DOI: 10.5061/dryad.xpnvx0kmn
title: Winners and losers of reef flattening: An assessment of coral reef fish species and traits

description: 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.
description: <strong>Reef and fish survey data</strong>
 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).
 <strong>Biophysical and Anthropogenic
 Predictors</strong> To model species densities at
 each survey site, we compiled biophysical and anthropogenic explanatory
 variables (Table 1) from in <em>situ</em>, 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
 <em>situ </em>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. <strong>Fish
 trait data compilation</strong> 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).
description: #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.,