Nutrient Supply from Fishes Facilitates Macroalgae and Suppresses Corals in a Caribbean

Nutrient supply from fishes facilitates macroalgae and suppresses corals in a Caribbean coral reef ecosystem

Deron E. Burkepile, Jacob E. Allgeier, Andrew A. Shantz, Catharine E. Pritchard, Nathan Lemoine,Laura Bhatti, and Craig A. Layman

Table S1. Summary of fish and benthic community data across reef sites.

Parameter / Mean / SE / Min / Max
Herbivorous fish mass (g/m2) / 22.5 / 4.5 / 10.9 / 66.6
Parrotfish mass (g/m2) / 16.3 / 3.5 / 5.6 / 49.7
Surgeonfish mass (g/m2) / 6.2 / 1.0 / 3.2 / 16.9
Carnivorous fish mass (g/m2) / 87.8 / 21.3 / 19.6 / 213.6
Corallivorous fish mass (g/m2) / 12.1 / 2.0 / 3.5 / 28.0
Macroalgae (% cover) / 38.9 / 3.6 / 22.6 / 65.1
Coral cover (% cover) / 3.4 / 1.0 / 0.9 / 11.1
Juvenile corals (indiv./m2) / 6.9 / 0.5 / 4.1 / 9.3
Territorial damselfish (indiv./m2) / 0.55 / 0.07 / 0.24 / 1.10
Rugosity (C) / 0.28 / 0.013 / 0.18 / 0.36
Distance from shore (km) / 8.9 / 0.4 / 7.0 / 10.3
Nitrogen excretion models
Family / # ind / Min wt (g) / Max wt (g) / Mean Model / Lower 95% CI Model / Upper 95% CI Model
Acanthuridae / 10 / 4 / 202 / y = 0.00019x + 0.0011 / y = 0.00017x + 0.00037 / y = 0.00021x + 0.0018
Haemulidae / 57 / 12 / 707 / y = 0.00027x + 0.011 / y = 0.00026x + 0.0095 / y = 0.00027x + 0.012
Holocentridae / 12 / 88 / 328 / y = 0.00029x + 0.011 / y = 0.00028x + 0.0097 / y = 0.0003x + 0.013
Kyphosidae / 2 / 58 / 752 / y = 0.000087x + 0.0031 / y = 0.000085x + 0.0018 / y = 0.000089x + 0.0044
Labridae / 4 / 2 / 378 / y = 0.00047x + 0.016 / y = 0.00046x + 0.015 / y = 0.00048x + 0.018
Lutjanidae / 36 / 31 / 394 / y = 0.00032x + 0.022 / y = 0.00031x + 0.020 / y = 0.00033x + 0.024
Mullidae / 3 / 68 / 279 / y = 0.00035x + 0.028 / y = 0.00035x + 0.027 / y = 0.00036x + 0.03
Scaridae / 28 / 3 / 1796 / y = 0.00011x / y = 0.00010x / y = 0.00011x + 0.00072
Serranidae / 19 / 15 / 2991 / y = 0.00016x + 0.014 / y = 0.00016x + 0.011 / y = 0.00016x + 0.017
Phosphorus excretion models
Family / # ind / Min wt (g) / Max wt (g) / Mean Model / Lower 95% CI Model / Upper 95% CI Model
Acanthuridae / 10 / 4 / 202 / y = 0.000012x / y = 0.000009x / y = 0.000014x
Haemulidae / 57 / 12 / 707 / y = 0.000029x + 0.00013 / y = 0.000029x / y = 0.00003x + 0.00028
Holocentridae / 12 / 88 / 328 / y = 0.000047x / y = 0.000044x / y = 0.000049x + 0.000056
Kyphosidae / 2 / 58 / 752 / y = 0.0000058x / y = 0.0000054x / y = 0.000006x
Labridae / 4 / 2 / 378 / y = 0.000052x + 0.0016 / y = 0.00005x + 0.0014 / y = 0.000053x + 0.0018
Lutjanidae / 36 / 31 / 394 / y = 0.000097x + 0.0061 / y = 0.000094x + 0.0053 / y = 0.000099x + 0.0068
Mullidae / 3 / 68 / 279 / y = 0.000039x + 0.0021 / y = 0.000038x + 0.002 / y = 0.000039x + 0.0024
Scaridae / 28 / 3 / 1796 / y = 0.0000072x / y = 0.0000068x / y = 0.0000076x
Serranidae / 19 / 15 / 2991 / y = 0.000058x + 0.0062 / y = 0.000057x + 0.0051 / y = 0.00006x + 0.0072

Table S2. Bioenergetics models for fish families representing >95% of the biomass across all study sites. “# ind” indicates the number of individuals used for stoichiometric analysis to parameterize the models. Minimum and maximum weights represent the range in biomass of fishes from the reef surveys. Model equation: y is the grams of nutrient excreted per day by an individual of x grams.

Table S3. Results of multiple linear regression using Akaike Information Criteria (AICc) assessing the factors that explain patterns in macroalgal cover or juvenile coral abundance. A positive estimate for a model term indicates a positive correlation bewteeen macroalgae or corals and that model term; a negative estimate indicates a negative correlation bewteeen macroalgae or corals and that model term. Regressions are with either lower or upper 95% confidence interval values of fish excretion data.

Lower 95% CI fish excretion / Upper 95% CI fish excretion
Macroalgal cover –
Model terms (estimate) / r2 / AICc / AICc / Macroalgal cover –
Model terms (estimate) / r2 / AICc / AICc
Log nitrogen excretion (10.23)
Log parrotfish biomass (-12.35) / 0.84 / 83.37 / 0 / Log nitrogen
excretion (10.89)
Log parrotfishbiomass (-12.94) / 0.83 / 85.52 / 0
Log phosphorus excretion (9.03)
Log parrotfishbiomass (-13.19) / 0.79 / 88.36 / 2.99 / Log phosphorus excretion (9.17)
Log parrotfishbiomass (-13.17) / 0.79 / 88.12 / 2.60
Log nitrogen excretion (9.56) / 0.50 / 94.06 / 10.69 / Log nitrogen excretion (9.82) / 0.47 / 94.81 / 9.29
Log phosphorus excretion (7.93) / 0.41 / 96.01 / 12.64 / Log phosphorus excretion (8.07) / 0.41 / 95.91 / 10.39
Log parrotfish biomass (-10.09) / 0.27 / 98.58 / 15.21 / Log parrotfish biomass (-10.09) / 0.27 / 98.58 / 13.06
Juvenile coral abundance - Model terms (estimate) / r2 / AICc / AICc / Juvenile coral abundance - Model terms (estimate) / r2 / AICc / AICc
Log nitrogen excretion (-1.11)
Log coral cover (1.12) / 0.77 / 43.81 / 0 / Log nitrogen excretion (-1.16)
Log coral cover (1.11) / 0.76 / 44.22 / 0
Log phosphorus excretion (-0.93)
Log coral cover (1.35) / 0.75 / 44.87 / 1.06 / Log phosphorus excretion (-0.95)
Log coral cover (1.35) / 0.75 / 44.77 / 0.55
Log nitrogen excretion (-1.43) / 0.51 / 48.06 / 4.25 / Log nitrogen excretion (-1.52) / 0.51 / 48.10 / 3.88
Log coral cover (1.46) / 0.49 / 48.42 / 4.63 / Log coral cover (1.46) / 0.49 / 48.42 / 4.20
Log phosphorus excretion (-1.06) / 0.33 / 51.77 / 7.96 / Log phosphorus excretion (-1.09) / 0.34 / 51.59 / 7.37

Table S4. Site names, locations, and depth ranges of the reefs surveyed.

Reef Site / Position N / Position W / Surveyed Depth Range
Alligator Reef / 2450.767’ / 8037.382’ / 5-7m
Carysfort Reef / 2512.405’ / 8013.262’ / 6-8m
Conch Reef / 2457.375’ / 8027.442’ / 5-7m
Davis Reef / 2455.341’ / 8030.347’ / 6-7m
Dry Rocks / 2507.343’ / 8017.859’ / 7-8m
Elbow Reef / 2508.457’ / 8015.545’ / 6-8m
French Reef / 2502.094’ / 8020.907’ / 6-8m
Maitland Reef / 2511.779’ / 8013.595’ / 5-7m
Molasses Reef / 2500.523’ / 8022.517’ / 6-8m
Pickles Reef / 2459.242’ / 8024.863’ / 6-8m
Pinnacles Reef / 2459.505’ / 8024.546’ / 5-7m
Snapper Ledge / 2458.911’ / 8025.301’ / 6-8m

Appendix S1

Supplementary Methods for Bioenergetics Modeling

Model Parameterization

Excretion estimates for nitrogen and phosphorus were modeled at the family level. Stoichiometry data for each family was determined by averaging the percent nutrient content for a suite of species within the family (Table S2). Use of parameters for closely related species may increase error in model estimates (Hansen et al. 1993, Ney 1993), however empirical work suggests that variation in excretion rates vary little within a family but widely among families (Vanni et al. 2002). Energy densities of prey items were obtained from Cummins and Wuycheck (1981). Assimilation efficiencies, which have been shown to have only marginal influence on model estimates (Hood et al. 2005), were assumed to be 80% for N and 70% for P, unless published values were found. The growth rate of an animal has been shown to be a particularly influential parameter in bioenergetics (Hood et al. 2005), as such published growth rate values were found for each taxon of interest (Table S3). Other parameter estimates were obtained from literature values specific to the given taxonomic level (Table S3). If published values were not found, we used those from closely related fish species (within the same family) or used assumed values based on widely used bioenergetics parameters for carnivores and herbivores (Schreck and Moyle 1990, Hanson et al. 1997).

We used Fish Bioenergetics 3.0 software (Hanson et al. 1997), to determine consumption rates for the dominant feeding guilds present in our datasets (predators – consuming a mixed diet of vertebrate and invertebrate prey (e.g., Lutjanidae), mesopredators – consuming almost exclusively invertebrate prey items (e.g., Labridae), and herbivores – consuming >90% primary producer material (e.g., Scaridae). To do this we chose the taxon per feeding guild for which we had the best parameter estimates (e.g., Lutjanidae >100 individuals, thousands of diet data points, etc.) and used the software to calculate consumption rates based on energetic demands of the taxon. These consumption rates were then used for all families within that particular guild, holding this parameter constant and allowing other important estimates (e.g., body stoichiometry, prey stoichiometry and growth rate to have influence over the model).

Additionally, uncertainty associated with consumption rates and diet were iteratively modeled using Monte Carlo simulations for each family (Owens et al. 2009). Specifically, a normal distribution of values was created for each parameter with a standard deviation of 5% of the maximum potential value of that parameter (in both cases the parameters represent a proportion, so the standard deviation was 0.05). For each model run, random draws were taken from within these distributions 500-10,000 times, depending on the mean size of the fish within that family. Note, the number of draws within this range did not change the outcome of the model.

Field and Laboratory methods

Fish and invertebrate specimens for predator and prey nutrient analysis were caught using hook and line, traps or netting.Gut passages were cleared, either through live captivity without feeding or via dissection. Macroalgal samples were hand collected from the reef, rinsed with clean seawater, and cleaned of all epiphytes. Samples were frozen and transported to the lab for processing. Samples were dried at 65F for >72 hours then ground to a powder with a ball mill grinder. Larger individuals required blending to homogeneity before mill grinding. Ground samples were analyzed for %C and N content with a CHN Carlo-Erba elemental analyzer (NA1500) CN Analyzer,and for %P using dry oxidation-acid hydrolysis extraction followed by a colorometric analysis (Aplkem RF300). Elemental content was calculated on a dry weight basis. For crustaceans, samples were first acidified to remove inorganic carbon.

References Cited

Coleman, David, and Brian Fry. 1991.Carbon Isotope Techniques.Academic

Press/Harcourt Brace Jovanovich, New York.

Cummins, K. W. and J. C. Wuycheck. 1981. Caloric Equivelances for Investigations in Ecological Energetics, Germany.

Hansen, M. J., D. Boisclair, S. B. Brandt, S. W. Hewett, J. F. Kitchell, M. C. Lucas, and J. J. Ney. 1993. Applications of bioenergetics models to fish ecology and management - where do we go from here? T. Am. Fish. Soc.122,1019-1030.

Hood, J. M., M. J. Vanni, and A. S. Flecker. 2005. Nutrient recycling by two phosphorus-rich grazing catfish: the potential for phosphorus-limitation of fish growth. Oecologia146, 247-257.

Layman, C. A. and B. R. Silliman. 2002. Preliminary survey and diet analysis of juvenile fishes of an estuarine creek on Andros Island, Bahamas. Bull. Mar. Sci.70, 199-210.

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Ney, J.J. 1993. Bioenergetics modeling today: growing pains on the cutting edge.

T. Am. Fish. Soc., 122, 736-748

Owens, J., R. Maillardet, and A. Robinson. 2009. Introduction to Scentific Programming and Simulation Using R. Chapman and Hall, Boca Raton, FL.

Schreck, C. B. and P. B. Moyle, editors. 1990. Methods for fish biology. American Fisheries Society, Bethesda, MD.

U. S. Environmental Protection Agency. 1983. Sample preservation. pp.xv-xx.

InMethods for Chemical Analysis of Water and Wastes, EPA-600/4-79-

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Vanni M.J., Flecker A.S., Hood J.M. & Headworth J.L. (2002). Stoichiometry of nutrient recycling by vertebrates in a tropical stream: linking species identity and ecosystem processes. Ecol. Lett., 5, 285-293.

Table S5 – Species list of prey taxa used for model parameters.

Taxa / Vert/Invert/Algae / N
Amphipoda spp. / Invertebrate / 3
Isopoda spp. / Invertebrate / 3
Microphyrus bicornutus / Invertebrate / 3
Mithrax sculptus / Invertebrate / 3
Palaemon floridana / Invertebrate / 3
Pagurus longicarpus / Invertebrate / 3
Paneopus herstii / Invertebrate / 3
Pitho aculeate / Invertebrate / 3
Portunus pelagicus / Invertebrate / 3
Squillid spp. / Invertebrate / 3
Synalpheus spp. / Invertebrate / 3
Anguila rostrata / Vertebrate / 1
Bahtygobius soporator / Vertebrate / 14
Eucinostoma melanopterus / Vertebrate / 1
Gnatholepis thompsoni / Vertebrate / 2
Haemulon flavolineatum / Vertebrate / 17
Haemulon melanurum / Vertebrate / 10
Haemulon parra / Vertebrate / 2
Haemulon plumierii / Vertebrate / 24
Haemulon sciurus / Vertebrate / 4
Halichoeres bivittatus / Vertebrate / 14
Halichoeres garnoti / Vertebrate / 1
Halichoeres poeyi / Vertebrate / 1
Halichoeres bifasciatum / Vertebrate / 3
Malacoctenus macropus / Vertebrate / 15
Nicholsina usta / Vertebrate / 2
Pseudupeneus maculatus / Vertebrate / 6
Salarias fasciatus / Vertebrate / 2
Scarus coeruleus / Vertebrate / 2
Scarus taeniopterus / Vertebrate / 1
Sparisoma chrysopterum / Vertebrate / 1
Sparisoma radians / Vertebrate / 9
Sparisoma spp. / Vertebrate / 11
Dictyota menstrualis / Algae / 10
Dictyota pulchella / Algae / 10

Table S6 – References for parameter estimates per genus. Assimilation efficiency was assumed to be 70% for P and 80% for N following Shreck and Moyel (1990) and Hanson et al. (1997) for all species.

Family / Length-Weight regressions / Growth Rate
Acanthuridae / Fishbase – averaged values across species / Robertson et al. 2005
Haemulidae / Allgeier unpublished / Grol et al. 2011
Holocentridae / Allgeier unpublished / Fishbase – averaged values across species
Kyphosidae / Fishbase – blue sea chub / Fishbase – blue sea chub
Labridae / Fishbase - Thalassoma / Gordoa et al. 1999
Lutjanidae / Allgeier unpublished / Rypel and Layman 2008
Mulliodichthidae / Holland et al. 1993 / Holland et al. 1993
Scaridae / Fishbase – averaged values across species / Paddack et al. 2009,
Fishbase
Serranidae / Fishbase, Sadovy et al. 1992 / Fishbase, Crabtree & Bullock 1998

References Cited

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Holland, K. N., J. D. Peterson, C. G. Lowe, and B. M. Wetherbee. 1993. Movements, distributions and growth-rates of the white goatfish Mulloides flavorineatus in a fisheries conservation zone. Bull. Mar. Sci.52, 982-992.

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Paddack M.J., Sponaugle S., Cowen R.K. (2009). Small-scale demographic variation in the stoplight parrotfish Sparisoma viride. J. Fish. Biol., 75, 2509-2526

Robertson D.R., Choat J.H., Posada J.M., Pitt J., Ackerman J.L. (2005). Ocean surgeonfish Acanthurus bahianus. II. Fishing effects on longevity, size and abundance? Mar. Ecol. Prog. Ser., 295, 245-256

Rypel A.L., Layman C.A. (2008). Degree of aquatic ecosystem fragmentation predicts population characteristics of gray snapper (Lutjanus griseus) in Caribbean tidal creeks. Can. J. Fish. Aquat. Sci., 65, 335-339

Schindler, D. E. and L. A. Eby. 1997. Stoichiometry of fishes and their prey: Implications for nutrient recycling. Ecology78, 1816-1831.

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Vanni, M. J., A. S. Flecker, J. M. Hood, and J. L. Headworth. 2002. Stoichiometry of nutrient recycling by vertebrates in a tropical stream: linking species identity and ecosystem processes. Ecol. Lett.5, 285-293.

Table S7. Possible terms correlated with macroalgal cover and/or juvenile coral density.

Model term / Possible correlation with macroalgal cover/juvenile coral abundance
Herbivore/
Parrotfish/
Surgeonfish
biomass / Herbivorous fish biomass is often positively correlated with grazing intensity, and with the abundance of juvenile corals, as grazing frees space for the settlement of coral larvae (Hughes et al. 2007; Mumby et al. 2007). Macroalgae can limit the survival of juvenile corals (Box & Mumby 2007). Thus, one could expect a negative correlation of herbivore biomass with macroalgal cover and a positive correlation with juvenile coral abundance.
Corallivore
biomass / Corallivory rates in the Florida Keys are very high (Burkepile 2011) suggesting that corallivores may influence the abundance of juvenile corals from preferred coral species. Thus, one could expect a negative correlation between corallivore biomass and juvenile coral abundance. There is no obvious correlation between corallivore biomass and macroalgal cover, and thus, it was not included in those models.
Nutrient excretion
by fishes / Primary producers in the Florida Keys, are often limited by either N or P (Fourqurean & Zieman 2002; Beach et al. 2006). Thus, one could expect a positive correlation between excretion by fishes and macroalgal cover. If fish excretion increases macroalgal cover, then one could expect a negative correlation between excretion rates and juvenile coral abundance if macroalgae limit coral settlement or survival. However, one might also expect a positive relationship between nutrient excretion and juvenile coral abundance if excretion facilitates coral growth. N and P were not included in the same models due to their collinearity.
Coral cover / Higher coral cover can limit the space that algae can colonize. Further, coral cover may be positively related to the abundance of juvenile corals, as higher coral cover on a reef would likely mean more coral larvae present to settle on that reef, especially if the majority of the corals are brooding species that release competent larvae as are many of the corals now dominant in the Florida Keys (e.g. Agaricia spp) (Porter & Meier 1992). Thus, one could expect a negative correlation with macroalgal cover and a positive correlation with juvenile coral density.
Damselfish
density / Territorial damselfishes create algal gardens that they defend from larger herbivorous fishes, and these algal gardens may limit recruitment of coral larvae (Ceccarelli et al. 2001). Thus, one could expect a positive correlation with macroalgal abundance and a negative correlation with juvenile coral abundance.
Distance to
land / Distance from land is a proxy of the influence of land-based nutrients as reefs closer to land in the Florida Keys have a greater input of anthropogenic N and P (Szmant & Forrester 1996). Reefs with more anthropogenic eutrophication may have more macroalgal cover and less coral cover. Thus, one could expect a positive correlation with macroalgal abundance if nutrient limitation is relieved and a negative correlation with juvenile coral density if macroalgae limit coral recruitment.
Reef
rugosity / Increasing reef rugosity may provide shelter for herbivorous fishes and allow more favorable microclimates for the settlement of coral larvae (Mumby et al. 2007). Thus, one could expect a negative correlation with macroalgal cover and a positive correlation with juvenile coral abundance.

References Cited

Beach K.S., Walters L.J. & Borgeas H.B. (2006). Irradiance and nutrient limitation of Dicytota spp. populations on Conch Reef, Florida Keys, USA. J. Exp. Mar. Biol. Ecol., 329, 101-112.

Box S.J. & Mumby P.J. (2007). Effect of macroalgal competition on growth and survival of juvenile Caribbean corals. Mar. Ecol.-Prog. Ser., 342, 139-149.

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Table S8. Simple Pearson correlations between possible model parameters and macroalgal cover and juvenile coral density. The correlation coefficients in bold represent those parameters used in multiple linear regression to determine which parameters best explain patterns in macroalgal cover and juvenile coral density.

Parameter / Macroalgal cover
(%) / Juvenile corals (indiv./m2)
Herbivore biomass / -0.45 / 0.12
Scarid biomass / -0.52 / -0.18
Acanthurid biomass / -0.13 / 0.21
Corallivore biomass / Not tested / 0.18
Total N rate / 0.68 / -0.71
Total P rate / 0.64 / -0.58
Coral cover / -0.41 / 0.70
Territorial damselfish density / 0.36 / -0.46
Rugosity / 0.07 / -0.17
Distance to land / -0.08 / 0.40