Asymmetric Competition Prevents the Outbreak of an Opportunistic Species After Coral Reef

Asymmetric competition prevents the outbreak of an opportunistic species after coral reef degradation

Manuel González-Rivero1, 2,* ζ, Yves-Marie Bozec1, Iliana Chollett1, 2 ξ, RenataFerrari1,φ, Christine H. L. Schönberg3, Peter J. Mumby1, 2

1. School of Biological Sciences and Australian Research Council Centre of Excellence for Coral Reef Studies, University of Queensland, St. Lucia, Qld 4072 Australia.

2. College of Life and Environmental Sciences, University of Exeter, EX44PS. United Kingdom.

3. Oceans Institute, University of WesternAustralia, 39 Fairway, Crawley, WA 6009, Australia

*Corresponding author: Telephone: +617 33653452. E-mail:

ζ Current address: Global Change Institute, University of Queensland, St Lucia, Qld, 4072, Australia.

ξ Current address: Smithsonian Marine Station, Smithsonian Institute, Fort Pierce, Florida. USA

φ Current address: University of Sydney, Coastal and Marine Ecosystems Group, School of Biological Sciences & Australian Centre for Field Robotics, NWS, Australia.

Author Contributions: MGR and PJM conceived and designed field surveys and the ecosystem model. MGR, RF and PJM performed field surveys. MGR, YMB, IC and PJM designed and performed data analysis. MGR, PJM, IC, and YMB wrote the manuscript; other authors provided editorial advice.

Online Resource1. Spatio-temporal variations of benthic community composition, detailing the composition of macroalgae

Coverage of benthic species was monitored over time from 1998 to 2009 using randomly placed photo-quadrats or transects for three locations at Glover’s atoll, Belize (as described on the manuscript and Online Resource 3). Here we present a comparative characterization of the benthic community across sites and between two periods of time. While benthic composition was monitored until 2009, here we present a spatial and temporal comparison between 1998 and 2007 because only one site (E1) was monitored in 2009.

When aggregating the benthic composition in functional groups, the results show a consistent decrease in coral cover between 1998 and 2007 across sites (Figure S1), from 18.5 % ± 4.8 % to 8.5 ± 0.3 % (mean ± se among sites). In contrast, cropped turf algae, defined in the model as available space for recruitment and growth of benthic species, increased from 35.7 ± 4.8 % to 43.1 ± 7.9 % (mean ± se among sites, Figure S1). Macroalgae remained the most dominant group of benthic functional groups across sites and between periods of time (Figure S1), averaging 36 % ± 3.9 % (mean +/se across sites and time). Similarly, sponges only represented an average of 1.7 % ± 0.32 % (mean +/se across sites and time), where Cliona tenuis dominated the composition (averaging 1.1 % ± 0.3 % cover, mean ± se across sites and time).

Looking in detail at the spatio-temporal variability of relative macroalgal composition (Figure S2), Lobophora variegata and Dictyota spp, the model macroalgae species in this study, consistently dominated the species composition across time and space, representing 78.1 +/- 0.1 % (mean +/- se across sites and time) of the total macroalgae coverage. Between time periods, Dictyota spp showed a consistent increase in cover, relative to total macroalgae abundance, from 46.2 +/- 0.1 % to 60.3 +/- 0.1 (mean +/- se across space and time, Figure S2). In contrary, L. variagata showed no changes between 1998 and 2007. While the figure provided (Figure S2) summarises other macroalgal species by algal groups (e.g., Rhodophyta, Clorophyta, Phaenophyta), here we provide the full taxonomic list of macroalgae identified to lowest possible taxonomic resolution (Table S1).

Fig S1. Spatial variability of benthic composition at Glover’s Atoll, Belize, between A) 1998 and B) 2007. Pie charts represent the percentage cover of functional groups of benthos monitored at each study site. Base map source: Millennium Coral Reef Mapping Project (MCMP).

Fig. S2. Macroalgal composition for each study site at Glover’s atoll and between two periods of time: A) 1998 and B) 2007. The values presented here are the relative proportion of each group for the total macroalgal cover for each site and period of time. For graphic representation, identified species of macroalgae have been categorised into major groups (e.g., Chlorophyta, Phaenophyta and Rodophyta) with the exception of Lobophora variegata, Dictyota spp and Halimeda spp, being the most dominant species. Note that Filamentous cyanobacteria (Cyanophyta) are also included here as a category despite the fact that cyanobacteria are not formally considered to be macroalgae. Base map source: Millennium Coral Reef Mapping Project (MCMP).

Table S1. Macroalgal species list recorded at Glover’s Atoll, Belize, from video surveys between 1998 and 2009.

Group / Species
Chlorophyta / Amphiroa spp
Microdictyon spp
Penicillus capitatus
Rhipocephalus spp
Halimeda spp
Phaeophyta / Padina spp
Sargassum hystrix
Turbinaria spp
Dictyota spp
Lobophora variegata
Rhodophyta / Galaxaura spp
Jania spp
Jania adhaerens
Laurencia spp
Wrangelia spp

Online Resource 2. Spatio-temporal variations of the population attributes of Cliona tenuis at Glover’s atoll between 1998 and 2009

Population attributes of Cliona tenuis, such as Skewness, Kurtosis, Geometric mean size and size distribution are compared among time periods and detailed in Table S2, and summarized in Figure S3. Size-frequency distribution at each period was compared against a lognormal distribution using Kolmogorov-Smirnov (K-S) normality test on log-transformed data. Sample size and sampled area are shown in Table S2. Populations attributes describing the structure of the sponge populations did not varied significantly among sites and over 11 years, describing a size distribution strongly biased towards individuals between 0 and 100cm2 (Fig. S3).

Table S2. Demographic attributes of Cliona tenuis at Glover’s Atoll from 1998 to 2009, showing the skewness, kurtosis, geometric mean size, Shapiro-Wilk normality test of the log transformed sample (W Shapiro Wilk statistic, p: significance level), and the sampling effort (sampled area, number sampling units and of sampled individuals) for specific sponge populations at each site and time. Populations were sampled in time and space using 1-m2 quadrats, which two exceptions highlighted in the table with the asterisks.

Year / Site / Skewness / Kurtosis / Geometric mean Size (cm2) / Log-Normality test / Sampling effort
W / p / Area (m2) / Sampling units / Ind.
1998 / E1 / 5.14 / 38.81 / 12.65 / 0.984 / 0.279 / 30 / 6* / 100
E2 / 0.96 / 2.64 / 9.97 / 0.960 / 0.694 / 5 / 1* / 15
W1 / 2.86 / 12.96 / 12.85 / 0.988 / 0.684 / 15 / 3* / 79
2003 / E1 / 1.12 / 3.22 / 27.06 / 0.958 / 0.087 / 13 / 13 / 47
E2 / 1.15 / 2.93 / 7.12 / 0.888 / 0.092 / 18 / 18 / 13
W1 / 10.01 / 102.13 / 3.57 / 0.953 / 0.001 / 19 / 19 / 106
2007 / E1 / 5.69 / 34.20 / 14.82 / 0.968 / 0.341 / 23 / 23 / 38
E2 / 5.24 / 33.95 / 28.18 / 0.959 / 0.043 / 40 / 40 / 60
W1 / 2.08 / 5.93 / 11.78 / 0.934 / 0.095 / 30 / 30 / 26
2009 / E1 / 5.22 / 33.78 / 9.89 / 0.993 / 0.291 / 100** / 10 / 241
E2 / 9.28 / 94.21 / 10.42 / 0.931 / <0.001 / 100** / 10 / 136

* Belt transects (0.5 x 10 m)

** Belt transects (1 x 10 m)

Fig.S3. Demographic attributes of Cliona tenuis populations between 1998 and 2009. The dotted line in panned A to C show the global averages among sites and years, dots represent averages among sites, and vertical bars denote the 95% confidence intervals. A) Geometric mean size of individuals. B) Skewness. C) Kurtosis. In panels D and E, the bars indicate the observed size frequency distribution and the red dashed lines show the fitted log-normal size frequency distribution given the mean and standard deviation. D) Size-frequency distribution of C. tenuis in 2009 at site E1. E) The same distribution when size is log-transformed.

Online Resource 3. Parameter estimation of the hypothesized drivers of Cliona tenuis population structure

Four processes are here hypothesized to drive the population structure of C. tenuis: Competition, Stock-Recruitment, individual mortality and partial tissue mortality. The parameterization of these drivers was estimated from Glover’s Atoll, Belize on the windward side of the atoll (E1 and E2, Fig.S4), and it is discussed in turn for each driver bellow.

Fig.S4. Location of study sites at Glover’s Atoll (A) in the Mesoamerican Barrier Reef, Belize (B), and the relative location of the atoll in the wider Caribbean region (C). Dashed line show the approximate boundaries of the Glover’s Atoll Marine Reserve. Vital rates of the Cliona tenuis populations were obtained from site E1, while sites E1, E2 and W1 were monitored over time and used for testing the model simulations. Map Source data: global coastline by the Global Self-consistent, Hierarchical, High-resolution Shoreline database (GSHHS) and Mesoamerican coastline and reef locations by the Millennium Coral Reef Mapping project (MCRM).

Competition: The growth rate of Cliona spp. is strongly dependent on the intensity of competition, given by the identity of the competitor and the proportion of tissue in direct contact (Cebrian and Uriz 2006; Chaves-Fonnegra and Zea 2011; López-Victoria et al. 2006). Pairwise competition coefficients, such as the rate of advance or retreat during confrontation, were obtained from a previous field study at Glover’s atoll (E1, Fig. S4 during 2009 (González-Rivero et al. 2012), and the results are summarized in the manuscript (Table S4).

In previous research, we observed that the linear extension of C. tenuis in competition with cropped algae, or turf, significantly varied as a function of the developing state of this algal community, indicating that the competitive strength of turf increase as it get denser and taller (González-Rivero et al. 2012). Here we estimate growth of the sponge in confrontation with the average cropped algae state at Glover’s Atoll (including tall and short turf algae). From tagged sponges in 2009 on the windward side of the atoll(see González-Rivero et al. 2012 for details), we selected those individuals which withstood over 90% of their perimeter in competition with turf algae (95.6 ± 0.2 %; mean ± CI0.95, n = 27). Assuming a radial expansion of C. tenuis, average linear extension was estimated by calculating the difference between the radius of the sponge (∆r) at two time steps (0 and 286 days during year 2009) from each individual (Equation 1).

eqn 1

Where A is the size of the individual at 0 (Ai) and 286 days (Af), estimated from video footage using the software VidAna (v 1.2.1; Hedley 2006). Linear extension (∆r) is linearly extrapolated to a year, using the constant coefficient of 1.35. The average growth of the sponge in confrontation with major benthic components is presented in the manuscript (Table S3).

Stock-recruitment dynamics:Given the poor swimming capabilities of clionaid larvae their abundance is strongly spatially correlated to the abundance of adults (Mariani et al. 2006; Mariani et al. 2005). Therefore, the modelled populations are assumed to be sustained by stock-recruitment dynamics determined by the number of individuals and the fecundity associated with each. Fecundity is a function of colony size (Ramirez Llodra 2002), and although the exact nature of the fecundity-size relationship has not yet been determined in clionaids, here we assume a simple linear increase in fecundity with tissue area. The reproductive index is the proportion of propagules per unit of tissue, and assuming that this index remains constant with size, the number of propagules and larvae produced will proportionately increase with the individual size of the sponge.

Whole-individual mortality:Newly settled individuals are prone to high mortality rates caused by extrinsic physical or biological selective pressures, and as individuals increase in size they become less vulnerable, eventually reaching a size at which they escape from these sources of mortality (Gosselin and Qian 1997). Thus, whole-individual mortality rates tend to decrease as benthic invertebrates grow (Babcock 1991; Hughes and Connell 1987; Jackson et al. 1985). Here we modelled mortality as a negative power function of size (Peterson and Wroblewski 1984): M=0.16(size-1.42) (eqn 2), where the parameters were estimated by the non-linear least square regression fitting, and size is the initial area of the sponge, described below. Equation 2 was used to calculate the mortality rate per year. We then divided this value by two to obtain the rate per time step in the model (6 months).

253 sponges were randomly tagged in January 2009 at E1 (Fig.S4), and followed during 286 days (see González-Rivero et al. 2012 for details). The number of dead individuals was recorded at the end of this period, and the initial size of each individual was estimated from high definition footage videos, using the software VidAna (Hedley 2006). The probability of mortality as a function of size was then calculated by subdividing the dataset into 5 cm2 size classes. The no-linear regression was fitted using R and the ‘nls’ package.

Partial tissue mortality: The age and size structure of sessile organisms are largely decoupled, and this especially true in marine ecosystems (Bak and Meesters 1998; Hughes 1984; Hughes and Connell 1987). Individuals of a given age can vary considerably in size by sustaining large partial tissue mortality or shrinkage (Bak and Meesters 1998; Hughes and Connell 1987). Here we modelled the partial tissue mortality of sponges by using the probability of shrinking per individual at each time step and, the probable extent of tissue mortality as a proportion of size.

Shrinking of the tagged sponges was commonly observed in the field. Although partial mortality is generally overlooked in demographic studies of benthic organisms, this attribute could be important for the dynamics of these populations. To calculate the per capita probability of occurrence and intensity of partial mortality we followed sponges that were not subjected to macroalgae or coral competition throughout the year in 2009. These sponges had 95.6 ± 0.2 % (mean ± CI0.95, n = 27) of the perimeter in contact with turf, therefore minimizing any possible confounding effect of competition in the estimates of partial mortality. The probability of partial mortality was then calculated as a proportion of shrinking sponges against those that did not change in size.

Online resource 4. Partitioning the direct competitive interaction of C. tenuis with other space occupiers at Glover’s atoll, Belize.

Over the course of a year, 247 individuals of C. tenuis were monitored at sites E1 and E2 (Figure S2, Online Resource 2). The methodological approach is described in Gonzalez-Rivero et al (2011), and data were used to parameterize the model as described in the Online Resource 2. During these observations, the perimeter of each sponge in contact with other benthic species was recorded at two points in time, January and December 2009. Here we summarize the relative proportion of tissue of C. tenuis individuals in contact with other functional groups of competitors, averaged for these two sampling periods (Figure S3). From these results, macroalgae comprise the main competitor averaging a percentage contact with the sponge of 37%±8.4% (mean ± ci95%). Together, available space for cropped turf algae and macroalgae represented an average of 68% ± 13.6% (mean ± ci95%) the perimeter of each sponge. A detailed analysis of the interaction with the identified species or groups of algae show that Lobophora variegata, Dictyota pulchella and dense algal mats have on average 26% ± 4.7% of their perimeter in contact with C. tenuis (Figure S4). Although some variability was observed between the two observation periods, these three groups of macroalgae represent the main competitors of C. tenuis throughout the study (Figure S4).

Fig S3. Average proportion of the perimeter of C. tenuis individuals in contact with other space occupiers at Glover’s atoll, Belize. Competitor categories are: Macroalgal species (Macroalgae), Cropped turf algae (turf), Coral species (Coral), Crustose coralline algae (CCA), Other invertebrates (Other), Sponges species (Sponges). Bars represent the average percentage of the tissue in contact with each competitor group across 247 individuals observed in January and December 2009. Error bars denote the 95% confidence intervals from each mean value.
Fig S4. Average proportion of the perimeter of C. tenuis individual in contact with macroalgae and cyanobacteria at Glover’s atoll, Belize and between two periods of evaluation. Competitor categories are: Dictyota pulchela (Dpul), Lobophora variegata (Lvar), Dense algal mat (Amat), Halimeda spp. (Hsp), Jania adherens (Jadh), Sargassum hystrix (Shys), Cyanobacteria (Cyan), Filamentous Rhodophyta (F.rhodo). Bars represent the average proportion of the tissue in contact with each competitor group across 247 individuals observed in January and December 2009. Error bars denote the 95% confidence intervals from each mean value.

Online Resource 5. Detailed description of the ecosystem model

Overview

The model is an individual-based cellular automaton simulating the population dynamics of benthic organisms dispatched across a regular square lattice of 20×20 cells. The lattice grid has a toroidal structure so that every reef cell has continuous boundaries formed by 4 neighbouring cells. Each cell contains a mixture of living substrata (Table S4) comprising multiple coral colonies, sponge individuals and patches of algae. A number of cells are assigned to the class “ungrazable substratum” (e.g., sand, soft-corals, etc) so that no benthic live cover can colonize those cells.

The model captures rates of recruitment, growth, reproduction and mortality of benthic individuals as well as their competitive interactions, calculated twice a year (every 6 months). At each time step, the toroidal lattice structure helps define probabilistic rules (within a 4-cell von Neumann neighbourhood) of competitive interactions of corals and sponges withmacroalgae, which reduce the growth rate of each coral and sponge taxon. Parrotfish grazing randomly allocated over the grid mediates competition of macroalgae with other benthic components. Grazing affects all algal classes and always results in cropped algae. The spatial arrangement of elements within an individual cell is not explicit, but coral-coral and coral-sponge competition can occur at intra-cellular scales.

Technically, the model updates a set of connected 20×20 matrices (one matrix for each benthic cover) at discrete time steps according to the deterministic and probabilistic rules (sub models) presented in Table S5. The model is implemented in MATLAB as a sequence of vectorized instructions (see Fig. S6), so that all the cells of the lattice grid are processed simultaneously for a given matrix. Each instruction reflects the action of a particular process occurring within a cell, in isolation or as a result of its immediate environment (4-cell von Neumann neighbourhood) defined at the previous time step. Within a time iteration, the four coral matrices are temporarily fusionned within a three-dimensional array (20×20×5) for processing simultaneously all coral and sponge species. At the initial step, a number of cells are randomly designated as “ungrazable cells” to match the specified cover of ungrazable substratum. The remaining cells are filled with coral colonies until the total cover of each coral species reaches the desired level (as a percentage of the total reef area). Colony sizes are created based on a uniform distribution and each colony is randomly allocated to a cell. Each cell cannot contain more than one colony per species (50×50 cm cells). Algal patches are created in a similar way by filling the remaining space according to their initial cover (Fig. S7). Cover matrices are then processed and the resulting benthic covers are stored after every time-iteration. The whole process (including initialisation) is repeated to obtain 100 independent reef trajectories over time.

Fig.S6. Overview of model implementation. For definitions of terms see Table 2 and Figure 4.
Fig.S7. Example diagram of the structure of benthic organisms and key processes represented in a lattice. Note that x and y provide the coordinates for each cell. This diagram also shows that corals and sponges are presented as individuals, while macroalgae, cropped algae and “ungrazable” substrate fills the remaining space.

Table S4. Contents of individual cells within the grid lattice