COMMUNITY SECONDARY PRODUCTION AS a MEASURE of Ecosystem Function: a Case Study with AQUATIC ECOSYSTEM FRAGMENTATION

BULLETIN OF MARINE SCIENCE. 87(4):913–937. 2011 http://dx.doi.org/10.5343/bms.2010.1043

COMMUNITY SECONDARY PRODUCTION AS A MEASURE of ecosystem function: a case study with AQUATIC ECOSYSTEM FRAGMENTATION

Lori Valentine-Rose, Andrew L Rypel, and Craig A Layman

ABSTRACT Secondary production is a composite measure of an animal population’s density, biomass, and growth over time. Thus, secondary production converts animal abundance and biomass data into a functional measure of energy flow through an ecosystem. In the present study, we used community secondary production as a composite measure of ecosystem functional response to anthropogenic disturbance, and applied this approach to examine effects of fragmentation on tidal creek ecosystems in The Bahamas. Community fish production decreased from a mean of 901 g m−2 yr−1 in unfragmented creeks to 166 g m−2 yr−1 in downstream areas of fragmented creeks to 29 g m−2 yr−1 upstream of creek blockages in fragmented creeks. When compared with several univariate measures (species richness, evenness, abundance, biomass), community composition (by ordination), and community distribution (via rank-curve analysis), community secondary production appeared to be the most consistent variable for distinguishing between three creek fragmentation categories. These data suggest that small, non-significant declines in diversity or other structural metrics can yield disproportionately large declines in ecosystem function. We suggest community secondary production can be an important component of future assessments of anthropogenic impacts, especially when alterations in diversity or community structure are small, but potentially have dramatic impacts on ecosystem function.

Human alteration of earth’s ecosystems is widespread and ongoing (Vitousek 1997, Foley et al. 2005), and habitat degradation is a leading cause of these declines (Ehrlich and Ehrlich 1981, Tilman et al. 1994). One common consequence of habitat alteration is biodiversity loss, which can drive dramatic changes in ecosystem function (Naeem et al. 1994, Chapin et al. 2000, Cardinale et al. 2006). As such, understanding the relationship between habitat degradation, biodiversity loss, and ecosystem function has become a central subdiscipline in the biological sciences (Chapin et al. 1997, Loreau et al. 2001). Many studies that evaluate ecosystem responses to habitat alteration have focused on structural response variables. For example, changes in species richness, abundance, or biomass are commonly used as indicators of ecosystem response to disturbance (Micheli 1999, Ceballos et al. 2005, Brooks et al. 2006, Cardinale et al. 2006, Mouillot et al. 2006). However, these structural variables may indicate little about ecosystem functional responses to habitat and biodiversity loss (Walker et al. 1999). A more thorough understanding of anthropogenic impacts may be gleaned from measures of ecosystem function that reflect and integrate multiple underlying processes (Pauly et al. 2002, Bellwood et al. 2004, Kremen and Ostfeld 2005, Mouillot et al. 2006).

Secondary production is a composite measure of an animal population’s density, biomass, and growth over time (Benke 1993). Thus, secondary production converts animal abundance and biomass data into a functional measure of energy flow through an ecosystem. When secondary production is measured for multiple taxa across an entire community, the sum of these values (defined as community secondary production) serves as a powerful response variable that reflects high levels of biological organization (Huryn and Wallace 1987, Edgar and Shaw 1995b, Huryn 1998, Cowley and Whitfield 2002). In terrestrial ecosystems, plant community primary production has been used extensively to evaluate response to disturbance and to examine biodiversity-ecosystem function relationships (Naeem et al. 1994, Tilman et al. 1994, 1996, Hector et al. 1999, Tilman et al. 2001, Haberl et al. 2002, Hooper et al. 2005, Tilman et al. 2006). However, in faunal communities, community biomass typically is used as a surrogate for community production. Direct estimates of secondary production at the population-level have been generated in various marine systems, e.g., mangroves (Faunce and Serafy 2008), oyster reefs (Peterson et al. 2003), artificial patch reefs (Powers et al. 2003), seagrass (Edgar and Shaw 1995a, McCay and Rowe 2003), and salt marshes (Kneib 2003, McCay and Rowe 2003). Yet, community-wide approaches to production in marine systems have been less common, and have usu- ally focused on invertebrate communities (Cebrian 2002, Munari and Mistri 2007, Madsen et al. 2008). In the present study, we demonstrate how community secondary production of fishes can be used as a composite measure of ecosystem response to anthropogenic- driven disturbance and apply this approach to examine effects of aquatic ecosystem fragmentation on tidal creek ecosystems in The Bahamas. We provide new estimates of community-wide secondary production in sub-tropical mangrove habitats and hypothesized that (1) increased fragmentation would correlate with significantly di- minished secondary production of fish communities, and (2) community secondary production would be a more sensitive variable to identify effects of ecosystem fragmentation relative to other structural response variables (e.g., richness, abundance, and biomass).

Methods

Site Description.—Our study systems were eight clear-water, mangrove-lined tidal creeks on the eastern shore of Andros Island, The Bahamas (Table 1). At low tide, water is restricted to the main creek channels providing permanent habitat for fishes (i.e., most fishes remain in the main creek channels even on the lowest tides). Main channel areas may contain multiple habitat types (e.g., mangrove, rock, seagrass, or sand) and vary substantially in microtopography among systems. Intertidal flats typically have a sandy, biogenically-derived substrate with interspersed dwarf red, Rhizophora mangle Linnaeus, and black, Avicennia germinans (Linnaeus) Linnaeus, mangroves. More detailed information on these systems can be found in previous studies (Layman et al. 2007, Rypel et al. 2007, Valentine-Rose et al. 2007a, Valentine-Rose and Layman 2011). In The Bahamas, estuary fragmentation results from construction of roads lacking flow conveyance structures (i.e., bridges or culverts), thereby decreasing habitat quality and al- tering floral and faunal assemblages (Layman et al. 2004, Valentine-Rose et al. 2007b, Rypel and Layman 2008). Similar to Dynesius and Nilsson (1994), we devised a three category system for classifying the relative degree of fragmentation in tidal creeks (Valentine- Rose et al. 2007b). The study sections of each creek were classified as either: unfragmented (UF, those with no blockage), fragmented-upstream (FU, those upstream of a major flow valentine-rose et al.: secondary production of fish communities 915

Table 1. Physical and biological charcteristics of study tidal creeks on Andros Island, The Bahamas. % columns refers to the proportions of various habitats available to fishes in each creek. N/A represents a habitat area so expansive that it could not be calculated in the field, but nonetheless was dry at low tide and therefore contained no fish species.

Area Max depth Fragmentation % % % % Species Ecosystem (low tide, m2) (low tide, m) classification mangrove seagrass sand rock richness Bowen up N/A 0.10 FU 0 0 N/A N/A 0 Man-o-War up 1,650 0.25 FU 10 0 86 4 6 Love Hill up 831 0.50 FU 18 1 36 45 9 Davis up 384 0.50 FU 54 0 46 0 0 Conch up 150 0.25 FU 67 0 0 33 6 Davis down 10,020 0.50 FD 10 2 84 4 23 Man-o-War down 11,718 0.25 FD 2 0 59 39 9 Love Hill down 8,350 3.00 FD 20 9 33 38 23 Bowen down 2,439 2.00 FD 33 27 10 30 11 Conch down 1,415 0.05 FD 11 0 21 68 0 Somerset 16,245 0.50 UF 14 73 3 10 25 Buryin Peace 1,810 0.50 UF 24 10 6 60 21 White Bight 1,008 1.00 UF 18 1 23 58 16 blockage), and fragmented-downstream (FD, those on the ocean-side of a major flow blockage). Fragmentation categories for each surveyed creek are provided in Table 2. Survey Methodology.—All production data were derived from field estimates of fish densities and lengths from surveys in May 2005. At each study site, we first calculated the spatial extent of four main habitat types (rock, mangrove roots and/or pneumatophores, sand/ silt, and seagrass) in the main channel of each creek at low tide. We then divided each habitat into 25 equally-sized sections, and in each subsection, we randomly selected a 1-m2 area for survey. Fish densities and lengths in each random area were then estimated with 1-m2 visual- ized quadrats using underwater visual census (UVC, Valentine-Rose et al. 2007b). Surveys were taken within 2 hrs of low tide to facilitate estimates of fish biomass, because fishes were constricted to the main channel during this time (Valentine-Rose et al. 2007b). While this may have resulted in inflated per unit area (m−2) estimates of subtidal channel areas, the ex- trapolated values of whole-creek production (see below) were more robust because surveys were conducted when fish biomass could be quantified most accurately. At the survey time, the investigator approached the predetermined quadrat area within ~2 m, waited ~1 min for fishes to acclimate to the observer’s presence, and then noted the number and length (L) of all individuals within the quadrat. For fragmented tidal creeks, this protocol was conducted in each habitat type both upstream and downstream of the road blockage. Further information on survey methodologies for production estimations in tidal creeks can be found in previous studies (Valentine-Rose et al. 2007a, Valentine-Rose and Layman 2011). Ages for each individual fish were estimated using a rearrangement of the Von Bertalanffy growth function that used fish lengths from the above surveys to predict age (Appendix 1). For two common species [gray and schoolmaster snapper, Lutjanus griseus (see Table 3 for fish species authorities) and Lutjanus apodus, together accounting for 18% of abundance and 20% of productivity], in situ growth parameters were developed by ageing otolith an- nuli from individuals in unfragmented and fragmented creeks on Andros and Abaco Islands (Valentine-Rose et al. 2007b, Rypel and Layman 2008). Published Von Bertalanffy growth functions (Appendix 1) from similar habitats were used for all other species. When more than one published growth rate was available for the same species in similar habitats, an average of those growth rates was used. When growth rates were not available for a species (n = 13, totaling 12% of abundance and 17% of productivity), growth rates from the most closely related taxa from the most similar habitats and environmental conditions were used. While growth data for all individual taxa in all creeks would be most desirable, here we focus on higher- order community-based metrics of production. We therefore assume that individual-based 916 BULLETIN OF MARINE SCIENCE. VOL 87, NO 4. 2011

Table 2. Univariate results for fish communities in unfragmented (UF), fragmented downstream (FD), and fragmented upstream (FU) tidal creeks in Andros Island, The Bahamas. SOM = Somerset, WB = White Bight, BP = Buryin Peace, LH = Love Hill, DAV = Davis, MOW = Man-o-War, BOW = Bowen, CNC = Conch Sound. DE = abundance m−2, BI = biomass g m−2, PR = production g m−2 yr−1, SP = total number of species per creek, H’ = Shannon diversity, E = Shannon evenness. * indicates a P value < 0.05 for ANOVA comparisons. 1, 2, or 3 indicates significance in Bonferroni comparisons between (1) UF and FD, (2) UF and FU, and (3) FD and FU creeks. – indicates an insignificant ANOVA or Bonferroni comparison at the 0.05 α-level. Dominance is based on largest value of each metric (i.e., highest DE, BI, or PR). E. spp. = Eucinostomus spp. and includes E. jonesi, E. gula, and E. lefroyi. See Table 3 for full species names.

Fragmentation Dominance Dominance Dominance Tidal creek category DE BI PR SP H’ E DE BI PR SOM UF 7.0 384 1,006 25 2.30 0.71 L. griseus L. griseus S. iserti WB UF 9.0 762 952 16 2.19 0.79 L. apodus L. apodus L. griseus BP UF 8.0 1,711 746 21 2.19 0.72 E. spp. L. griseus L. griseus LH FD 7.0 848 397 23 2.14 0.68 E. spp. L. griseus L. griseus DAV FD 3.0 83 82 23 2.32 0.74 L. griseus L. apodus S. testudineus MOW FD 3.0 25 74 9 1.54 0.70 L. griseus L. griseus E. spp. BOW FD 2.0 50 111 11 1.59 0.66 E. spp. S. testudineus E. spp. LH-Up FU 0.6 6 71 9 1.88 0.85 L. griseus L. griseus L. griseus MOW-Up FU 0.5 14 30 6 1.22 0.68 L. griseus L. griseus L. griseus CNC-Up FU 0.2 2 14 6 1.48 0.83 L. apodus S. testudineus L. griseus DAV-Up FU 0.0 0 0 0 0.00 0.00 N/A N/A N/A ANOVA * * * * – – Bonferroni 1,2 2,3 1,2 – differences in some metrics (e.g., growth) are less important than the overall community-level patterns we sought to describe. Secondary Production.—Secondary production calculations utilized an adapted form of the “instantaneous growth method” (Waters 1977, Valentine-Rose et al. 2007b, Valentine- Rose and Layman 2011). This methodology involves summing the accumulation of biomass for a population across fish age-classes (Waters 1977). To calculate fish biomass, in situ length/weight (L/W) regressions were derived based on field-collected individuals ranging over the entire size distribution in creeks for that species. Individuals were collected by sein- ing or hook-and-line from each tidal creek in May 2004 (1 yr prior to the survey period). For species that could not be collected in high enough abundance to derive L/W regressions in situ (n = 22, totaling 7% of abundance and 11% of productivity), published L/W regressions derived from similar environments were used (Appendix 2). Age-class biomass was calculated by multiplying the density of each age-class by weights predicted from L/W regressions, and total biomass was found by summing age-class biomass estimates for each species. Thus, low tide estimates of biomass were calculated for each species in each habitat in each creek (example calculation in Appendix 3). Annual secondary production was calculated for all species in each habitat of each creek by subtracting current total biomass estimates from total biomass estimates predicted 1 yr later by Von Bertalanffy growth functions. Production was calculated for each habitat, and then each habitat production value was weighted by the percentage of that habitat type in the survey area. Weighted habitat production values were then summed, giving total creek production for each species (example calculation in Appendix 3). Individual species’ production values were then summed for all species in each creek (or upstream/downstream area of each creek) to yield estimates of total fish community production (g −2m yr−1) for each tidal creek. It is important to note that annual fish production estimates calculated with the instantaneous growth method are frequently developed using samples taken from a single month (Huryn 1996, 1998, Peterson et al. 2003, Powers et al. 2003). This is possible because in the increment valentine-rose et al.: secondary production of fish communities 917 summation method, accrued biomass is summed across the representative age-classes rather than across months (e.g., in the size-frequency method of production calculation). To support the assumption that a single month survey was representative of fish production across the creek systems despite migration, mortality, and recruitment variation, additional surveys were conducted monthly (January–October 2005). Statistical analysis via a two factor (A: unfragmented vs fragmented, and B: month) repeated measures analysis with repeti- tion on one factor (month) suggested that natural variability of secondary production within tidal creeks among months was not significant (F4, 39 = 0.04, P = 0.99), while differences in secondary production between creek fragmentation categories was significant (F1, 39 = 63.21, P < 0.001). This further suggests among-creek comparisons based on a single set of surveys (May 2005) are sufficient to compare fish secondary production among creek fragmentation categories. Similar precautions should be taken to assess in-stream variability when repeating this methodology. Assumptions.—These production estimates are intended for comparisons among tidal creeks within our study (i.e., relative comparisons). Care should be taken in extending these numbers for comparisons with community production estimates in other systems. Due to the limitations of our sampling method, our values are not exact whole-ecosystem values, as they do not include in situ growth rates for every species. However, the use of published growth rates from similar environmental conditions to estimate secondary production is a common practice when in situ growth rates cannot be obtained (Waters 1977, Benke 1993, Peterson et al. 2003, Powers et al. 2003). Furthermore, we assume our samples represent the whole of the fish communities although cryptic, transient, nocturnal, or small fishes might have been undersampled. Presumably these fishes would be undersampled consistently across study sites and thus should not affect the relative comparisons among creeks. Error in production estimates also may have resulted from using the same growth rates in both fragmented and unfragmented tidal creeks (for species other than gray and schoolmaster snapper). Since growth rates of several fishes are slower in fragmented tidal creeks (Rypel and Layman 2008), differences in community secondary production estimates among fragmentation categories are likely conservative (i.e., production differences between fragmented and unfragmented creeks were likely underestimated). Production estimates, regardless of these assumptions, may be more sensitive than abundance or biomass data because production adjusts these data to account for rapid growth of smaller species and individuals, transforming data to an energetics (i.e., functional) scale. Therefore, the above-mentioned limitations do not compromise the value of the estimates in our study to indicate ecosystem functional response to fragmentation among sites. Density, Diversity, Evenness, and Rank-Production.—To evaluate alternative structural and functional response variables, total creek weighted (by habitat) densities for each species in each creek were derived from the above-described methodology, and Shannon diversity and evenness indices were calculated for each tidal creek fish community. Rank curves provide a graphical representation of the distribution of species richness, diversity, and evenness within a community (Begon et al. 2006). Rank curves may be preferable to diversity indices alone because no information is lost through collapsing patterns into a single value. Rank curves have most commonly been based on relative abundance or biomass (reviews in Tokeshi 1990, 1993, Fesl 2002) in plant, bird, or insect communities where large ranges in size and abundance (i.e., a mixture of very small, abundant individuals and large, less abundant individuals) are not common among species (Motomura 1932, Fisher et al. 1943, Preston 1948, MacArthur 1957, Whittaker 1977, Tokeshi 1990, 1993). However, because of the size and abundance differences among species in fish communities, traditional metrics (i.e., biomass and abundance) may not be strong enough to elucidate patterns among fish communities in different areas. Because production integrates abundance, size, and growth into a single functional metric, it may be more sensitive to differences in communities in various locations. To demonstrate the difference that alternative response variables can have on the 918 BULLETIN OF MARINE SCIENCE. VOL 87, NO 4. 2011 interpretation of species distribution within an ecosystem, we developed rank-abundance, -biomass, and -production curves separately (Magurran 1988). Statistical Analysis.—Single-factor ANOVA was used to compare differences in abundance, biomass, production, species richness, diversity, and evenness among UF, FD, and FU creek categories. When appropriate, data were log-transformed to meet assumptions of nor- mality. If ANOVA indicated a P-value < 0.05, Bonferroni post-hoc t-test analysis was per- formed to indentify which categories differed significantly. We used non-metric multidimensional scaling (NMDS) to test if species contribution to community production varied among UF, FD, and FU creek categories. To demonstrate the difference that alternative response variables can have on interpretation of community composition within an ecosystem, we developed NMDS for relative community abundance and biomass as well. NMDS graphically represents, in two dimensions, relationships between ob- jects using the Bray-Curtis similarity index (Bray and Curtis 1957). In ordination plots, as distance between points (i.e., species presence recorded in a single census) increases, similarity of biotic species composition between the two surveys decreases. Analysis of similarities (ANOSIM), a multivariate, non-metric analog to MANOVA, was used to test for significant differences in contribution to relative production between tidal creek categories for each functional group designation. If ANOSIM revealed significant (P < 0.05) effects, then similarity percentage analysis (SIMPER, Clarke and Warwick 1994) was used to identify which species or group contributed to the observed differences. All NMDS, ANOSIM, and SIMPER analyses were conducted in Primer 5 (version 5.2.9, PRIMER-E Ltd., Plymouth, UK). To de- termine potential differences alternative response variables can have on the interpretation of community distribution within an ecosystem, we used DOM-DIS (dominance-distribution) in Primer 6 (version 6, PRIMER-E Ltd., Plymouth, UK) to analyze differences among UF, FD, and FU creeks in community abundance, biomass, and production rank-curves.

Results

Secondary Production.—Fish community production (i.e., as a univariate metric) differed significantly between two creek fragmentation categoriesF ( = 51.5, P < 0.0001), sometimes by as much as an order of magnitude (Table 2). Post-hoc comparisons revealed significant differences in community production between UF and FD categories (UF > FD, Tukey’s: P < 0.0001) and UF and FU categories (UF > FU, Tukey’s: P < 0.0001). FU and FD categories did not differ significantly (Tukey’s: P = 0.14, Fig. 1). Community production ranged from 746 to 1006 g m−2 yr−1 in unfragmented tidal creeks, from 74 to 397 g m−2 yr−1 in downstream areas of fragmented creeks, and from 0 to 71 g m−2 yr−1 in upstream areas of fragmented tidal creeks (Table 2, see Table 3 for production values for each population and habitat). Univariate estimates of community abundance and biomass were also significantly different among fragmentation categories (F = 23.8, P < 0.001; F = 51.5, P < 0.0001, respectively), and abundance most closely paralleled differences in community production regarding creek fragmentation category differentiation (Bonferroni multiple comparisons, Table 2). Community composition (i.e., a multivariate metric) based on either abundance or biomass did not differ significantly among fragmentation categories (ANOSIM: r = 0.05, P = 0.32; r = 0.01, P = 0.88, respectively; Fig. 2). Community composition using production as the measure did differ significantly among all fragmentation categories (ANOSIM: r = 0.42, P = 0.02). This difference was due to communities dominated by production of reef species, those associated most often with complex reef habitat as adults, vs bay species, generalists associated with shallow inshore habitats throughout their life cycles (defined in Nagelkerken et al. 2000 and listed in Table 2). valentine-rose et al.: secondary production of fish communities 919

Figure 1. Mean community secondary production of fishes in Bahamian tidal creeks classified into three fragmentation categories. Error bars represent the mean ± 1 SE. Letters denote categories that do not differ significantly (Bonferroni P > 0.05) from one another.

In UF creeks, a larger proportion of production was comprised of reef species (e.g., Scarus iserti, Haemulon sciurus, Haemulon parra, and Halichoeres bivattatus; Table 3). A larger proportion of production in FD creeks was made up of bay species (e.g., Eucinostomus spp., Sphoeroide testiduneus, Gerres cinereus, and Sphyraena barracuda) than in UF creeks. FU creeks had low species richness, and secondary production was typically dominated by one species (Lutjanus griseus, Table 3). Because production of only two, albeit dominant, species was calculated using in situ derived growth rates that differed between fragmentation categories, differences in community production estimates between fragmentation categories was likely due to one of two factors: differences in individual species’ abundances or differences in individual species’ size distributions among fragmentation categories (i.e., the other main components to production calculations). Differences between UF and FU creek categories were primarily due to a decrease in species richness (Table 2), and lower biomass for those species absent in FU creek areas (i.e., obviously no biomass for nonexistent species). When comparing UF and FD fragmentation categories, in which species richness was similar (Table 2), underlying reasons for production differences varied. For example, based on the species-specific results discussed above, some species had a greater proportion of production in UF creeks primarily due to greater abundance (S. iserti: 120 UF, 15 FD; H. sciurus: 23 UF, < 1 FD; H. parra: 35 UF, < 1 FD; mean values, standard deviations not included for simplicity). But size distributions drove differences in production between UF and FD creeks for species present in both creek categories. For example, for Eucinostomus spp., there was a greater proportion of smaller individuals in FD creeks, and since smaller individuals grow faster, more biomass would be expected to be produced over the course of a year. 920 BULLETIN OF MARINE SCIENCE. VOL 87, NO 4. 2011 0 0 0 0 0 0 0 0 0 FU 2.4 (2.2) 1.2 (1.9) 0.6 (1.0) 2.9 (4.5) 0.3 (0.6) 12.0 (14.6) 0 FD 1.5 (1.7) 6.8 (6.2) 0.1 (0.1) 2.6 (3.9) 2.2 (3.6) 2.1 (2.1) 1.4 (2.8) 0.1 (0.2) 0.1 (0.1) 5.5 (3.2) 6.5 (5.6) 2.1 (4.1) 21.1 (9.8) 27.2 (26.7) 0 0 0 0 UF 0.2 (0.3) 4.9 (8.5) 8.4 (5.4) 39.8 (29.6) 36.0 (39.0) 34.1 (54.6) 13.2 (10.9) 10.8 (18.4) 40.4 (40.0) 35.4 (16.9) 74.3 (126.3) S F S F F S F S S S S M M M M Growth ). R B B R N E. gula C, R C, R EFG C, N C, N C, N C, N C, N C, N C, N C, N . 1 ), and jenny silver ), ( 2 ), and ecological functional group (EFG) classification (Nagelkerken 2000) of fish taxa E. lefroyi E. S. notata ) −1 yr 2 Common name Needlefish spp. Nassau grouper Yellow-finned mojarra Yellow-finned French grunt Four-eye butterfly Four-eye Horse-eye jack Sailor’s choice grunt Sailor’s Blue-striped grunt Spotfin butterfly Mutton snapper Schoolmaster snapper Cubera snapper Gray snapper Yellowtail snapper Yellowtail Mojarra spp. ), mottled), mojara ( S. marina ) and redfin needlefish ( (Desmarest, 1823) Eucinostomus jonesi Eucinostomus (Linnaeus, 1758) (Bloch, 1787) (Bloch, 1792) Strongylura spp. Strongylura Epinephelus striatus 1792) (Walbaum, cinereus Gerres Haemulon flavolineatum Caranx latus (Agassiz, 1831) Haemulon parra (Desmarest, 1823) 1803) Haemulon sciurus (Shaw, Chaetodon capistratus Chaetodon ocellatus (Cuvier, 1828) Lutjanus analis (Cuvier, (Walbaum, 1792) Lutjanus apodus (Walbaum, 1828) Lutjanus cyanopterus (Cuvier, Lutjanus griseus (Linnaeus, 1758) Ocyurus chrysurus (Bloch, 1791) Eucinostomus spp. Needlefish spp. includes Atlantic needlefish ( Needlefish spp. includes Mojarra includes spp. slender mojara ( Table 3. Mean Table (± SD) total creek weighted production values (g m 1 2 in tidal creeks on Andros Island, The Bahamas. UF = unfragmented tidal creeks, FD = downstream areas of C = commercial. Relative R growth = rates reef, of fragmented B the fragmented = species tidal (F bay, creeks, = N fast = growth nursery, rate, M = tidal medium growth rate, S creeks, FU = upstream areas of = slow growth) are provided for qualitative comparisons. Species listed phylogenetically Species Belonidae Serranidae Haemulidae Carangidae Chaetodontidae Lutjanidae Gerreidae valentine-rose et al.: secondary production of fish communities 921 0 0 0 0 0 0 0 0 0 0 0 0 FU 3.8 (3.7) 3.5 (3.6) 0.3 (0.4) 12.6 (21.8) 0 0 0 FD 0.1 (0.1) 0.3 (0.7) 4.5 (8.7) 4.6 (9.3) 0.1 (0.1) 5.6 (7.5) 1.0 (0.8) 3.0 (4.9) 0.2 (0.4) 11.8 (13.3) 11.8 18.4 (18.4) 15.3 (17.5) 37.6 (74.2) 0 UF 0.4 (0.4) 1.8 (2.9) 0.1 (0.2) 9.4 (9.5) 0.1 (0.1) 1.4 (1.8) 1.9 (3.0) 1.7 (3.0) 0.6 (1.0) 0.7 (1.2) 15.7 (15.7) 22.5 (25.9) 28.3 (48.5) 14.9 (24.3) 86.4 (50.1) ). F F S F F F S S F M M M M M M M Growth S. viride R B R B R B B R R R R R N N N N EFG 3 Common name Sergeant major Sergeant Doctorfish Porcupinefish Spotted goatfish Dusky damselfish French angel Great barracuda Checkered puffer Beaugregory Slipperydick Yellowhead wrasse Yellowhead Blackear wrasse Puddingwife Bluehead wrasse Striped parrotfish Parrotfish spp. ) , and stoplight parrotfish ( S. radians ), redband parrotfish ( aurofrenatum (Bloch, 1793) (Linnaeus, 1758) (Bloch, 1791) (Müller and Troschel, 1848) Troschel, (Müller and (Linnaeus, 1758) (Steindachner, 1867) (Steindachner, (Linnaeus, 1758) Abudefduf saxatilis Acanthurus chirurgus (Bloch, 1787) Acanthurus chirurgus Pseudupeneus maculates (Troschel, 1865) Stegastes adustus (Troschel, Pomacanthus paru (Bloch, 1787) (Walbaum, 1792) Sphyraena barracuda (Walbaum, Diodon hystix Stegastes leucostictus Sphoeroide testudineus Sphoeroide Halichoeres bivittatus Halichoeres (Valenciennes, 1839) garnoti (Valenciennes, Halichoeres Halichoeres poeyi Halichoeres radiatus (Linnaeus, 1758) Halichoeres Thalassoma bifasciatum (Bloch, 1791) Scarus iserti (Bloch, 1789) Sparisoma spp. Parrotfish spp. includes bucktooth parrotfish ( Table 3. Continued. Table Species 3 Pomacentridae Acanthuridae Mullidae Sphyraenidae Diodontidae Tetraodontidae Labridae Scaridae 922 BULLETIN OF MARINE SCIENCE. VOL 87, NO 4. 2011

Figure 2. Non-metric Multi Dimensional Scaling (NMDS) ordinations of relative (A) density (N), (B) biomass (B), and (C) production (P) surveys of Bahamian tidal creeks classified into three fragmentation categories. Tidal creek abbreviations: BOW Bowen, BP Buryin Peace, CONCH Conch Sound, DAV Davis, LH Love Hill, MOW Man-o-War, SOM Somerset, WB White Bight. valentine-rose et al.: secondary production of fish communities 923

Rank-Production.—Species richness, diversity, and evenness were not significantly different in any two fragmentation comparisons (Bonferroni multiple comparisons, all P-values > 0.05, Table 2). These patterns were apparent when comparing rank-abundance, -biomass, and -production curves among the three fragmentation categories (Fig. 3). In rank-abundance plots, slopes and shapes showed little difference among fragmentation categories (DOM-DIS: r = 0.36, P = 0.71). This pattern reflects little difference in species richness, diversity, and evenness comparisons among fragmentation categories. Rank-biomass curves also showed little separation in slope and shape among fragmentation categories (DOM-DIS: r = 0.17, P = 0.81). Slopes of biomass curves were steeper compared to abundance and production, sug- gesting that biomass was less evenly distributed in fish communities (i.e., some species contributed very high biomass and while others contributed little). Compared to rank-abundance and -biomass curves, rank-production curves showed the most separation in slope and shape among fragmentation categories, and approached significance (DOM-DIS: r = 0.63, P = 0.061). The “dominant” species within each creek changed when using different variables to identify dominance (i.e., greatest abundance, greatest biomass, or greatest production), resulting in different patterns among fragmentation categories (Table 2). For example, only two creeks (Man-o-War and Love Hill upstream) had the same species (L. griseus) with the highest abundance, biomass, and productivity. All other creeks had different results for dominant species between at least two defining variables, and two creeks had different dominant species for all three variables. When dominance was assigned based on abundance, no patterns were apparent among fragmentation categories, as each creek tended to have a unique dominant species (Table 2). When dominance was assigned based on biomass, again, no patterns were apparent among fragmentation categories because the species with the highest biomass tended to be L. griseus or L. apodus in all creeks. However, when the most productive species was used to assign dominance, distinct patterns emerged. While unfragmented creeks were dominated by commercially important (L. griseus) or reef (S. iserti) species, downstream areas of fragmented creeks were dominated by generalist bay species such as S. testudineus and Eucinostomus spp. Upstream areas of fragmented creeks were also dominated by L. griseus, but this single species made up a much larger proportion of relative production compared to UF creeks (Fig. 2) and consisted entirely of age-0 individuals.

Discussion

Our data suggest that community secondary production can be a more sensitive response variable (compared with species richness, diversity, density, and biomass) when assessing ecosystem-level impacts of tidal creek fragmentation. Even when declines in species richness and diversity were small and non-significant, these changes drove a disproportionate erosion of ecosystem function (i.e., production). Our study complements the suggestion by the US NOAA National Marine Fisheries Service that of the four criteria used to identify essential fish habitat (presence-absence, density, growth, and production), production may be the most distinguishing and sensitive metric (Able 1999, Searcy et al. 2007). Below we examine (1) possible effects of ecosystem fragmentation on secondary production of fishes, (2) the implications of using various univariate and multivariate metrics to interpret ecological data, and (3) 924 BULLETIN OF MARINE SCIENCE. VOL 87, NO 4. 2011

Figure 3. Rank-abundance (DE), -biomass (BI), and -production (PR) curves in unfragmented (UF), fragmented-downstream (FD), and fragmented-upstream (FU) creek areas in The Bahamas.

the potential usefulness of rank-production curves in examining effects of human impacts like marine ecosystem fragmentation. Secondary production was significantly lower in fragmented creeks for all analyses. Significant declines in community secondary production imply major changes to the energetic structure of fish communities and marine food webs. Previous work on effects of tidal creek fragmentation have documented how numerous biotic and abiotic factors can be altered following fragmentation (Layman et al. 2007, Valentine- Rose et al. 2007a, Allgeier et al. 2010, Layman et al. 2011). As in most cases of aquatic valentine-rose et al.: secondary production of fish communities 925 ecosystem fragmentation (Pringle 2001, 2003a,b), variability in physiochemical conditions increases and habitat heterogeneity decreases (Roman et al. 1984, Warren et al. 2002, Valentine-Rose et al. 2007a), both of which may serve to directly affect primary and secondary production (Valentine-Rose et al. 2007b, Allgeier et al. 2010). These changes may have lead to the selective decrease in production of dominant species in our marine systems. For example, fish production in UF creeks was dominated by parrotfishes—important herbivores that associate with reef and seagrass habitat. However, production of parrotfishes was significantly lower in FD creeks, and parrotfishes were not found in FU creeks, likely a response to these underlying changes in habitat and physiochemical conditions (Valentine-Rose et al. 2007a). As with any space-for-time comparative study, underlying ecological mechanisms can be questioned. In the present case, there are multiple reasons to believe that the fragmentation is the major driver in production differences among creeks. First, recent restoration projects led to a rapid increase in secondary production following removal of the sources of creek fragmentation (Valentine-Rose and Layman 2011), consistent with the assumption that ecosystem fragmentation initially drove the differences in production values among creeks. Second, more recently blocked creek systems tend to have higher abundance and biomass of fishes than those blocked for many decades (C Layman, per obs). Third, local fishermen also confirm that many currently-blocked creeks supported productive fisheries before they were fragmented. Fragmented creeks had a higher proportion of smaller fishes. Differences in fish growth rates and decreased migration into fragmented creeks are likely key sources for variation in fish sizes among creek fragmentation categories. Significantly slower growth rates have been documented for two fish species in fragmented tidal creeks (Valentine-Rose et al. 2007b, Rypel and Layman 2008), mirroring patterns of fishes in fragmented freshwater rivers (Rypel et al. 2006, Penczak 2007, Rypel and Bayne 2009, Weyl et al. 2009). Growth differences among creek fragmentation categories are likely the product of structural and functional modifications to tidal creek food webs follwing fragmentation. For example, fragmented creeks support lower diversity and abundance of prey items (Valentine-Rose et al. 2007a) and an increase in trematode parasite loads for fishes (Rypel and Layman 2008), both of which can function to reduce fish growth. Therefore, a greater proportion of small individuals in fragmented creeks may be a product of slower growth rates of fishes in these systems. If this is the case, production differences between UF and FD/FU creeks may be even greater than our data suggest. Further, tidal creek fragmentation may result in a physical exclusion of large motile fishes. Fragmented tidal creeks are often shallower due to increased siltation and a lack of flow (Valentine-Rose and Layman 2011), thereby impeding access to these areas by larger individuals. Concomitant with the loss of larger fishes, there may also be decreased predation on smaller individuals due to fewer large predators, as well as decreased predator detection and capture efficiency in the more turbid downstream waters (Aksnes and Giske 1993). Targeted studies of growth rates (Faunce and Serafy 2008, Rypel and Layman 2008) and predation rates (Rypel et al. 2007) in these systems may reveal which of these processes are most important in driving production differences among tidal creeks (Searcy et al. 2007). The most important general finding of our study was that community secondary production was the best metric for discerning ecosystem-level effects of tidal creek fragmentation on fish communities. Considering all univariate and multivariate 926 BULLETIN OF MARINE SCIENCE. VOL 87, NO 4. 2011 metrics used to discriminate among fragmentation categories in our study, secondary production was the only metric that consistently distinguished ecosystems based on fragmentation categories. Even in creeks where declines in diversity or changes to community structure due to fragmentation were minor (e.g., Love Hill), this was associated with significant declines in fish production. Abundance and biomass are two of the most commonly used ecological response variables, yet our results suggest that analysis of secondary production estimates may be essential for more integrated ecological patterns to emerge. For example, studies of species “dominance” nor- mally define the dominant as either the most abundant species or the species with greatest biomass (Ricklefs and Miller 2000). Although univariate abundance and production metrics paralleled in discriminating between fragmentation categories, defining dominance by different metrics (i.e., abundance, biomass, or production) suggested conflicting interpretations of how communities were structured and how they were affected by fragmentation. Defining dominance by biomass revealed no differences among fragmentation categories. According to the dominant abundant species, fragmentation reduced a different species in each creek with no apparent pattern. However, production of selective discrete functional groups appeared to be particularly sensitive to the effects of tidal creek fragmentation (e.g., reef-associated parrotfishes discussed above). UF creeks also had higher production of commercially important and top predator species. Rank-production curves provided a novel way to view how community distribution patterns differed among fragmentation categories, and again, secondary production was the most sensitive metric in this approach. Rank-production in UF creeks was best approximated by a lognormal curve, where many species had similar relative production and there were relatively few dominant or rare species. The lognormal pattern is typical for stable and well developed communities (Ugland and Gray 1982, Sugihara 1989). FU creeks had the steepest rank-production curve (most similar to a convex curve), indicating dominance by few taxa. Evenness of production among species was highest in FD creeks, and appeared to be approximately linearly distributed (except for the rarest species). While detailed examination of rank models was beyond the scope of the present study, using production in developing future models of community distribution may be an important tool and deserves further con- sideration. Studies that have not found significant changes following anthropogenic disturbances in rank-abundance analyses (e.g., Hoyle and Harborne 2005) may have reached different conclusions if secondary production were employed as the measure of dominance (but see Buffagni and Comin 2000). We showed how community secondary production can be an integrative variable for assessing effects of ecosystem fragmentation, and perhaps a more sensitive metric with which to assess affects of ecosystem fragmentation. However, diversity remains a key metric, even though diversity alone showed less clear patterns among fragmentation categories (species richness and community secondary production were significantly and positively related; linear regression: r2 = 0.46, P = 0.02). Our study should not be construed as rationale that fisheries managers or conservation biolo- gists focus on production over diversity. Instead these data are best interpreted as examples of how even non-significant declines in diversity can lead to disproportionately large declines in ecosystem function. Community secondary production estimates could therefore be an especially useful tool in scenarios where diversity- or community-based metrics do not detect an anthropogenic effect, but where an valentine-rose et al.: secondary production of fish communities 927 ecosystem-level effect is nonetheless suspected (e.g., Hoyle and Harborne 2005). In a broader context, it is often assumed that diversity change reduces species richness regardless of particular species or functional groups (Micheli 1999, Ceballos et al. 2005, Cardinale et al. 2006), yet here we showed that when realistic impacts on ecosystems occur (Zavaleta and Hulvey 2004, Bracken et al. 2008), disproportionate and specific changes in production of important functional groups result. In sum, we recommend that community secondary production be considered in future studies of anthropogenic stress on ecosystem function.

Acknowledgments

This study would not have been possible without the ideas, efforts, and persistence of D Albrey Arrington. We owe sincere thanks to M Blackwell, Bahamas Environmental Research Center, The Natural Conservancy Bahamas, Forfar Field Station, and the College of The Bahamas for providing research facilities, support, and help with logistics and planning. This project was funded by the National Science Foundation through an IGERT training grant to A Ward and the University of Alabama (DGE0319143), NSF OCE0746164, the Acorn Alcinda Foundation, and an RJKOSE grant through The Nature Conservancy. We would also like to thank the many Androsians that assisted with field work, particularly C “Tutu” Bains. We thank A Benke, J Benstead, and all anonymous reviewers for their time and advice reviewing this manuscript.

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Date SuBmitted: 19 May, 2010. Date Accepted: 16 June, 2011. AvailaBle Online: 20 July, 2011.

Addresses: (LV-R) Department of Life Sciences, Hill College, 2712 Mayﬁ eld Dr, Cleburne, Texas 76033. (ALR) Department of Fish and Wildlife Conservation, Virginia Tech, 100 Cheatham Hall, Blacksburg, Virginia 24061-0321. (CAL) Marine Sciences Program, Department of Biological Sciences, Florida International University, 3000 NE 151st Street, North Miami, Florida 33181. Corresponding Author: (LV-R) E-mail: . 932 BULLETIN OF MARINE SCIENCE. VOL 87, NO 4. 2011

Appendix 1. Growth rate data used for fishes from Bahamian tidal creeks. Parameters are given forthe −k(t−t0) Von Bertalanfy (VB) growth equation, Lage+1 = L∞ (1 − e ), where Lage = field estimated lengths, L∞ = asymptotic maximum length, t0 = age at zero length, and k = Brody growth coefficient with all lengths L representing standard length (SL, mm). Temperatures given for published VB equations used (°C), while for those experienced in situ during the sampling month were 33–35 °C in upstream areas of fragmented creeks (FU) and 25–30 °C in unfragmented (UF) creeks and downstream areas of fragmented (FD) creeks. Growth rates for schoolmaster in unfragmented tidal creeks fit a linear model, SL, mm = 24.711 (age, y) + 50.826 (Valentine-Rose et al. 2007). Citations for this appendix are located below.

Species/common name L∞ k t0 °C Location Ref Belonidae Strongylura spp. 1,230 0.610 27.0 India 8 Needlefish spp. Serranidae Epinephelus striatus (Bloch, 1792) 698 0.145 −1.080 Florida Keys 3 Nassau grouper Carangidae Caranx latus (Agassiz, 1831) 560 0.143 −1.730 27.0 Cuba 13 Horse-eye jack Lutjanidae Lutjanus analis (Cuvier, 1828) 696 0.246 27.2 Cuba 9 Mutton snapper 1,054 0.100 Lutjanus apodus (Walbaum, 1792) Schoolmaster snapper FD and FU 416 0.061 −1.900 The Bahamas 18 Lutjanus cyanopterus (Cuvier, 1828) 877 0.125 27.2 Cuba 19 Cubera snapper Lutjanus griseus (Linnaeus, 1758) Andros and Abaco 18 Gray snapper Islands, The Bahamas UF 947 0.036 −2.500 FD 238 0.303 0.140 FU 191 0.352 0.100 Ocyurus chrysurus (Bloch, 1791) 413 0.279 −0.355 South Florida 7 Yellowtail snapper Gerreidae Eucinostomus spp. 207 0.430 27.0 Mexico 1 Mojarra spp. Gerres cinereus (Walbaum, 1792) 268 0.600 27.0 Cuba 19 Yellow-finned mojarra Haemulidae Haemulon flavolineatum (Desmarest, 1823) 208 0.350 27.2 Jamaica 4 French grunt 224 0.179 Haemulon parra (Desmarest, 1823) 349 0.240 −0.270 27.2 SW Cuba 19 Sailor’s choice grunt Haemulon sciurus (Shaw, 1803) 305 0.220 −0.140 27.2 SW Cuba 19 Blue-striped grunt 334 0.300 0.000 Puerto Rico 2 Chaetodontidae Chaetodon capistratus (Linnaeus, 1758) 182 1.700 27.0 India 12 Four-eye butterfly Chaetodon ocellatus (Bloch, 1787) 182 1.700 27.0 India 12 Spotfin butterfly Pomacentridae Abudefduf saxatilis (Linnaeus, 1758) 189 0.850 27.0 India 12 Sergeant major Stegastes adustus (Troschel, 1865) 108 0.450 27.0 Jamaica 20 Dusky damselfish Stegastes leucostictus (Müller and Troschel, 1848) 108 0.450 27.0 Jamaica 20 Beaugregory valentine-rose et al.: secondary production of fish communities 933

Appendix 1. Continued.

Species/common name L∞ k t0 °C Location Ref Labridae Halichoeres bivittatus (Bloch, 1791) 170 0.750 Florida 14 Slipperydick Halichoeres garnoti (Valenciennes, 1839) 400 0.600 28.2 US Virgin Islands 16 Yellowhead wrasse Halichoeres poeyi (Steindachner, 1867) 400 0.600 28.2 US Virgin Islands 16 Blackear wrasse Halichoeres radiatus (Linnaeus, 1758) 400 0.600 28.2 US Virgin Islands 16 Puddingwife Thalassoma bifasciatum (Bloch, 1791) 170 0.750 Florida 14 Bluehead wrasse Scaridae Scarus iserti (Bloch, 1789) 290 0.600 27.0 The Bahamas 5 Striped parrotfish Sparisoma spp. 260 0.200 27.2 Caribbean 17 Parrotfish spp. Acanthuridae Acanthurus chirurgus (Bloch, 1787) 258 0.254 28.2 US Virgin Islands 14 Doctorfish 272 0.500 27.0 Jamaica 10 Pomacanthus paru (Bloch, 1787) 395 0.214 28.2 US Virgin Islands 14 French angel Sphyraenidae Sphyraena barracuda (Walbaum, 1792) 1,560 0.113 25.0 Florida 14 Great barracuda 1,780 0.087 6 Tetraodontidae Sphoeroide testudineus (Linnaeus, 1758) 250 0.510 25.4 Biscayne Bay 15 Checkered puffer Mullidae Pseudupeneus maculates (Bloch, 1793) 235 0.350 27.0 Jamaica 11 Spotted goatfish 254 0.700

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6De Sylva DP. 1963. Systematics and life history of the great barracuda Sphyraena barracuda Walbaum. Studies of Tropical Oceanography Miami (1), University of Miami Press, Coral Gables, Florida, USA. 7Johnson AG. 1983. Age and growth of yellowtail snapper from South Florida. Trans Amer Fish Soc. 112:173–177. http://dx.doi.org/10.1577/1548-8659(1983)112<173:AAGOYS>2.0.CO;2 8Kasim HM, Ameer Hamsa KMS, Balasubramanian TS, Rajapackiam S. 1996. Fishery of full beaks and half beaks with special reference on the growth, mortality and stock assessment of Ablennes hians (Valenciennes) along the Tuticorin coast, Gulf of Mannar. Ind J Fish. 43:51–59. 9Manooch CS. 1987. Age and growth of snappers and groupers. In: Polovina J, Ralston S, editors. Tropical snappers and groupers: biology and fisheries management, ocean resources marine policy series. Westview Press, Boulder, CO. 10Munro JL. 1974. The biology, ecology, exploitation and management of Caribbean reef fishes. Scientific Report of the ODA/UWI Fisheries Research Project: 1969–1973, Part V. In: Summary of biological and ecological data pertaining to Caribbean reef fishes. Research Report, Zoology Department, University of the West Indies. 3:24. 11Munro JL. 1983. The biology, ecology and bionomics of the goatfishes, Mullidae. ICLARM 7:142–154. 12Murty VS. 2002. Marine ornamental fish resources of Lakshadweep. CMFRI Special Publication Number 72. 13Palomares ML, Pauly D. 1989. A multiple regression model for predicting the food consumption of marine fish populations. Aust J Mar Freshwat Res. 40:259–273. http://dx.doi.org/10.1071/ MF9890259 14Pauly D. 1978. A preliminary compilation of fish length growth parameters. Ber Inst Meereskd Christian-Albrechts-Univ Kiel. 55:1–200. 15Pauly D. 1991. Growth of the checkered puffer Sphoeroides testudineus: postscript to papers by Targett and Pauly & Ingles. Fishbyte. 9:19–22. 16Randall JE. 1962. Tagging reef fishes in the Virgin Islands. Proc Gulf Car Fish Inst. 14:201–241. 17Stephenson D. 1977. Management of a tropical fish pot fishery for maximum sustainable yield. Proceedings of the Gulf Caribbean Fisheries Institute 30th Anniversary Session. p. 95–115. 18Valentine-Rose L, Layman CA, Arrington DA, Rypel AL. 2007. Habitat fragmentation decreases fish secondary production in Bahamian tidal creeks. Bull Mar Sci. 80:863–877. 19Valle SV, García-Arteaga JP, Claro R. 1997. Growth parameters of marine fishes in Cuban waters. Naga ICLARM Q. 20:34–37. 20Williams AH. 1978. Ecology of the threespot damselfish: social organization, age structure and population stability. J Exp Mar Biol Ecol. 34:197–213. http://dx.doi.org/10.1016/ S0022-0981(78)80002-1 valentine-rose et al.: secondary production of fish communities 935

Appendix 2. Length-weight regression data sources. 1 = present study, 2 = Bohnsack and Harper (1988)1. Parameters represent the equation, log W = log a + b log L, where L = length (mm), W = weight (g), and a and b are coefficients.

Min–max Length Species/common name a b Source Location (mm) measure Belonidae Strongylura spp. −7.19 3.62 1 Andros Island 160–350 SL Needlefish spp. Serranidae Epinephelus striatus (Bloch, 1792) −4.89 3.03 2 Puerto Rico 210–635 FL Nassau grouper Carangidae Caranx latus (Agassiz, 1831) −5.36 3.23 2 Florida 32–377 FL Horse-eye jack Lutjanidae Lutjanus analis (Cuvier, 1828) −4.83 3.03 2 St. Croix 260–630 FL Mutton snapper Lutjanus apodus (Walbaum, 1792) −4.30 2.91 1 Andros Island 50–180 SL Schoolmaster snapper Lutjanus cyanopterus (Cuvier, 1828) −4.06 2.75 1 Andros Island 74–310 SL Cubera snapper Lutjanus griseus (Linnaeus, 1758) −4.27 2.87 1 Andros Island 40–290 SL Gray snapper Ocyurus chrysurus (Bloch, 1791) −3.15 2.33 2 Puerto Rico 29–562 FL Yellowtail snapper Gerreidae Eucinostomus spp. −4.46 2.94 1 Andros Island 19–100 SL Mojarra spp. Gerres cinereus (Walbaum, 1792) −4.07 2.77 1 Andros Island 40–200 SL Yellow-finned mojarra Haemulidae Haemulon flavolineatum −5.04 3.15 2 Florida 32–389 FL (Desmarest, 1823) French grunt Haemulon parra (Desmarest, 1823) −4.44 2.98 1 Andros Island 50–135 SL Sailor’s choice grunt Haemulon sciurus (Shaw, 1803) −4.4 2.94 1 Andros Island 63–174 SL Blue-striped grunt Chaetodontidae Chaetodon capistratus (Linnaeus, 1758) −4.84 3.18 2 Florida 43–115 TL Four-eye butterfly Chaetodon ocellatus (Bloch, 1787) −4.48 2.98 2 Florida 103–181 TL Spotfin butterfly Pomacentridae Abudefduf saxatilis (Linnaeus, 1758) −4.78 3.14 2 Florida 18–143 FL Sergeant major Stegastes adustus (Troschel, 1865) −4.39 3.02 1 Andros Island 25–70 SL Dusky damselfish Stegastes leucostictus (Müller and Troschel, 1848) −4.39 3.02 1 Andros Island 25–70 SL Beaugregory Labridae Halichoeres bivittatus (Bloch, 1791) −4.81 2.93 2 Florida 36–152 TL Slipperydick Halichoeres garnoti (Valenciennes, 1839) −5.65 3.37 2 Florida 26–105 TL Yellowhead wrasse Halichoeres poeyi (Steindachner, 1867) −5.65 3.37 2 Florida 26–105 TL Blackear wrasse Halichoeres radiatus (Linnaeus, 1758) −4.92 3.03 2 Florida 24–36 TL Puddingwife Thalassoma bifasciatum (Bloch, 1791) −4.88 2.91 2 Florida 15–118 TL Bluehead wrasse 936 BULLETIN OF MARINE SCIENCE. VOL 87, NO 4. 2011

Appendix 2. Continued. Min–max Length Species a b Source Location (mm) measure Scaridae Scarus iserti (Bloch, 1789) −5.14 3.35 1 Andros Island 38–60 SL Striped parrotfish

Sparisoma spp. −5.75 3.42 2 Florida 130–240 FL Parrotfish spp. Acanthuridae Acanthurus chirurgus (Bloch, 1787) −5.92 3.53 2 Florida 39–304 FL Doctorfish Pomacanthus paru (Bloch, 1787) −4.81 3.12 2 Florida 21–413 TL French angel Sphyraenidae Sphyraena barracuda (Walbaum, 1792) −4.81 2.88 1 Andros Island 150–500 SL Great barracuda Tetraodontidae Sphoeroide testudineus (Linnaeus, 1758) −3.94 2.77 1 Andros Island 70–185 SL Checkered puffer Mullidae Pseudupeneus maculates (Bloch, 1793) −4.82 3.02 2 Florida 149–290 FL Spotted goatfish

1Bohnsack JA, Harper DE. 1988. Length-weight relationships of selected marine reef species from the southeastern United States and the Caribbean. NOAA Tech Memorandum NMFS- SEFC-215, 31 p. valentine-rose et al.: secondary production of fish communities 937

Appendix 3. Example of calculation of annual production: schoolmaster snapper in White Bight tidal creek, Andros Island, The Bahamas. DE = density 25 m−2, BI = biomass g 25 m−2, PR = secondary production, and PR (weighted) = PR multiplied by percentage of habitat contribution for each habitat. Equations can be found in the Methods section.

Creek: PR (weighted) −2 −1 −2 −1 White Bight Age (i) DE BIage field BIage + 1 PR g m yr g m yr Rock 58% 0 24 400 0 1 18 1,563 3,078 2 7 1,745 7,593 3 1 811 2,935 4 0 0 930 Total 4,519 14,536 401 232 Mangrove 18% 0 41 266 0 1 8 1,320 5,313 2 22 5,484 11,926 3 20 12,887 6,585 4 0 0 9,306 Total 19,957 33,130 527 95 Seagrass 23% 0 3 12 0 1 0 0 244 Total 12 244 9 2 Sand 1% 0 1 4 0 1 0 0 85 Total 4 85 3 0.03 Total PR 329