a very low incidence of competitive interaction before the next disturbance arrived. Average space at the site that was unoccupied by any of the physiognomic groups was about 68% and maximum coral cover was 40%. In other habitats with comparable colony sizes and low total coral cover, interactions among established corals and other macro-benthos may similarly be of minor importance. In summary, the model suggests that recruitment and inter-specific interactions play a minor role in structuring this community over the 2-decade timescale of observations, and that the major drivers of community structure are disturbance history and growth.
There is widespread concern that global change is degrading the fundamental structure and function of provision of services by, ecosystems of all kinds. For coral reefs, numerous studies have shown that their degradation is often manifest as a persistent phase shift to non-reef-building forms such as macro-algae, soft corals or to overgrazed barrens (Done 1992; Hughes 1994; Wooldridge et al. 2005). There was no such tendency observed in the site in the period 1981–2003. However, the need to introduce background mortality to accurately model from 1997 to 2003 suggests that a period of changed disturbance regime and community dynamics may have already commenced.
The model was therefore used to assess community persistence and resilience under a range of different disturbance frequencies. The model in which a major disturbance event arises every 16 years (which is about twice as long as the average observed interval) produced a community that after 100 years became dominated by the hard coral A. hyacinthus (97%). Although the maximum cover of A. hyacinthus recorded at this site is only 17%, a cover of 75% and more is not uncommon in similar habitats on the GBR (personal observations of the authors). Disturbance every 2 years led to a community that no longer supported hard coral and only low levels of soft coral persisted. There are places where soft corals do monopolize large areas (Dinesen 1983; Fabricius 1997; Ninio et al. 2000) and it is plausible that soft corals could expand in the site, especially under increased disturbance frequency and background mortality levels that limit hard coral recovery. A disturbance interval of 8 years with current observed rates of recruitment, growth and mortality most closely reproduced the mixed hard coral dominance observed at the site in the 1980s and 1990s (but only for the first 20–30 years of the 100 year projection). These 100 year projections did not include the chronic background mortality that this study suggests has retarded hard coral growth since 1996. This raises the question as to the likelihood of a community that suffers chronic background mortality will be able to recover its former abundance, species composition and size class following acute disturbances, or declining even further.
Thanks to Lyndon DeVantier and Emre Turak for their ongoing assistance with the long-term photographic study of GBR coral communities, and to the master and crews of AIMS ships RV Lady Basten, RV Harry Messel and RV Cape Ferguson. Thanks are also due to Steve Delean, John Carleton and Shaun Wilson for critical comments and advice on the manuscript. Involvement of C. R. Johnson in the modelling was through the Modelling and Decision Support working group of the CRTR Project.
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