Winners and losers of reef flattening: An assessment of coral reef fish species and traits
Data files
Sep 01, 2023 version files 4.87 MB
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README.md
10.50 KB
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SiteEnv.xlsx
1.28 MB
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SpecAbund.xlsx
3.54 MB
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traits_combined_2023.xlsx
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Abstract
Anthropogenic stressors are causing widespread coral mortality, leading to loss of coral cover and decreased structural complexity that threatens reef biodiversity, functioning, and ecosystem services. Reef fishes are intimately linked to coral reef complexity, but we lack a generic understanding of which species are particularly affected by reef flattening and what traits make them susceptible. We used extensive species- and trait-based analyses to build a framework for western Atlantic fish association with both structural complexity and coral cover to better understand the implications of reef degradation. These analyses also investigated the relative importance of live coral versus the value of the structure it provides to reef fishes. We modeled how 25 biophysical and anthropogenic factors correlated with the densities of 109 fish species across 3292 Floridian reef sites. The importance of a metric of structural complexity and coral cover to the abundance of each species was then isolated. Species with positive associations were categorized as likely future ‘losers’ and negative associations as ‘winners’. We showed that structure loss was more critical than loss of coral cover, as 53% of species were predicted as losers on low-relief reefs, while only 11% were losers with decreased coral cover. We found morphological, behavioral, and ecological traits mediate species’ responses to reef degradation, and shared evolutionary history is unlikely to be a strong driver of trait relationships. Eight traits explained 79.7% of variation in species’ associations with relief and six traits explained 27.8% of associations with coral cover. Smaller, streamlined, habitat and trophic generalists are more likely winners on flattened reefs and large-bodied predators and species with deeper bodies and intermediate caudal fin shapes are likely losers. Identifying these important traits provides insight into mechanisms that may link fish and complex habitats, which allows us to better predict assemblage-wide responses to future reef flattening.
#Winners and losers of reef flattening: An assessment of coral reef fish species and traits
Datasets include density data for 109 fish species included in the analysis, environmental data used for the density analysis, and a trait table which includes the response variables of association with relief and coral cover.
Description of the data and file structure
SpecAbund.xlsx - data used for the boosted regression tree models for fish species density. Includes all density information 109 species and all environmental variables
SiteEnv.xlsx - environmental data extracted from geospatial layers and matched to fish survey sites. Already merged with the species dataset in SpecAbund.xlsx but included as a smaller file
traits_combined_2023.xlsx - trait data and response variables used for the trait analysis. Response variables extracted from boosted regression tree models and traits derived from literature and FishBase
Columns in SpecAbund and SiteEnv:
site: numeric site descriptor matching NOAA Reef Visual Census sites
model: factor used to subset data for two separate models - not used in these analyses
Year: year of RVC fish survey
Month: month of RVC fish survey
Latitude: latitude of RVC fish survey
Longitude: longitude of RVC fish survey
Depth: depth of RVC fish survey averaged for each surveyor
Region: jurisdiction of RVC fish survey. FLA KEYS = Florida Keys; DRY TORT = Dry Tortugas; SEFCRI = Southeast Florida Coral Reef Initiative (Renamed to Coral Ecological Conservation Area)
Coral_cover: percentage of benthos made up of living hard coral visually estimated by RVC surveyors
Reef_complexity: maximum hard relief measured by averaging the height of the highest rigid point above the lowest point in 8 segments of the cylinder for RVC surveys
SST: minimum monthly average sea surface temperature in Celcius derived from CoRTAD database from 2012-2016
NPP: net primary productivity derived from remotely sensed chlorophyll-a from the OSU VGPM model
Wave_exposure: exposure calculated using linear wave theory
Habitat_type_classLV0: habitat classification of each site according to the FWC Unified Reef Map level 0
Habitat_type_classLV2: habitat classification of each site according to the FWC Uniifed Reef Map level 2
Coral_area_UFRTM_20km: area classified as reef by Unified Reef Map level 0 within 20km of each site
Coral_area_UFRTM_200km: area classified as reef by Unified Reef Map level 0 within 200km of each site
Depth_Sbracco: remotely sensed depth of survey sites
Deepwater: euclidean distance in meters over water to the 30-meter bathymetric line
FSA: euclidean distance in meters over water to the nearest fish spawning aggregation site
Marina_slips_10km: number of marina slips over 45ft within 10km of each site
Marina_slips_25km: number of marina slips over 45ft within 25km of each site
Marine_reserve: protected status of site; whether fishing was allowed or not
Population_20km: human population living within 20km of reef sites derived from LandScan dataset
Population_50km: human population living within 50km of reef sites derived from LandScan dataset
Recreational_fishermen_50km: number of recreational fishing licenses within 50km of reef sites derived by zip code
Tourist_fishing: statistics from Johns et al. 2001 and publicly available dataset of hotel units in Florida
Artificial_reefs_1km: number of artificial reefs within 1 km
SG_permits_50km: number of recreational snapper-grouper fishing permits within 50km
SG_charter_permits_50km: number of commercial snapper-grouper fishing permits within 50km
total_gravity_intercept: number of people in population centers within 500km divided by the square of travel time (same as Total_gravity and this column was not used)
Total_gravity: number of people in population centers within 500km divided by the square of travel time
Keys_Division: sub-jurisdictions of Florida Keys including Upper, Middle, Lower Keys and Marquesas; NAs for non Florida Keys sites
FKNMS: Florida Keys National Marine Sanctuary sites; NAs for non Florida Keys sites
DryTortugas: Dry Tortugas sites; NAs for non-Dry Tortugas sites
BNP: Biscayne National Park sites; NAs for non-BNP sites
CoralECA: Coral Ecological Conservation Area sites; NAs for non-ECA sites (also known as SEFCRI)
Nursery_seagrass: connectivity of reef sites to continuous seagrass patches within 10 km
Nursery_mangroves: connectivity of reef sites to mangrove stands within 12 km
connectivity: number of larva from upstream modeledto a connectivity matrix; model does not extend to further north reefs and those sites were assigned NAs
Comm_engagement: metrics of commercial engagement based on landings and permits provided by NOAA
Comm_reliance: metrics of commercial engagement based on landings and permits relative to size of fishing community provided by NOAA
Rec_engagement: metrics of recreational engagement based on landings and permits provided by NOAA
Rec_reliance: metrics of recreational engagement based on landings and permits relative to size of fishing community provided by NOAA
pop_per_area_reef_20km: human population divided by area of reef within 20km
Commerical_pounds_landed: annual number of pounds of fish reported by commercial anglers
Random: random number assigned to each column; not used in final model
impact: fishing impact variable derived in previous project; not used in final model
YEAR: Year of RVC surveys, repeat of earlier column
HABITAT_CD: habitat code used by NOAA RVC surveys to stratify sites
PCT_CORALCOVER: percent coral cover, same as Coral_cover column
REGION: jurisdiction of RVC survey sites, repeat of earlier column
MAX_HARD_RELIEF: maximum hard relief; same as Reef_complexity column
Columns in SpecAbund (not in SiteEnv):
no.divers: number of divers for RVC survey
Remaining variables are individual reef fish species that were present on at least 4% of reef surveys
Columns in traits_combined_2023:
Species: full species name with space
Sp: full species name with underscore
Family: family
Genus: genus
Spec: species epithet
MaxLengthTL: maximum total length in cm for South Florida/Caribbean where available
Body_size_max: maximum total length in cm overall - contains typos and not used
MaximumLengthSL: maximum standard length in cm for South Florida/Caribbean where available
MaxJuvLength: maximum length of juvenile stages
AspectRatio: aspect ratio of caudal fin measured as the Area^2/Height; derived from FishBase or calculated from photos
AspectRatio_Q: aspect ratio of caudal fin measured as the Area^2/Height; derived from Quimbayo et al. 2021
Log_AR: natural log of AspectRatio column
swim_type: combination of fins used to propel fish
swim_mode: combination of fins and body movement used to propel fish
body_shape: categorical variable describing body fineness
Total.length.body.depth.ratio: numerical variable describing body fineness; calculated by total length of fish divided by height at the pectoral fin
presence_defense: binary variable of whether species has chemical or physical defenses
ComDepthMax: common maximum depth of species
DepthMax: maximum recorded depth of species
Depth_min: minimum recorded depth
Depth_range: DepthMax minus Depth_min
Troph: trophic level of species
Trophic_level: trophic level of species - contains typos and not used
Diet.x: categorical variable of feeding type; H = Herbivore, C = Carnivore, P = Piscivore, Z = Planktivore - not used
Diet.y: categorical variable of feeding type; om = omnivore, im = invertivore, is = invertivore and omnivore, pk = planktivore, hd = herbivore, fc = piscivore - not used
Noctural: binary variable of whether species is active at night
Diel_activity: categorical variable of whether species is active during day, night, or both
shoaling: categorical variable of group size; solitary, paired, shoaling (loose aggregation), or schooling - not used
Size_group: categorical variable of group size; solitary, paired, small group, medium group, large group
Position.in.water.column: common position in water column of species
level_water: common position in water column - not used
Specialist: binary variable of specialization to reef; 1 = found only on coral reefs
Fished: binary variable of whether species is targeted by anglers; 1 = fished
Spawn: spawning mode of species; PEL = Pelagic, BAL = Balistiform, DEM = Demersal
Spawning: spawning mode of species
Brackish: species found in lower salinity habitats
Multihabitat: species found in habitats other than coral reefs
Rafter: species larvae rafting
Relief: percent contribution of maximum hard relief to overall fish density derived from density models
Coral: percent contribution of coral cover to overall fish density derived from density models
Cryptic: binary variable of whether species are considered cryptic and to be excluded from trait models
random: random variable used to determine significance in trait models
Home_range: size of home range; mob = mobile, vmob = highly mobile; sed = sedentary
Sharing/Access information
Data was derived from the following sources:
- Geospatial data was extracted using the protocols described in: Zuercher, R., D. P. Kochan, R. D. Brumbaugh, K. Freeman, R. Layko, and A. R. Harborne. 2023. Identifying correlates of coral-reef fish biomass on Florida’s Coral Reef to assess potential management actions. Aquatic Conservation: Marine and Freshwater Ecosystems 33:246–263.
- Fish density data extracted from NOAA RVC Surveys: https://grunt.sefsc.noaa.gov/rvc_analysis20/ and reproduced under open data sharing licenses.
- Trait data were extracted from a variety of sources, described in the paper and shared under open data licenses. Sources include FishBase (Froese and Pauly, 2000), Quimbayo, J. P., F. C. Silva, T. C. Mendes, D. S. Ferrari, S. L. Danielski, M. G. Bender, V. Parravicini, M. Kulbicki, and S. R. Floeter. 2021. Life‐history traits, geographical range, and conservation aspects of reef fishes from the Atlantic and Eastern Pacific. Ecology 102.,
Green, S. J., and I. M. Côté. 2014. Trait-based diet selection: Prey behaviour and morphology predict vulnerability to predation in reef fish communities. Journal of Animal Ecology 83:1451–1460., Bridge, T. C. L., O. J. Luiz, R. R. Coleman, C. N. Kane, and R. K. Kosaki. 2016. Ecological and morphological traits predict depth-generalist fishes on coral reefs. Proceedings of the Royal Society B: Biological Sciences 283:20152332.,
Reef and fish survey data
Since 1979, NOAA has conducted fishery-independent coral reef fish and benthic surveys across southeast Florida as part of the Reef Visual Census (RVC) and National Coral Reef Monitoring Program (NCRMP) (Bohnsack et al. 1999; Smith et al. 2011; NOAA 2017). Sites were surveyed annually (before 2014) or biennially (2014 to present) using a two-stage stratified random sampling scheme in conjunction with a 100x100 m resolution grid covering Florida’s Coral Reef. The first stage stratified grid cells by habitat type within three sub-jurisdictions (Florida Keys, Dry Tortugas/Marquesas, and Southeast Florida), and survey sites were randomly selected within each 100x100 m cell. One or two diver pairs conduct stationary point counts and size assessments of over 500 species of fish (see Bohnsack and Bannerot 1986 for details of this approach), visually estimate benthic coral cover, and measure rugosity based on maximum vertical relief of the substrate within individual 7.5m radius cylinders. Briefly, each diver created a list of fish identified to species level that entered the cylinder in the first five minutes, counting and sizing highly mobile species. The following ten minutes were used to count and size individuals remaining in the cylinder while adding any new fish entering the cylinder. The rest of the dive was used to assess the benthos, including measuring maximum height of hard relief and visually estimating live hard coral cover from an overhead view within the cylinder (NOAA 2017). Maximum hard relief is a metric of reef complexity measured as the average of the highest rigid point within 8 segments of the 7.5m survey cylinder. Coral cover was measured as the average of live hard coral visually estimated within 8 segments of the 7.5m survey cylinder. Data from the pair of divers (or from multiple pairs of divers where appropriate) at the same site were averaged. We used survey data from 2005 to 2018, excluding 2010 when a significant cold snap caused fish kills (Kemp et al. 2016). Out of 7046 unique sites in the region surveyed since 2005, using data from the most recent surveys if surveyed multiple times, 3292 sites were designated as coral reef or hardbottom by the FWC UFRTM Level 2 classifications. We included 109 reef fish species from 25 families (Table 1), all of which were present at 4% or more of sites, which was experimentally determined as the minimum number of non-zero values necessary to run the density models. We excluded cryptic species that are difficult to survey with point counts (e.g. species in families Gobiidae, Blennidae, and Muraenidae) (Willis 2001).
Biophysical and Anthropogenic Predictors
To model species densities at each survey site, we compiled biophysical and anthropogenic explanatory variables (Table 1) from in situ, remotely sensed, and published data sources. Justification for inclusion and full derivation of each predictor is available in the supplementary materials (SI). Maximum hard relief, coral cover, and depth were all measured in situ by NOAA NCRMP divers. Maximum hard relief provides a proxy for large scale complexity, capturing boulders and drop offs, while coral cover generally captures small-scale complexity. Month and year of surveys were included as categorical variables to account for seasonal and longer-term trends in species densities.
The remaining predictors were extracted (using the coordinates of the underwater surveys) from continuous geospatial data layers with a grid size of 100 x 100 m in ArcGIS Pro (v10.7 ESRI). The biophysical variables used to predict individual species’ densities from these layers included UFRTM habitat level 2 class, the total area of coral reef and hardbottom habitats within 20 km of each site, connectivity to mangrove and seagrass nursery habitats, distance to deep water habitats, lowest monthly mean sea surface temperature (SST), net primary productivity (NPP), larval connectivity, wave exposure, and distance to the nearest mapped fish spawning aggregation (FSA). We also included 10 anthropogenic variables to account for human impacts, specifically impacts from fishing which has major effects on populations of reef fishes like snappers and groupers (Zuercher et al. 2023). These predictors included the number of recreational anglers within 50 km, human population within 50 km, human population per reef area within 50 km, the number of marina slips for boats under 14 m within 25 km, gravity of all potential fish markets (within 500 km), the number of federal commercial (within 50km) and charter (within 25 km) snapper-grouper permits, metrics of community fishing engagement and reliance, the estimated number of tourist fishers, and the protected status of reefs.
Fish trait data compilation
We assembled 13 morphological, behavioral, and life history traits to identify which traits were predictive of a species’ relationship to relief or coral cover (Table 2). Justification for inclusion and full derivation of each set of fish traits is available in the supplementary materials (SI). Trait values were collected from a combination of published literature, online databases (particularly FishBase, Froese and Pauly 2022), and measurements from publicly available photographs. For morphological traits derived from photographs, a single value was obtained from the mean of three lateral images of adults of the species of interest. Trait values from local specimen images (i.e., in Florida or northern Caribbean) were used where available. When possible, continuous, quantifiable analogs were used in place of categorical variables to produce higher quality functional spaces (Maire et al. 2015). Maximum total length and body fineness (total length to body depth ratio), were included based on the known importance of morphology in determining predation risk and availability of spatial refuge (Green and Côté 2014). We included the presence of physical or chemical defenses as categorized by Green and Côté (2014) or as described in the ‘Biology’ or ‘Threat to humans’ sections of FishBase (Froese and Pauly 2022). Defenses such as sharp spines or barbs that make capture or ingestion by predators difficult (see Price et al. 2015) or toxins that harm predators or decrease palatability (see Harris and Jenner 2019) may reduce the need for physical refuge. Swim mode (fin and body region and movement combinations used for propulsion, e.g. labriform or subcarangiform) and aspect ratio (ratio of height squared to surface area) of the caudal fin were used to highlight the importance of swimming performance for predator avoidance and maneuverability for navigating complex reefs (Fulton 2010). We included schooling (Green and Côté 2014) as an important anti-predator defense (Magurran 1990) that may reduce reliance on physical refuge. Additionally, nocturnality (Green and Côté 2014) was included as a potential behavior to reduce the risk of visual predators (Kronfeld-Schor and Dayan 2003); however, nocturnal species may also rely on structure for diurnal refuge (Ménard et al. 2008). Home range size, depth range, and the use of multiple habitat types (hereafter referred to as multihabitat) were included as proxies for specialization to shallow coral reef habitats (Luiz et al. 2013). Position in water column and spawning mode were included to identify specific associations with the benthos (Luiz et al. 2013). Finally, trophic level (Froese and Pauly 2022) was included because reef structure has a variety of effects on feeding, for example, complexity may increase the surface area for herbivores to graze (González-Rivero et al. 2017) or provide hiding places for ambush predators (Harborne et al. 2022).