Tellier et al. 2008 FWRI File Code: F2196-05-08-F
Final report
for
Florida’s Wildlife Legacy Initiative
Florida’s State Wildlife Grants Program
Monitoring the Flora and Fauna of the nearshore hardbottom habitats of the Florida Keys
June 16 th , 2008
Report Authors: Marie-Agnès S. Tellier 1 Rodney Bertelsen 1
Principal Investigators: John H. Hunt1 Mark Butler 2 Thomas R. Matthews 1
1 Florida Fish and Wildlife Conservation Commission Fish and Wildlife Research Institute South Florida Regional Laboratory 2796 overseas highway, suite 119 Marathon, Florida 33050
2 Old Dominion University Department of Biological Science, 110 Mills Godwin Life Science Blds. Norfolk, VA 23529-0266
Tellier et al. 2008 i FWRI File Code: F2196-05-08-F
ABSTRACT
Florida’s Comprehensive Wildlife Conservation Strategy (CWCS) has specifically identified hard-bottom habitat as an area of concern. This project was developed to meet the data needs of the CWCS and allow an evaluation of the status of the hard-bottom community at an ecosystem level. Nearshore hard-bottom surveys of the Florida Keys were conducted from 2002 to 2007 at 32 sites ranging from west of Key West to the Upper Keys. All sites were surveyed visually by divers using SCUBA. Surveys of sessile fauna were conducted during the summer whereas motile fauna (fish and invertebrates) and algal surveys were conducted quarterly in the first year, then spring and fall thereafter. Data were analyzed primarily using multivariate techniques (regression, logistics, factor, discriminant, and non-linear canonical correlation) to explore which taxa, structures, and physical parameters best describe the nearshore hard-bottom community. There were three faunistically district regions in the Florida Keys designated as Oceanside, Channels, and Inner Bay. The centrally positioned Outer Bay region was not distinguishable from any other geographical area in the nearshore hard-bottom habitat. We found that the Anomura spp., Cerithiidae, and Astraea spp. (all small common invertebrates) with a larger decapod, Menippe mercenaria , formed a principal group that responded favorably to hard-bottom structure. While members of this group could be found throughout the Florida Keys, they best characterized the Gulfside region. The Oceanside region could be characterized with the abundance of echinoderms, and octocorals. The majority of sessile invertebrate structure was composed of sponge and octocoral taxa. Sponge structure were correlated with decapod abundance ( Panulirus argus , Menippe mercenaria , and Mithrax spinosissimus ), whereas octocoral structure was correlated with echinoderm abundance, especially Clypeaster rosaceus and Echinometra lucunter . We found two significant trends from 2003 to 2007 in the algal data. The presence of Halimeda spp. increased, and both Halimeda spp and Laurencia spp. increased in percent cover. A majority of the fishes observed during these surveys were juvenile fish. More than 85% of the fish were less than 15 cm total length. Only 79% of all fish taxa had reached up to one-third of their maximum size. We recommend that any management decisions and future regulations regarding the nearshore hard-bottom community account for the unique regional characteristics of the hard-bottom community and recognize that hard-bottom habitat, like seagrass habitat, has an important role as a nursery area for both fish and invertebrates. We also recommend that future monitoring include efforts to link factors between seagrass and hard-bottom by conducting simultaneous surveys in adjacent seagrass and hard-bottom sites.
ACKNOWLEDGMENTS
Support was provided for this project by the Florida Fish and Wildlife Conservation Commission’s program, Florida’s Wildlife Legacy Initiative, and the U.S. Fish and Wildlife Service’s State Wildlife Grants program (grant number: F2196-05-F). We would also like to thank Dr. Mark Butler IV and his students from Old Dominion University, Dr. William Herrnkind and his students from Florida State University, and the Florida Keys National Marine Sanctuary for assisting in the data collection. Tellier et al. 2008 ii FWRI File Code: F2196-05-08-F
TABLE OF CONTENT
ABSTRACT ...... i ACKNOWLEDGMENTS ...... i INTRODUCTION ...... 1 STUDY AREA ...... 2 METHODS...... 3 Sampling Procedure...... 3 Sessile Invertebrates ...... 4 Algae and Seagrass...... 4 Motile Invertebrates...... 4 Fish ...... 7 Data Handling and Analysis ...... 8 Agreement Analysis...... 8 Regression Analysis...... 8 Discriminant Analysis...... 8 Non-linear Canonical Correlation Analysis...... 9 Principal Factor Extraction Analysis ...... 9 RESULTS...... 9 Sessile Invertebrates ...... 9 Presence-Absence Analysis...... 10 Algae and Seagrasses...... 10 Motile invertebrates...... 13 Fish ...... 16 Percent Cover and Density Analysis ...... 17 Algae-Seagrass ...... 17 Agreement Analysis among Algae and Seagrass Data Collection Methods ...... 19 Motile invertebrates...... 21 Fish ...... 24 Discriminant analysis...... 25 Algae-Seagrass and Region ...... 25 Sessile Invertebrates and Region ...... 25 Motile Invertebrates/Fish and Region...... 25 Non-Linear Canonical Correlation Analysis ...... 27 Principal Factor Extraction...... 27 DISCUSSION...... 30 MANAGEMENT IMPLICATIONS AND RECOMMENDATIONS ...... 37 Recommendations for Future Studies...... 37 Recommendations for Future Hardbottom Studies...... 37 LITERATURE CITED...... 38
Hunt et al. 2008 1 Report: F219-05/08-F
INTRODUCTION
Nearshore habitat can be defined geographically as exposed rocky bottom within 2 km of the shore on the Gulf and Florida Bay side or Oceanside of the Florida Keys (Chiappone 1996). Another definition uses hydrodynamic characteristics to describe nearshore habitats, and includes areas of high velocity water flow such as channels and cuts and low flow areas such as basins (Schomer and Drew 1982). A third strategy to characterize nearshore hardbottom uses the benthic sessile plant and animal communities (Chiappone 1996). Macroalgal-dominated hardbottom constitutes approximately 40% of the shallow coastal zone surrounding the Florida Keys (Schomer and Drew 1982, Zieman et al. 1989, Chiappone 1996, Herrnkind et al. 1997) and is one of the most heavily impacted communities in the nearshore marine environment. With few exceptions, the abundance and distribution of sponges, corals, and motile fauna living in this community are poorly documented, and remarkably little is known about the community structure or ecological function of hardbottom communities (Zischke 1973; Chiappone and Sullivan 1994 a,b; Stevely and Sweat 1999). It is clear to even the most casual observer that the community has diminished in recent decades and species that were once common are at risk of being no longer common. Sponges, octocorals, and scleractinian corals are typically the most conspicuous sessile fauna in the hardbottom communities of the Florida Keys. Massive sponges are a common and striking component of hardbottom habitat throughout the Florida Keys and dominate the sponge community with respect to biomass; although, other species of small-to-medium sized, encrusting sponges dominate numerically. The octocoral and scleractinian coral assemblages found in hardbottom areas are depauperate compared to the Oceanside reefs. Approximately 15 species of octocorals are present in shallow hardbottom areas, but these areas are dominated by just a few taxa (Opresko 1973). A few small and inconspicuous stony corals commonly occur in shallow hardbottom areas, although the smooth star coral forms corals heads approaching 2 m in diameter. Nearshore hardbottom habitats provide shelter and forage for many invertebrates and fishes. A large but unknown number of motile macrofauna dwell in hardbottom areas (Sogard et al. 1989 a). Many of these species use hardbottom habitat opportunistically (e.g., stone crabs, bonefish, tarpon, various sharks, and turtles), for others hardbottom serves as a nursery (e.g., Caribbean spiny lobster, red grouper, Nassau grouper, hogfish, various species of snapper and parrotfish, etc.), while others are obligate dwellers of hardbottom and are rarely found elsewhere (e.g., spider crabs, octopus, numerous shrimps and other species). The need for comprehensive, up-to-date surveys of the shallow hardbottom communities of the Florida Keys has been highlighted in recent years by environmental change and resource exploitation that are affecting this habitat. Since the early 1990s, hardbottom communities in south central Florida Bay have experienced several episodes of sponge die-off (Butler et al. 1995, Herrnkind et al. 1997), presumably tied to massive blooms of cyanobacteria. This same region, because of its hydrological linkage with the Everglades, experiences remarkable annual and intra- annual fluctuations in salinity (Fourqurean and Robblee 1999) and there are concerns about the possible impact of the Everglades’ restoration on hardbottom habitat. An independent science advisory panel for the Environmental Protection Agency (EPA) and Florida Keys National Marine Sanctuary (FKNMS) stated that the hardbottom community has been neglected and its ecological significance must be explored. In addition, Florida’s Comprehensive Wildlife Conservation Strategy (CWCS) has specifically identified hardbottom habitat as an area of concern. The current project was developed to meet the data needs of the CWCS and allow an evaluation of the status of the hardbottom community at an ecosystem level. We completed a Hunt et al. 2008 2 Report: F219-05/08-F detailed, baseline assessment of the sessile macrofauna and flora of the Florida Keys hardbottom areas, and we have monitored representative sites to assess any changes in the community structure between regions or over time. This assessment was done at time scales sufficient to determine if any decline in community structure is correlated with episodic events like hurricanes, and toxic algal blooms, or anthropogenic events such as nearshore water quality deterioration or other persistent influences. We also examined and quantified sessile structure, motile invertebrates, and fishes in order to assess the relationship between fish, motile and sessile invertebrates, and seagrass and algae. Our project will detail the distribution, abundance, and size-structure of prominent species of interest to the CWCS that dwell in shallow hardbottom habitats throughout the Florida Keys. These data will provide the requisite long-term database necessary for sound resource management and assessment of subsequent alterations in hardbottom community structure in response to ecosystem change.
STUDY AREA
Using earlier FWRI aerial surveys, we estimated that hardbottom and hardbottom mixed with seagrass areas comprises 67,000 ha or 29% of the entire nearshore benthos. The dominant nearshore habitat of the Florida Keys is a softbottom community containing seagrasses (continuous and patchy) covering 147,000 ha or 62% of the total habitat. The amount of area that could not be interpreted with aerial photography was 17,000 ha or about 7%. These areas were predominately located Oceanside along the middle and lower Florida Keys. The shallow waters around the Florida Keys were partitioned into five regions during a previous benthic community survey project conducted in 1995 (Figure 1) (see Herrnkind et al 1997, Hunt et al 2005, Bertelsen et al. in review). The partitioning followed a hydrodynamic model partly derived from Schomer and Drew (1982) and Browder et al. (1976) in addition to our personal observations. The region “Inner bay” consists of a set of semi-enclosed basins and banks. These banks can significantly restrict water flow. Although fresh water enters this region through the Everglades from the north, the salinity in the areas we surveyed was identical to the other regions. The region “Outer bay” refers to the shallow waters north of the central portion of the Florida Keys. Relatively large openings between some of the islands and the lack of any large banks permit a greater exchange of water with the Oceanside than any other region. To the west, the open bayside waters are replaced by channels and islands that run the southeast to northwest. These lower Keys channels, which can exceed 20 km in length, can feature strong currents generated by wind or tides, and have been referred as "high velocity channels" (Browder et al. 1976). In the middle Keys, those types of channels are shorter, such as those around Craig and Fiesta Key (Chiappone, 1996), but were not part of our survey. Within the Channels region are the Turkey and Waltz Key basins. These two basins share characteristics of the Inner bay region such as being surrounded by banks and island. In the case of the Basins region, these bank and island barriers tend to increase water retention time and impede currents. In addition, the lower Keys Basins do not have freshwater input from the mainland. Finally, the “Gulf” and “Oceanside” regions refer respectively to the shallow nearshore waters lying adjacent to the Gulf of Mexico and to the Florida Straits.
Hunt et al. 2008 3 Report: F219-05/08-F
Fig. 1. Sampling locations of the nearshore hardbottom throughout the Florida Keys based on regions, from fall 2003 to spring 2007.
METHODS
Sampling Procedure
In a pilot study done in 2002, more than 100 sites were surveyed between Key Largo and the Marquesas Keys, to estimate the structural complexity and species richness of their sessile invertebrate communities (Hunt et al. 2005). In our study, 32 sites scattered throughout the Keys were selected based on the presence of hardbottom, and the amount of structural complexity and species richness of their sessile communities estimated in the pilot study(Figure 1) . The 32 sites were sampled quarterly from fall 2003 to summer 2004, then bi-annually from fall 2005 to spring 2007. Due to unfavorable environmental conditions, one site in the Oceanside region was not sampled in fall 2003, and one Inner bay site was not sampled in fall 2005. Each site was marked by a subsurface buoy attached to a center stake and four stakes placed at the end of four 28-meter long transects running north, east, south and west. We repeatedly collected quantitative data on fish, motile invertebrates, and seagrass-algae communities through visual census, along with environmental data such as temperature, salinity, and depth. To maintain a minimum standard through all these surveys, they were all conducted during daylight and only when horizontal water visibility, assessed by Secchi distance, exceeded 2 m. Bottom temperature and salinity were recorded using an YSI 650 Multiparameter Display System with an YSI 600XL sonde (YSI Incorporated, Yellow Spring, Ohio). In addition, each summer, from 2002 to 2006, we Hunt et al. 2008 4 Report: F219-05/08-F surveyed the sessile invertebrate communities of each site to obtain an estimate of sessile invertebrate surface area and volume. Sessile Invertebrates.—At each site, we surveyed the abundance and measured the size of selected sessile organisms, as well as the crevice structures present (solution holes), within four 25- meter long by 2-meter wide transects that were oriented north, south, east, and west from the permanent central stake. Up to six randomly selected individuals of the targeted species were measured (Table 1). Sponges and coral taxa were measured for height and diameter, whereas octocorals were only measured for height. Total sessile volume and surface area at each site were estimated and will respectively be referred as “all sessile surface” and “all sessile volume”. Other taxa specific volume and surface estimates, such as sponge and octocoral, were also calculated. To estimate the surface area and volume of sessile invertebrate fauna, we used the formulae for the cylinder or cone to model each taxon. For each site and survey, the mean height and diameter of the selected taxa were used in these formulae. For all sponges, except Ircinia campana , the surface area was estimated using the formula for the surface area of a cylinder minus the one of its base, which is in contact with the substratum ( 2πrh + 2πr2). To estimate the volume of the sponges, the standard volume of a cylinder was used (πr2h). The surface area of I. campana was estimated using the surface of a cone (πr(r2 + h2)0.5 + πr2). For octocorals, only height was measured, so in order to estimate volume and surface area, we estimated an average aspect ratio (height / width) for each taxon by examining the height and width of octocorals in our digital image library collected during the project. The estimated mean height and width, determined from the aspect ratio, were used in the equations of a cone as for I. campana . An exception was made for the widths of Briarium abestinium and ‘miscellaneous sea rods’ because they were generally found in single stalks approximately 1 cm wide. The overall estimated volume and surface area of sessile structure at each site were a simple sum of all the estimated volumes and surface areas. These estimates were also subdivided into sponge and octocoral volumes and surface areas. To lessen the influence of a few outlier estimates from certain surveys, we chose the median of all the yearly estimates to serve as a baseline number. Algae and Seagrass.—Data for algae and seagrass percent cover were collected using two methods: (1) a linear intercept transect method and (2) a Braun-Blanquet method. The latter was implemented in spring 2006 and used in addition to the linear transect method thereafter. The linear transect survey was used to evaluate the percent cover of dominant macroalgae and seagrasses. The targeted species were Laurencia spp., Thalassia testudinum , Syringodium filiforme , Halodule wrightii , Halimeda spp., and Dasycladus vermicularis . All other taxa were grouped and recorded as “Plantae”. When more than two species were present, this category also included any mix of algae or seagrass present at the second percent cover level and under. The percent cover of D. vermicularis was only assessed from fall 2003 to summer 2004. Data were collected over 25 meters, from the 3-meter mark to the end of each transect. Only seagrass and seaweed patches exceeding 20 cm in length on the linear transect were measured. The modified Braun-Blanquet method was used to evaluate the abundance of all algae and seagrass at a finer scale. The taxa considered for this survey are presented in Table 2. Four 1-m2 quadrats were placed at 8, 17, 19 and 25 m from the center buoy on each of the four transects. The percent cover of each taxa was recorded as a score based on similar percent cover criteria presented by Fourqurean et al. (2001) (Table 3). Motile Invertebrates.—The abundance of thirty-eight motile invertebrate taxa was surveyed during this study using the belt transect method. The four transects were surveyed starting 3 m from the center buoy, and was 25 m long by 2 m wide, 1 m on each side of the linear belt. Cerithiidae and Astraea spp. were not surveyed as separate taxa in fall 2003, but were part of the
ute l 08 5 Report: F219-05/08-F Hunt et al. 2008 Table 1. Taxa shown in bold indicate the taxa measured to estimate the sessile surface area and volume. Average density (individual per 100 m 2) of sessile invertebrate taxa by region from summer 2002 to summer 2006. The numerically dominant sponges, ( Anthosigmella varians , Ircinia spp, Geodia spp.) typically have very irregular shapes; therefore, height and diameter were not measured. Each taxon was ordered by decreasing average density within its major taxa to show dominant species. Region Overall Taxa Basins Channel Gulf Inner Bay Outer Bay Ocean average Sponges: Anthosigmella varians 87.700 31.160 36.683 0.678 25.578 52.714 39.086 Ircinia spp. 24.100 17.093 7.400 13.250 15.403 24.071 16.886 Geodia spp. 6.800 22.804 0.767 21.918 14.334 34.200 16.804 Aplysina spp. 2.000 9.249 17.017 0.393 8.763 50.343 14.627 Spheciospongia vesparia 3.300 17.649 16.650 11.913 8.058 10.229 11.300 Porifera 6.875 9.510 4.938 9.463 11.305 7.911 8.333 Ircinia campana 1.500 13.715 5.167 2.163 6.664 4.514 5.621 Sponge complex 2.100 10.096 0.350 5.621 1.874 2.871 3.819 Tedania ignis 1.600 3.023 3.867 8.487 2.923 1.071 3.495 Spongia graminea 2.900 3.065 3.117 0.887 2.639 1.600 2.368 Spongia cheiris 0.200 3.740 1.867 0.017 1.661 1.071 1.426 Aaptos spp. 0.200 0.824 0.500 0.837 0.818 3.271 1.075 Ircinia strobilina 0.000 0.740 0.750 0.413 0.819 2.343 0.844 Lissodendoryx spp. 1.300 1.442 0.100 0.113 1.276 0.443 0.779 Spongia dura 0.200 0.680 0.867 0.217 0.480 1.757 0.700 Spongia barbara 0.100 0.529 0.950 0.106 0.169 1.257 0.518 Hippospongia lachne 1.100 0.221 0.150 0.187 0.578 0.714 0.492 Total sponges: 141.975 145.540 101.138 76.661 103.342 200.382 128.173
ute l 08 6 Report: F219-05/08-F Hunt et al. 2008 Table 1. Continued
Region Overall Taxa Basins Channel Gulf Inner Bay Outer Bay Ocean average Fire coral: Millepora alcicornis 0.000 0.000 4.333 0.067 0.020 0.957 0.896 Octocorals: Pterogoriga anceps 2.600 4.621 11.200 24.079 21.567 55.014 19.847 Pseudopterogorgia spp. 0.200 0.112 0.050 17.528 1.423 50.157 11.578 Briareum asbestinum 5.000 1.215 14.550 5.908 6.575 31.071 10.720 Pterogorgia citrina 0.000 0.862 36.767 0.688 0.360 9.757 8.072 Pseudoplexaura spp. 0.000 0.000 21.950 0.401 0.688 23.971 7.835 Eunicea spp. 0.000 0.000 11.900 0.373 0.081 28.400 6.792 Plexaurella spp. 0.000 0.000 4.783 0.011 0.163 17.386 3.724 Muricea spp. 0.000 0.000 0.350 0.089 0.079 5.629 1.024 Pseudopterogorgia americana 0.000 0.000 0.000 1.194 0.064 3.614 0.812 Pterogorgia spp. 0.000 0.033 3.033 0.100 0.053 0.729 0.658 Soft Coral Complex 0.100 0.050 0.750 0.921 0.821 1.029 0.612 Plexauridae 0.000 0.000 2.333 0.000 0.110 0.529 0.495 Total octocorals: 7.900 6.894 107.667 51.292 31.983 227.286 72.170 Anemones: Bartholomea annulata 0.600 0.701 0.000 144.433 1.157 0.486 24.563 Condylactis gigantea 0.300 0.506 0.150 0.390 0.177 0.114 0.273 Stichodactyla helianthus 0.000 0.014 0.150 0.028 0.089 0.257 0.090 Actinoporus elegans 0.000 0.029 0.000 0.000 0.100 0.114 0.040 Total anemones: 0.900 1.250 0.300 144.851 1.523 0.971 24.966 Corals: Solenastrea hyades 0.000 0.000 0.000 3.411 0.063 1.300 0.796 Other Large Corals 0.100 0.062 0.133 0.039 0.286 1.457 0.346 Solenastrea bournoni 0.000 0.129 0.000 0.033 1.091 0.200 0.242 Siderastrea radians 0.000 0.500 0.000 0.000 0.063 0.143 0.118 Siderastrea siderea 0.125 0.042 0.000 0.046 0.000 0.250 0.077 Total corals: 0.225 0.732 0.133 3.529 1.503 3.350 1.579 Solution hole: Solution hole 0.000 0.095 0.050 0.247 0.162 0.071 0.104 Total Sessile: 151.000 154.512 213.621 276.647 138.533 433.018 227.888
Hunt et al. 2008 7 Report: F219-05/08-F
Table 2. Algae and seagrass taxa used for the Braun-Blanquet survey. Phylum Taxa used in this study Magnoliophyta: Thalassia testudinum Halodule wrightii Syringodium filiforme Halophila engelmannii Ruppia maritima Chlorophyta: Caulerpa spp. Acetabularia spp. Batophora oerstedii Halimeda spp. Penicillus spp. Other calcareous green algae Other green algae Rhodophyta: Drift red algae ( Laurencia spp., Gracilaria spp.) Other red algae Phaeophyta: Sargassum spp. Other brown algae Unknown: Other algae
Table 3. Braun-Blanquet abundance scores and their significance (Fourqurean et al. 2001), and estimated equivalence of percent cover ranges obtained using belt transect intercept method. Belt transect %cover Score Significance equivalence 0 Taxa absent from the quadrat 0 0.1 Taxa represented by a solitary shoot, and less than 5% cover 0-0.2% 0.5 Taxa represented by 2 to 5 shoots, and less than 5% cover 0.2-1% 1 Taxa represented by more than 5 shoots, and less than 5% cover 1-5% 2 Taxa represented by 5% to 25% cover 5-25% 3 Taxa represented by 25% to 50% cover 25-50% 4 Taxa represented by 50% 50 75% cover 50-75% 5 Taxa represented by 75% to 100% cover 75-100% taxa Mollusca. After observing the high abundance of those two particular taxa, Cerithiidae and Astraea spp. were surveyed as separate taxa, from winter 2004 and thereafter. Echinometra lucunter was added in winter 2004. Asteroidea was only surveyed in winter 2003. Echinaster spinulosus and Callinectes sapidus were added to the motile invertebrates in fall 2005. This latter taxa replaced Callinectes spp., which was only surveyed from fall 2003 to summer 2004. Fish.—Since fish are highly motile and wary, their survey was always executed by a team of two divers before any other survey. Each diver surveyed two of the four transects without influence from any other diver. Through visual census, each diver identified, counted, and estimated the size of every fish they encountered within the motile invertebrate survey belt- transects areas (25 m long by 2 m wide, starting 3 m mark from the center buoy). The total length (TL ) of each fish was binned within the following classes: 0-2 cm, 2-4 cm, 4-6 cm, 6-8 cm, 8-10 cm, 10-15 cm, 15-20 cm, 20-25 cm, 25-30 cm, 30-35 cm, 35-40 cm, 40-45 cm, 45-50 cm, 50- 55cm, 55-60 cm, 60-70 cm, 70-80 cm, 80-90 cm, 90-100 cm. Total lengths larger than 100 cm were recorded as an actual number.
Hunt et al. 2008 8 Report: F219-05/08-F
Data Handling and Analysis
All statistical analyses were performed with SPSS, Systat, Minitab, and R packages, unless specified otherwise. Agreement Analysis.—In order to assess the consistency of the results for algae and seagrass percent cover obtained by linear belt transect and Braun-Blanquet methods, we transformed the linear transect data into the same ordinal score than the one used for Braun-Blanquet (Table 3). Only data for Laurencia spp., H. wrightii , Halimeda spp., S. filiforme, and T. testudinum , collected from spring 2006 to spring 2007 were considered. The category 0.1 was combined with the category 0 in this analysis since the linear transect method did not encompass any patches smaller than 20 cm in length. We first measured the chance-adjusted data agreement of the two methods by calculating kappa (K) and the Fleiss-Cohen quadratic weighted Kappa ( Kc ), which is believed to be a more appropriate measure of agreement when the categories are ordinal (Cohen 1960, 1968; Cook 1998 a). Landis and Koch (1977) provided ranges for the interpretation of kappa: K > 0.75 represents excellent agreement, 0.40 < K < 0.75 fair to good agreement and K < 0.40 poor agreement. Kappa has been proven to depend on the marginal frequencies, the prevalence of the trait examined (e.g. the distribution of seagrass and algae across the categories) and the bias of the method/observers (Cicchetti and Feinstein 1990, Feinstein and Cicchetti 1990, Lantz and Nebenzahl 1996, Cook 1998 b). So we calculated the prevalence adjusted kappa (PAK) and the prevalence adjusted bias adjusted kappa (PABAK), using the program PEPI (Byrt et al. 1993, Abramson and Gahlinger 2001). Since the ordinal ratings implied a latent continuous trait, we estimated the association between the two methods with the polychoric correlation, rho , through a latent class analysis (LCA) using the program LLCA v1.0 (Chandler 1969; Agresti and Young 1986; Uebersax 1993, 2006 a). We tested the overall marginal homogeneity using both the Stuart-Maxuell test and the Bhapkar test (Stuart 1955, Bhapkar 1966, Maxwell 1970, Everitt 1977, Keefe 1982, Agresti 2002). Calculations were conducted using the MH Program v1.2 (Uebersax 2006 b). Finally, we tested for differences in method thresholds associated with each category and for a difference between the methods’ overall bias using McNemar tests (McNemar 1947, Everitt 1977, Somes 1983, Sheskin 2000) Regression Analysis.—The relative importance of physical, environmental, and community parameters to the distribution of algae, motile invertebrates, and fish were examined using a two- step process. The factors influencing the presence of algae-seagrass, motile invertebrates, and fish were tested using a logistic approach, while their percent cover and densities were modeled using multiple regression techniques. To minimize problems of non-normality and heteroscedasticity, all percent cover and densities were log-transformed prior to analysis. Dummy variables for season and regions were created when necessary. For both logistic and multiple regression approaches, the environmental and community parameters were entered into the models stepwise with P = 0.05 criterion and removed if P > 0.1. Only taxa that appeared more than 12 times during the entire study were considered for analysis. High performances of the logistic models were assessed using receiver–operating characteristic (ROC) curves and the area under those curves (AUC): 0.5 < AUC < 0.7 indicates low accuracy, 0.7 < AUC < 0.9 indicates good accuracy and useful applications, and AUC > 0.9 indicates high accuracy (Swets 1988). Results from the multiple regression analyses were examined for normality, linearity, homoscedasticity of residuals and absence of strong outliners. Only taxa that did not display obvious violations of all assumptions are presented here. Discriminant Analysis.—Discriminant analysis was used to determine the best combination of variables that detail the differences between geographic regions of the Keys. We ran separate discriminant analyses on the algal data, sessile abundance, and the motile fauna (both invertebrates Hunt et al. 2008 9 Report: F219-05/08-F and fishes). In each case, region was used as the grouping variable. Discriminant analysis performs best when memberships within the grouping variables are roughly equal. Therefore, because of the relative rarity of sites surveyed in the Basin and Gulf regions, sites from these regions were not included in discriminant analyses. Non-linear Canonical Correlation Analysis.—Non-linear canonical correlation analysis was used to explore the relationships between motile and sessile fauna (Meulan and Heiser, 2004). The advantages of the nonlinear canonical correlation are (1) its robustness to many zeros, (2) ordinal and nominal variables can both be used in a single analysis, and (3) highly correlated variables can be placed into a single variable set that prevents those correlations from dominating the analysis and masking other relationships (Gifi, 1990). Categorical variables were first formulated from invertebrates and juvenile and adult fish. An ‘equal membership’ technique was used to define categories wherein observations are ranked then divided into bins of equal size with the first and last member in a bin defining the bounds of a category. This technique is useful when data contain widely divergent distributions and ranges. Principal Factor Extraction Analysis .—We used exploratory principal factor extraction on the motile taxa abundance data (both fishes and invertebrates) to find groups of taxa that best explain correlations in abundance. The factors produced by the analysis attempts to explain the greatest amount of variance in the original data in the fewest number of taxa (SPSS, 2004). We used the scree test to determine the number of factors to retain (Costello and Obsborne, 2005). To better meet the assumption of normality, we performed a natural log transformation on the abundance data. Taxa chosen for this analysis represent taxa present at least 25% of the time or were in the top 15 most abundant taxa. In addition, the taxa chosen were identified at least to the family level. Taxa included for principal factor extraction were Anomura, Astraea spp., Cerithiidae, Clypeaster rosaceus , Echinometra lucunter , Fasciolaria tulipa , Lytechinus variegatus , Menippe mercinaria , Mithrax spinosissimus , Panulirus argus , Strombus costatus , Anisotremus virginicus , Coryphopterus glaucofraenum , Diplectrum formosum , Epinephelus morio , Haemulon plumierii , H. sciurus , Halichoeres bivittatus , Lutjanus analis , L. griseus , L. synagris , Ocyurus chrysurus , Calamus spp., and Lagodon rhomboides .
RESULTS
The sites were at depth ranging from 2 to 10 feet (x- = 5.89 ± 1.6 ft). At such shallow depths, season and air temperature had a large influence on the water temperature at some sites. Water temperature ranged from 18 to 39 ºC during this study ( x- = 26.63 ± 3.87 ºC). The Florida Keys receive fresh water during wet season from the Florida Bay and salt water from the Gulf and the Ocean. Both have a large effect on salinity at such shallow depths, especially when they are close to shore. In this study, salinity varied from 20.70 to 42.50 ( x- = 36.17 ± 1.91).
Sessile Invertebrates
We counted 70,113 sessile invertebrates during surveys conducted from summer 2002 to summer 2006. The ten most abundant were Anthosigmella varians (31.0 individuals per 100 m2), Pterogorgia anceps (25.4 individuals per 100 m2), Geodia spp. (19.8 individuals per 100 m2), Aplysina spp. (16.9 individuals per 100 m2), Ircinia spp. (16.8 individuals per 100 m2), Bartholomea annulata (15.9 individuals per 100 m2), Pseudopterogorgia spp. (15.0 individuals per 100 m2), B. asbestinum (11.6 individuals per 100 m2), Spheciospongia vesperia (11.6 individuals per 100 m2), and miscellaneous Porifera (9.7 individuals per 100 m2). Hunt et al. 2008 10 Report: F219-05/08-F
Sponges and octocorals, as major taxonomic groups, dominated the sessile nearshore hardbottom benthic community (Table 1), encompassing approximately 90% of all sessile invertebrate organisms. Taxa shown in bold were used to estimate the total surface area and volume of the sessile invertebrate community at each site. Sponges that were not used to estimate surface area and volume of the sessile invertebrates (e.g. A. varians ) are usually irregularly shaped; therefore, it was not possible to estimate their size reliably. Basins and Channel areas differed from the other regions in that their sponge densities were 18 and 21 times respectively more abundant than octocorals, whereas in all other areas the abundance of these major taxa were approximately equal. Corals were most abundant in the Inner Bay and Ocean regions where their densities were at least twice as high as in other regions. Anemones were typically as abundant as corals; however, a few Inner Bay sites contained large numbers of B. annulata , which greatly influenced the total average of anemones for this region. The total volume of all sessile invertebrate measured ranged from 6,787.88 cm 3 per 100 m 2 to 955,448.70 cm 3 per 100 m 2, and their total surface from 2,718.11 cm 2 per 100 m 2 to 404,656.28 cm 2 per 100 m 2. The sponge volume per site ranged from 3,301.94 cm 3 per 100 m 2 and its surface area from 1,589.20 cm 2 per 100 m2 to 195,730.49 cm 2 per 100 m 2. The octocoral volume per site ranged from 0.00 cm 3 per 100 m 2 to 871,739.24 cm 3 per 100 m 2 and its surface from 0.00 cm 2 per 100 m 2 to 359,263.56 cm 2 per 100 m 2. Table 4. Average total sessile invertebrate volume (cm 3 per 100 m 2), sponge volume (cm 3 per 100 m 2), octocoral volume (cm 3 per 100 m 2), total sessile surface (cm 2 per 100 m 2), sponge surface (cm 2 per 100 m 2), and octocoral surface (cm 2 per 100 m 2), for each region and all regions together in the nearshore hardbottom of the Florida Keys, USA, from fall 2003 to spring 2007.
Region Basins Channels Gulf Inner bay Outer bay Ocean All regions All sessile volume 57,271.39 234,510.88 411,519.54 170,670.68 167,398.74 150,826.99 191,685.49 Sponge volume 56,240.55 222,370.68 127,422.62 128,110.68 147,866.85 95,661.38 140,135.54 Octocoral volume 750.90 5,325.89 292,693.55 11,884.76 7,474.03 54,527.25 39,924.17 All sessile surface 20,306.83 72,894.51 161,330.45 92,033.30 60,201.85 123,797.57 89,281.01 Sponge surface 18,186.85 67,696.39 42,976.65 33,587.09 43,871.73 28,628.09 41,254.59 Octocoral surface 993.92 3,731.84 120,209.45 51,201.50 10,758.68 88,228.46 43,362.78
The region with the highest overall sessile volume and surface was the Gulf region, with more than 1.75 times more sessile volume and 1.3 times more surface area than any other region (Table 4). This is mainly due to the relatively high content of octocoral in the Gulf region, where it represents 71.1 % of the sessile invertebrate volume and 74.5 % of its surface. Sponges are the dominant component of sessile invertebrate volume and surface in Basins, Channels, and Outer bay regions. In the Inner bay region and on the Ocean side, the average sessile invertebrate volume is mostly determined by the sponge volume, representing respectively 75.1 % and 63.4 % of all sessile volume. However, the average sessile invertebrate surface is mostly driven by the octocoral, representing 55.6% and 71.3% respectively of all sessile invertebrate surface areas.
Presence-Absence Analysis
Algae and Seagrasses.—According to the data collected by the linear transect method, Laurencia spp. is the most common algae in the nearshore hardbottom of the Florida Keys, being present in 82.7 % of the surveys, and T. testudinum is the most common seagrass (83.1 % of the surveys) (Table 5). Laurencia spp. and T. testudinum are also the most common algae and seagrass Hunt et al. 2008 11 Report: F219-05/08-F
in the Basins, Channels, Inner and Outer bay regions, but the Gulf region was mostly dominated by T. testudinum , present at all times and locations, and S. filiforme , present 85.0 % of the surveys. Ocean side was mostly a mix of Laurencia spp (present 85.7 % of), and other algae-seagrass (69.6%). Table 5. Average presence of Plantae (other algae or seagrass, or mix of more than two taxa), Halimeda spp., Dasycladus vermicularis , Laurencia spp., Thalassia testudinum , Halodule wrightii , Syringodium filiforme , and all algae-seagrass for each and all regions together, per site per sampling period, based on the linear transect survey in the nearshore hardbottom of the Florida Keys from fall 2003 to spring 2007. All Halimeda Dasycladus Laurencia Thalassia Halodule Syringodium Regions Plantae algae- spp. vermicularis spp. testudinum wrightii filiforme seagrass Basins 0.667 0.533 0.143 0.667 0.933 0.133 0.000 0.464 Channels 0.750 0.458 0.125 0.938 0.917 0.083 0.083 0.506 Gulf 0.650 0.450 0.300 0.250 1.000 0.050 0.850 0.523 Inner bay 0.784 0.373 0.208 0.824 0.961 0.176 0.020 0.500 Outer bay 0.797 0.578 0.031 0.938 0.813 0.031 0.016 0.490 Ocean 0.696 0.571 0.071 0.857 0.571 0.089 0.036 0.440 All regions 0.744 0.500 0.120 0.827 0.831 0.091 0.098 0.485
Table 6. Average presence of Acetabularia spp., Batophora oerstedii , Caulerpa spp., Halimeda spp., Penicillus spp., Codiacea (other calcareous green algae), Chlorophyta (other green algae), Sargassum spp., Phaeophyta (other brown algae), Rhodophyta (drift) (e.g. Laurencia spp. and Gracilaria spp.), Rhodophyta (other red algae not drift), Halodule wrightii , Ruppia maritima , Syringodium filiforme , Thalassia testudinum , Plantae (unknown), and all algae-seagrass for each and all regions together, per site per sampling period, based on the Braun-Blanquet survey in the nearshore hardbottom of the Florida Keys from spring 2006 to spring 2007. Taxa were ordered in decreasing average presence per site per sampling period for all regions considered to show the taxa most present. Region All Taxa Basins Channels Gulf Inner bay Outer bay Ocean regions Penicillus spp. 1.000 1.000 1.000 0.905 1.000 1.000 0.979 Rhodophyta (drift) 1.000 0.944 0.667 1.000 1.000 1.000 0.969 Halimeda spp. 1.000 1.000 1.000 0.762 1.000 1.000 0.948 Codiaceae 1.000 1.000 0.833 0.762 0.958 1.000 0.927 Thalassia testudinum 1.000 0.833 1.000 0.952 0.750 0.619 0.813 Batophora oerstedii 0.500 0.667 1.000 0.905 0.458 0.714 0.688 Caulerpa spp. 0.833 0.667 1.000 0.619 0.833 0.286 0.646 Rhodophyta 0.333 0.889 0.833 0.429 0.542 0.667 0.615 Chlorophyta 0.500 0.556 1.000 0.524 0.500 0.571 0.563 Acetabularia spp. 0.667 0.556 0.333 0.667 0.333 0.381 0.479 Phaeophyta 0.000 0.167 1.000 0.333 0.250 0.286 0.292 Sargassum spp. 0.000 0.111 0.833 0.095 0.042 0.095 0.125 Halodule wrightii 0.000 0.000 0.167 0.238 0.083 0.095 0.104 Syringodium filiforme 0.000 0.000 1.000 0.095 0.083 0.000 0.104 Plantae 0.000 0.000 0.000 0.048 0.000 0.000 0.010 Ruppia maritima 0.000 0.000 0.167 0.000 0.000 0.000 0.010 All algae-seagrass 0.490 0.524 0.740 0.521 0.490 0.482 0.517 Hunt et al. 2008 12 Report: F219-05/08-F
According to the Braun-Blanquet data, calcareous green algae, like Penicillus spp. and Halimeda spp, but also other Codiaceae (such as Udotea spp. or Avrainvillea spp.), are the most common algae in the nearshore hardbottom of the Florida Keys, being present in 92.7 to 97.9 % of the surveys, along with drift red algae, such as Laurencia spp. or Gracilaria spp., present 96.9 % of the surveys (Table 6). This method confirmed that T. testudinum was the most common seagrass found in this habitat, present 81.3 % of the time. The average presence of all algae-seagrass obtained with the Braun-Blanquet method is higher for each and all regions than the one obtained with the linear transect method. With the exception of the Gulf region where the Braun-Blanquet method give an average presence of all algae-seagrass 41.5% higher, the Braun-Blanquet method seems to estimate the average presence of algae- seagrass between 0 to 9.5 % higher than the linear transect method. Nevertheless, both methods seemed to present the same relative results. Both methods revealed that S. filiforme was mostly present in the Gulf, whereas Laurencia spp. was the least present in the Gulf, and Halimeda spp. in the Inner bay, and T. testudinum in on the ocean side region. H. wrightii seemed to be mostly present in the Inner bay region, but the difference between this region and others was found to be non-significant (PE χ2 = 8.069; df = 5; P = 0.152). Ruppia maritima was exclusively found in the Gulf region (16.7 % of the surveys), and Sargassum spp., with other brown algae, were mostly present in the Gulf (83.3 % of the surveys). Laurencia spp. and T. testudinum were, on average, the most common algae and seagrass during all seasons, with the exception of summer, when other algae-seagrass mix, Plantea, was the most common along with T. testudinum (Table 7). With the exception of S. filiforme , Halimeda spp., D. vermicularis , Laurencia spp., T. testudinum , and H. wrightii were the least present in winter, and Halimeda spp., D. vermicularis , T. testudinum , S. filiforme , and other algae-seagrass mix were mostly present in summer. Table 7. Average Presence of Halimeda spp., Dasycladus vermicularis , Laurencia spp., Thalassia testudinum , Halodule wrightii , Syringodium filiforme , Plantae (other algae or seagrass, or mix of more than two taxa), and all algae-seagrass for each and all seasons, per site per sampling period, based on the linear transect survey in the nearshore hardbottom of the Florida Keys from fall 2003 to spring 2007. All Halimeda Dasycladus Laurencia Thalassia Halodule Syringodium Seasons Plantae algae- spp. vermicularis spp. testudinum wrightii filiforme seagrass Fall 0.747 0.516 0.063 0.789 0.832 0.179 0.084 0.500 Winter 0.355 0.194 0.065 0.710 0.774 0.032 0.129 0.323 Spring 0.813 0.531 0.094 0.885 0.823 0.052 0.083 0.508 Summer 0.906 0.656 0.250 0.875 0.906 0.000 0.156 0.536 All seasons 0.744 0.500 0.118 0.827 0.831 0.091 0.098 0.485
A logistic analysis of the presence-absence data of the algae and seagrass suggested that Laurencia spp. and Halimeda spp. had the highest probability of presence in summer, whereas H. wrightii was most probable to occur in fall (Appendix C). Furthermore, S. filiforme and T. testudinum are more likely to be present in the Gulf than any other region, whereas Laurencia spp. is the most likely to be present in the Outer bay and Channels regions. When considering other factors, such as depth, salinity, temperature, and the sessile invertebrates present, it appears that the presence of S. filiforme and T. testudinum are not only dependent on the region, but also on depth. The probability of S. filiforme being present significantly decreases with increasing depth, whereas that of T. testudinum increases. Furthermore, the probability of presence of the latter significantly decreases with the increase in sessile invertebrate surface, while the probability of Halimeda spp. significantly increases with sponge volume. Hunt et al. 2008 13 Report: F219-05/08-F
Motile invertebrates.— The Oceanside nearshore hardbottom of the Florida Keys presents the highest motile invertebrate species richness of all regions with 92.1 % of the species surveyed being present, followed by the Bay regions (Outer bay 86.8 %, and Inner bay 81.6 %) (Table 8). The Basins region displayed the lowest species richness of all with only 19 species recorded out of 38, or half of the species considered. All species considered were present in fall, whereas only 97.3 % of the species surveyed were present in spring, 91.4% in winter, and 80.6 % in summer. Strombus raninus was absent from the sites we surveyed in spring and summer; Octopus spp. was absent in winter; Melongenidae, and S. alatus were absent in winter and summer; and Aplysia dactylomela , Pleuroploca gigantea and S. costatus were not found in summer. The ten most common taxa surveyed during this study were Anomura (present in 69.4 % of the surveys), Astraea spp. (52.9 %), Cerithiidae (40.4 %), Mollusca (other mollusks, 38.0 %), P. argus (37.6 %), Holothuroidea (28.6 %), M. spinosissimus (28.6 %), M. mercenaria (27.8 %), Crustacea (other crustaceans, 24.7 %) and L. variegatus (23.1 %). All other taxa were encountered about 20 % of the time or less. Region did not explain the presence of Anomura (PE χ2 = 9.386, df = 5, P = 0.095), F. tulipa (PE χ2 = 6.835, df = 5, P = 0.233), Phyllonotus pomum (PE χ2 = 4.731, df = 5, P = 0.450), and Portunus sebae (PE χ2 = 3.004, df = 5, P = 0.699). But the probability of presence of C.rosaceus , E. lucunter , Eucidaris tribuloides , Holothuroidea, Strombus gigas , S. costatus , and Vasum muricatum were the highest on the Ocean side of the Florida Keys (Appendix B); Astraea spp. , Cerithiidae, M. mercenaria , L. variegatus , Mithrax sculptus , and M. spinosissimus were most probable in the Gulf, Ophiuroidea in the Outer bay, and Panulirus argus in the Channels. None of the collected parameters could explain the presence of Lysmata wurdemanni , E. spinulosus , Stomatopoda, and Periclimenes pedersoni . Season did not have any significant effect on the presence of Anomura (PE χ2 = 5.359, df = 3, P = 0.147), Cerithiidae (PE χ2 = 1.575, df = 3, P = 0.665), C. rosaceus (PE χ2 = 1.627, df = 3, P = 0.653), E. lucunter (PE χ2 = 0.325, df = 3, P = 0.947), E. tribuloides (PE χ2 = 0.569, df = 3, P = 0.903), F. tulipa (PE χ2 = 1.791, df = 3, P = 0.617), L. variegatus (PE χ2 = 1.001, df = 3, P = 0.801), M. sculptus (PE χ2 = 4.999, df = 3, P = 0.172), M. spinosissimus (PE χ2= 0.869, df = 3, P = 0.833), Ophiuroidea (PE χ2 = 1.401, df = 3, p = 0.705), P. argus (PE χ2= 4.079, df = 3, P = 0.253), P. sebae (PE χ2 = 1.101, df = 3, P = 0.777), S. costatus (PE χ2 = 5.788, df = 3, P = 0.122), and S. gigas (PE χ2 = 3.890, df = 3, P = 0.274). However, Astraea spp., Holothuroidea, M. mercenaria , and V. muricatum had the highest probability of presence in fall, whereas P. pomum was summer. The probability of presence of Anomura, Astraea spp., and Cerithiidae increased with increasing sessile invertebrate volume, whereas the one of Ophiuroidea decreased. The probability of presence of C. rosaceus , E. lucunter , Ophiuroidea, and V. muricatum increased with increasing sessile invertebrate surface. The probability of presence of Astraea spp. and L.variegatus increased with increasing depth, whereas S. gigas ’ decreased with depth. The probability of presence of Portunus sebae increased with salinity. The probability of presence of E. tribuloides increased with increasing amount of octocoral surface, whereas the one of P. pomum increased with the amount of octocoral volume. The probability of presence of M. mercenaria , M.sculptus , and M. spinosissimus increased with increasing amount of sponge surface, whereas the one of P. argus increased with the amount of sponge volume. The probability of presence of M. sculptus increased with increasing percent cover of Laurencia spp, whereas the one of F. tulipa and M. spinosissimus decreased with Laurencia spp. percent cover. The probability of presence of Holothuroidea and V. muricatum decreased with increasing percent cover of T. testudinum .
ute l 08 14 Report: F219-05/08-F Hunt et al. 2008
Table 8. Presence of Anomura, Aplysia dactylomela , Asteroidea, Astraea spp., Callinectes sapidus , Callinectes spp., Cerithiidae, Clypeaster rosaceus, Crustacea (other crustaceans), Cypraea spp., Diadema antillarum , Echinaster spinulosus , Echinodermata (other echinoderms), Echinometra lucunter , Eucidaris tribuloides , Fasciolaria tulipa , Holothuroidea, Lysmata wurdemanni , Lytechinus variegatus , Melongenidae, Menippe mercenaria , Mithrax sculptus , M. spinosissimus , Mollusca (other mollusks), Octopus spp., Ophiuroidea, Oreaster reticulatus , Panulirus argus , Periclimenes pedersoni , Phyllonotus pomum , Pleuroploca gigantea , Portunus sebae , Stomatopoda, Strombus alatus , S. costatus , S. gigas , S. raninus , Vasum muricatum , and all motile invertebrates for each and all regions, and for each season, per site and sampling period, from fall 2003 to spring 2007. Taxa were ordered in decreasing presence per site per sampling period for all regions together to show the taxa most present. Species richness is the number of species present divided by the number of species surveyed. Note: Cerithiidae and Astraea spp . were not surveyed as separate taxa in fall 2003, but were part of the taxa Mollusca. They were added as separate taxa in winter 2004. Echinometra lucunter was added to the list in winter 2004. Asteroidea was only surveyed in winter 2003. Echinaster spinulosus and Callinectes sapidus were only surveyed from fall 2005 and after. Callinectes spp. was only surveyed from fall 2003 to summer 2004. Region Season Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Anomura 0.625 0.729 0.900 0.569 0.672 0.750 0.694 0.716 0.656 0.740 0.531 Astraea spp. 0.571 0.571 0.882 0.378 0.607 0.408 0.529 0.683 0.500 0.417 0.594 Cerithiidae 0.643 0.357 0.824 0.333 0.250 0.469 0.404 0.397 0.406 0.438 0.313 Mollusca 0.313 0.313 0.600 0.353 0.344 0.446 0.380 0.579 0.250 0.292 0.188 Panulirus argus 0.250 0.563 0.350 0.314 0.453 0.232 0.376 0.453 0.375 0.323 0.313 Holothuroidea 0.250 0.208 0.150 0.137 0.328 0.500 0.286 0.368 0.344 0.260 0.063 Mithrax spinosissimus 0.313 0.292 0.650 0.196 0.234 0.286 0.286 0.295 0.219 0.302 0.281 Menippe mercenaria 0.313 0.375 0.600 0.216 0.219 0.196 0.278 0.358 0.125 0.292 0.156 Crustacea 0.313 0.146 0.350 0.176 0.203 0.393 0.247 0.253 0.125 0.271 0.281 Lytechinus variegatus 0.000 0.083 0.700 0.039 0.438 0.196 0.231 0.263 0.219 0.219 0.188 Ophiuroidea 0.125 0.146 0.000 0.196 0.328 0.232 0.208 0.221 0.156 0.229 0.156 Clypeaster rosaceus 0.000 0.167 0.150 0.059 0.063 0.571 0.196 0.200 0.250 0.198 0.125 Asteroidea 1.000 0.000 0.000 0.333 0.000 0.286 0.188 0.188 Mithrax sculptus 0.250 0.042 0.400 0.020 0.125 0.393 0.176 0.232 0.094 0.177 0.094 Fasciolaria tulipa 0.125 0.104 0.350 0.176 0.156 0.143 0.161 0.179 0.219 0.135 0.125 Echinometra lucunter 0.000 0.024 0.000 0.044 0.018 0.531 0.135 0.127 0.156 0.125 0.156 Echinaster spinulosus 0.375 0.125 0.000 0.185 0.188 0.000 0.134 0.079 0.188 Stomatopoda 0.000 0.104 0.050 0.078 0.094 0.304 0.129 0.137 0.094 0.135 0.125 Periclimenes pedersoni 0.125 0.063 0.050 0.098 0.125 0.107 0.098 0.105 0.125 0.063 0.156 Echinodermata 0.125 0.063 0.000 0.039 0.156 0.089 0.086 0.063 0.156 0.063 0.156 Phyllonotus pomum 0.063 0.063 0.200 0.078 0.094 0.054 0.082 0.053 0.156 0.052 0.188
ute l 08 15 Report: F219-05/08-F Hunt et al. 2008 Table 8. Continued.
Region Season Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Eucidaris tribuloides 0.000 0.000 0.000 0.000 0.047 0.286 0.075 0.084 0.063 0.063 0.094 Vasum muricatum 0.000 0.042 0.100 0.039 0.031 0.179 0.071 0.126 0.094 0.021 0.031 Portunus sebae 0.063 0.042 0.150 0.078 0.063 0.054 0.067 0.063 0.031 0.083 0.063 Strombus costatus 0.063 0.000 0.200 0.000 0.016 0.179 0.063 0.105 0.031 0.052 0.000 Callinectes spp 0.000 0.042 0.000 0.208 0.031 0.036 0.063 0.094 0.031 0.063 0.063 Strombus gigas 0.000 0.021 0.100 0.000 0.000 0.214 0.059 0.063 0.063 0.031 0.125 Callinectes sapidus 0.000 0.083 0.000 0.111 0.031 0.000 0.047 0.032 0.063 Lysmata wurdemanni 0.000 0.042 0.100 0.020 0.031 0.071 0.043 0.042 0.063 0.010 0.125 Cypraea spp. 0.000 0.000 0.100 0.098 0.031 0.018 0.039 0.021 0.094 0.021 0.094 Diadema antillarum 0.000 0.000 0.050 0.020 0.000 0.143 0.039 0.032 0.063 0.042 0.031 Pleuroploca gigantea 0.000 0.000 0.150 0.000 0.016 0.071 0.031 0.021 0.031 0.052 0.000 Strombus alatus 0.000 0.083 0.000 0.020 0.016 0.036 0.031 0.074 0.000 0.010 0.000 Octopus spp. 0.000 0.042 0.000 0.059 0.031 0.000 0.027 0.021 0.000 0.042 0.031 Oreaster reticulatus 0.000 0.000 0.000 0.039 0.031 0.054 0.027 0.042 0.031 0.010 0.031 Strombus raninus 0.000 0.000 0.000 0.000 0.000 0.107 0.024 0.032 0.094 0.000 0.000 Aplysia dactylomela 0.000 0.000 0.000 0.000 0.000 0.071 0.016 0.011 0.031 0.021 0.000 Melongenidae 0.000 0.000 0.000 0.000 0.016 0.018 0.008 0.011 0.000 0.010 0.000 All motile invertebrates 0.133 0.133 0.225 0.116 0.149 0.218 0.159 0.177 0.153 0.151 0.139 Species richness 0.500 0.737 0.632 0.816 0.868 0.921 1.000 1.000 0.914 0.973 0.806 Hunt et al. 2008 16 Report: F219-05/08-F
Fish.—One hundred and fifty one taxa were censussed during this study (Appendix C). Ocean side was the region with the highest species richness, with 91 different species. The region of the Basins was the region with the lowest species richness, with only 25 species. Fall had the highest species diversity with 107 species, while winter uncovered 57 species, the lowest count of species for all seasons. The ten most common fish taxa during this study were: H. plumierii (present in 41.2 % of the surveys), L. synagris (34.1 %), D. formosum (32.2 %), L. griseus (24.3 %), Eucinostomus spp. (23.5 %), Calamus spp. (22.4 %), L. rhomboides (22.0 %), C. glaucofraenum (21.6 %), Haemulon spp. (17.3 %), and Microgobius microlepis (15.7 %). All other taxa were, on average, present less than 15 % of the time. None of the environmental parameters collected could statistically explain the presence of Acanthostracion quadricornis . Season did not have a significant effect on the presence of Archosargus rhomboidalis (PE χ2 = 0.066, df = 3, P = 0.996), Calamus spp. (PE χ2 = 2.476, df = 3, P = 0.480), Chilomycterus schoepfii (PE χ2 = 1.384, df = 3, P = 0.709), C. glaucofraenum (PE χ2 = 4.480, df = 3, P = 0.214), D. formosum (PE χ2 = 2.773, df = 3, P = 0.428), H. sciurus (PE χ2 = 6.943, df = 3, P = 0.074), L. rhomboides (PE χ2 = 1.921, df = 3, P = 0.589), Malacoctenus macropus (PE χ2 = 1.216, df = 3, P = 0.749), M. microlepis (PE χ2 = 6.674, df = 3, P = 0.083), Pareques acuminatus (PE χ2 = 2.336, df = 3, P = 0.506), Stegastes leucostictus (PE χ2 = 5.421, df = 3, P = 0.143), and Urobatis jamaicensis (PE χ2 = 1.464, df = 3, P = 0.691). H. plumierii , Haemulon spp., L. griseus and L. synagris have the highest probability of presence in fall, whereas H. flavolineatum in winter, and Eucinostomus gula , Eucinostomus spp. and Sphoeroides spengleri in summer. Region did not explain the presence of A. rhomboidalis (PE χ2 = 4.826, df = 5, P = 0.438), H. flavolineatum (PE χ2 = 5.721, df = 3, P = 0.126), H. sciurus (PE χ2 = 6.895, df = 5, P = 0.229), O. chrysurus (PE χ2 = 4.622, df = 5, P = 0.464), or S. spengleri (PE χ2 = 7.746, df = 5; P = 0.171). Region also had no influence on the presence of Haemulon spp. smaller than 4 cm TL (PE χ2 = 8.548, df = 5, p = 0.129). Calamus spp. had the highest probability of presence in the Gulf, C. schoepfii and M. microlepis in the Inner bay, C. glaucofraenum and Eucinostomus spp. in the Outer bay, E. gula and L. rhomboides in the Channels, D. formosum , H. plumierii , L. griseus and L. synagris in the Basins; and H. bivittatus , M. macropus , P. acuminatus and S. leucostictus had the highest probability of presence in the Ocean side region (Appendix D). The probability of presence of O. chrysurus increased with increasing sessile invertebrate volume, whereas the one of M. microlepis decreased with increasing sessile volume (Appendix D). The probability of presence of A. virginicus , Calamus spp., and L. griseus increased with increasing sessile invertebrate surface. The probability of presence of C. schoepfii, H. plumierii , H. sciurus , H. bivittatus , M. macropus , P. acuminatus , S. leucostictus , and U. jamaicensis increased with increasing amount of octocoral surface, whereas the probability of D. formosum and Eucinostomus spp. decreased as octocoral surface increased. The probability of presence of S. spengleri increased with octocoral volume. The probability of presence of Calamus spp. increased with increasing sponge surface, whereas the one of A. rhomboidalis and L. rhomboides increased with increasing sponge volume. The probability of presence of C. glaucofraenum , D. formosum , H. flavolineatum and M. microlepis increased with increasing depth, whereas the one of Eucinostomus spp. and L. griseus decreased with increasing depth. The probability of presence of Calamus spp. decreased with increasing salinity. The probability of presence of H. bivittatus and O. chrysurus increased with temperature. The probability of presence of H. plumierii , H. sciurus and L. synagris increased with increasing percent cover of Laurencia spp, whereas the one of Calamus spp. decreased with Hunt et al. 2008 17 Report: F219-05/08-F
increasing Laurencia spp. percent cover. The probability of presence of A. rhomboidalis , H. plumierii, and H. sciurus increased with percent cover of T. testudinum, the one of Haemulon spp. with H. wrightii , and the one of Eucinostomus spp. with increasing total algae-seagrass percent cover. The probability of presence of A. virginicus increased with increasing percent cover of Plantae (algae-seagrass mix), whereas the one of C. glaucofraenum and L. rhomboides decreased with Plantae percent cover.
Percent Cover and Density Analysis
Algae-Seagrass.—Using the linear transect data, Laurencia spp. and T. testudinum were the most abundant algae and seagrass of the nearshore hardbottom of the Florida Keys, with 14.7 and 10.4 percent cover respectively (Table 9). When considering all algae and seagrass together, their highest percent cover was in summer and their lowest in winter. Nevertheless, season had no significant effect of the percent cover of Halimeda spp. (F (1, 115) = 1.288; P = 0.282), Laurencia spp. (F (3, 206) = 1.782; P = 0.152), H. wrightii (F (2,20) = 1.992; P = 0.163), T. testudinum (F (3, 207) = 0.303; P = 0.674), or S. filiforme (F (3, 21) = 0.691; P = 0.568). Table 9. Average percent cover per site and sampling period, of Plantae (other algae or seagrass, or mix of more than two taxa), Halimeda spp., Dasycladus vermicularis , Laurencia spp., Thalassia testudinum , Halodule wrightii , Syringodium filiforme , and all algae-seagrass for each and all seasons together, based on the linear transect surveys in the nearshore hardbottom of the Florida Keys from fall 2003 to spring 2007. All Halimeda Dasycladus Laurencia Thalassia Halodule Syringodium Seasons Plantae algae- spp. vermicularis spp. testudinum wrightii filiforme seagrass Fall 7.701 2.658 0.108 12.996 11.385 1.550 0.400 36.797 Winter 0.747 0.206 0.854 9.006 8.827 0.042 1.110 20.793 Spring 6.878 2.760 0.194 18.076 9.628 0.034 0.610 38.178 Summer 6.900 3.935 3.737 15.089 11.305 0.000 0.983 41.948 All seasons 6.440 2.558 1.226 14.693 10.399 0.597 0.639 36.552 Table 10. Average percent cover of Plantae (other algae or seagrass, or mix of more than two taxa), Halimeda spp., Dasycladus vermicularis , Laurencia spp., Thalassia testudinum , Halodule wrightii , Syringodium filiforme , and all algae-seagrass for each and all regions together, per site per sampling period, based on the linear transect surveys in the nearshore hardbottom of the Florida Keys from fall 2003 to spring 2007. All Halimeda Dasycladus Laurencia Thalassia Halodule Syringodium Regions Plantae algae- spp. vermicularis spp. testudinum wrightii filiforme seagrass Basins 3.010 3.220 0.046 11.501 8.152 1.612 0.000 27.541 Channels 6.359 1.588 0.208 17.926 10.335 0.712 2.472 39.598 Gulf 13.864 1.171 6.841 0.607 14.932 0.531 2.005 39.952 Inner bay 6.575 0.883 3.034 11.888 15.371 0.179 0.005 37.935 Outer bay 6.407 2.471 0.023 21.896 10.226 0.264 0.014 41.301 Ocean 4.693 5.332 0.300 12.130 5.106 1.014 0.045 28.619 All regions 6.440 2.558 1.246 14.693 10.399 0.597 0.639 36.572
According to the linear transect data, the Outer bay region has the most algae-seagrass cover (41.3 %), mostly due to a high percent cover of Laurencia spp (21.9 %) (Table 10). Laurencia spp. and T. testudinum are the most abundant algae and seagrass in the Basins, Channels, Inner, and Hunt et al. 2008 18 Report: F219-05/08-F
Outer bay region, representing percent covers of 19.7 %, 28.3 %, 27.3 % and 32.1 % respectively, whereas Halimeda spp. and Laurencia spp. are the most abundant algae of the Ocean side region, and Plantae and T. testudinum in the Gulf. Nevertheless, region had no significant effect on the percent cover of Halimeda spp. (F (5, 121) = 1.510; P = 0.192), nor did it on H. wrightii (F (5, 17) = 1.397; P = 0.275). Through modeling of the algae and seagrass percent cover obtained by the linear transect method, T. testudinum is expected to have the highest percent cover in the Gulf region, S. filiforme in the Channels, and Laurencia spp. in the Outer bay region (Appendix E). Furthermore, the percent cover of Halimeda spp. and H. wrightii increased with water temperature. The percent cover of T. testudinum increased with depth, whereas the ones of Laurencia spp. and H. wrightii decreased with increasing depth. The percent cover of Halimeda spp. also increased with the total sessile invertebrate surface, the one of H. wrightii increased with increasing octocoral volume, and the one of T. testudinum decreased with increasing sponge surface area. Finally, it seems that the percent cover of Halimeda spp. and Laurencia spp. increased through time, while the one of S. filiforme decreased (Appendix E, and Fig. 2).
Halimeda spp. Laurencia spp. Dasycladus vermicularis
0.30 ]
] ]
r 0.20 e
v ]
o c
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% ] ] ]
] 0.10 ]
] ] ] ] ]
] ] ] ] ] 0.00 Thalassia testudinum Halodule wrightii Syringodium filiforme
0.30
r 0.20
e
v o
] c
]
] % ] ] ] ] ] ] 0.10 ] ]
] ] ]
] ] ] ] ] ] ] 0.00 ] ] 1234 9 11 13 15 1234 9 11 13 15 1234 9 11 13 15 Collection period Collection period Collection period
Figure 2. Mean percent cover of Halimeda spp., Laurencia spp., Dasycladus vermicularis , Thalassia testudinum , Halodule wrightii and Syringodium filiforme through time. Error bars show mean percent cover ± 1 standard error. 1: fall 2003, 2: winter 2004, 3: spring 2004, 4: summer 2004, 9: fall 2005, 11: spring 2006, 13: fall 2006, 15: spring 2007. The Braun-Blanquet method confirmed that the most abundant algae were red algae drift, such as Laurencia spp. (17.9 percent cover), and Halimeda spp. (5.5 percent cover), while the most abundant seagrass was T. testudinum (4.6 percent cover) (Table 11). However, for each and all regions, the Braun-Blanquet method estimated the average percent cover of all algae-seagrass between 6.7 to 36.5 % higher than the linear transect method, with the exception of the Inner bay Hunt et al. 2008 19 Report: F219-05/08-F
Table 11. Average percent cover of Acetabularia spp., Batophora oerstedii , Caulerpa spp., Halimeda spp., Penicillus spp., Codiacea (other calcareous green algae), Chlorophyta (other green algae), Sargassum spp., Phaeophyta (other brown algae), Rhodophyta (drift) (e.g. Laurencia spp. and Gracilaria spp.), Rhodophyta (other red algae, not drift), Halodule wrightii , Ruppia maritima , Syringodium filiforme , Thalassia testudinum , Plantae (unknown), and all algae-seagrass for each and all regions together, per site per sampling period, based on the Braun-Blanquet survey in the nearshore hardbottom of the Florida Keys from spring 2006 to spring 2007. Taxa were ordered in decreasing average percent cover per site per sampling period for all regions considered to show the most abundant taxa. Region All Taxa Basins Channels Gulf Inner bay Outer bay Ocean regions Rhodophyta (drift) 25.190 25.711 0.538 10.549 24.701 13.585 17.883 Halimeda spp. 4.880 3.702 5.141 1.802 6.186 10.119 5.475 Thalassia testudinum 1.623 1.353 9.785 6.884 5.529 3.469 4.614 Penicillus spp. 3.465 3.578 0.678 1.867 4.023 1.259 2.619 Batophora oerstedii 0.184 2.423 7.811 2.340 0.620 1.904 2.037 Rhodophyta 0.308 3.459 2.528 0.520 2.462 1.777 1.944 Chlorophyta 0.048 0.281 11.097 0.328 2.376 0.733 1.575 Codiaceae 0.378 1.063 1.158 0.672 0.878 1.126 0.908 Phaeophyta 0.000 0.125 6.319 0.399 0.719 0.754 0.850 Caulerpa spp. 0.108 0.528 3.180 0.777 1.003 0.042 0.735 Acetabularia spp. 0.029 0.037 1.631 0.173 0.040 0.169 0.195 Sargassum spp. 0.000 0.009 2.781 0.018 0.000 0.004 0.180 Syringodium filiforme 0.000 0.000 1.864 0.013 0.041 0.000 0.129 Halodule wrightii 0.000 0.000 0.015 0.398 0.025 0.020 0.099 Plantae 0.000 0.000 0.000 0.011 0.000 0.000 0.002 Ruppia maritima 0.000 0.000 0.013 0.000 0.000 0.000 0.001 All algae-seagrass 36.214 42.270 54.539 26.749 48.602 34.960 39.247 where the Braun-Blanquet method gave an estimate of the average total algae-seagrass 29.5 % lower than the linear transect method (Table 10 and 11). In addition, the Braun-Blanquet method pointed to the Gulf region as the region with the most abundant algae-seagrass coverage instead of the Outer bay. When examining the average percent cover of each taxon per region, we noticed that neither method was systematically overestimating nor underestimating the average percent cover of an algae or seagrass. Agreement Analysis among Algae and Seagrass Data Collection Methods.—In an effort to determine if there were any directional tendencies among the two data collection methods, we looked at the agreement of all algae and seagrass data collected from spring 2006 to spring 2007.All PABAK values, with the exception of the ones reported for H. wrightii and S. filiforme , suggested poor agreements between the Braun-Blanquet and the linear intercept methods Table 12). The high PABAK values for H. wrightii and S. filiforme can be explained by the high number of empty cells in the ‘Braun-Blanquet * linear transect’ contingency tables. The absence of data points also explained the significant symmetry found in the data, and the non-significant results obtained using the McNemar test for each category, which would incorrectly suggest perfect concordance of those specific categories. Furthermore, the homogeneity analysis of the marginal data for every algae/seagrass considered revealed high chi-square values, using either Bhapkar analysis or Stuart-Maxwell. Finally, the data showed a significant overall bias in the methods with a tendency for the linear transect method to assign a higher category to the percent cover than the Braun-Blanquet method, with the exception of Halimeda spp.
ute l 08 20 Report: F219-05/08-F Hunt et al. 2008 Table 12. Agreement analysis between Braun-Blanquet and linear transect methods, using kappa ( K), Fleiss-Cohen weighted kappa ( Kc ), Bias-adjusted kappa (BAK), Prevalence-adjusted bias-adjusted kappa (PABAK), Likelihood ratio chi-square and rho of latent class analysis , McNemar test, and marginal homogeneity analysis through Bhapkar and Stuart-Maxwell chi-square tests. Values in parenthesis are the approximate standard error of the kappas. Statistical analysis Laurencia spp. Halodule wrightii Halimeda spp. Thalassia testudinum Syringodium filiforme Kappa: K 0.210 (0.015) -0.018 (0.100) 0.079 (0.014) 0.182 (0.019) 0.158 (0.100) Kc 0.602 (0.028) -0.021 (0.008) 0.260 (0.025) 0.414 (0.025) 0.261 (0.007) 95% CI for Kc 0.547 ; 0.657 -0.037 ; -0.005 0.212 ; 0.309 0.366 ; 0.463 0.246 ; 0.275 BAK 0.20 -0.02 -0.02 0.16 0.15 PABAK 0.25 0.93 0.14 0.36 0.95 Latent Class Analysis: 84.75; df = 29 3.10; df = 29 99.68; df = 29 48.29; df = 29 22.02; df = 29 Likelihood ratio chi-square P < 0.001 P = 0.9999 P < 0.001 P = 0.0137 P = 0.8194 rho 0.837 0.035 0.617 0.734 0.326 McNemar tests for each category: Chi-square for category 0 42.261; P = 0.0000* 4.393; P = 0.0263 633.322; P = 0.0000* 135.752; P = 0.0000* 23.290; P = 0.0000* Chi-square for category 0.5 146.174; P = 0.0000* 0.133; P = 0.7150 268.800; P = 0.0000* 63.439; P = 0.0000* 0.077; P = 0.7815 Chi-square for category1 40.129; P = 0.0000* 3.846; P = 0.0499 245.333; P = 0.0000* 7.243; P = 0.0071* 237.769; P = 0.0000* Chi-square for category2 0.871; P = 0.3508 0.286; P = 0.5930 0.958; P = 0.3278 102.820; P = 0.0000* exact test; P = 0.3750 Chi-square for category3 29.568; P = 0.0000* 24.000; P = 0.0000* 9.600; P = 0.0019* 129.605; P = 0.0000* exact test; P = 0.1250 Chi-square for category4 52.760; P = 0.0000* exact test; P = 0.1250 22.091; P = 0.0000* 10.646; P = 0.0000* exact test; P = 1.0000 Chi-square for category5 8.853; P = 0.0029* exact test; P = 1.0000 0.286; P = 0.5930 0.067; P = 0.7963 exact test; P = 1.0000 * p < Bonferroni-adjusted significance criterion of 0.008 Marginal homogeneity analysis: 355.144; df = 6 32.958; df = 5 1397.037; df = 6 513.021; df = 6 30.947; df = 4 Bhapkar chi-square P = 0.0000 P = 0.0000 P = 0.0000 P = 0.0000 P = 0.0000 288.450; df = 6 32.265; df = 5 731.613; df = 6 384.574; df = 6 30.335; df = 4 Stuart-Maxwell chi-square P = 0.0000 P = 0.0000 P = 0.0000 P = 0.0000 P = 0.0000 McNemar test of overall bias 311.831; df = 21 32265; df = 21 756.863; df = 21 400.634; df = 21 30.879; df = 21 Bowker symmetry test P = 0.0000 P = 0.0550 P = 0.0000 P = 0.0000 P = 0.0757 32.596; df = 1 4.939; df = 1 283.250; df = 1 267.471; df = 1 19.059; df = 1 Chi-square for overall bias P = 0.0000 P = 0.0263 P = 0.0000 P = 0.0000 P = 0.0000 BB = Braun-Blanquet higher LT LT BB LT LT LT = Linear transect higher Hunt et al. 2008 21 Report: F219-05/08-F
Motile invertebrates.—We recorded 29,465 motile invertebrates, among 38 taxa, from fall 2003 to spring 2007 in the nearshore hardbottom of the Florida Keys. The ten most abundant motile invertebrates in the nearshore hardbottom of the Florida Keys were Cerithiidae (38.7 individuals per 100 m 2 per surveys), Astraea spp. spp. (9.9 individuals per 100 m 2), Anomura (6.2 individuals per 100 m 2), C. rosaceus (2.6 individuals per 100 m 2), Mollusca (1.7 individuals per 100 m 2), L. variegatus (0.7 individuals per 100 m 2), P. argus (0.6 individuals per 100 m 2), E. lucunter (0.5 individuals per 100 m 2), Holothuroidea (0.3 individuals per 100 m 2), and M. spinosissimus (0.3 individuals per 100 m 2) (Table 13). All other motile invertebrates we surveyed accounted for about 4 % of all motile invertebrate densities per site per sampling period. The highest average density of motile invertebrates was in the Gulf region (573.6 individuals per 100 m 2), where we found, on average, 490.9 Cerithiidae per 100 m 2; the lowest average motile invertebrate density was in the Channels region, where we counted 10.6 individuals per 100 m 2. Summer was the season with the highest average motile invertebrate density (149.8 individuals per 100 m 2), while winter had the lowest (17.9 individuals per 100 m 2). None of the recorded parameters could explain the density distribution of F. tulipa , M. sculptus , P .pedersoni , P. pomum , P. sebae , and Stomatopoda. Season had no significant influence on the density distribution of Anomura (F (3, 173) = 1.269; P = 0.287), Astraea spp. (F (3, 114) = 0.661; P = 0.578), Cerithiidae (F (3,86) = 0.406; P = 0.749), E. lucunter (F (3, 26) = 0.047; P = 0.986), E. tribuloides (F (3, 15) = 0.156; P = 0.924), Holothuroidea (F (3, 69) = 2.053; P = 0.114), L. variegatus (F (3, 55) = 0.392; P= 0.759), M. spinosissimus (F (3,069) = 0.684; P = 0.565), Ophiuroidea (F (3, 49) = 0.652; P = 0.586), P. argus (F (3, 92) = 0.871; P = 0.459), S. costatus (F (2, 13) = 0.766; P = 0.485) or S. gigas (F (3, 11) = 0.920; P = 0.463). However, season had a significant effect on the distribution of V. muricatum , as its density was the highest in spring (Appendix F). Region had no significant effect on the density distribution of E. lucunter (F (3, 26) = 2.231; P = 0.108), L. variegatus (F (4, 54) = 1.945; P = 0.116), M. spinosissimus (F (5, 67) = 1.725; P = 0.141), S. costatus (F (3, 12) = 0.784; P = 0.526), or S. gigas (F (2, 12) = 0.997; P = 0.398). However, the density of Anomura and Astraea spp. was statistically higher in the Gulf than any other region, as was the density of Cerithiidae in the Inner bay (Appendix F). The density of E. tribuloides was the highest on the Ocean side, M. mercenaria in the Basins, Ophiuroidea in the Outer bay, and P. argus in the Channels region. The density distribution of motile invertebrate was also influenced by the amount of sessile invertebrates present. The density of M. spinosissimus increased with increasing amount of sessile invertebrate surface. The density of P. argus significantly increased with increasing sponge surface; whereas the densities of Anomura, Cerithiidae, E. lucunter, Holothuroidea, M. mercenaria , and Ophiuroidea increased with increasing octocoral volume. Furthermore, the densities of Anomura and L. variegatus decreased with increasing percent cover of T. testudinum , while the ones of M. mercenaria , M. spinosissimus , and Anomura decreased with increasing percent cover of Laurencia spp. The density of Ophiuroidea decreased with increasing total algae-seagrass percent cover, while the density of L. variegatus increased with increasing S. filiforme percent cover. The density of Astraea spp. increased with increasing percent cover of Halimeda spp. Finally, it is also interesting to point out that the density distributions of certain motile invertebrates of the nearshore hardbottom of the Florida Keys were influenced by environmental factors such as depth, temperature, and salinity. For instance, the density of Holothuroidea, S. costatus , and V. muricatum significantly decreased with increasing depth. The density of M. mercenaria significantly increased with temperature, while the one of S. gigas decreased. Lastly, the density of S. costatus also significantly decreased with increasing salinity.
ute l 08 22 Report: F219-05/08-F Hunt et al. 2008 Table 13. Density per 100 m 2 per site per sampling period of Anomura, Aplysia dactylomela , Asteroidea, Astraea spp., Callinectes sapidus , Callinectes spp., Cerithiidae, Clypeaster rosaceus, Crustacea (other crustaceans), Cypraea spp., Diadema antillarum , Echinaster spinulosus , Echinodermata (other echinoderms), Echinometra lucunter , Eucidaris tribuloides , Fasciolaria tulipa , Holothuroidea, Lysmata wurdemanni , Lytechinus variegatus , Melongenidae, Menippe mercenaria , Mithrax sculptus , M. spinosissimus , Mollusca (other mollusks), Octopus spp., Ophiuroidea, Oreaster reticulatus , Panulirus argus , Periclimenes pedersoni , Phyllonotus pomum , Pleuroploca gigantea , Portunus sebae , Stomatopoda, Strombus alatus , S. costatus , S. gigas , S. raninus , Vasum muricatum , and all motile invertebrates for each and all regions, and for each season, from fall 2003 to spring 2007. Taxa were ordered in decreasing densities for all regions together to show the most abundant taxa. Note: Cerithiidae and Astraea spp . were not surveyed as separate taxa in fall 2003, but were part of the taxa Mollusca. They were added as separate taxa in winter 2004. Echinometra lucunter was added to the list in winter 2004. Asteroidea was only surveyed in winter 2003. Echinaster spinulosus and Callinectes sapidus were only surveyed from fall 2005 and after. Callinectes spp. was only surveyed from fall 2003 to summer 2004.
Regions All Seasons Taxa Basins Channels Gulf Inner bay outer bay Ocean regions Fall Winter Spring Summer Cerithiidae 1.536 0.631 490.941 2.911 0.250 1.939 38.717 26.595 3.000 41.359 90.375 Astraea spp. 2.179 2.060 13.382 1.989 28.304 3.806 9.890 3.524 4.125 1.917 52.109 Anomura 2.219 2.271 58.775 1.716 0.898 2.098 6.206 11.400 2.281 4.146 0.891 Clypeaster rosaceus 0.000 1.115 0.125 0.029 0.047 10.768 2.602 3.032 2.250 2.516 1.938 Mollusca 0.531 1.146 2.275 3.010 1.063 1.679 1.665 3.679 0.672 0.453 0.313 Lytechinus variegatus 0.000 0.177 3.650 0.020 0.555 1.000 0.682 0.684 1.109 0.693 0.219 Panulirus argus 0.156 1.573 0.275 0.412 0.594 0.268 0.618 0.789 0.813 0.469 0.359 Echinometra lucunter 0.000 0.024 0.000 0.022 0.009 2.398 0.538 0.405 0.703 0.552 0.594 Holothuroidea 0.188 0.156 0.275 0.108 0.234 0.902 0.341 0.358 0.484 0.339 0.156 Mithrax spinosissimus 0.219 0.250 1.050 0.147 0.219 0.384 0.312 0.353 0.234 0.297 0.313 Mithrax sculptus 0.188 0.021 0.400 0.010 0.078 0.857 0.257 0.379 0.281 0.193 0.063 Ophiuroidea 0.063 0.073 0.000 0.255 0.367 0.438 0.257 0.179 0.422 0.307 0.172 Crustacea 0.313 0.292 0.400 0.088 0.109 0.464 0.253 0.316 0.203 0.234 0.172 Menippe mercenaria 0.406 0.313 0.625 0.147 0.133 0.143 0.227 0.358 0.063 0.182 0.141 Asteroidea 2.500 0.000 0.000 0.167 0.000 0.143 0.219 0.219 Phyllonotus pomum 0.031 0.031 0.100 0.059 0.430 0.205 0.180 0.126 0.109 0.031 0.859 Echinodermata 0.125 0.052 0.000 0.078 0.109 0.518 0.175 0.311 0.109 0.063 0.172 Stomatopoda 0.000 0.083 0.025 0.059 0.047 0.330 0.114 0.147 0.047 0.115 0.078 Strombus gigas 0.000 0.010 0.050 0.000 0.000 0.446 0.104 0.084 0.281 0.063 0.109 Eucidaris tribuloides 0.000 0.000 0.000 0.000 0.023 0.438 0.102 0.121 0.078 0.094 0.094 Echinaster spinulosus 0.313 0.063 0.000 0.148 0.141 0.000 0.098 0.071 0.125 Fasciolaria tulipa 0.063 0.052 0.175 0.127 0.086 0.080 0.092 0.105 0.109 0.078 0.078
ute l 08 23 Report: F219-05/08-F Hunt et al. 2008 Table 13. Continued.
All Taxa Regions regions Seasons Vasum muricatum 0.000 0.021 0.075 0.020 0.023 0.259 0.076 0.095 0.094 0.073 0.016 Strombus costatus 0.031 0.000 0.125 0.000 0.008 0.268 0.073 0.089 0.016 0.099 0.000 Periclimenes pedersoni 0.063 0.042 0.025 0.059 0.086 0.080 0.065 0.063 0.063 0.057 0.094 Diadema antillarum 0.000 0.000 0.300 0.010 0.000 0.134 0.055 0.021 0.063 0.099 0.016 Portunus sebae 0.063 0.021 0.250 0.039 0.039 0.027 0.051 0.037 0.016 0.047 0.141 Lysmata wurdemanni 0.000 0.021 0.175 0.010 0.031 0.080 0.045 0.047 0.031 0.010 0.156 Callinectes spp 0.000 0.021 0.000 0.146 0.016 0.036 0.043 0.063 0.016 0.047 0.047 Cypraea spp. 0.000 0.000 0.075 0.098 0.031 0.018 0.037 0.011 0.063 0.036 0.094 Callinectes sapidus 0.000 0.042 0.000 0.056 0.016 0.000 0.024 0.016 0.031 Strombus alatus 0.000 0.042 0.000 0.010 0.008 0.027 0.018 0.037 0.000 0.010 0.000 Oreaster reticulatus 0.000 0.000 0.000 0.029 0.016 0.027 0.016 0.021 0.031 0.005 0.016 Pleuroploca gigantea 0.000 0.000 0.075 0.000 0.008 0.036 0.016 0.011 0.016 0.026 0.000 Aplysia dactylomela 0.000 0.000 0.000 0.000 0.000 0.063 0.014 0.005 0.063 0.010 0.000 Octopus spp. 0.000 0.021 0.000 0.029 0.016 0.000 0.014 0.011 0.000 0.021 0.016 Strombus raninus 0.000 0.000 0.000 0.000 0.000 0.063 0.014 0.021 0.047 0.000 0.000 Melongenidae 0.000 0.000 0.000 0.000 0.008 0.027 0.008 0.005 0.000 0.016 0.000 All motile invertebrates 11.183 10.621 573.624 12.007 34.000 30.446 64.216 53.787 17.891 54.813 149.797
Hunt et al. 2008 24 Report: F219-05/08-F
Fish.—We counted, identified, and estimated the total length of 12,894 fish in the nearshore hardbottom of the Florida Keys from fall 2003 to spring 2007. The ten most abundant fish taxa were L. rhomboides (with an average density of 3.3 individuals per 100 m 2 per survey), L. griseus (3.1 individuals per 100 m 2), H. plumierii (2.8 individuals per 100 m 2), Eucinostomus spp. (2.4 individuals per 100 m 2), Menidia spp.(1.7 individuals per 100 m 2), Engraulidae (1.2 individuals per 100 m 2), Harengula spp. (0.9 individuals per 100 m 2), Haemulon spp. (0.8 individuals per 100 m 2), A. rhomboidalis (0.6 individuals per 100 m 2), and L. synagris (0.6 individuals per 100 m 2) (Appendix G). Those ten taxa represented, on average, 71.5 % of all fish density in the nearshore hardbottom habitat. The size distribution of all fish observed during this study was right-skewed (Figure 3). More than 85 % of the fish were less than 15 cm in TL. When reporting the relative size of each fish to its maximum possible size, we found that 51.8 % had reached up to only 20 % of their maximum size, and 79.0 % of fish up to 30 % of their maximum size. In addition, many of the fish that had reached larger relative size were often gobies. Therefore, a large majority of the fish we encountered were juveniles or subadults. About 29.5% of all the fish smaller than 20% of their maximum size were grunts, 18.5% snappers, 12.2% L. rhomboides , 7.7% Eucinostomus spp., 6.8% Harengula spp. and 4.5% Menidia spp. Those six taxa accounted for 79.2% of all the fish smaller than 20% of their maximum length. Those same taxa also accounted for 71.3% of all fish smaller than 30% of their maximum size: 20.7% were grunts, 16.6% snappers, 12.3% pinfish, 8.7% Menidia spp., 8.5% Eucinostomus spp., and 4.5% Harengula spp.
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Fig. 3. Size distribution (in cm) and relative size to their maximum size (%) of all fish surveyed on the nearshore hardbottom of the Florida Keys, USA, from fall 2003 to spring 2007. The Channels had on average the highest fish density per site and sampling period (32.9 fish per 100 m 2), having more than twice the fish density of the Outer bay, the region with the lowest average fish density of all regions (15.6 fish per 100 m 2) (Appendix G). The average fish density in summer (54.1 fish per 100 m 2) was more than four times the fish density in winter (12.5 fish per 100 m 2). None of the recorded environmental factors or densities of any invertebrate present could explain the density distribution of A. virginicus , C. schoepfii , E. gula , H. sciurus , L. griseus , M. macropus , O. chrysurus , P. acuminatus , S. spengleri , S. leucostictus , and U. jamaicensis . Season Hunt et al. 2008 25 Report: F219-05/08-F
had no significant effect in the density distribution of C. glaucofraenum : (F (3, 51) = 0.035; P = 0.991), D. formosum , (F (3, 78) = 1.616; P = 0.192), H. flavolineatum : (F (3, 12) = 1.750; P = 0.210), H. bivittatus (F (2, 23) = 0.599; P = 0.558), L. rhomboides (F (3, 52) = 0.424; P = 0.737), L. synagris (F (3, 83) = 1.688; P = 0.176), and M. microlepis (F (3, 36) = 0.984; P = 0.411). However, season had a significant effect on the density distribution of Haemulon spp., being significantly higher in fall than any other season (F (3, 125) = 2.707; P = 0.048). Furthermore, season had a significant effect on the density distribution of A. rhomboidalis , Eucinostomus spp, and H. plumierii , which have their highest density in summer, and on Calamus spp., which is most abundant in winter (Appendix H). Region had no effect on the density distribution of A. rhomboidalis (F (5, 17) = 1.086; P = 0.403), Calamus spp. (F (5, 50) = 0.817; P = 0.543), D. formosum (F (5, 76) = 1.331, P = 0.260), H. plumierii (F (5, 99) = 0.743; P = 0.593), H. bivittatus (F (4, 21) = 0.568; P = 0.689), and M. microlepis (F (4, 35) = 1.210; P = 0.324). However, C. glaucofraenum and L. synagris were the most abundant in the Basins, whereas Eucinostomus spp. were significantly more abundant in the Inner bay, H. flavolineatum and L. rhomboides in the Channels, and Haemulon spp. in the Gulf (Appendix H). The density of A. rhomboidalis , Calamus spp., Eucinostomus spp., and L. rhomboides decreased with increasing depth (Appendix H). The density of M. microlepis and L. synagris increased with temperature, but the density of the latter decreased with increasing salinity. Furthermore, the density of H. bivittatus increased with increasing percent cover of Laurencia spp., while the density of H. plumierii and Haemulon spp. increased with increasing H. wrightii percent cover, the one of Eucinostomus spp. with increasing T. testudinum percent cover, and the density of H. plumierii with Plantae percent cover. By opposition, the density of C. glaucofraenum decreased with increasing percent cover of Plantae. Finally, the density of H. plumierii and Haemulon spp. increased with total sessile invertebrate surface area, while the one of L. synagris decreased with increasing octocoral surface. The density of D. formosum seemed to increase with increasing density of M. sculptus .
Discriminant analysis
Discriminant analysis provides a means to interpret which taxa best characterize the nearshore hardbottom community with respect to region (e.g.; Oceanside, Florida Bay, Lower Keys Channels). Overall, we found the Oceanside hardbottom community to be the most unique, followed by the Channels and Inner bay region. The Outer bay region was the least unique. Algae-Seagrass and Region.—The first two functions in this discriminant analysis explained 85% of the variance (64% function 1; 21% function 2). Function 1 best differentiated the Inner bay and Channels regions with the taxa ‘red algae’ and Codiaceae loading positively and H. wrightii loading negatively (Figure 4). Function 2 best differentiated the Oceanside region from all other regions with Caulerpa spp., Penicillus spp., and Sargassum spp. loading positively and Halimeda spp. loading negatively. Sessile Invertebrates and Region.—The first two functions explained 89% of the variance (57% function 1; 32% function 2). Function 1 best differentiated the Oceanside from all other regions using all octocoral taxa except sea plumes ( Pterogorgia spp.). Function 2 best differentiated the Inner bay from all other regions (Figure 5) with I. campana , Spongia cheris loading positively, and Solenastrea hyades and Pterogorgia spp. loading negatively. Motile Invertebrates/Fish and Region.—The first two functions explained 92% of the variance (73% function 1; 19% function 2). Function 1 best differentiated the Oceanside from the Channels region where E. lucunter , Halichoeres bivittatus , C. rosaceus , C. glaucofraenum , and L. variegatus loaded positively (Figure 6). Function 2 best differentiated the Channels and Oceanside regions Hunt et al. 2008 26 Report: F219-05/08-F
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Function 1 (+Sea rods and Sea whips) Fig. 5. Scatterplot of the discriminant scores of the first two functions using sessile fauna abundance data. Grouping variable was regions and 95% confidence ellipses are plotted for each region. The sea rod taxa included in Function 1 are Eunicea spp., Pseudoplexaura spp., Briareum asbestinum , Plexauridae, and Muricea spp. The sea whip taxa included in Function 1 are Pterogoriga anceps , Pterogorgia citrina , and Pterogorgia spp. Hunt et al. 2008 27 Report: F219-05/08-F from the Inner and Outer bay where L. rhomboides , P. argus , L. griseus , A. virginicus , Calamus spp., H. plumierii, and M. spinosissimus loaded positively. The Inner and Outer bay regions are indistinguishable using abundance of motile fauna.
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+ ( -4 -4 -2 0 2 4 6 Function 1 (+ E. lucunter, H. bivittatus, C. rosaceus, C. glaucofraenum, L. variegatus ) Fig. 6. Scatterplot of the discriminant scores of the first two functions using motile fauna (invertebrates and fish) data. Grouping variable was regions and 95% confidence ellipses are plotted for each region.
Non-Linear Canonical Correlation Analysis
Two separate non-linear canonical correlation analyses were run, one for sessile invertebrates and motile invertebrates, the second for sessile invertebrates and fish. In both analyses, the sessile fauna separated in a similar fashion with octocorals primarily defining the first axis and sponges defining the second axis. The pattern on the first two axes formed by the motile invertebrates was one of a continuum running between the sponges and octocorals (Figure 7). Nearest to the sponges were all the crustacean taxa with P. argus closest to the sponges. Occupying the middle were the gastropods. The echinoderms were generally closest to the octocorals, although, L.variegatus was relatively weakly associated with the octocorals. The pattern of the distribution of the fish on the first two axes (Figure 8) was more difficult to interpret. H. bivittatus was clearly found among the octocorals, whereas H. plumierii and A. virginicus occupied an intermediate position. L. rhomboides , Calamus spp., and L. synagris are associated with sponges.
Principal Factor Extraction
Taxa from the initial model (see Methods for taxa included in the initial model) were removed stepwise by examining anti-image scores until the Kaiser-Meyer-Olkin test exceeded 0.6. Four factors were extracted from 19 taxa (Table 14). We used the results from the logistic and Hunt et al. 2008 28 Report: F219-05/08-F
.8 S. vesparia S. cheris Motile fauna I. campana .6 S. graminea Crustacea Gastropoda
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Dimension 1 Figure 7. Scatterplot of the first two non-linear canonical correlation dimension axes for the surface area estimates of the top four sponge taxa and top four octocoral taxa with the invertebrate taxa Clypeaster rosaceus , Echinometra lucunter , Cerithiidae, Lytechinus variegatus , Astraea spp., Mithrax spinosissimus , Menippe mercenaria , and Panulirus argus . 0.4 C. glaucofraenum Motile fauna 0.2 Pseudopterogorgia spp. D. formosum Fishes (total) Pseudoplexura spp. 0.0
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s -0.2 n Sessile fauna A. virginicus e P. citrina m i Calamus spp. P. anceps Sponges D L. rhomboides -0.4 Sea Whips S. graminea -0.6 Sea Rods S. cheris S. vesparia I. campana -0.8 Sea Plumes -0.4 -0.2 0.0 .02 0.4 0.6 0.8
Dimension 1 Figure 8. Scatterplot of the first two non-linear canonical correlation dimension axes for the surface area estimates of the top four sponge taxa and top four octocoral taxa with the fish taxa Lagodon rhomboides , Lutjanus synagris , Calamus spp, Diplectrum formosum , Haemulon plumierii , Anisotremus virginicus , Coryphopterus glaucofraenum , and Halichoeres bivittatus. Hunt et al. 2008 29 Report: F219-05/08-F
Table 14. Results from principal factor extraction of selected fish and motile invertebrate taxa. Membership of taxa with factors 1 through 4 indicated by bold factor loading scores. Taxa without a bold loading factor either were cross-loaded into two or more factors or did not have any significant loading factors (i.e. loading factor did not exceed 0.3). Rotated Component Matrix Taxa Component (ln+1 transformed) 1 2 3 4 Cerithiidae 0.793 0.056 0.032 -0.100 Anomura. 0.767 -0.072 0.117 -0.082 Astraea spp. 0.571 -0.036 0.194 0.233 Menippe mercenaria 0.516 0.211 -0.286 -0.134 Mithrax spinosissimus 0.467 0.244 0.029 0.112 Haemulon plumierii 0.189 0.723 0.043 -0.007 Ocyurus chrysurus 0.026 0.658 0.234 0.090 Lutjanus synagris -0.015 0.628 -0.151 -0.180 Lutjanus griseus 0.188 0.462 -0.058 0.214 Haemulon sciurus -0.159 0.391 -0.050 0.196 Halichoeres bivittatus -0.038 0.382 0.771 -0.154 Echinometra lucunter 0.055 0.061 0.758 -0.068 Clypeaster rosaceus 0.024 -0.100 0.530 -0.241 Strombus costatus 0.020 -0.010 0.359 0.073 Calamus spp. 0.077 0.045 -0.060 0.770 Lagodon rhomboides -0.193 0.112 -0.245 0.596 Lutjanus analis 0.137 0.039 0.047 0.419 Coryphopterus glaucofraenum -0.005 -0.015 -0.025 -0.362 Anisotremus virginicus 0.526 0.358 0.119 -0.047 Epinephelus morio 0.453 -0.068 0.062 0.323 Lytechinus varigatus 0.325 -0.207 0.528 0.260 Fasciola tulipa 0.292 -0.180 -0.067 0.195 Extraction Method: Principal Factor Extraction. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 5 iterations. multiple linear regression analyses to assist the interpretation of these factors. Component 1: Anomura spp., Astraea spp., Cerithiidae, M. mercenaria , and M. spinosissimus loaded positively into Component 1. We interpreted this first factor as a regionally Gulf oriented invertebrate (Mollusca and Decapoda) group that positively responded to structure especially octocoral structure (i.e.; abundance of this group increased with increasing sessile structure). Component 2: H. plumierii , H. sciurus , L. griseus , L. synagris , and O. chrysurus loaded positively into Component 2. Component 2 is a grunt and snapper group without any strong regional orientation, but with a positive general response to any type of structure. Component 3: H. bivittatus , E. lucunter , C. rosaceus , and S. costatus loaded positively into Component 3. Component 3 is regionally oriented toward the Oceanside. The invertebrates are grazers while the fish ( H. bivittatus ) preys upon these grazers (Wainwright 1988). Component 4: Calamus spp., L. rhomboides , and L. analis loaded positively into Component 4, whereas C. glaucofraenum loaded negatively. Compoennt 4 is comprised of fish taxa that show a weak positive response to sponge structure and negative toward octocoral structure.
Hunt et al. 2008 30 Report: F219-05/08-F
DISCUSSION
Sullivan et al. (1995) defined the nearshore hardbottom community of the Florida Keys as close to shore, less than 4 m deep, with relief less than 0.5 m, and sitting on limestone bedrock. While two primary objectives of this study were to (1) establish a baseline, and (2) implement a monitoring program, we also hoped to characterize the nearshore hardbottom of the Florida Keys using algae-seagrass, sessile and motile invertebrates, and fish. Several environmental and biological factors influenced the presence and density distribution of all these living components of the nearshore hardbottom communities, and they are the key structures that make the nearshore hardbottom of the Florida Keys a unique community. Region, season, and depth were the factors that were the most useful in assessing the probability of presence of algae and seagrass (Table 15). In the case of the motile invertebrates, region was the most important factor to assess their probability of presence as it was a significant factor in the analysis of more than 83% of the taxa selected. Sessile invertebrates as a group (e.i. surface or volume of overall sessile, sponge, or octocoral) were a significant factor for almost 78% of the taxa in estimating the probability of their presence. Fish were mostly responding to region (70%), sessile invertebrates (83% - especially octocoral surface: 43%), and percent cover of Laurencia spp. and T. testudinum (52% combined). When considering the community as a whole, if one wants to assess the species community presence, one must record the region, algae/seagrass percent cover, season, sessile invertebrate surface or volume (with a preference in the case of fish for octocoral surface), and depth. On the other hand, when considering the density of nearshore hardbottom community, region (used 52% of the time), sessile invertebrate (45%, with 23% being octocoral volume), percent cover of algae and seagrass in general (48%), and depth (32%) were the most useful in analysis. There may be a fundamental difference in the abundance and distribution patterns of motile and sessile fauna in nearshore hardbottom habitat as revealed by discriminant analyses. Motile fauna abundance and distribution best discriminated between Oceanside and Channel regions. We also found that the discriminant analysis separated Oceanside and Channels when invertebrates and fish were analyzed separately. Sessile fauna discriminated between the Inner bay and Oceanside regions. Algae discriminated in a pattern that could be interpreted as intermediate between sessile and motile fauna. The algal pattern showed differences among Channels, Oceanside, and Inner bay with outer bay, as always, occupying the center. We do not have a working hypothesis for this pattern, but we will speculate that perhaps the pattern is due to factors that involve differences in dispersal (active or passive) of propagules, subsequent juvenile or adult mobility, and longevity. Perhaps the most unexpected result from the discriminant analysis was that the region defined as the Outer bay, once called “the sluiceway” by Schomer and Drew (1982), was generally indistinguishable from the other regions whether we tried to characterize it by flora or fauna, motile or sessile. The outer bay was also perhaps, not coincidentally, geographically in the middle of all the other regions. A similar result was found with an earlier survey conducted in 1994 (Bertelsen et al. in review). When we compared both methods of measurement of algae and seagrass percent cover, we found that except for Halimeda spp., the linear transect had a tendency to provide an estimate larger than the Braun-Blanquet method. It is important to note that our divers reported that the linear transect method is more subjective than Braun-Blanquet. Indeed, we had extensive discussion on the subject of algae or seagrass continuity. The linear transect method calls for any continuous patch larger than 20 cm long, but on site, several divers had difficulties in assessing if a patch was truly continuous, especially when the seagrass or algae were semi-sparse. As a result, one might have recorded a patch of relatively sparse algae whereas another diver would not.
ute l 08 31 Report: F219-05/08-F Hunt et al. 2008
Table 15. Number of times a factor was determinant in the estimation of probability of presence of taxa and in the density distribution analysis or the algae-seagrass, motile invertebrate and fish. Numbers in italic : total number of times any sessile invertebrate factors and total number of any algae or seagrass related factors had a significant effect in either the presence-absence analysis or density analysis. Presence-Absence Analysis Density analysis Factors Algae/ Motile Algae/ Motile Overall Fish Total Fish Total seagrass invertebrates seagrass invertebrates Region 3 15 16 34 3 7 6 16 50 Season 3 5 8 16 0 1 4 5 21 Depth 2 3 6 11 3 3 4 10 21 Temperature 0 0 2 2 2 2 2 6 8 Collection period 1 1 0 2 3 0 0 3 5 Salinity 0 1 1 2 0 1 1 2 4 Sessile invertebrates: 2 14 19 35 3 8 3 14 49 All sessile invertebrate surface 1 4 3 8 1 1 2 4 12 Octocoral surface 0 1 10 11 0 0 1 1 12 Octocoral volume 0 1 1 2 1 6 0 7 9 All sessile invertebrate volume 0 4 2 6 0 0 0 0 6 Sponge surface 0 3 1 4 1 1 0 2 6 Sponge volume 1 1 2 4 0 0 0 0 4 Algae and Seagrass: 5 12 17 9 6 15 32 Laurencia spp. 3 4 7 3 1 4 11 Thalassia testudinum 2 3 5 2 1 3 8 Plantae 0 3 3 1 2 3 6 Halodule wrightii 0 1 1 0 2 2 3 Total algae-seagrass percent cover 0 1 1 1 0 1 2 Halimeda spp. 0 0 0 1 0 1 1 Syringodium filiforme 0 0 0 1 0 1 1 Dasycladus vermicularis 0 0 0 0 0 0 0 Motile invertebrates 8 8 1 8 16 Number of taxa studied 5 18 23 46 5 14 12 31 77 Tellier et al. 2008 32 FWRI File Code: F219-05-08-F
Furthermore, even if the linear transect method was providing a feeling of accuracy because the diver could readily read length on a tape, this method was not assessing the sparseness of the algae, nor its actual distribution. Two patches 30 cm long on the linear transect could be either 5 cm wide or 2 m wide, and would be reported in the transect data the same way. Since no algae patch smaller than 20 cm long on the linear transect was recorded, several smaller patches could potentially have been intentionally missed, and thus reduced the estimation of the algae or seagrass percent cover. The quadrat method of Braun-Blanquet and the linear transect method use the same amount of time. For this reason, we highly recommend using a Braun-Blanquet method to assess the percent cover of algae and seagrass, as it provides more details, is less subjective, and offers a more accurate estimate of actual percent cover than the linear transect. Nevertheless, to fully validate the comparison of methods, we should also test them in areas with higher seagrass densities, as in this study, we were limited in areas with little seagrass. Laurencia spp. was the most common and the most abundant algae of the nearshore hardbottom, especially in the Outer bay and Channel regions. As a drift macroalgae, the presence of Laurencia spp. increases the complexity of the nearshore hardbottom habitat, therefore attracting and providing shelter to fish and invertebrates, especially in their early life-history stages (Kingsford and Choat 1985, Kingsford 1995). Laurencia spp. not only provides refuge against predation to young lobsters, P. argus , and fish, such as Nassau grouper, Epinephelus striatus , but also supports the fauna they prey upon, such as small mollusks and arthropods (Andree 1981; Marx and Herrnkind 1985 a, b; Dahlgren and Eggleston 2001). We observed a significant seasonal increase in the presence of Laurencia spp. in spring and summer, but unlike Sullivan et al. (1995), who reported a significant increase in percent cover during spring, we did not encountered any seasonality in Laurencia spp. percent cover. The diminished presence of Laurencia spp. in winter might be the result of the movement of the algae by winds and currents, usually stronger during this season. Zieman et al. (1989) observed high abundance of Laurencia spp. at low-energy sites and almost absence from high-energy areas of the Florida Bay. Halimeda spp., as a calcareous green alga, plays a significant role as primary producer, sediment producer, and sediment stabilizers (Drew 1983, Collado-Vides et al. 2005). Collado- Vides et al. (2005) described the same seasonal pattern in its abundance, as we noticed in its presence (i.e. highest in summer). We also noted that its abundance significantly increased with water temperature. It has been demonstrated that Halimeda spp. ceases to grow when the temperature drops under 20ºC, but above this temperature, its growth increases with increasing temperature (Wefer 1980). Unfavorable conditions for Halimeda spp., in terms of temperature, are usually met in the nearshore water of the Florida Keys from December to March (according to the data from NOAA’s National Data Buoy Center, www.ndbc.noaa.gov ), but we only encountered water temperatures less 20ºC 8.9% of the time. Thus, we conclude that the nearshore hardbottom of the Florida Keys is a favorable environment for the growth and development of Halimeda spp. The distribution of H. wrightii in the nearshore hardbottom of the Florida Keys is apparently driven by depth and temperature, as its density is higher in shallower sites with higher temperatures. A previous study in Indian River Lagoon, Florida, also found that H. wrightii dominated shallow water in comparison to other seagrasses (Virnstein and Carbonara 1985). During our study, we recorded temperatures averaging 30ºC in fall and summer, and reaching up to 39ºC in some areas. When comparing the temperature tolerance of H. wrightii to other seagrasses, H. wrightii demonstrated the highest heat tolerance of all, surviving beyond 48 hrs after exposure to 39ºC (McMillan 1984). This faculty to withstand heat would favor its presence at shallower sites over less heat tolerant seagrasses, such as T. testudinum , the most abundant seagrass of the Florida Keys. Tellier et al. 2008 33 FWRI File Code: F219-05-08-F
From fall 2003 to spring 2007, we observed a significant increase in the presence of Halimeda spp., and an increase in percent cover of both Halimeda spp. and Laurencia spp. Collado-Vides et al. (2005) observed a similar increasing, long-term trend in Halimeda spp. percent cover in the Florida Keys National Marine Sanctuary between 1996 and 2002. Although Halimeda spp. and Laurencia spp. are common and abundant in most regions of the Florida Keys, an increase in their presence or abundance could be the result of a degradation of the ecosystem due to either anthropogenic nutrient input or a die-off of herbivores that maintained the ecosystem in equilibrium (Shulman and Robertson 1996). Collado-Vides et al. (2005) found that the increase in abundance of Halimeda spp. was a function of the distance from land. During 1996, Lapointe et al. (2004) observed a gradient in nitrogen content in Laurencia spp. in the Lower Keys that ranged from values typical of algae growing on sewage nitrogen close to shore to lower values out on patch reefs. They attributed the eutrophication to agricultural runoff from the Everglades watershed and local sewage discharges. Nutrient enrichment is believed to result in an increase of macroalgae, which cause the decline of seagrass, such as T. testudinum (Lapointe et al 1994). The presence of macroalgae, especially in thick mats or clumps, would significantly attenuate the light received by seagrass, reduce the amount of dissolved oxygen in the water, and, as a result, decrease the seagrass’ photosynthetic capabilities. Holmquist (1977) demonstrated that after 6 months of drifting algal cover, the density of T. testudinum and S. filiforme dropped to 12 and 4% of their original value respectively. Laurencia spp., and to a certain extent all macroalgae, provides habitat complexity and has a beneficial effect on the nearshore hardbottom community. However, a long-term increase in macroalgae would have dramatic consequences not only on the nearshore hardbottom community, but also on its surroundings. The increase of macroalgae could result in a die-off of seagrass in and around the nearshore hardbottom region, but it could also impair the growth and survival of its coral. Eutrophication and the presence of macroalgae have shown to reduce coral growth and limit its recruitment (Tomascik 1991, Wittenberg and Hunte 1992, Miller and Hay 1996). Corals provide additional shelter and food for fish in the nearshore hardbottom community, and their disappearance would have unmistakable negative consequences. The increase in Laurencia spp. abundance might also accentuate any effect that grazers would have on the abundance of seagrass. For instance, under normal, moderate density, L. variegatus has very little negative effect on the abundance of T. testudinum if Laurencia spp. is not present; however, if Laurencia spp. is present, the damage caused by the grazing from L. variegatus and the shading from Laurencia spp. spp. would have a “synergistic effect” and reduce the seagrass shoot density” (Maciá 2000). Furthermore, if a continuous grazing event from L. variegatus occurs in winter, T. testudinum will not be able to recover due to its natural winter low productivity (Heck and Valentine 1995), and the nearshore hardbottom community could suffer a permanent loss of T. testudinum and all the organisms it normally supports. On a similar note, the decrease in abundance of S. filiforme might be the result of overgrazing by a grazer such as L. variegatus . In a previous study, Sharp and Hunt (2003) reported the complete defoliation of approximately 9 km 2 of manatee grass by a dense aggregation of variegated urchins in the Outer Bay. The results from our principal factor extraction analysis resembled a principal component analysis performed with a earlier dataset (Tellier and Bertelsen 2005) in that three taxa (Cerithiidae, Anomura and Astraea spp.) are found together in Component 1 (the analysis in this report adds M. mercenaria and M. spinosissimus to Component 1). The members of this first factor now appear to have significant ecological relationships. In addition, the logistic analysis suggests that the amount of octocoral structure is an important predictive element for these taxa. Anomura, Cerithiidae, and Astraea spp. responded to the same environmental factors. They preferred for the Gulf region and high sessile invertebrate volume, especially octocoral. It is Tellier et al. 2008 34 FWRI File Code: F219-05-08-F interesting to note that Cerithiidae and Astraea spp. are the most common and abundant in the region where some of their predators are least abundant. For instance, we found that P. argus were less abundant in the Gulf region, where Cerithiidae and Astraea spp. are the most present. Therefore, the distribution of these gastropods might be driven in part by predation, while the distribution of Anomura seemed to be driven by commensalisms with those two gastropod groups to a certain extent. Most Anomura observed in the nearshore hardbottom of the Florida Keys occupied empty shells of Cerithiidae or Astraea spp. (personal observation), supporting the well- known close relationship between the hermit crab and gastropod shells (Hazlett 1981). The portable home of the hermit crab provides his new owner protection against predation and physical stress, and increases its survival. Studies revealed that in addition to shell availability, r internal shell volume and weight are determinant factors in choosing the ‘right’ shell (Bertness 1981, Hazlett 1981, Wilber 1990, Wada et al. 1997, Garcia and Mantelatoo 2001). Cerithium spp. are light shells (compared to Anachis spp. for instance), costing less energy to carry around while foraging for detritus, amphipods, or isopods, and provide excellent resistance to thermal stress (Bertness 1981). The gulf region has a relatively high density of T. testudinum , providing an ideal source of epiphytes (Turnberg et al. 1994), prime diet for the hermit crabs, The Gulf region also hosted one of the major predators of Anomura, M. mercenaria . Even though Anomura are known to choose shells colonized with hydroids as a deterrent to predation (Grant and Pontier 1973), it is a prime element in the stone crab’s diet. In a previous experimental study conducted by Brooks and Mariscal (1985), Anomura survived an average of seven days longer when in a hydroid-colonized shell than in a bare shell while in the presence of M. mercenaria . Despite this protection against M. mercenaria , Anomura is still vulnerable along with the presence and high abundance of Astraea spp. and other gastropods, these prey items cause M. mercenaria to be highly present in this region. Furthermore, the Gulf region had an average density of 100 sponges per 100 m 2; sponges offer shelter to stone crabs. Structural complexity and the availability of refuge are not only critical against predation, but also drive the density of a population and the growth of its members. The refuge limitation not only creates a demographic bottleneck in the stone crab population, but it also slows the growth of large specimens (Beck 1995). Shelter can also affect population fecundity as they attract gravid females and seem to promote egg mass production (Beck 1995). According to our data, the presence of M. mercenaria is also driven by season and its abundance by temperature. We found through our regression analyses that its abundance significantly increases with increasing temperature, and that the presence of stone crab in the Gulf significantly diminishes in winter. Wilber and Herrnkind (1986) described an increase in movement when water temperature was dropped from 27ºC to 20ºC, and therefore, we think that M. mercenaria probably migrates to deeper regions of the Gulf in winter where the water is not as affected by the drop in air temperature. M. sculptus and M. spinosissimus are also part of the Gulf motile invertebrate community and have been described as herbivores (Wilber and Wilber 1989, Stachowicz et Hay 1996). According to our Braun-Blanquet data, the Gulf region is not only the region with the most abundant overall algae-seagrass percent cover, but it is also the region with the most T. testudinum , green, and brown algae. So, not only does this region have probably the highest concentration of epiphytes, but it also has a high concentration of some of the macroalgae comprising these two species’ diet: Dictyota spp., Padina spp., and Halimeda spp. (Wilber and Wilber 1989, Stachowicz and Hay 1996). Furthermore, these crabs have repetitively been observed sheltering in sponges. Since M. sculptus and M. spinosissimus occur in the same region as stone crabs, they are in direct competition with M. mercenaria for shelter. Therefore, the limitation in sponge density might aggravate the bottleneck effect and growth constraints on these three crab species (Wilber and Wilber 1989). In the case of M. spinosissimus , a limitation of shelter might even originate Tellier et al. 2008 35 FWRI File Code: F219-05-08-F conspecific aggression resulting in high mortality, as it would incite the spider crabs to kill soft post-molt individuals by puncturing their ventral carapace near the base of their mouth (Wilber and Wilbert 1991). Finally, another motile invertebrate seemed to have specifically chosen the Gulf region as its favorite region, as it is the most common in the Gulf: the variegated urchin, L. variegatus . This can be explained by the high presence and abundance of S. filiforme and T. testudinum in this region. S. filiforme and T. testudinum are the primary food of L. variegatus , in addition to epiphytes and detritus (Beddingfield and McClintock 1998, Sharp and Hunt 2003). Panulirus argus was found to be most common and the most abundant in the Channels region and to have a significant positive relationship with sponge volume and sponge surface. We found that the Channel region has not only the largest sponge volume and sponge surface area of all regions, but as we previously mentioned, this region also has the highest percent cover of Laurencia spp., according to the Braun-Blanquet data, or one of the highest percent cover if we are referring to the linear transect data. These two properties of the Channel region are highly correlated to the development and ecology of P. argus . Laurencia spp. not only provides shelter and protection against predation by camouflage thanks to its structural complexity, but it also provide forage for the light-colored, newly settled, postlarvae and juvenile spiny lobsters (Marx and Herrnkind 1985 a, b; Herrnkind and Butler 1986; Marx 1986; Herrnkind et al. 1988). After approximately 3 to 5 months, when the juvenile spiny lobsters reach a size that does not allow them to stay inconspicuous any longer, they move to shelters under sponges (Andree 1981). At this point, they become gregarious, and one can easily encounter several lobsters under one sponge. P. argus is dependent on the number and size of available sponge shelter for its survival and would suffer from the same demographic bottleneck and growth limitation as the one previsouly mentioned for M. mercenaria (Childress and Herrnkind 1994). C. rosaceus and E. lucunter occupied primarily Oceanside hardbottom, which is also a habitat that supports octocorals. E. lucunter favors bare rocky substrate typically feeding on drift algae (McPherson 1969) whereas C. rosaceus favors coarse sands near Thalassia beds (Poddubiuk 1985). C. rosaceus is unusual from other Clypeasters in that it does not bury into sand (Poddubiuk 1985), and therefore, we sometimes found C. rosaceus and E. lucunter in the same Oceanside sites where thin sands were adjacent to bare substrate. H. bivittatus was strongly associated with octocorals in our non-linear canonical correlation analysis, as were C. rosaceus and E. lucunter . This association agrees with our factor analysis where grazing invertebrates such as C. rosaceus and E. lucunter were placed in the same factor with H. bivittatus . C. rosaceus and E. lucunter are prey items of H. bivittatus (Wainwright, 1988). We reported that more than 85 % of the fish were less than 15 cm in TL. When reporting the relative size of each fish to its maximum possible size, we found that 51.8 % of the fish had reached up to only 20 % of their maximum size, and 79.0 % up to 30 % of their maximum size (Fig. 3). In previous studies of the nearshore hardbottom in other regions of Florida, such as Broward county, newly settled and early juveniles were also found to constitute more than 80% of the fish community (Linderman and Snyder 1999, Baron et al. 2004). In these studies, a large majority of the fish were grunts, whereas in the nearshore hardbottom of the Florida Keys, only 20% were newly settled and juvenile grunts. It seems that in the Keys, the nearshore hardbottom habitat not only serves as nursery and juvenile habitat for grunts, but also for a multitude of other fish species and invertebrates (e.g. the Florida spiny lobster). The nearshore hardbottom is not an obligate nursery habitat for these juveniles. Grunts, snappers, and pinfish are also known to utilize seagrass beds and/or mangrove as nursery habitat (Sogard et al 1989 a,b; Nagelkerken et al. 2000 b, 2001; Spitzer et al 2000; Rydene and Matheson 2003; Verweij et al 2006; Acosta et al 2007; Kopp et al 2007). We found (1) a strong positive Tellier et al. 2008 36 FWRI File Code: F219-05-08-F relationship between the percent cover of T. testudinum and Laurencia spp., and the presence or density of these fish (e.g. H. plumierii , H. sciurus , and L. synagris ), or (2) a strong relationship between a region of the nearshore hardbottom identified as having high percent cover of Laurencia spp. (e.g. Channels or Basins) and the presence or density distribution of those species (e.g. L. rhomboides , and L. griseus ). As we previously mentioned, the T. testudinum and Laurencia spp. not only provide shelter against predation through their structural complexity, but also support an abundant source of food (Stoner 1982, Spitzer et al 2000). Rydene and Matheson (2003) also pointed out the importance of Laurencia spp. as essential habitat for L. rhomboides in Tampa Bay, especially during winter. Several studies examined the fish community in seagrass beds and mangroves (Parrish 1989, Sheridan 1992, Kopp et al 2007), some studied the relationships between the mangrove, seagrass beds and the coral reefs (Nagelkerken et al 2000 b, Cocheret de la Morinière et al. 2002, Aguilar-Perera and Appeldoorn 2008), but little is know about the association between the seagrass beds and the nearshore hardbottom (Nagelkerken et al 2000 b, Parrish 1989), especially in the Florida Keys where the hardbottom habitat extend both Oceanside and Bayside. In our principal factor extraction analysis, Components 2 and 4 are entirely composed of fishes and do not show any clear regional orientation. In addition, their response to structure is not as clear as with some invertebrates. This relatively muted response to region and structure may be due to fish’s generally greater mobility than invertebrates. In this study, the principal factor extraction grouped H. plumierii , H. sciurus , L. griseus, L. synagris, and O. chrysurus together into Component 2, and grouped Calamus spp., L. rhomboides , L. analis in Component 4. With the data collected in this study, we could not explain with certainty why our factor analysis separated the species categorized by Component 2 from the ones categorized by Component 4. All fish in Component 2, with the exception of L. synagris , responded positively to an increase in octocoral surface areas. Octocorals increase the habitat complexity and provide additional shade, which increases the chances of remaining inconspicuous to predator. Cocheret de la Morinière et al (2004) studied the effect of structural complexity and mangrove shade on fish density. Both structural complexity and shade had a significant positive effect on the density of H. sciurus , whereas O. chrysurus only responded positively to shade. Calamus spp. and L. rhomboides (in Component 4) responded positively to an increase in sponge surface area and sponge volume respectively. These two factors increase the habitat’s structural complexity, shade, and increase the number of available shelter. On the other hand, Nagelkerken et al. (2001) suggested that L. analis (in Component 4) favors mudflats even if the mutton snapper still depends on mangrove and seagrass bed as nursery habitats. A comprehensive study of the association between the seagrass beds, the mangroves, and the nearshore hardbottom might shed more light on the importance and true role of the nearshore hardbottom as a nursery habitat. Surveys were only done during daylight, when visual observations by the divers could be made fairly easily. Several studies point out the diel movements of fish between seagrass beds and surrounding reefs (Nagelkerken et al 2000 a, Kopp et al 2007, Verweij and Nagelkerken 2007) . For instance, Verweij and Nagelkerken (2007) described how H. sciurus and H. flavolineatum moved from the mangrove and shore line to the seagrass beds before night in order to feed, and how they would return from the seagrass beds in the morning to seek shelter. In order to fully assess the dynamic of the nearshore hardbottom habitat, a nocturnal survey would probably be necessary. In addition, the hardbottom habitat cannot be seined or trawled like seagrass beds without destroying the habitat itself. The use of nocturnal underwater cameras or new sampling gear would have to be designed to assess the nearshore hardbottom community at night since divers would have very little visibility and will most probably scare away the fish while sampling. In conclusion, studies such as the present one can be critical to natural resource managers as they underscore the importance of ecosystem management versus more traditional management Tellier et al. 2008 37 FWRI File Code: F219-05-08-F paradigms. Ecosystem management endeavors to “conserve the structure, diversity, and function of ecosystems through management actions that focus on the biophysical components of ecosystems (FAO, 2003).” The ecosystem approach can be used in fisheries management as the narrow focus of traditional single-species fisheries management could miss certain factors that have critical repercussions on the abundance of fisheries species. For example, our data shows that the presence and abundance of stone crabs are related to their prey items and habitat preferences. This result highlights the importance of the interaction between fishery resources and the ecosystem in which they exist (FAO, 2003).
MANAGEMENT IMPLICATIONS AND RECOMMENDATIONS
Much of the community analyses (Principal factor extraction and discriminant analysis) have identified the Oceanside, Channels, and Gulfside nearshore hard bottom communities as relatively unique regions from other areas in the Florida Keys, whereas the Outer bay region was found not to be particularly unique. This suggests that best management practices for the nearshore hard bottom community should account for each region’s uniqueness. In others words, what is best for the Oceanside nearshore hardbottom may not be best for the Channel nearshore hardbottom. The Outer bay region which is the “middle ground”, both from a fauna community perspective and a geographical perspective, might make a good general long term monitoring area because this region shares some characteristics with all other regions.
Recommendations for Future Studies
Two possible directions for future studies are (1) to conduct more comprehensive surveys that incorporate all habitat types in nearshore waters (e.g. grassbeds), or (2) to conduct future hardbottom studies by narrowing the focus to only those organisms that have been shown to respond to hard bottom structure (e.g.; Cerithiidae, Anomura, P. argus , C. roseus , E. lucunter ). A more comprehensive survey that would include grassbeds and hardbottom could enable us to understand better those taxa that utilize both. Perhaps many of the taxa that we identified as exhibiting strong relationships to hardbottom structure are hardbottom obligates. The fishes comprising Component 2 (grunts and snappers) in our factor analysis exhibited only mild relationships to hardbottom structure. From other studies (Parrish 1989, Sheridan 1992, Kopp), we know grassbeds play a role in their ecology; yet the grunts and snappers formed a factor that was an important source of variation in our hardbottom surveys. This suggests that both hardbottom and seagrass were important to this group. A narrowly focused study could permit a greater number of surveys to be conducted. This would allow a greater geographical spread to site selection. The Gulfside of the lower Keys and the area in and around Everglades National Park are two areas with hardbottom habitat that could perhaps benefit from additional surveys.
Recommendations for Future Hardbottom Studies
One of the objectives of our study was to investigate potential relationships between sessile invertebrate structure and motile fauna (invertebrates and fish). A number of factors could have impaired our analysis. Structure measurements were not given nearly the emphasis as abundance counts. One reason structure measurements received less attention is that some taxa were difficult to measure. For example, A. varians (variable sponge) did not have a predictable shape, although, it numerically dominated many sites. In some places, Ircinia spp. (one of the finger sponges) grew Tellier et al. 2008 38 FWRI File Code: F219-05-08-F to sizes of approximately 0.3 m in height and they acquired a shape approximating seawhips; yet these were not measured. Measurements of these common sponges should be collected. For most sponges, a single height and a single diameter was measured. This was probably adequate. However, for octocorals only height was measured. Octocoral structure estimates would benefit from a second measurement perhaps a widest spot near the top. Collecting many more measurements across many more taxa will increase time commitment. If additional time is not available, then something else must be given up. Among the things to consider would be the overall number of surveys, whether to perform the quadrat surveys, how many transects to perform, or whether to perform roving surveys. The use of a “structure index” to analyze structure relationships with motile fauna is not adequate, although, it can serve a useful purpose in making decisions about site stratification. The “structure index” is comprised of one dimensional measurements of sponges and corals (height and width) and octocorals (height only). If “structure” is to be critically estimated, accounting for structures’ three-dimensional nature is critical. The 2-by-25-meter transect was of adequate size to surveys the invertebrates. Motile invertebrates, unlike fish, are in physical contact with a substrate or sessile invertebrates. Therefore, more discriminating power with the motile invertebrate data might be achieved if an additional substrate observation is made (i.e.; was the Astraea found on a loggerhead sponge, bare substrate, or elsewhere). Collecting this information will allow us to better determine habitat preferences. Fish, with few exceptions, are not in physical contact with the substrate. In addition, most fish can and do swim across this sized transect in seconds. Consideration should be given to altering the fish sampling design, although, we do not have any specific recommendations to make. In general, if surveying a larger area was considered, the expanded survey would need to insure that it was representative of the transects (e.g. the transects and expanded area must have roughly the same density and composition of sessile fauna).
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Appendix A. Multiple logistic regression models of the presence-absence of Laurencia spp., Halimeda spp., Syringodium filiforme , Halodule wrightii , and Thalassia testudinum , in the nearshore hardbottom of the Florida Keys, from fall 2003 to spring 2007. SE: standard error of the coefficients ( β) of the logistic regression equations. 95% CI: 95% confidence interval for the estimated odds ratios. Variable for which the odds ratio would be zero were not reported in this table. Goodness of fit of the models using Pearson chi-square (PE χ2), Deviance chi-square (D χ2), and Hosmer-Lemeshow Brown chi- square (HLB χ2), Naglekerke’s R-square (R 2), and the area under the receiver-operating characteristic curve (AUC). Coefficient Odds Variables SE Wald χ2 P 95% CI Goodness of fit (β) Ratio Laurencia spp.: Constant 0.963 0.559 2.969 0.085 Outer bay 0.943 0.651 2.098 0.147 2.569 0.717 9.207 Channels 0.943 0.718 1.729 0.189 2.569 0.629 10.483 PE χ2 = 19.923, df = 15, P = 0.175 Gulf -3.080 0.674 20.910 < 0.001 0.046 0.012 0.172 D χ2 = 22.707, df = 15, P = 0.091 Inner bay -0.277 0.542 0.262 0.609 0.046 0.012 0.172 HLB χ2 = 4.840, df = 6, P = 0.564 Basins -1.233 0.691 3.183 0.074 0.291 0.075 1.129 R2 = 0.326 Fall 0.530 0.540 0.962 0.327 1.699 0.589 4.899 AUC = 0.804 Spring 1.489 0.598 6.197 0.013 4.434 1.373 14.321 Summer 1.502 0.791 3.608 0.058 4.493 0.953 21.174 Halimeda spp.: Constant -1.880 0.485 0.769 < 0.001 Sponge volume 8.0E-07 4.0E-07 3.474 0.062 1.000 1.000 1.000 PE χ2 = 254.012, df = 248, P = 0.383 Collection period 0.103 0.031 11.438 0.001 1.109 1.044 1.177 D χ2 = 320.461, df = 248, P = 0.001 Fall 0.935 0.535 3.056 0.081 2.547 0.893 7.269 HLB χ2 = 3.032, df = 8, P = 0.932 Spring 0.795 0.557 2.039 0.153 2.214 0.744 6.590 R 2 = 0.156 Summer 1.907 0.596 10.234 0.001 6.731 2.093 21.653 AUC = 0.691 Syringodium filiforme: Constant -1.131 1.234 0.840 0.359 Channels 0.884 0.901 0.964 0.326 2.421 0.414 14.143 HLB χ2 = 4.957, df = 8, P = 0.762 Gulf 6.343 1.316 23.222 0.000 568.402 43.079 7499.746 R2 = 0.603 Inner bay -0.152 1.274 0.014 0.905 0.859 0.071 10.431 AUC = 0.933 Outer bay -0.195 1.305 0.022 0.881 0.822 0.064 10.618 Depth -0.456 0.236 3.738 0.053 0.634 0.399 1.006
ele ta.20 46 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix A. Continued.
Coefficient Odds Variables SE Wald χ2 P 95% CI Goodness of fit (β) Ratio Halodule wrightii: Constant -3.401 1.017 11.195 0.001 HLB χ2 = 0.003, df = 2, P = 0.998 Fall 1.878 1.051 3.191 0.074 6.538 0.833 51.315 R 2 = 0.142 Spring 0.500 1.115 0.201 0.654 1.648 0.185 14.674 AUC = 0.724 Thalassia testudinum: Constant -1.032 0.856 1.452 0.228 Basins 1.102 1.134 0.945 0.331 3.012 0.326 27.820 PE χ2 = 193.336, df = 125, P < 0.001 Channels 1.885 0.644 8.569 0.003 6.588 1.864 23.277 D χ2 = 98.058, df = 125, P = 0.964 Gulf 9.048 20.208 0.200 0.654 8505.556 0.000 1.351E+21 HLB χ2 = 15.190; df = 8, P = 0.056 Inner bay 2.711 0.880 9.500 0.002 15.049 2.684 84.387 R2 = 0.476 Outer bay -0.149 0.566 0.070 0.792 0.861 0.284 2.611 AUC = 0.868 Depth 0.592 0.179 10.921 0.001 1.807 1.272 2.568 All sessile surface -6.8E-06 1.3E-06 26.423 < 0.001 1.000 1.000 1.000
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Appendix B. Multiple logistic regression models of the presence-absence of Anomura, Astraea spp., Clypeaster rosaceus , Cerithiidae, Echinometra lucunter , Eucidaris tribuloides , Fasciolaria tulipa , Holothuroidea, Menippe mercenaria , Lytechinus variegatus , Mithrax sculptus , Mithrax spinosissimus , Ophiuroidea, Panulirus argus , Phyllonotus pomum , Portunus sebae , Strombus gigas , and Vasum muricatum , in the nearshore hardbottom of the Florida Keys, from fall 2003 to spring 2007. SE: standard error of the coefficients ( β) of the logistic regression equations. 95% CI: 95% confidence interval for the estimated odds ratios. Variable for which the odds ratio would be zero were not reported in this table. Goodness of fit of the models using Pearson chi-square (PE χ2), Deviance chi-square (D χ2), and Hosmer-Lemeshow Brown chi-square (HLB χ2), Naglekerke’s R-square (R 2), and the area under the receiver-operating characteristic curve (AUC). Coefficient Odds Variables SE Wald χ2 P 95% CI Goodness of fit (β) Ratio Anomura: Constant 0.290 0.199 2.122 0.145 PE χ2 = 39.396, df = 35, P = 0.280 All sessile volume 1.6E-06 5.0E-07 9.800 0.002 1.000 1.000 1.000 D χ2 = 46.349, df = 35, P = 0.095 HLB χ2 = 2.500, df = 7, P = 0.927 R 2 = 0.078 AUC = 0.641 Astraea spp.: Constant -1.935 0.742 6.799 0.009 Depth 0.302 0.110 7.523 0.006 1.353 1.090 1.679 All sessile volume 1.1E-06 5.0E-07 5.067 0.024 1.000 1.000 1.000 Basins 0.962 0.657 2.140 0.144 2.616 0.721 9.487 PE χ2 = 184.075, df = 182, P = 0.443 Channels 0.660 0.463 2.032 0.154 1.934 0.781 4.791 D χ2 = 227.761, df = 182, P = 0.012 Gulf 1.708 0.862 3.925 0.048 5.519 1.018 29.905 HLB χ2 = 5.328, df = 8, P = 0.722 Inner bay -0.453 0.463 0.958 0.328 0.636 0.257 1.575 R2 = 0.240 Outer bay 0.526 0.437 1.449 0.229 1.692 0.719 3.983 AUC = 0.739 Fall 0.210 0.488 0.185 0.667 1.234 0.474 3.213 Winter -0.442 0.544 0.662 0.416 0.642 0.221 1.865 Spring -0.968 0.455 4.523 0.033 0.380 0.156 0.927 Clypeaster rosaceus: Constant -0.258 0.340 0.576 0.448 All sessile surface 2.4E-06 1.0E-06 6.346 0.012 1.000 1.000 1.000 PE χ2 = 87.746, df = 30, P < 0.001 Channels -1.730 0.481 12.945 < 0.001 0.177 0.069 0.455 D χ2 = 90.017, df = 30, P < 0.001 Gulf -2.458 0.745 10.871 0.001 0.086 0.020 0.369 HLB χ2 = 16.061, df = 7, P = 0.025 Inner bay -3.041 0.666 20.838 < 0.001 0.048 0.013 0.176 R2 = 0.382 Outer bay -2.774 0.591 22.029 < 0.001 0.062 0.020 0.199 AUC = 0.846
ele ta.20 48 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix B. Continued.
Coefficient Odds Variables SE Wald χ2 P 95% CI Goodness of fit (β) Ratio Cerithiidae: Constant -0.433 0.314 1.910 0.167 All sessile volume 1.0E-06 4.0E-07 6.206 0.013 1.000 1.000 1.000 PE χ2 = 38.727, df = 30, P = 0.132 Basins 0.904 0.632 2.042 0.153 2.468 0.715 8.524 D χ2 = 43.560, df = 30, P = 0.052 Channels -0.670 0.445 2.267 0.132 0.512 0.214 1.224 HLB χ2 = 5.200, df = 7, P = 0.636 Gulf 1.188 0.727 2.670 0.102 3.280 0.789 13.630 R 2 = 0.172 Inner bay -0.593 0.430 1.906 0.167 0.552 0.238 1.283 AUC = 0.700 Outer bay -1.050 0.430 -2.444 0.015 0.350 0.151 0.812 Echinometra lucunter: Constant -1.098 0.425 6.676 0.010 PE χ2 = 53.949, df = 30, P = 0.005 Channels -3.756 1.077 12.161 < 0.001 0.023 0.003 0.193 D χ2 = 37.450, df = 30, P = 0.164 Inner bay -3.916 1.021 14.713 < 0.001 0.020 0.003 0.147 HLB χ2 = 11.127, df = 7, P = 0.133 Outer bay -3.947 1.080 13.367 < 0.001 0.019 0.002 0.160 R2 = 0.622 All sessile surface 6.1E-06 1.7E-06 12.270 < 0.001 1.000 1.000 1.000 AUC = 0.929 Eucidaris tribuloides: Constant -4.018 0.945 18.070 < 0.001 PE χ2 = 17.642, df = 25, P = 0.957 Octocoral surface 1.8E-05 4.6E-06 15.179 < 0.001 1.000 1.000 1.000 D χ2 = 16.358, df = 25, P = 0.904 Outer bay 0.338 0.962 0.123 0.726 1.402 0.213 9.238 HLB χ2 = 7.707, df = 6, P = 0.260 R 2 = 0.714 AUC = 0.939 Fasciolaria tulipa: Constant -1.269 0.217 34.197 < 0.001 PE χ2 = 197.638, df = 209, P = 0.703 Laurencia spp. -3.192 1.385 5.309 0.021 0.041 0.003 0.621 D χ2 = 166.185, df = 209, P = 0.987 HLB χ2 = 13.418, df = 7, P = 0.063 R2 = 0.045 AUC = 0.625
ele ta.20 49 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix B. Continued.
Coefficient Odds Variables SE Wald χ2 P 95% CI Goodness of fit (β) Ratio Holothuroidea: Constant -1.640 0.770 4.528 0.033 Thalassia testudinum -4.215 1.771 5.666 0.017 0.015 0.000 0.475 Fall 2.392 0.786 9.265 0.002 10.933 2.344 51.004 Winter 2.058 0.854 5.805 0.016 7.829 1.468 41.752 PE χ2 = 224.101, df = 215, P = 0.321 Spring 1.761 0.788 4.996 0.025 5.820 1.242 27.273 D χ2 = 219.987, df = 215, P = 0.393 Basins -1.361 0.720 3.575 0.059 0.256 0.063 1.051 HLB χ2 = 3.797, df = 8, P = 0.875 Channels -1.335 0.467 8.169 0.004 0.263 0.105 0.657 R2 = 0.228 Gulf -1.447 0.714 4.105 0.043 0.235 0.058 0.954 AUC = 0.753 Inner bay -1.592 0.520 9.388 0.002 0.204 0.074 0.563 Outer bay -0.598 0.403 2.197 0.138 0.550 0.250 1.212 Menippe mercenaria: Constant -2.511 0.617 16.585 < 0.001 Sponge surface 3.9E-06 1.7E-06 5.487 0.019 1.000 1.000 1.000 Fall 1.258 0.561 5.021 0.025 3.519 1.171 10.576 Winter -0.283 0.755 0.140 0.708 0.754 0.172 3.307 PE χ2 = 137.643, df = 128, P = 0.264 Spring 0.927 0.565 2.695 0.101 2.527 0.835 7.641 D χ2 = 138.090, df = 128, P = 0.256 Basins 0.733 0.652 1.264 0.261 2.081 0.580 7.465 HLB χ2 = 10.014, df = 8, P = 0.264 Channels 0.597 0.489 1.491 0.222 1.816 0.697 4.731 R2 = 0.172 Gulf 1.880 0.594 10.003 0.002 6.550 2.044 20.996 AUC = 0.711 Inner bay 0.083 0.488 .1705070^2 0.865 1.087 0.417 2.829 Outer bay -0.021 0.473 0.002 0.964 0.979 0.388 2.474 Lytechinus variegatus: Constant -3.287 0.815 16.286 < 0.001 Depth 0.337 0.127 7.071 0.008 1.401 1.093 1.796 PE χ2 = 32.582, df = 5, P = 0.979 Channels -0.881 0.630 1.953 0.162 0.414 0.121 1.425 D χ2 = 36.483, df = 51, P = 0.938 Gulf 1.756 0.625 7.897 0.005 5.787 1.701 19.688 HLB χ2 = 0.9168, df = 7, P = 0.996 Inner bay -2.126 0.814 6.831 0.009 0.119 0.024 0.588 R2 = 0.369 Outer bay 0.838 0.439 3.642 0.056 2.312 0.978 5.470 AUC = 0.839
ele ta.20 50 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix B. Continued.
Coefficient Odds Variables SE Wald χ2 P 95% CI Goodness of fit (β) Ratio Mithrax sculptus: Constant -1.118 0.342 10.687 0.001 Basins -0.485 0.667 0.529 0.467 0.616 0.167 2.276 PE χ2 = 257.735, df = 221, P = 0.045 Channels -3.600 0.875 16.906 < 0.001 0.027 0.005 0.152 D χ2 =156.423, df = 221, P = 1.000 Gulf 0.261 0.559 0.218 0.641 1.298 0.434 3.885 HLB χ2 = 8.137, df = 8, P = 0.420 Inner bay -3.658 1.059 11.922 0.001 0.026 0.003 0.206 R2 = 0.334 Outer bay -2.294 0.580 15.630 < 0.001 0.101 0.032 0.315 AUC = 0.837 Sponge surface 5.0E-06 2.4E-06 4.461 0.035 1.000 1.000 1.000 Laurencia spp. 3.164 1.150 7.565 0.006 23.666 2.483 225.591 Mithrax spinosissimus: Constant -0.904 0.326 7.687 0.006 Sponge surface 4.5E-06 1.7E-06 6.832 0.009 1.000 1.000 1.000 PE χ2 = 230.825, df = 221, P = 0.311 Laurencia spp. -2.462 1.061 5.387 0.020 0.085 0.011 0.682 D χ2 = 246.344df = 221, P = 0.116 Basins 0.302 0.631 0.229 0.632 1.353 0.392 4.664 HLB χ2 = 3.740= 8, P = 0.880 Channels -0.246 0.476 0.268 0.605 0.782 0.308 1.986 R2 = 0.137 Gulf 1.149 0.569 4.072 0.044 3.155 1.034 9.628 AUC = 0.686 Inner bay -0.538 0.465 1.336 0.248 0.584 0.234 1.454 Outer bay -0.237 0.441 0.290 0.590 0.789 0.332 1.871 Ophiuroidea: Constant -1.947 0.522 13.937 < 0.001 Collection period 0.075 0.033 5.038 0.025 1.077 1.010 1.150 PE χ2 = 244.009, df = 246, P = 0.524 All sessile volume -2.7E-06 9.0E-07 10.265 0.001 1.000 1.000 1.000 D χ2 = 220.668, df = 246, P = 0.876 All sessile surface 3.5E-06 1.0E-06 11.525 0.001 1.000 1.000 1.000 HLB χ2 = 9.524, df = 8, P = 0.300 Basins -0.424 0.870 0.237 0.626 0.655 0.119 3.604 R 2 = 0.227 Channels 0.253 0.585 0.187 0.665 1.288 0.409 4.052 AUC = 0.772 Inner bay 0.004 0.523 0.000 0.993 1.004 0.360 2.799 Outer bay 1.028 0.484 4.514 0.034 2.795 1.083 7.214
ele ta.20 51 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix B. Continued.
Coefficient Odds Variables SE Wald χ2 P 95% CI Goodness of fit (β) Ratio Panulirus argus: Constant -1.563 0.335 21.793 < 0.001 Sponge volume 1.9E-06 5.0E-07 12.839 < 0.001 1.000 1.000 1.000 PE χ2 = 51.148, df = 30, P = 0.009 Basins 0.256 0.661 0.150 0.699 1.291 0.354 4.714 D χ2 = 59.628, df = 30, P = 0.001 Channels 1.022 0.453 5.099 0.024 2.779 1.144 6.749 HLB χ2 = 11.002, df = 7, P = 0.139 Gulf 0.452 0.568 0.635 0.426 1.572 0.517 4.783 R 2 = 0.162 Inner bay 0.323 0.446 0.527 0.468 1.382 0.577 3.310 AUC = 0.696 Outer bay 0.881 0.415 4.513 0.034 2.413 1.071 5.439 Phyllonotus pomum: Constant -1.609 0.467 11.886 0.001 PE χ2 = 119.488, df = 103, P = 0.127 Octocoral volume 1.2E-06 5.0E-07 6.896 0.009 1.000 1.000 1.000 D χ2 = 77.206, df = 103, P = 0.973 Fall -1.493 0.662 5.085 0.024 0.225 0.061 0.823 HLB χ2 = 9.204, df = 8, P = 0.325 Winter -0.230 0.680 0.115 0.735 0.794 0.209 3.014 R 2 = 0.120 Spring -1.502 0.662 5.153 0.023 0.223 0.061 0.815 ROC = 0.703 Portunus sebae: Constant -26.439 7.830 11.403 0.001 PE χ2 = 40.777, df = 61, P = 0.978 Salinity 0.647 0.211 9.436 0.002 1.909 1.264 2.885 D χ2 = 37.394, df = 61, P = 0.993 HLB χ2 = 5.817, df = 8, P = 0.668 R 2 = 0.120 AUC= 0.761 Strombus gigas: Constant 2.799 1.136 6.068 0.014 PE χ2 = 40.105, df = 116, P = 1.000 Channels -2.847 1.094 6.773 0.009 0.058 0.007 0.495 D χ2 = 34.038, df= 116, P = 1.000 Depth -0.866 0.249 12.091 0.001 0.421 0.258 0.685 HLB χ2= 0.955, df= 8, P = 0.999 R 2 = 0.495 AUC = 0.942
ele ta.20 52 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix B. Continued.
Coefficient Odds Variables SE Wald χ2 P 95% CI Goodness of fit (β) Ratio Vasum muricatum: Constant -3.401 1.136 8.958 0.003 All sessile surface 2.7E-06 1.1E-06 6.619 0.010 1.000 1.000 1.000 PE χ2 = 191.342, df = 234, P = 0.981 Thalassia testudinum -11.749 5.231 5.044 0.025 7.9E-06 0.000 0.224 D χ2 = 77.242, df = 234, P = 1.000 Fall 1.567 1.104 2.016 0.156 4.793 0.551 41.698 HLB χ2= 4.502, df = 8, P = 0.809 Winter 1.169 1.233 0.898 0.343 3.218 0.287 36.101 R 2 = 0.271 Spring -0.560 1.278 0.192 0.661 0.571 0.047 6.986 AUC = 0.841
ele ta.20 53 FWRI File Code: F219-05-08-F Tellier et al. 2008
Appendix C. Presence, per site and sampling period, of all fish taxa found in the nearshore hardbottom of the Florida Keys from fall 2003 to spring 2007 for each region, all regions together, and for each season. Taxa were ordered in decreasing presence per site per sampling period for all regions together to show the taxa most present. Regions Seasons Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Haemulon plumierii 0.688 0.396 0.600 0.275 0.516 0.286 0.412 0.547 0.344 0.260 0.531 Lutjanus synagris 0.500 0.354 0.300 0.176 0.469 0.304 0.341 0.589 0.125 0.083 0.594 Diplectrum formosum 0.438 0.354 0.250 0.176 0.469 0.250 0.322 0.316 0.438 0.281 0.344 Lutjanus griseus 0.438 0.292 0.300 0.235 0.203 0.179 0.243 0.326 0.125 0.156 0.375 Eucinostomus spp. 0.313 0.438 0.050 0.216 0.297 0.054 0.235 0.295 0.156 0.135 0.438 Calamus spp. 0.125 0.313 0.500 0.157 0.234 0.125 0.224 0.189 0.313 0.240 0.188 Lagodon rhomboides 0.188 0.438 0.050 0.137 0.328 0.054 0.220 0.211 0.313 0.198 0.219 Coryphopterus glaucofraenum 0.438 0.146 0.000 0.275 0.203 0.250 0.216 0.263 0.250 0.146 0.250 Haemulon spp. 0.188 0.083 0.200 0.098 0.172 0.304 0.173 0.232 0.063 0.188 0.063 Microgobius microlepis 0.188 0.104 0.000 0.294 0.125 0.161 0.157 0.232 0.094 0.125 0.094 Anisotremus virginicus 0.063 0.104 0.400 0.118 0.078 0.143 0.129 0.179 0.125 0.063 0.188 Sparisoma spp. 0.000 0.104 0.050 0.098 0.109 0.232 0.122 0.168 0.094 0.094 0.094 Gobiidae 0.188 0.083 0.000 0.098 0.172 0.107 0.114 0.137 0.188 0.042 0.188 Urobatis jamaicensis 0.000 0.063 0.100 0.039 0.141 0.232 0.114 0.137 0.125 0.083 0.125 Pareques acuminatus 0.063 0.042 0.150 0.039 0.078 0.250 0.106 0.084 0.156 0.094 0.156 Halichoeres bivittatus 0.000 0.021 0.050 0.020 0.016 0.393 0.102 0.168 0.000 0.063 0.125 Archosargus rhomboidalis 0.063 0.146 0.050 0.059 0.125 0.054 0.090 0.084 0.094 0.094 0.094 Chilomycterus schoepfii 0.000 0.021 0.200 0.137 0.016 0.179 0.090 0.105 0.125 0.073 0.063 Eucinostomus gula 0.063 0.167 0.000 0.059 0.125 0.018 0.082 0.137 0.000 0.021 0.188 Ocyurus chrysurus 0.063 0.021 0.100 0.039 0.109 0.089 0.071 0.084 0.000 0.031 0.219 Haemulon sciurus 0.000 0.021 0.000 0.098 0.109 0.071 0.067 0.116 0.031 0.052 0.000 Scarus spp. 0.000 0.042 0.000 0.098 0.031 0.143 0.067 0.063 0.125 0.052 0.063 Sphoeroides spengleri 0.063 0.000 0.150 0.059 0.109 0.054 0.067 0.095 0.031 0.021 0.156 Haemulon flavolineatum 0.000 0.063 0.000 0.059 0.094 0.071 0.063 0.084 0.125 0.021 0.063 Malacoctenus macropus 0.000 0.000 0.000 0.000 0.000 0.268 0.059 0.042 0.094 0.063 0.063 Osteichthyes (unidentified) 0.000 0.042 0.100 0.137 0.016 0.054 0.059 0.084 0.000 0.063 0.031 Stegastes leucostictus 0.000 0.000 0.000 0.020 0.000 0.250 0.059 0.074 0.063 0.021 0.125 Acanthurus chirurgus 0.000 0.042 0.000 0.000 0.016 0.196 0.055 0.063 0.063 0.031 0.094
ele ta.20 54 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix C. Continued.
Regions Seasons Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Acanthostracion quadricornis 0.000 0.021 0.050 0.039 0.047 0.107 0.051 0.021 0.031 0.094 0.031 Opsanus beta 0.000 0.021 0.050 0.078 0.094 0.018 0.051 0.032 0.063 0.073 0.031 Stegastes variabilis 0.000 0.021 0.050 0.000 0.000 0.196 0.051 0.074 0.000 0.031 0.094 Pomacanthus paru 0.000 0.125 0.050 0.020 0.016 0.054 0.047 0.042 0.094 0.042 0.031 Sparisoma aurofrenatum 0.000 0.021 0.050 0.000 0.047 0.125 0.047 0.074 0.063 0.000 0.094 Epinephelus morio 0.000 0.064 0.250 0.000 0.016 0.036 0.043 0.021 0.125 0.031 0.065 Caranx crysos 0.000 0.021 0.150 0.059 0.031 0.036 0.043 0.074 0.000 0.031 0.031 Pomacanthus arcuatus 0.000 0.104 0.150 0.000 0.031 0.000 0.039 0.053 0.031 0.021 0.063 Cryptotomus roseus 0.000 0.000 0.000 0.000 0.031 0.125 0.035 0.042 0.031 0.031 0.031 Haemulon parra 0.000 0.021 0.000 0.039 0.047 0.054 0.035 0.053 0.094 0.010 0.000 Lutjanus analis 0.000 0.042 0.200 0.020 0.031 0.000 0.035 0.053 0.031 0.031 0.000 Monacanthus ciliatus 0.000 0.021 0.100 0.039 0.063 0.000 0.035 0.032 0.063 0.031 0.031 Parablennius marmoreus 0.000 0.021 0.000 0.000 0.016 0.125 0.035 0.053 0.063 0.010 0.031 Carangoides ruber 0.000 0.042 0.050 0.059 0.016 0.018 0.031 0.053 0.031 0.010 0.031 Equetus spp. 0.000 0.000 0.150 0.020 0.016 0.054 0.031 0.053 0.031 0.021 0.000 Lachnolaimus maximus 0.000 0.000 0.100 0.020 0.031 0.054 0.031 0.032 0.000 0.031 0.063 Opistognathus spp. 0.000 0.000 0.000 0.000 0.063 0.071 0.031 0.042 0.063 0.000 0.063 Gerreidae 0.063 0.021 0.050 0.039 0.031 0.000 0.027 0.053 0.000 0.021 0.000 Haemulon aurolineatum 0.000 0.021 0.000 0.020 0.047 0.036 0.027 0.042 0.000 0.010 0.063 Scaridae 0.000 0.000 0.000 0.039 0.063 0.018 0.027 0.042 0.000 0.031 0.000 Synodus foetens 0.000 0.083 0.000 0.020 0.016 0.018 0.027 0.032 0.000 0.031 0.031 Hypoplectrus puella 0.063 0.042 0.150 0.000 0.000 0.000 0.024 0.032 0.031 0.021 0.000 Stephanolepis hispidus 0.000 0.021 0.000 0.059 0.031 0.000 0.024 0.011 0.031 0.010 0.094 Lutjanus spp. 0.000 0.000 0.000 0.039 0.031 0.018 0.020 0.000 0.000 0.000 0.156 Mycteroperca microlepis 0.000 0.000 0.150 0.000 0.016 0.018 0.020 0.000 0.063 0.021 0.031 Scarus taeniopterus 0.063 0.000 0.000 0.000 0.016 0.054 0.020 0.032 0.000 0.021 0.000 Sphyraena barracuda 0.000 0.042 0.050 0.020 0.000 0.018 0.020 0.021 0.031 0.010 0.031 Stegastes fuscus 0.000 0.000 0.000 0.000 0.000 0.089 0.020 0.021 0.031 0.000 0.063 Abudefduf saxatilis 0.000 0.000 0.000 0.000 0.000 0.071 0.016 0.021 0.000 0.000 0.063 Carangoides bartholomaei 0.000 0.000 0.000 0.059 0.000 0.018 0.016 0.042 0.000 0.000 0.000
ele ta.20 55 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix C. Continued.
Regions Seasons Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Ctenogobius saepepallens 0.063 0.021 0.000 0.020 0.000 0.018 0.016 0.021 0.000 0.021 0.000 Diodon holocanthus 0.000 0.000 0.000 0.000 0.000 0.071 0.016 0.021 0.000 0.021 0.000 Hypoplectrus unicolor 0.000 0.000 0.100 0.000 0.031 0.000 0.016 0.032 0.000 0.000 0.031 Opistognathus macrognathus 0.000 0.000 0.000 0.059 0.016 0.000 0.016 0.011 0.000 0.031 0.000 Scarus iseri 0.000 0.000 0.000 0.000 0.047 0.018 0.016 0.032 0.000 0.000 0.031 Sphoeroides nephelus 0.000 0.021 0.000 0.020 0.016 0.018 0.016 0.011 0.000 0.031 0.000 Stegastes spp. 0.000 0.000 0.000 0.000 0.000 0.071 0.016 0.042 0.000 0.000 0.000 Syngnathus spp. 0.000 0.021 0.000 0.020 0.031 0.000 0.016 0.000 0.063 0.010 0.031 Acanthurus spp. 0.000 0.000 0.000 0.020 0.000 0.036 0.012 0.021 0.000 0.010 0.000 Chaetodon ocellatus 0.000 0.021 0.100 0.000 0.000 0.000 0.012 0.021 0.000 0.000 0.031 Equetus lanceolatus 0.000 0.021 0.050 0.020 0.000 0.000 0.012 0.021 0.031 0.000 0.000 Floridichthys carpio 0.000 0.000 0.000 0.059 0.000 0.000 0.012 0.011 0.031 0.000 0.031 Halichoeres radiatus 0.000 0.000 0.000 0.000 0.000 0.054 0.012 0.011 0.000 0.021 0.000 Hypoplectru s spp. 0.000 0.000 0.150 0.000 0.000 0.000 0.012 0.011 0.000 0.010 0.031 Lucania parva 0.000 0.000 0.000 0.059 0.000 0.000 0.012 0.000 0.063 0.000 0.031 Mycteroperca bonaci 0.000 0.000 0.100 0.000 0.016 0.000 0.012 0.011 0.000 0.021 0.000 Paraclinus fasciatus 0.063 0.000 0.050 0.000 0.016 0.000 0.012 0.000 0.000 0.031 0.000 Paradiplogrammus bairdi 0.000 0.000 0.000 0.039 0.016 0.000 0.012 0.000 0.000 0.031 0.000 Sparisoma radians 0.000 0.000 0.000 0.000 0.016 0.036 0.012 0.011 0.031 0.010 0.000 Sparisoma viride 0.000 0.000 0.000 0.020 0.000 0.036 0.012 0.032 0.000 0.000 0.000 Syngnathus floridae 0.000 0.021 0.000 0.020 0.016 0.000 0.012 0.011 0.031 0.010 0.000 Acanthurus bahianus 0.000 0.000 0.000 0.000 0.000 0.036 0.008 0.011 0.000 0.010 0.000 Archosargus probatocephalus 0.000 0.000 0.050 0.020 0.000 0.000 0.008 0.011 0.000 0.010 0.000 Canthigaster rostrata 0.063 0.000 0.000 0.000 0.000 0.018 0.008 0.000 0.000 0.021 0.000 Carangidae 0.000 0.000 0.000 0.020 0.000 0.018 0.008 0.000 0.031 0.010 0.000 Caranx spp. 0.000 0.000 0.000 0.020 0.016 0.000 0.008 0.021 0.000 0.000 0.000 Chaetodipterus faber 0.000 0.000 0.000 0.039 0.000 0.000 0.008 0.021 0.000 0.000 0.000 Chaetodon sedentarius 0.000 0.021 0.000 0.000 0.000 0.018 0.008 0.000 0.000 0.010 0.031 Chriodorus atherinoides 0.000 0.021 0.050 0.000 0.000 0.000 0.008 0.011 0.000 0.010 0.000 Echeneis naucrates 0.000 0.000 0.000 0.039 0.000 0.000 0.008 0.021 0.000 0.000 0.000
ele ta.20 56 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix C. Continued.
Regions Seasons Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Engraulidae 0.000 0.021 0.000 0.000 0.000 0.018 0.008 0.000 0.031 0.000 0.031 Gerres cinereus 0.000 0.000 0.000 0.000 0.016 0.018 0.008 0.021 0.000 0.000 0.000 Gymnothorax funebris 0.000 0.000 0.000 0.000 0.000 0.036 0.008 0.000 0.000 0.021 0.000 Holacanthus bermudensis 0.000 0.000 0.100 0.000 0.000 0.000 0.008 0.000 0.031 0.010 0.000 Menidia spp. 0.000 0.021 0.000 0.020 0.000 0.000 0.008 0.000 0.031 0.000 0.031 Ogcocephalus pantostictus 0.000 0.000 0.000 0.000 0.031 0.000 0.008 0.021 0.000 0.000 0.000 Paraclinus marmoratus 0.000 0.000 0.000 0.000 0.000 0.036 0.008 0.021 0.000 0.000 0.000 Scorpaena brasiliensis 0.000 0.000 0.000 0.000 0.000 0.036 0.008 0.000 0.031 0.000 0.031 Serranus subligarius 0.000 0.021 0.050 0.000 0.000 0.000 0.008 0.021 0.000 0.000 0.000 Sparisoma chrysopterum 0.000 0.000 0.000 0.000 0.000 0.036 0.008 0.021 0.000 0.000 0.000 Stegastes diencaeus 0.000 0.000 0.000 0.000 0.000 0.036 0.008 0.011 0.000 0.000 0.031 Acanthemblemaria aspera 0.000 0.000 0.050 0.000 0.000 0.000 0.004 0.000 0.000 0.000 0.031 Acanthurus coeruleus 0.000 0.000 0.000 0.000 0.016 0.000 0.004 0.000 0.000 0.000 0.031 Aluterus scriptus 0.000 0.000 0.000 0.020 0.000 0.000 0.004 0.011 0.000 0.000 0.000 Anchoa spp. 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.000 0.010 0.000 Apogon spp. 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.000 0.010 0.000 Balistes capriscus 0.000 0.021 0.000 0.000 0.000 0.000 0.004 0.011 0.000 0.000 0.000 Bathygobius spp. 0.000 0.000 0.000 0.000 0.016 0.000 0.004 0.011 0.000 0.000 0.000 Blenniidae 0.000 0.000 0.050 0.000 0.000 0.000 0.004 0.000 0.000 0.010 0.000 Bothidae 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.000 0.010 0.000 Caranx hippos 0.000 0.000 0.050 0.000 0.000 0.000 0.004 0.000 0.000 0.010 0.000 Cephalopholis cruentata 0.000 0.000 0.000 0.000 0.016 0.000 0.004 0.000 0.000 0.010 0.000 Chaetodon capistratus 0.000 0.021 0.000 0.000 0.000 0.000 0.004 0.000 0.031 0.000 0.000 Clupeidae 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.000 0.000 0.031 Cyprinodontidae 0.000 0.000 0.000 0.020 0.000 0.000 0.004 0.011 0.000 0.000 0.000 Dactylopterus volitans 0.000 0.000 0.050 0.000 0.000 0.000 0.004 0.011 0.000 0.000 0.000 Diplogrammus pauciradiatus 0.063 0.000 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.010 0.000 Diplogrammus spp. 0.000 0.000 0.000 0.000 0.016 0.000 0.004 0.000 0.000 0.000 0.031 Echeneis neucratoides 0.000 0.000 0.000 0.020 0.000 0.000 0.004 0.011 0.000 0.000 0.000 Elacatinus oceanops 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.000 0.010 0.000
ele ta.20 57 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix C. Continued.
Regions Seasons Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Entomacrodus nigricans 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.000 0.010 0.000 Equetus punctatus 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.011 0.000 0.000 0.000 Gerres spp. 0.000 0.021 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.010 0.000 Ginglymostoma cirratum 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.000 0.000 0.031 Gymnothorax moringa 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.011 0.000 0.000 0.000 Gymnothorax spp. 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.011 0.000 0.000 0.000 Haemulon melanurum 0.000 0.000 0.000 0.000 0.016 0.000 0.004 0.011 0.000 0.000 0.000 Harengula spp. 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.000 0.000 0.031 Hippocampus erectus 0.000 0.000 0.000 0.020 0.000 0.000 0.004 0.000 0.000 0.010 0.000 Hippocampus spp. 0.000 0.000 0.000 0.000 0.016 0.000 0.004 0.000 0.000 0.010 0.000 Holacanthus ciliaris 0.000 0.021 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.000 0.031 Hypleurochilus bermudensis 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.031 0.000 0.000 Lutjanus apodus 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.011 0.000 0.000 0.000 Lutjanus mahogoni 0.000 0.000 0.000 0.000 0.016 0.000 0.004 0.011 0.000 0.000 0.000 Microgobius spp. 0.000 0.000 0.000 0.000 0.016 0.000 0.004 0.011 0.000 0.000 0.000 Mugil spp. 0.000 0.000 0.000 0.020 0.000 0.000 0.004 0.000 0.000 0.010 0.000 Nicholsina usta 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.000 0.010 0.000 Oligoplites saurus 0.000 0.000 0.050 0.000 0.000 0.000 0.004 0.011 0.000 0.000 0.000 Opsanus spp. 0.000 0.000 0.000 0.020 0.000 0.000 0.004 0.000 0.000 0.010 0.000 Orthopristis chrysoptera 0.000 0.021 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.000 0.031 Paraclinus nigripinnis 0.000 0.000 0.000 0.000 0.016 0.000 0.004 0.011 0.000 0.000 0.000 Paralichthys albigutta 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.011 0.000 0.000 0.000 Prionotus scitulus 0.000 0.000 0.000 0.020 0.000 0.000 0.004 0.000 0.000 0.010 0.000 Prionotus spp. 0.000 0.000 0.000 0.020 0.000 0.000 0.004 0.011 0.000 0.000 0.000 Ptereleotris calliura 0.063 0.000 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.010 0.000 Sciaenidae 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.000 0.010 0.000 Scomberomorus regalis 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.011 0.000 0.000 0.000 Scombridae 0.000 0.000 0.000 0.020 0.000 0.000 0.004 0.000 0.000 0.010 0.000 Scorpaena plumieri 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.031 0.000 0.000 Scorpaena spp. 0.000 0.021 0.000 0.000 0.000 0.000 0.004 0.011 0.000 0.000 0.000
ele ta.20 58 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix C. Continued.
Regions Seasons Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Stegastes partitus 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.011 0.000 0.000 0.000 Thalassoma bifasciatum 0.000 0.000 0.000 0.000 0.016 0.000 0.004 0.011 0.000 0.000 0.000 Xyrichtys splendens 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.011 0.000 0.000 0.000 All fish 0.025 0.027 0.035 0.025 0.031 0.039 0.031 0.038 0.028 0.022 0.038 Number of species 25 61 50 70 73 92 151 107 57 91 69
ele ta.20 59 FWRI File Code: F219-05-08-F Tellier et al. 2008
Appendix D. Multiple logistic regression models of the presence-absence of Anisotremus virginicus , Archosargus rhomboidalis , Calamus spp., Chilomycterus schoepfii , Coryphopterus glaucofraenum , Diplectrum formosum , Eucinostomus gula , Eucinostomus spp. (TL < 4 cm), Haemulon flavolineatum , Haemulon plumierii , Haemulon sciurus , Haemulon spp (TL < 4 cm), Halichoeres bivittatus , Lagodon rhomboides , Lutjanus griseus , Lutjanus synagris , Malacoctenus macropus , Microgobius microlepis , Ocyurus chrysurus , Pareques acuminatus , Sphoeroides spengleri , Stegastes leucostictus , and Urobatis jamaicensis , in the nearshore hardbottom of the Florida Keys, from fall 2003 to spring 2007. SE: standard error of the coefficients ( β) of the logistic regression equations. 95% CI: 95% confidence interval for the estimated odds ratios. Variable for which the odds ratio would be zero were not reported in this table. Goodness of fit of the models using Pearson chi-square (PE χ2), Deviance chi-square (D χ2), and Hosmer- Lemeshow Brown chi-square (HLB χ2), Naglekerke’s R-square (R 2), and the area under the receiver-operating characteristic curve (AUC). Coefficient Wald Odds Variables SE P 95% CI Goodness of fit (β) χ2 Ratio Anisotremus virginicus: Constant -3.118 0.340 84.082 < 0.001 PE χ2 = 234.853, df = 216, P = 0.180 Plantae 3.724 1.416 6.913 0.009 41.426 2.580 665.100 D χ2 = 156.431, df = 216, P = 0.999 All sessile surface 3.9E-06 8.0E-07 22.884 < 0.001 1.000 1.000 1.000 HLB χ2 = 9.565, df = 8, P = 0.297 R 2 = 0.210 AUC = 0.750 Archosargus rhomboidalis: Constant -4.024 0.542 60.978 < 0.001 PE χ2 = 199.405, df = 221, P = 0.849 Sponge volume 3.3E-06 6.0E-07 31.918 < 0.001 1.000 1.000 1.000 D χ2 = 98.087, df = 221, P = 1.000 Thalassia testudinum 4.820 1.891 6.494 0.011 123.998 3.043 5051.956 HLB χ2 = 13.703, df = 8, P = 0.090 R2 = 0.286 AUC = 0.767 Calamus spp.: Constant 5.038 3.159 1.595 0.111 Laurencia spp. -3.347 1.295 6.677 0.010 0.035 0.003 0.446 All sessile surface 2.7E-06 1.0E-06 7.526 0.006 1.000 1.000 1.000 Sponge surface 5.8E-06 2.2E-06 6.950 0.008 1.000 1.000 1.000 PE χ2 = 249.179, df = 232, P = 0.209 Salinity -0.214 0.087 6.026 0.014 0.808 0.681 0.958 D χ2 = 212.335, df = 232, P = 0.818 Basins 0.876 0.938 0.873 0.350 2.402 0.382 15.093 HLB χ2 = 4.740, df = 8, P = 0.785 Channels 0.915 0.664 1.901 0.168 2.497 0.680 9.174 R2 = 0.269 Gulf 1.472 0.685 4.616 0.032 4.360 1.138 16.703 AUC = 0.770 Inner bay 0.387 0.610 0.403 0.526 1.473 0.445 4.873 Outer bay 1.238 0.620 3.990 0.046 3.448 1.023 11.613
ele ta.20 60 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix D. Continued.
Coefficient Wald Odds Variables SE P 95% CI Goodness of fit (β) χ2 Ratio Chilomycterus schoepfii: Constant -2.384 0.474 25.278 < 0.001 Octocoral surface 3.6E-06 1.0E-06 13.712 < 0.001 1.000 1.000 1.000 PE χ2 = 29.522, df = 25, P = 0.243 Channels -1.485 1.115 1.774 0.183 0.226 0.025 2.014 D χ2 = 26.678, df = 25, P = 0.372 Gulf -0.293 0.734 0.160 0.690 0.746 0.177 3.142 HLB χ2 = 2.286, df = 6, P = 0.892 Inner bay 0.057 0.585 0.009 0.923 1.058 0.336 3.332 R2 = 0.286 Outer bay -1.847 1.108 2.780 0.095 0.158 0.018 1.383 AUC = 0.811 Coryphopterus glaucofraenum: Constant -5.269 0.950 30.777 < 0.001 Depth 0.794 0.151 27.495 < 0.001 2.211 1.644 2.975 PE χ2 = 154.435, df = 213, P = 0.999 Plantae -10.173 3.860 6.947 0.008 3.8E-05 0.000 0.074 D χ2 = 164.267, df = 213, P = 0.994 Basins 0.730 0.717 1.037 0.308 2.075 0.509 8.459 HLB χ2 = 5.894, df = 8, P = 0.659 Channels -0.341 0.560 0.370 0.543 0.711 0.237 2.133 R2 = 0.313 Inner bay -0.367 0.499 0.543 0.461 0.692 0.261 1.840 AUC = 0.813 Outer bay 0.965 0.507 3.628 0.057 0.381 0.141 1.028 Diplectrum formosum: Constant -2.053 0.635 10.452 0.001 Octocoral surface -1.7E-06 1.1E-06 2.551 0.110 1.000 1.000 1.000 PE χ2 = 127.300, df = 117, P = 0.243 Depth 0.231 0.101 5.176 0.023 1.259 1.032 1.536 D χ2 = 155.294, df = 117, P = 0.010 Basins 0.566 0.619 0.834 0.361 1.761 0.523 5.926 HLB χ2 = 5.009, df = 8, P = 0.757 Channels 0.291 0.463 0.395 0.530 1.338 0.540 3.314 R2= 0.108 Gulf -0.296 0.640 0.214 0.644 0.744 0.212 2.606 AUC = 0.675 Inner bay -0.816 0.507 2.590 0.108 0.442 0.164 1.195 Outer bay 0.448 0.441 1.031 0.310 1.565 0.659 3.716
ele ta.20 61 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix D. Continued.
Coefficient Wald Odds Variables SE P 95% CI Goodness of fit (β) χ2 Ratio Eucinostomus gula: Constant -3.069 1.088 7.958 0.005 Basins 1.338 1.468 0.830 0.362 3.810 0.214 67.735 PE χ2 11.581, df = 15, P = 0.710 Channels 2.541 1.099 5.341 0.021 12.690 1.471 109.463 D χ2 = 13.324, df = 15, P = 0.577 Inner bay 1.290 1.185 1.184 0.276 3.633 0.356 37.097 HLB χ2 = 5.264, df = 7, P = 0.628 Outer bay 2.160 1.092 3.910 0.048 8.669 1.019 73.736 R2 = 0.280 Fall -0.438 0.573 0.586 0.444 0.645 0.210 1.981 AUC = 0.841 Spring -2.519 0.866 8.457 0.004 0.081 0.015 0.440 Eucinostomus spp.(TL < 4 cm): Constant 0.777 1.020 0.580 0.446 Total algae-seagrass cover 2.307 0.763 9.150 0.002 10.046 2.253 44.793 Octocoral surface -1.1E-05 4.8E-06 4.756 0.029 1.000 1.000 1.000 Depth -0.605 0.153 15.680 < 0.001 0.546 0.405 0.737 Basins 1.937 0.937 4.270 0.039 6.940 1.105 43.582 PE χ2= 196.332, df = 242, P = 0.986 Channels 2.205 0.767 8.269 0.004 9.067 2.018 40.748 D χ2 = 189.812, df = 242, P = 0.994 Gulf 0.484 1.333 0.132 0.716 1.623 0.119 22.115 HLB χ2 = 4.661, df = 8, P = 0.793 Inner bay 2.233 0.824 7.341 0.007 9.329 1.855 46.927 R2 = 0.440 Outer bay 2.321 0.818 8.052 0.005 10.189 2.050 50.634 AUC = 0.863 Fall -0.380 0.549 0.480 0.489 0.684 0.233 2.004 Winter -1.599 0.748 4.567 0.033 0.202 0.047 0.876 Spring -2.002 0.596 11.271 0.001 0.135 0.042 0.435 Haemulon flavolineatum: Constant -5.222 1.337 15.255 < 0.001 PE χ2 = 44.007, df = 37, P = 0.199 Depth 0.432 0.179 5.852 0.016 1.541 1.086 2.188 D χ2 = 31.729, df = 37, P = 0.714 Fall 0.019 0.839 0.001 0.982 1.020 0.197 5.277 HLB χ2 = 3.272, df = 7, P = 0.859 Winter 0.762 0.923 0.681 0.409 2.142 0.351 13.069 R 2 = 0.126 Spring -1.454 1.042 1.945 0.163 0.234 0.030 1.802 AUC = 0.744
ele ta.20 62 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix D. Continued.
Coefficient Wald Odds Variables SE P 95% CI Goodness of fit (β) χ2 Ratio Haemulon plumierii: Constant -1.495 0.548 7.447 0.006 Thalassia testudinum 5.752 1.551 13.763 < 0.001 314.929 15.079 6577.335 Basins 2.494 0.727 11.780 0.001 12.115 2.915 50.343 Channels 0.646 0.485 1.771 0.183 1.908 0.737 4.940 Gulf 1.032 0.626 2.719 0.099 2.806 0.823 9.569 PE χ2 = 248.217, df = 241, P = 0.361 Inner bay -0.437 0.495 0.777 0.378 0.646 0.245 1.706 D χ2 = 279.431, df = 241, P = 0.045 Outer bay 1.066 0.458 5.428 0.020 2.905 1.184 7.124 HLB χ2 = 9.971, df = 8, P = 0.267 Octocoral surface 2.4E-06 9.0E-07 6.768 0.009 1.000 1.000 1.000 R2 = 0.293 Fall 0.128 0.459 0.078 0.780 1.137 0.463 2.793 AUC = 0.780 Winter -0.565 0.571 0.980 0.322 0.568 0.185 1.740 Spring -1.359 0.476 8.140 0.004 0.257 0.101 0.653 Laurencia spp. 1.929 0.918 4.415 0.036 6.880 1.138 41.579 Haemulon sciurus: Constant -4.198 0.614 46.727 < 0.001 PE χ2 = 296.615, df = 248, P = 0.019 Laurencia spp. 3.457 1.343 6.629 0.010 31.716 2.282 440.711 D χ2 = 111.881, df = 248, P = 1.000 Thalassia testudinum 4.214 1.937 4.731 0.030 67.622 1.517 3014.793 HLB χ2 = 9.293, df = 8, P = 0.318 Octocoral surface 3.4E-06 1.1E-06 9.362 0.002 1.000 1.000 1.000 R2 = 0.128 AUC = 0.710 Haemulon spp. (TL < 4cm): Constant -2.777 0.266 108.878 < 0.001 PE χ2 = 28.605, df = 22, P = 0.156 Halodule wrightii 4.058 1.588 6.533 0.011 57.854 2.576 1299.494 D χ2 = 31.089, df = 22, P = 0.094 Fall 0.400 0.298 1.800 0.180 1.492 0.832 2.675 HLB χ2 = 0.301, df = 3, P = 0.960 Winter -0.041 0.383 0.012 0.914 0.960 0.453 2.032 R 2 = 0.031 Spring -0.427 0.325 1.731 0.188 0.652 0.345 1.233 AUC = 0.621
ele ta.20 63 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix D. Continued.
Coefficient Wald Odds Variables SE P 95% CI Goodness of fit (β) χ2 Ratio Halichoeres bivittatus: Constant -15.264 3.926 15.116 < 0.001 PE χ2 = 156.632, df = 231, P = 1.000 Temperature 0.501 0.134 14.047 < 0.001 1.650 1.270 2.145 D χ2 = 59.538, df = 231, P = 1.000 Inner bay -3.997 1.179 11.489 0.001 0.018 0.002 0.185 HLB χ2 = 2.661, df = 8, P = 0.954 Outer bay -4.142 1.389 8.893 0.003 0.016 0.001 0.242 R2 = 0.669 Octocoral surface 4.7E-06 1.6E-06 8.444 0.004 1.000 1.000 1.000 AUC = 0.971 Lagodon rhomboides: Constant -2.922 0.607 23.180 < 0.001 Plantae -9.028 3.191 8.005 0.005 1.2E-04 0.000 0.062 PE χ2 = 248.706, df = 211, P = 0.039 Sponge volume 1.7E-06 5.0E-07 12.466 0.000 1.000 1.000 1.000 D χ2 = 196.785, df = 211, P = 0.750 Basins 1.566 0.884 3.135 0.077 4.785 0.846 27.070 HLB χ2 = 3.508, df = 8, P = 0.899 Channels 2.433 0.683 12.708 < 0.001 11.396 2.990 43.429 R2 = 0.300 Gulf -0.027 1.197 0.000 0.982 0.974 0.093 10.170 AUC = 0.803 Inner bay 0.960 0.735 1.705 0.192 2.612 0.618 11.034 Outer bay 2.137 0.667 10.270 0.001 8.472 2.293 31.302 Lutjanus griseus: Constant -0.350 0.789 0.197 0.657 Depth -0.476 0.137 11.992 0.001 0.621 0.475 0.813 All sessile surface 5.3E-06 1.0E-06 28.721 < 0.001 1.000 1.000 1.000 Basins 2.981 0.778 14.677 < 0.001 19.704 4.288 90.539 PE χ2 = 222.987, df = 204, P = 0.172 Channels 1.467 0.578 6.444 0.011 4.338 1.397 13.471 D χ2 = 208.867, df = 204, P = 0.393 Gulf 1.400 0.759 3.401 0.065 4.053 0.916 17.939 HLB χ2 = 7.422, df = 8, P = 0.492 Inner bay 1.441 0.606 5.663 0.017 4.226 1.289 13.849 R2 = 0.304 Outer bay 1.716 0.627 7.495 0.006 5.561 1.628 18.994 AUC = 0.811 Fall 0.078 0.495 0.025 0.874 1.082 0.410 2.853 Winter -1.612 0.704 5.248 0.022 0.200 0.050 0.792 Spring -1.152 0.524 4.829 0.028 0.316 0.113 0.883
ele ta.20 64 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix D. Continued.
Coefficient Wald Odds Variables SE P 95% CI Goodness of fit (β) χ2 Ratio Lutjanus synagris: Constant -0.186 0.511 0.132 0.716 Laurencia spp. 2.564 1.099 5.443 0.020 12.991 1.507 111.999 Basins 1.482 0.749 3.914 0.048 4.401 1.014 19.099 PE χ2 = 210.519, df = 217, P = 0.611 Channels 0.318 0.505 0.398 0.528 1.375 0.511 3.696 D χ2 = 193.611, df = 217, P = 0.871 Gulf 0.301 0.688 0.191 0.662 1.351 0.351 5.203 HLB χ2 = 7.858, df = 8, P = 0.448 Inner bay -0.811 0.537 2.280 0.131 0.445 0.155 1.273 R2 = 0.450 Outer bay 0.904 0.493 3.365 0.067 2.468 0.940 6.482 AUC = 0.851 Fall 0.031 0.448 0.005 0.945 1.031 0.429 2.480 Winter -2.323 0.678 11.727 0.001 0.098 0.026 0.370 Spring -3.248 0.583 31.036 < 0.001 0.039 0.012 0.122 Malacoctenus macropus: Constant -1.646 0.425 15.001 < 0.001 PE χ2 = 6.321, df = 25, P = 1.000 Octocoral surface 3.1E-06 1.2E-06 6.232 0.013 1.000 1.000 1.000 D χ2 = 8.661, df = 25, P = 0.999 Region: exclusively on Ocean side HLB χ2 = 0.135, df = 6, P = 1.000 R 2 = 0.544 AUC = 0.950 Microgobius microlepis: Constant -3.605 0.940 14.722 < 0.001 All sessile volume -1.6E-06 9.0E-07 3.403 0.065 1.000 1.000 1.000 PE χ2 = 115.831, df = 125, P = 0.710 Depth 0.424 0.135 9.914 0.002 1.527 1.173 1.988 D χ2 = 112.871, df = 125, P = 0.774 Basins -0.044 0.769 0.003 0.954 0.957 0.212 4.321 HLB χ2 = 4.292, df = 8, P = 0.830 Channels -0.117 0.624 0.035 0.852 0.890 0.262 3.021 R2 = 0.201 Inner bay 0.488 0.505 0.933 0.334 1.629 0.605 4.384 AUC = 0.763 Outer bay -0.854 0.570 2.247 0.134 0.426 0.139 1.300 Ocyurus chrysurus: Constant -13.986 3.397 16.949 < 0.001 PE χ2 = 160.736, df = 237, P = 1.000 All sessile volume 1.5E-06 5.0E-07 9.402 0.002 1.000 1.000 1.000 D χ2 = 93.244, df = 237, P = 1.000 Temperature 0.379 0.113 11.213 0.001 1.460 1.170 1.823 HLB χ2= 4.173, df = 8, P = 0.841 R 2 = 0.233 AUC = 0.813
ele ta.20 65 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix D. Continued
Coefficient Wald Odds Variables SE P 95% CI Goodness of fit (β) χ2 Ratio Pareques acuminatus: Constant -1.717 0.404 18.044 < 0.001 Octocoral surface 3.0E-06 1.0E-06 8.366 0.004 1.000 1.000 1.000 PE χ2 = 31.088, df = 25, P = 0.186 Basins -0.997 1.109 0.808 0.369 0.369 0.042 3.243 D χ2 = 33.598, df = 25, P = 0.117 Channels -1.433 0.826 3.008 0.083 0.239 0.047 1.205 HLB χ2 = 5.699, df = 6, P = 0.458 Gulf -1.065 0.763 1.947 0.163 0.345 0.077 1.539 R 2 = 0.190 Inner bay -1.924 0.802 5.758 0.016 0.146 0.030 0.703 AUC = 0.760 Outer bay -0.820 0.608 1.820 0.177 0.441 0.134 1.449 Sphoeroides spengleri: Constant -1.811 0.500 13.119 < 0.001 PE χ2 = 129.247, df = 103, P = 0.041 Octocoral volume 1.0E-06 5.0E-07 3.876 0.049 1.000 1.000 1.000 D χ2 = 74.310, df = 103, P = 0.985 Fall -0.592 0.609 0.943 0.332 0.553 0.168 1.827 HLB χ2 = 10.729, df = 8, P = 0.218 Winter -1.789 1.138 2.471 0.116 0.167 0.018 1.555 R 2 = 0.123 Spring -2.212 0.875 6.396 0.011 0.109 0.020 0.608 AUC = 0.727 Stegastes leucostictus: Constant -2.017 0.457 19.445 0.000 PE χ2 = 17.7833, df = 25, P = 0.851 Octocoral surface 4.3E-06 1.3E-06 11.028 < 0.001 1.000 1.000 1.000 D χ2 = 12.021, df = 25, P = 0.986 Inner bay -2.710 1.097 6.108 0.013 0.067 0.008 0.571 HLB χ2 = 0.985, df = 6, P = 0986 R 2 = 0.529 AUC = 0.955 Urobatis jamaicensis: Constant -1.797 0.418 18.451 < 0.001 Octocoral surface 2.8E-06 1.0E-06 7.273 0.007 1.000 1.000 1.000 PE χ2 = 37.019, df = 25, P = 0.057 Channels -0.925 0.727 1.620 0.203 0.397 0.095 1.648 D χ2 = 35.530, df = 25, P = 0.079 Gulf -1.425 0.867 2.706 0.100 0.240 0.044 1.314 HLB χ2 = 11.442, df = 6, P = 0.076 Inner bay -1.817 0.804 5.108 0.024 0.163 0.034 0.786 R2 = 0.170 Outer bay -0.078 0.541 0.021 0.886 0.925 0.321 2.671 AUC = 0.743
ele ta.20 66 FWRI File Code: F219-05-08-F Tellier et al. 2008
Appendix E. Multiple linear regression models of the percent cover of Laurencia spp., Halimeda spp., Syringodium filiforme , Halodule wrightii , and Thalassia testudinum , in the nearshore hardbottom of the Florida Keys, Florida, USA, from fall 2003 to spring 2007, based on environmental factors. Unstandardized Standardized 95% Confidence Collinearity % Coefficients Coefficients Interval for B Statistics Variables t P explained Goodness of fit Lower Upper B SE Beta VIF by factor Bound Bound Halimeda spp.: Constant -3.419 0.464 -7.365 0.000 -4.339 -2.500
Collection period 0.029 0.011 0.214 2.568 0.012 0.007 0.051 1.000 4.98% F (3, 115) = 9.570 Temperature 0.045 0.016 0.227 2.723 0.007 0.012 0.077 1.001 10.41% P < 0.001 All sessile surface 1.0E-06 2.6E-07 0.325 3.891 0.000 5.0E-07 1.5E-06 1.001 4.59% R2 = 0.200 Laurencia spp.: Constant -0.949 0.156 -6.085 0.000 -1.257 -0.642 Collection period 0.031 0.007 0.284 4.291 0.000 0.017 0.046 1.051 7.33%
Depth -0.074 0.026 -0.205 -2.866 0.005 -0.125 -0.023 1.230 2.67% F (7, 202) = 5.348 Basins 0.086 0.182 0.033 0.473 0.637 -0.272 0.444 1.155 P < 0.001 Channels 0.132 0.108 0.097 1.219 0.224 -0.082 0.346 1.525 R2 = 0.156 Gulf -0.359 0.251 -0.098 -1.430 0.154 -0.854 0.136 1.130 5.64% Inner bay 0.092 0.113 0.066 0.817 0.415 -0.131 0.315 1.577 Outer bay 0.299 0.106 0.242 2.817 0.005 0.090 0.508 1.773 Thalassia testudinum : Constant -1.478 0.146 -10.127 0.000 -1.766 -1.191 Depth 0.050 0.022 0.165 2.299 0.023 0.007 0.092 1.273 7.17%
Basins 0.115 0.144 0.059 0.801 0.424 -0.168 0.398 1.344 F (7, 203) = 6.312 Channels 0.156 0.114 0.131 1.366 0.174 -0.069 0.381 2.264 P < 0.001 Gulf 0.365 0.136 0.221 2.689 0.008 0.097 0.633 1.663 6.21% R2 = 0.179 Inner bay 0.350 0.104 0.305 3.361 0.001 0.145 0.556 2.038 Outer bay 0.093 0.104 0.083 0.895 0.372 -0.112 0.298 2.110 Sponge surface -1.7E-06 5.1E-07 -0.248 -3.331 0.001 -2.7E-06 -6.9E-07 1.373 4.49%
ele ta.20 67 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix E. Continued.
Unstandardized Standardized 95% Confidence Collinearity % Coefficients Coefficients Interval for B Statistics Variables t P explained Goodness of fit Lower Upper B SE Beta VIF by factor Bound Bound Halodule wrightii :
Constant -3.359 1.369 -2.454 0.025 -6.247 -0.471 F (3, 17) = 4.634 Depth -0.305 0.131 -0.446 -2.324 0.033 -0.582 -0.028 1.136 8.23% P = 0.015 Temperature 0.111 0.043 0.466 2.578 0.020 0.020 0.202 1.009 17.56% R2 = 0.450 Octocoral volume 5.4E-06 2.2E-06 0.466 2.436 0.026 7.2E-07 1.0E-05 1.130 19.20% Syringodium filiforme : Constant -1.943 0.197 -9.841 0.000 -2.356 -1.530
Collection period -0.027 0.014 -0.213 -1.978 0.063 -0.056 0.002 1.246 18.74% F (5, 19) = 17.599 Channels 1.456 0.235 0.953 6.200 0.000 0.965 1.948 2.529 P < 0.001 Gulf 0.402 0.206 0.335 1.955 0.065 -0.028 0.833 3.145 R2 = 0.822 63.51% Inner bay -0.360 0.347 -0.126 -1.038 0.312 -1.087 0.366 1.579 Outer bay -0.049 0.332 -0.017 -0.146 0.885 -0.744 0.647 1.446
ele ta.20 68 FWRI File Code: F219-05-08-F Tellier et al. 2008
Appendix F. Multiple linear regression models of the percent cover of Anomura, Astraea spp. , Cerithiidae, Echinometra lucunter, Eucidaris tribuloides , Holothuroidea, Lytechinus variegatus , Menippe mercenaria , Mithrax spinosissimus , Ophiuroidea, Panulirus argus , Strombus costatus , Strombus gigas , and Vasum muricatum in the nearshore hardbottom of the Florida Keys, Florida, USA, from fall 2003 to spring 2007, based on environmental factors. Unstandardized Standardized 95% Confidence Collinearity % Coefficients Coefficients Interval for B Statistics Variables t P explained Goodness of fit Lower Upper B SE Beta VIF by factor Bound Bound Anomura: Constant 0.240 0.075 3.188 0.002 0.091 0.389 Octocoral volume 5.2E-07 1.2E-07 0.350 4.380 0.000 2.8E-07 7.5E-07 1.661 25.89% Basins 0.129 0.162 0.054 0.800 0.425 -0.190 0.449 1.163
Channels 0.172 0.104 0.129 1.665 0.098 -0.032 0.377 1.566 F (8, 167) = 11.540 Gulf 0.436 0.152 0.248 2.875 0.005 0.137 0.735 1.935 7.36% P < 0.001 Inner bay 0.071 0.112 0.049 0.631 0.529 -0.151 0.293 1.591 R2 = 0.356 Outer Bay -0.116 0.100 -0.094 -1.156 0.249 -0.314 0.082 1.700 Thalassia testudinum -0.668 0.314 -0.143 -2.129 0.035 -1.287 -0.048 1.178 1.39% Laurencia spp. -0.315 0.200 -0.107 -1.576 0.117 -0.711 0.080 1.197 0.96% Astraea spp.: Constant 0.507 0.130 3.887 0.000 0.248 0.766 Basins -0.294 0.244 -0.116 -1.206 0.230 -0.776 0.189 1.274
Channels -0.095 0.170 -0.064 -0.560 0.576 -0.431 0.241 1.791 F (7, 109) = 4.169 Gulf 0.568 0.200 0.316 2.841 0.005 0.172 0.964 1.705 13.51% P < 0.001 Inner bay -0.277 0.185 -0.162 -1.495 0.138 -0.644 0.090 1.626 R2 = 0.211 Outer Bay -0.193 0.157 -0.146 -1.230 0.222 -0.505 0.118 1.943 Plantea (other plant) -1.392 0.516 -0.244 -2.697 0.008 -2.415 -0.369 1.129 4.10% Halimeda spp. 2.391 1.086 0.192 2.203 0.030 0.240 4.543 1.055 3.51% Cerithiidae: Constant 0.172 0.117 1.471 0.145 -0.061 0.404 Octocoral volume 1.2E-06 1.7E-07 0.697 7.406 0.000 9.0E-07 1.6E-06 1.750 53.54%
Basins -0.070 0.217 -0.026 -0.320 0.750 -0.502 0.363 1.266 F (6, 83) = 19.148 Channels -0.186 0.184 -0.085 -1.012 0.315 -0.552 0.180 1.396 P < 0.001 Gulf 0.054 0.223 0.024 0.242 0.809 -0.390 0.498 1.945 4.52% R2 = 0.581 Inner bay 0.256 0.183 0.117 1.395 0.167 -0.109 0.621 1.391 Outer Bay -0.305 0.187 -0.135 -1.627 0.108 -0.677 0.068 1.372
ele ta.20 69 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix F. Continued.
Unstandardized Standardized 95% Confidence Collinearity % Coefficients Coefficients Interval for B Statistics Variables t P explained Goodness of fit Lower Upper B SE Beta VIF by factor Bound Bound Echinometra lucunter:
Constant -0.079 0.146 -0.544 0.591 -0.378 0.219 F (1, 28) = 12.650 Octocoral volume 2.8E-06 7.9E-07 0.558 3.557 0.001 1.2E-06 4.4E-06 1.000 31.12% P = 0.001 R 2 = 0.311 Eucidaris tribuloides:
Constant 0.130 0.055 2.355 0.031 0.013 0.246 F (1,17) = 9.677 Outer Bay -0.431 0.138 -0.602 -3.111 0.006 -0.723 -0.139 1.000 36.28% P = 0.006 R 2 = 0.363 Holothuroidea:
Constant 0.154 0.132 1.163 0.249 -0.110 0.417 F (2, 70) = 10.984 Depth -0.053 0.021 -0.262 -2.475 0.016 -0.095 -0.010 1.028 6.66% P < 0.001 Octocoral volume 1.3E-06 3.6E-07 0.372 3.515 0.001 5.5E-07 2.0E-06 1.028 17.22% R2 = 0.239 Lytechinus variegatus:
Constant 0.176 0.090 1.963 0.055 -0.004 0.355 F (2, 56) = 4.753 Thalassia testudinum -1.344 0.634 -0.265 -2.120 0.038 -2.614 -0.074 1.026 6.86% P = 0.012 Syringodium filiforme 12.442 4.880 0.319 2.550 0.014 2.666 22.218 1.026 7.65% R2 = 0.145 Menippe mercenaria: Constant -0.805 0.211 -3.822 0.000 -1.227 -0.383 Temperature 0.022 0.007 0.326 3.029 0.004 0.008 0.037 1.113 16.06% Octocoral volume 2.3E-07 6.8E-08 0.391 3.361 0.001 9.2E-08 3.6E-07 1.295 11.28%
Laurencia spp. -0.217 0.127 -0.206 -1.705 0.094 -0.471 0.038 1.407 3.03% F (8, 58) = 4.738 Basins 0.209 0.098 0.262 2.133 0.037 0.013 0.406 1.452 P < 0.001 Channels 0.090 0.072 0.183 1.248 0.217 -0.054 0.234 2.068 R2 = 0.395 Gulf -0.037 0.081 -0.065 -0.457 0.649 -0.198 0.124 1.945 9.15% Inner bay 0.021 0.077 0.037 0.271 0.787 -0.134 0.176 1.790 Outer Bay 0.010 0.081 0.020 0.128 0.899 -0.151 0.172 2.344
ele ta.20 70 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix F. Continued.
Unstandardized Standardized 95% Confidence Collinearity % Coefficients Coefficients Interval for B Statistics Variables t P explained Goodness of fit Lower Upper B SE Beta VIF by factor Bound Bound Mithrax spinosissimus:
Constant -0.114 0.055 -2.081 0.041 -0.223 -0.005 F (2, 70) = 7.915 All sessile surface 4.1E-07 1.5E-07 0.313 2.800 0.007 1.2E-07 7.0E-07 1.070 13.70% P = 0.001 Laurencia spp. -0.503 0.249 -0.225 -2.018 0.047 -1.000 -0.006 1.070 4.75% R2 = 0.184 Ophiuroidea: Constant -0.233 0.120 -1.947 0.058 -0.474 0.008 Octocoral volume 3.0E-06 6.3E-07 0.699 4.799 0.000 1.8E-06 4.3E-06 1.771 32.93%
Basins 0.079 0.215 0.046 0.368 0.715 -0.354 0.513 1.323 F (6, 46) = 6.253 Channels 0.030 0.147 0.032 0.206 0.837 -0.266 0.327 1.960 P < 0.001 7.29% Inner bay 0.112 0.122 0.134 0.913 0.366 -0.134 0.357 1.795 R2 = 0.449 Outer Bay 0.231 0.117 0.347 1.979 0.054 -0.004 0.466 2.561 Total algae-
seagrass cover -0.300 0.133 -0.249 -2.250 0.029 -0.569 -0.032 1.025 4.70% Panulirus argus: Constant -0.074 0.085 -0.872 0.385 -0.243 0.095 Basins -0.169 0.170 -0.101 -0.996 0.322 -0.507 0.169 1.257
Channels 0.313 0.106 0.418 2.948 0.004 0.102 0.523 2.472 F (6, 89) = 5.630 Gulf -0.138 0.139 -0.107 -0.993 0.323 -0.415 0.139 1.429 17.34% P < 0.001 Inner bay 0.099 0.111 0.110 0.891 0.375 -0.122 0.320 1.863 R2 = 0.275 Outer Bay 0.028 0.100 0.038 0.278 0.782 -0.170 0.226 2.280 Sponge surface 4.5E-07 3.0E-07 0.150 1.510 0.135 -1.4E-07 1.0E-06 1.206 10.18% Strombus costatus:
Constant 10.402 2.004 5.192 0.000 6.073 14.730 F (2, 13) = 15.824 Depth -0.116 0.027 -0.709 -4.303 0.001 -0.174 -0.058 1.212 41.47% P < 0.001 Salinity -0.271 0.053 -0.839 -5.092 0.000 -0.386 -0.156 1.212 29.42% R2 = 0.709 Strombus gigas:
Constant 1.806 0.753 2.397 0.034 0.165 3.448 F (1, 12) = 5.643 Temperature -0.064 0.027 -0.566 -2.376 0.035 -0.123 -0.005 1.000 31.99% P = 0.035 R 2 = 0.320
ele ta.20 71 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix F. Continued.
Unstandardized Standardized 95% Confidence Collinearity Coefficients Coefficients Interval for B Statistics % explained Variables t P Goodness of fit Lower Upper by factor B SE Beta VIF Bound Bound Vasum muricatum: Constant 0.575 0.280 2.055 0.062 -0.035 1.184
Depth -0.146 0.036 -0.646 -4.001 0.002 -0.225 -0.066 1.325 64.79% F (4, 12) = 9.724 Fall 0.151 0.181 0.229 0.833 0.421 -0.244 0.546 3.837 P = 0.001 Winter 0.243 0.216 0.260 1.125 0.283 -0.228 0.714 2.725 11.63% R2 = 0.764 Spring 0.471 0.220 0.505 2.143 0.053 -0.008 0.950 2.822
ele ta.20 72 FWRI File Code: F219-05-08-F Tellier et al. 2008
Appendix G. Average density per site per sampling period of all fish taxa found in the nearshore hardbottom of the Florida Keys from fall 2003 to spring 2007 for each region, all regions together, and for each season. Taxa were ordered in decreasing density per site per sampling period for all regions together to show the taxa most present . Region Season Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Lagodon rhomboides 1.875 10.500 0.050 0.216 4.508 0.036 3.280 1.479 5.016 4.021 4.672 Lutjanus griseus 5.531 5.094 3.425 4.402 1.109 1.563 3.076 5.237 0.938 0.984 5.078 Haemulon plumierii 5.719 2.510 8.150 1.020 2.539 2.027 2.757 4.779 1.078 0.552 5.047 Eucinostomus spp. 0.844 2.979 0.275 7.216 1.336 0.080 2.431 2.753 6.406 0.172 4.281 Menidia spp. 0.000 0.781 0.000 7.843 0.000 0.000 1.716 0.000 1.172 0.000 12.500 Engraulidae 0.000 1.042 0.000 0.000 0.000 4.464 1.176 0.000 7.813 0.000 1.563 Harengula spp. 0.000 0.000 0.000 0.000 0.000 4.018 0.882 0.000 0.000 0.000 7.031 Haemulon spp. 0.344 0.271 2.650 0.245 0.250 1.634 0.751 1.516 0.109 0.370 0.266 Archosargus rhomboidalis 0.031 2.406 0.075 0.088 0.641 0.045 0.649 0.068 0.156 1.125 1.438 Lutjanus synagris 1.563 0.823 0.275 0.245 0.813 0.491 0.635 1.132 0.094 0.083 1.359 Coryphopterus glaucofraenum 3.625 0.104 0.000 0.216 0.344 0.750 0.541 0.711 0.531 0.365 0.578 Anchoa spp. 0.000 0.000 0.000 0.000 0.000 2.232 0.490 0.000 0.000 1.302 0.000 Halichoeres bivittatus 0.000 0.042 0.025 0.020 0.070 2.063 0.484 0.984 0.000 0.120 0.578 Calamus spp. 0.281 0.719 2.000 0.216 0.227 0.098 0.431 0.189 0.844 0.552 0.375 Eucinostomus gula 0.313 0.958 0.000 0.255 0.477 0.036 0.378 0.600 0.000 0.021 1.172 Haemulon flavolineatum 0.000 1.156 0.000 0.059 0.461 0.134 0.375 0.274 1.078 0.057 0.922 Diplectrum formosum 0.625 0.469 0.300 0.108 0.563 0.232 0.365 0.279 0.625 0.359 0.375 Osteichthyes (unidentified) 0.000 0.052 1.425 0.206 0.023 0.580 0.296 0.332 0.000 0.250 0.625 Sparisoma spp. 0.000 0.146 0.025 0.118 0.086 0.964 0.286 0.595 0.109 0.078 0.172 Carangoides ruber 0.000 0.052 0.575 0.882 0.016 0.071 0.251 0.532 0.047 0.120 0.016 Scarus spp. 0.000 0.042 0.000 0.255 0.016 0.813 0.241 0.400 0.078 0.120 0.297 Gerreidae 0.063 1.125 0.075 0.020 0.055 0.000 0.239 0.626 0.000 0.016 0.000 Caranx crysos 0.000 0.010 0.850 0.510 0.086 0.098 0.214 0.316 0.000 0.193 0.188 Haemulon aurolineatum 0.000 0.021 0.000 0.490 0.422 0.018 0.212 0.474 0.000 0.005 0.266 Anisotremus virginicus 0.031 0.198 0.500 0.235 0.078 0.196 0.188 0.321 0.250 0.047 0.156 Microgobius microlepis 0.719 0.083 0.000 0.353 0.109 0.125 0.186 0.347 0.094 0.094 0.078 Sparisoma aurofrenatum 0.000 0.010 0.025 0.000 0.078 0.616 0.159 0.121 0.094 0.000 0.813 Clupeidae 0.000 0.000 0.000 0.000 0.000 0.536 0.118 0.000 0.000 0.000 0.938
ele ta.20 73 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix G. Continued.
Region Season Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Floridichthys carpio 0.000 0.000 0.000 0.569 0.000 0.000 0.114 0.095 0.016 0.000 0.609 Haemulon parra 0.000 0.177 0.000 0.020 0.031 0.313 0.114 0.195 0.313 0.005 0.000 Stegastes variabilis 0.000 0.010 0.025 0.000 0.000 0.464 0.106 0.158 0.000 0.031 0.281 Carangoides bartholomaei 0.000 0.000 0.000 0.490 0.000 0.018 0.102 0.274 0.000 0.000 0.000 Gobiidae 0.094 0.063 0.000 0.069 0.156 0.143 0.102 0.153 0.109 0.031 0.156 Pareques acuminatus 0.031 0.073 0.100 0.029 0.047 0.268 0.100 0.095 0.078 0.094 0.156 Stegastes leucostictus 0.000 0.000 0.000 0.010 0.000 0.446 0.100 0.184 0.031 0.021 0.156 Haemulon sciurus 0.000 0.010 0.000 0.147 0.117 0.170 0.098 0.179 0.063 0.063 0.000 Acanthurus chirurgus 0.000 0.063 0.000 0.000 0.008 0.375 0.096 0.163 0.094 0.016 0.141 Lucania parva 0.000 0.000 0.000 0.461 0.000 0.000 0.092 0.000 0.484 0.000 0.250 Ocyurus chrysurus 0.031 0.021 0.175 0.020 0.141 0.080 0.076 0.074 0.000 0.063 0.203 Lutjanus analis 0.000 0.031 0.725 0.010 0.016 0.000 0.069 0.026 0.031 0.146 0.000 Urobatis jamaicensis 0.000 0.031 0.050 0.020 0.070 0.170 0.069 0.095 0.063 0.047 0.063 Chriodorus atherinoides 0.000 0.271 0.025 0.000 0.000 0.000 0.053 0.137 0.000 0.005 0.000 Mycteroperca microlepis 0.000 0.000 0.625 0.000 0.008 0.009 0.053 0.000 0.109 0.099 0.016 Equetus spp. 0.000 0.000 0.100 0.010 0.008 0.179 0.051 0.079 0.016 0.052 0.000 Malacoctenus macropus 0.000 0.000 0.000 0.000 0.000 0.232 0.051 0.047 0.063 0.047 0.063 Epinephelus morio 0.000 0.032 0.475 0.000 0.008 0.018 0.049 0.016 0.234 0.026 0.032 Chilomycterus schoepfii 0.000 0.010 0.100 0.069 0.008 0.107 0.049 0.053 0.063 0.036 0.063 Abudefduf saxatilis 0.000 0.000 0.000 0.000 0.000 0.205 0.045 0.053 0.000 0.000 0.203 Sphoeroides spengleri 0.031 0.000 0.100 0.039 0.055 0.036 0.039 0.058 0.031 0.010 0.078 Pomacanthus arcuatus 0.000 0.125 0.125 0.000 0.016 0.000 0.037 0.037 0.031 0.016 0.109 Caranx spp. 0.000 0.000 0.000 0.108 0.031 0.000 0.029 0.079 0.000 0.000 0.000 Ctenogobius saepepallens 0.219 0.021 0.000 0.029 0.000 0.027 0.029 0.053 0.000 0.026 0.000 Opsanus beta 0.000 0.010 0.025 0.039 0.063 0.009 0.029 0.016 0.031 0.047 0.016 Acanthostracion quadricornis 0.000 0.010 0.050 0.020 0.023 0.054 0.027 0.011 0.031 0.047 0.016 Parablennius marmoreus 0.000 0.010 0.000 0.000 0.008 0.107 0.027 0.053 0.031 0.005 0.016 Pomacanthus paru 0.000 0.083 0.025 0.010 0.008 0.027 0.027 0.021 0.047 0.021 0.047 Lachnolaimus maximus 0.000 0.000 0.150 0.020 0.016 0.027 0.025 0.021 0.000 0.036 0.031 Scarus taeniopterus 0.156 0.000 0.000 0.000 0.008 0.054 0.024 0.032 0.000 0.031 0.000
ele ta.20 74 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix G. Continued.
Region Season Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Cryptotomus roseus 0.000 0.000 0.000 0.000 0.016 0.080 0.022 0.032 0.016 0.016 0.016 Monacanthus ciliatus 0.000 0.010 0.050 0.020 0.047 0.000 0.022 0.021 0.047 0.016 0.016 Scarus iseri 0.000 0.000 0.000 0.000 0.055 0.036 0.022 0.037 0.000 0.000 0.063 Hypoplectrus spp. 0.000 0.000 0.250 0.000 0.000 0.000 0.020 0.032 0.000 0.010 0.031 Scaridae 0.000 0.000 0.000 0.020 0.055 0.009 0.020 0.037 0.000 0.016 0.000 Sparisoma chrysopterum 0.000 0.000 0.000 0.000 0.000 0.089 0.020 0.053 0.000 0.000 0.000 Stegastes spp. 0.000 0.000 0.000 0.000 0.000 0.089 0.020 0.053 0.000 0.000 0.000 Stephanolepis hispidus 0.000 0.010 0.000 0.029 0.039 0.000 0.018 0.005 0.031 0.005 0.078 Acanthurus spp. 0.000 0.000 0.000 0.049 0.000 0.027 0.016 0.037 0.000 0.005 0.000 Carangidae 0.000 0.000 0.000 0.059 0.000 0.018 0.016 0.000 0.031 0.031 0.000 Opistognathus spp. 0.000 0.000 0.000 0.000 0.031 0.036 0.016 0.021 0.031 0.000 0.031 Acanthurus bahianus 0.000 0.000 0.000 0.000 0.000 0.063 0.014 0.021 0.000 0.016 0.000 Hypoplectrus unicolor 0.000 0.000 0.125 0.000 0.016 0.000 0.014 0.032 0.000 0.000 0.016 Synodus foetens 0.000 0.042 0.000 0.010 0.008 0.009 0.014 0.016 0.000 0.016 0.016 Archosargus probatocephalus 0.000 0.000 0.125 0.010 0.000 0.000 0.012 0.005 0.000 0.026 0.000 Hypoplectrus puella 0.031 0.021 0.075 0.000 0.000 0.000 0.012 0.016 0.016 0.010 0.000 Lutjanus spp. 0.000 0.000 0.000 0.029 0.016 0.009 0.012 0.000 0.000 0.000 0.094 Sparisoma viride 0.000 0.000 0.000 0.010 0.000 0.045 0.012 0.032 0.000 0.000 0.000 Sphyraena barracuda 0.000 0.031 0.025 0.010 0.000 0.009 0.012 0.011 0.016 0.005 0.031 Stegastes fuscus 0.000 0.000 0.000 0.000 0.000 0.054 0.012 0.016 0.016 0.000 0.031 Gerres cinereus 0.000 0.000 0.000 0.000 0.008 0.036 0.010 0.026 0.000 0.000 0.000 Sphoeroides nephelus 0.000 0.010 0.000 0.010 0.008 0.018 0.010 0.005 0.000 0.021 0.000 Caranx hippos 0.000 0.000 0.100 0.000 0.000 0.000 0.008 0.000 0.000 0.021 0.000 Diodon holocanthus 0.000 0.000 0.000 0.000 0.000 0.036 0.008 0.011 0.000 0.010 0.000 Equetus lanceolatus 0.000 0.010 0.050 0.010 0.000 0.000 0.008 0.011 0.031 0.000 0.000 Halichoeres radiatus 0.000 0.000 0.000 0.000 0.000 0.036 0.008 0.005 0.000 0.016 0.000 Oligoplites saurus 0.000 0.000 0.100 0.000 0.000 0.000 0.008 0.021 0.000 0.000 0.000 Opistognathus macrognathus 0.000 0.000 0.000 0.029 0.008 0.000 0.008 0.005 0.000 0.016 0.000 Paradiplogrammus bairdi 0.000 0.000 0.000 0.020 0.016 0.000 0.008 0.000 0.000 0.021 0.000 Syngnathus spp. 0.000 0.010 0.000 0.010 0.016 0.000 0.008 0.000 0.031 0.005 0.016
ele ta.20 75 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix G. Continued.
Region Season Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Chaetodon ocellatus 0.000 0.010 0.050 0.000 0.000 0.000 0.006 0.011 0.000 0.000 0.016 Cyprinodontidae 0.000 0.000 0.000 0.029 0.000 0.000 0.006 0.016 0.000 0.000 0.000 Microgobius spp. 0.000 0.000 0.000 0.000 0.023 0.000 0.006 0.016 0.000 0.000 0.000 Mycteroperca bonaci 0.000 0.000 0.050 0.000 0.008 0.000 0.006 0.005 0.000 0.010 0.000 Paraclinus fasciatus 0.031 0.000 0.025 0.000 0.008 0.000 0.006 0.000 0.000 0.016 0.000 Paraclinus marmoratus 0.000 0.000 0.000 0.000 0.000 0.027 0.006 0.016 0.000 0.000 0.000 Serranus subligarius 0.000 0.010 0.050 0.000 0.000 0.000 0.006 0.016 0.000 0.000 0.000 Sparisoma radians 0.000 0.000 0.000 0.000 0.008 0.018 0.006 0.005 0.016 0.005 0.000 Syngnathus floridae 0.000 0.010 0.000 0.010 0.008 0.000 0.006 0.005 0.016 0.005 0.000 Acanthemblemaria aspera 0.000 0.000 0.050 0.000 0.000 0.000 0.004 0.000 0.000 0.000 0.031 Canthigaster rostrata 0.031 0.000 0.000 0.000 0.000 0.009 0.004 0.000 0.000 0.010 0.000 Chaetodipterus faber 0.000 0.000 0.000 0.020 0.000 0.000 0.004 0.011 0.000 0.000 0.000 Chaetodon sedentarius 0.000 0.010 0.000 0.000 0.000 0.009 0.004 0.000 0.000 0.005 0.016 Echeneis naucrates 0.000 0.000 0.000 0.020 0.000 0.000 0.004 0.011 0.000 0.000 0.000 Gerres spp. 0.000 0.021 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.010 0.000 Gymnothorax funebris 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.000 0.010 0.000 Holacanthus bermudensis 0.000 0.000 0.050 0.000 0.000 0.000 0.004 0.000 0.016 0.005 0.000 Holacanthus ciliaris 0.000 0.021 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.000 0.031 Hypleurochilus bermudensis 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.031 0.000 0.000 Ogcocephalus pantostictus 0.000 0.000 0.000 0.000 0.016 0.000 0.004 0.011 0.000 0.000 0.000 Scombridae 0.000 0.000 0.000 0.020 0.000 0.000 0.004 0.000 0.000 0.010 0.000 Scorpaena brasiliensis 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.000 0.016 0.000 0.016 Stegastes diencaeus 0.000 0.000 0.000 0.000 0.000 0.018 0.004 0.005 0.000 0.000 0.016 Acanthurus coeruleus 0.000 0.000 0.000 0.000 0.008 0.000 0.002 0.000 0.000 0.000 0.016 Aluterus scriptus 0.000 0.000 0.000 0.010 0.000 0.000 0.002 0.005 0.000 0.000 0.000 Apogon spp. 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.000 0.000 0.005 0.000 Balistes capriscus 0.000 0.010 0.000 0.000 0.000 0.000 0.002 0.005 0.000 0.000 0.000 Bathygobius spp. 0.000 0.000 0.000 0.000 0.008 0.000 0.002 0.005 0.000 0.000 0.000 Blenniidae 0.000 0.000 0.025 0.000 0.000 0.000 0.002 0.000 0.000 0.005 0.000 Bothidae 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.000 0.000 0.005 0.000
ele ta.20 76 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix G. Continued.
Region Season Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Cephalopholis cruentata 0.000 0.000 0.000 0.000 0.008 0.000 0.002 0.000 0.000 0.005 0.000 Chaetodon capistratus 0.000 0.010 0.000 0.000 0.000 0.000 0.002 0.000 0.016 0.000 0.000 Dactylopterus volitans 0.000 0.000 0.025 0.000 0.000 0.000 0.002 0.005 0.000 0.000 0.000 Diplogrammus pauciradiatus 0.031 0.000 0.000 0.000 0.000 0.000 0.002 0.000 0.000 0.005 0.000 Diplogrammus spp. 0.000 0.000 0.000 0.000 0.008 0.000 0.002 0.000 0.000 0.000 0.016 Echeneis neucratoides 0.000 0.000 0.000 0.010 0.000 0.000 0.002 0.005 0.000 0.000 0.000 Elacatinus oceanops 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.000 0.000 0.005 0.000 Entomacrodus nigricans 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.000 0.000 0.005 0.000 Equetus punctatus 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.005 0.000 0.000 0.000 Ginglymostoma cirratum 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.000 0.000 0.000 0.016 Gymnothorax moringa 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.005 0.000 0.000 0.000 Gymnothorax spp. 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.005 0.000 0.000 0.000 Haemulon melanurum 0.000 0.000 0.000 0.000 0.008 0.000 0.002 0.005 0.000 0.000 0.000 Hippocampus erectus 0.000 0.000 0.000 0.010 0.000 0.000 0.002 0.000 0.000 0.005 0.000 Hippocampus spp. 0.000 0.000 0.000 0.000 0.008 0.000 0.002 0.000 0.000 0.005 0.000 Lutjanus apodus 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.005 0.000 0.000 0.000 Lutjanus mahogoni 0.000 0.000 0.000 0.000 0.008 0.000 0.002 0.005 0.000 0.000 0.000 Mugil spp. 0.000 0.000 0.000 0.010 0.000 0.000 0.002 0.000 0.000 0.005 0.000 Nicholsina usta 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.000 0.000 0.005 0.000 Opsanus spp. 0.000 0.000 0.000 0.010 0.000 0.000 0.002 0.000 0.000 0.005 0.000 Orthopristis chrysoptera 0.000 0.010 0.000 0.000 0.000 0.000 0.002 0.000 0.000 0.000 0.016 Paraclinus nigripinnis 0.000 0.000 0.000 0.000 0.008 0.000 0.002 0.005 0.000 0.000 0.000 Paralichthys albigutta 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.005 0.000 0.000 0.000 Prionotus scitulus 0.000 0.000 0.000 0.010 0.000 0.000 0.002 0.000 0.000 0.005 0.000 Prionotus spp. 0.000 0.000 0.000 0.010 0.000 0.000 0.002 0.005 0.000 0.000 0.000 Ptereleotris calliura 0.031 0.000 0.000 0.000 0.000 0.000 0.002 0.000 0.000 0.005 0.000 Sciaenidae 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.000 0.000 0.005 0.000 Scomberomorus regalis 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.005 0.000 0.000 0.000 Scorpaena plumieri 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.000 0.016 0.000 0.000 Scorpaena spp. 0.000 0.010 0.000 0.000 0.000 0.000 0.002 0.005 0.000 0.000 0.000
ele ta.20 77 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix G. Continued.
Region Season Taxa All Basins Channels Gulf Inner bay Outer bay Ocean regions Fall Winter Spring Summer Stegastes partitus 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.005 0.000 0.000 0.000 Thalassoma bifasciatum 0.000 0.000 0.000 0.000 0.008 0.000 0.002 0.005 0.000 0.000 0.000 Xyrichtys splendens 0.000 0.000 0.000 0.000 0.000 0.009 0.002 0.005 0.000 0.000 0.000 All fish 22.281 32.917 24.800 27.892 15.625 28.429 25.283 27.300 28.891 12.474 54.110
ele ta.20 78 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix H. Multiple linear regression models of the percent cover of Archosargus rhomboidalis , Calamus spp., Coryphopterus glaucofraenum , Diplectrum formosum , Eucinostomus spp., Haemulon flavolineatum , Haemulon plumierii , Haemulon spp., Halichoeres bivittatus , Lagodon rhomboides , Lutjanus synagris , and Microgobius microlepis in the nearshore hardbottom of the Florida Keys, Florida, USA, from fall 2003 to spring 2007, based on environmental factors.
Unstandardized Standardized 95% Confidence Collinearity % Coefficients Coefficients Interval for B Statistics Variables t P explained Goodness of fit Std. Lower Upper by factor B Error Beta Bound Bound VIF Archosargus rhomboidalis:
Constant 1.755 0.502 3.498 0.003 0.701 2.809 F (4, 18) = 4.130 Depth -0.187 0.093 -0.389 -2.015 0.059 -0.382 0.008 1.284 11.77% P = 0.015 Fall -0.893 0.365 -0.669 -2.450 0.025 -1.659 -0.127 2.572 R2 = 0.479 Winter -0.624 0.434 -0.331 -1.439 0.167 -1.536 0.287 1.823 22.39% Spring -0.066 0.388 -0.051 -0.171 0.866 -0.882 0.749 3.060 Calamus spp.: Constant 0.513 0.189 2.711 0.009 0.133 0.893
Fall -0.215 0.147 -0.308 -1.469 0.148 -0.509 0.079 2.987 F (4, 51) = 4.274 Winter 0.053 0.155 0.063 0.344 0.732 -0.258 0.365 2.254 25.11% P = 0.005 Spring -0.235 0.141 -0.352 -1.674 0.100 -0.518 0.047 3.007 R2 = 0.251 Depth -0.066 0.030 -0.278 -2.179 0.034 -0.126 -0.005 1.106 6.97% Coryphopterus glaucofraenum: Constant 0.301 0.103 2.928 0.005 0.094 0.507
Plantea (other plant) -1.416 1.278 -0.123 -1.108 0.273 -3.986 1.154 1.109 1.37% F (5, 48) = 8.324 Basins 0.523 0.176 0.357 2.970 0.005 0.169 0.877 1.294 P < 0.001 Channel -0.431 0.166 -0.314 -2.600 0.012 -0.763 -0.098 1.306 R2 = 0.464 45.07% Inner bay -0.417 0.138 -0.396 -3.015 0.004 -0.695 -0.139 1.550 Outer bay -0.237 0.138 -0.220 -1.714 0.093 -0.515 0.041 1.478 Diplectrum formosum:
Constant -0.094 0.034 -2.784 0.007 -0.161 -0.027 F (1, 80) = 6.085 Mithrax sculptus 0.301 0.122 0.266 2.467 0.016 0.058 0.544 1.000 7.07% P = 0.016
ele ta.20 79 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix H. Continued.
Unstandardized Standardized 95% Confidence Collinearity % Coefficients Coefficients Interval for B Statistics Variables t P explained Goodness of fit Std. Lower Upper by factor B Error Beta Bound Bound VIF Eucinostomus spp. (TL < 4 cm): Constant 0.836 0.353 2.367 0.022 0.126 1.545 Thalassia testudinum 1.577 0.437 0.365 3.614 0.001 0.700 2.455 1.060 17.31% Basins 0.133 0.367 0.058 0.362 0.719 -0.605 0.871 2.715 Channel 0.327 0.307 0.247 1.064 0.293 -0.291 0.945 5.621 F = 6.130 14.80% (9, 49) Inner bay 0.828 0.326 0.509 2.541 0.014 0.173 1.482 4.174 P < 0.001 Outer bay 0.374 0.317 0.276 1.179 0.244 -0.263 1.011 5.697 R2 = 0.530 Fall -0.174 0.167 -0.137 -1.041 0.303 -0.509 0.162 1.794 Winter -0.082 0.258 -0.036 -0.316 0.753 -0.601 0.437 1.342 12.83% Spring -0.602 0.191 -0.394 -3.153 0.003 -0.986 -0.218 1.626 Depth -0.149 0.052 -0.332 -2.890 0.006 -0.253 -0.046 1.377 8.02% Haemulon flavolineatum: Constant 0.035 0.232 0.149 0.884 -0.471 0.540
Channel 1.206 0.354 0.770 3.405 0.005 0.434 1.978 1.422 F (3, 12) = 5.259 Inner bay -0.135 0.354 -0.086 -0.381 0.710 -0.907 0.637 1.422 56.80% P = 0.015 Outer bay 0.366 0.299 0.290 1.222 0.245 -0.287 1.018 1.563 R2 = 0.568 Haemulon plumierii: Constant 0.518 0.143 3.629 0.000 0.235 0.801 Fall -0.252 0.151 -0.207 -1.669 0.098 -0.551 0.047 2.115
Winter -0.542 0.208 -0.273 -2.604 0.011 -0.955 -0.129 1.513 11.64% F (6,98) = 6.573 Spring -0.591 0.168 -0.415 -3.520 0.001 -0.925 -0.258 1.907 P < 0.001 Plantea (other plant) 0.987 0.425 0.203 2.320 0.022 0.143 1.831 1.050 5.75% R2 = 0.287 Halodule wrightii 2.396 1.232 0.181 1.946 0.055 -0.048 4.840 1.190 2.75% All sessile surface 4.9E-07 2.6E-07 0.174 1.899 0.060 -2.2E-08 1.0E-06 1.160 8.55%
ele ta.20 80 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix H. Continued.
Unstandardized Standardized 95% Confidence Collinearity % Coefficients Coefficients Interval for B Statistics Variables t P explained Goodness of fit Std. Lower Upper by factor B Error Beta Bound Bound VIF Haemulon spp. (TL < 4 cm): Constant 0.102 0.110 0.926 0.356 -0.116 0.319 Halodule wrightii 1.918 0.825 0.202 2.325 0.022 0.284 3.551 1.435 12.27% All sessile surface 6.8E-07 2.6E-07 0.274 2.637 0.009 1.7E-07 1.2E-06 2.057 11.82%
Basins 0.137 0.181 0.065 0.758 0.450 -0.220 0.494 1.414 F (7, 121) = 9.876 Channel 0.198 0.140 0.123 1.415 0.159 -0.079 0.476 1.431 P< 0.001 Gulf 0.534 0.171 0.255 3.120 0.002 0.195 0.873 1.271 12.28% R2 =0.364 Inner bay -0.219 0.139 -0.132 -1.574 0.118 -0.495 0.056 1.345 Outer bay -0.146 0.123 -0.121 -1.190 0.236 -0.389 0.097 1.950 Halichoeres bivittatus:
Constant 0.130 0.138 0.937 0.358 -0.156 0.415 F (1, 24) = 5.870 Laurencia spp. 1.300 0.536 0.443 2.423 0.023 0.193 2.407 1.000 19.65% P = 0.023 R2 = 0.197 Lagodon rhomboides: Constant 0.603 0.627 0.961 0.341 -0.657 1.862 Depth -0.151 0.091 -0.228 -1.655 0.104 -0.333 0.032 1.306 3.96%
Basins 0.606 0.574 0.179 1.057 0.296 -0.546 1.759 1.981 F (6, 49) = 3.353 Channel 1.131 0.425 0.718 2.662 0.010 0.277 1.985 5.026 P = 0.008 Gulf 0.452 0.807 0.078 0.559 0.578 -1.171 2.074 1.357 25.15% R2 =0.291 Inner bay 0.283 0.478 0.123 0.593 0.556 -0.677 1.243 2.964 Outer bay 0.763 0.426 0.484 1.791 0.079 -0.093 1.618 5.047
ele ta.20 81 FWRI File Code: F219-05-08-F Tellier et al. 2008 Appendix H. Continued.
Unstandardized Standardized 95% Confidence Collinearity % Coefficients Coefficients Interval for B Statistics Variables t P explained Goodness of fit Std. Lower Upper by factor B Error Beta Bound Bound VIF Lutjanus synagris: Constant 1.618 1.007 1.606 0.113 -0.390 3.625 Octocoral surface -5.1E-07 2.5E-07 -0.228 -2.040 0.045 -1.0E-06 -1.2E-08 1.374 2.78% Salinity -0.082 0.026 -0.332 -3.211 0.002 -0.133 -0.031 1.170 9.41%
Temperature 0.050 0.013 0.368 3.787 0.000 0.023 0.076 1.035 13.08% F (8, 73) = 4.565 Basins 0.300 0.143 0.246 2.103 0.039 0.016 0.584 1.495 P < 0.001 Channel 0.005 0.125 0.005 0.038 0.970 -0.244 0.253 1.837 R2 = 0.333 Gulf -0.085 0.161 -0.056 -0.527 0.600 -0.406 0.236 1.243 8.08% Inner bay -0.012 0.133 -0.010 -0.088 0.930 -0.277 0.254 1.447 Outer bay 0.010 0.105 0.013 0.095 0.924 -0.199 0.219 2.101 Microgobius microlepis:
Constant -0.880 0.408 -2.155 0.038 -1.708 -0.053 F (1, 37) = 3.978 Temperature 0.030 0.015 0.312 1.995 0.053 0.000 0.059 1.000 9.71% P = 0.053 R 2 = 0.097