DOI: 10.5061/dryad.jdfn2z3dd
title: Western Indian Ocean coral and fish normalized site richness collected between 1991 to 2000

description: Aim: Strong social-ecological trade-offs between resource extraction and
 protection have created challenges for large, protected area management in
 natural-resource-dependent countries. Therefore, local governments and
 community conservation activities are becoming common and need information
 about low environmental exposure and high biodiversity for planning
 localized conservation activities. Location: the western Indian Ocean
 Methods: Coral reef sites were evaluated for local scale environmental and
 species richness to elucidate local patterns in spatial heterogeneity.
 Local coral and fish taxonomic richness were normalized to partially
 account for common and heterogeneous disturbances to coral cover and fish
 biomass. Residuals were evaluated for patterns of local diversity with
 geography, environmental stress, and by machine learning to evaluate the
 relationship with 21 specific environmental variables. Results: High
 variability in richness was found at similar latitudes where richness was
 high. Relationships with specific environmental and human influences
 variables were complex and spatially heterogeneous. Expected large-scale
 biogeographic variables influenced richness but variability and
 environmental influences were highly specific and localized. Among the
 environmental and human influence variables examined, ~ 8 variables
 contributed 8 to 25% of the variance to the richness of both coral and
 fishes. Main conclusions: Decisions to focus small-scale conservation on
 locally biodiverse locations could contribute to species persistence by
 planning for local heterogeneity in richness and stress. From this
 specific data set, sites in the Pemba Channel between the Tanzanian
 mainland and Pemba Island, and northern Mozambique and Madagascar fit
 these characteristics.
description: Field study sites were
 undertaken in 4 ecoregions and 7 countries that ranged in latitude
 from 2.04°S (Kenya) to 26.08°S (Mozambique) and longitudes of 32.96°E
 (Mozambique) to 57.71°E in (Mauritius). Sites were located on the windward
 and leeward sides of coral reefs in depths from 1.5 to 20 meters depth at
 low tide (the region’s tidal range is ~1 to 4 meters). Sites were all
 located on calcium carbonate coral bottoms colonized by hard and soft
 corals and various algae, with sand and seagrass being a smaller portion
 of the benthic cover (McClanahan & Muthiga 2016). Sites were
 distributed among four fisheries management categories, namely, high
 compliance reserves (no-take closures), low compliance reserves, and
 restricted and unrestricted fishing, as previously described (McClanahan
 et al. 2015). Low compliance closures were areas legally gazetted as
 marine reserves but where fishing was evident by personal observations or
 reports in the literature. Restricted fishing locations had restrictions
 on the usage of small-meshed nets or spearguns. Sites were not randomly
 selected but biased towards sampling marine protected areas and comparable
 reference sites. At each location, coral surveys
 were conducted in 7 countries (Comoros,
 Kenya, Madagascar, Mauritius, Mayotte, Mozambique, and Tanzania)
 using a roving observer method. Two experienced observers (N.A.
 Muthiga and T.R. McClanahan) sampled
 ~40 m<sup>2</sup> over a broad range of ~1000 meters or 40 minutes of
 sampling. For each survey, an observer assessed the coral
 community in a series of haphazardly selected replicate quadrats (~2
 m<sup>2</sup>), such that richness was the number of taxa
 encountered per ~40
 m<sup>2</sup> (McClanahan et al. 2007). Within each quadrat,
 hard coral colonies (&gt;5 cm) were identified to genus and the
 coverage of hard coral was estimated to the nearest 5%. One identification exception was <em>Porites</em>,
 which was classified as either branching, massive, or <em>Porites
 rus</em>. A second was
 <em>Galaxea</em>, which was classified as
 <em>G. astreata</em> or <em>G. fasciscularis</em>.
 These divisions were made because of the different life histories and possible functions of
 these two common genera. Fish families were
 sampled by snorkel and scuba diving
 using two separate belt transect methods. The first method estimated the number of
 species and biomass for the
 same 500 m<sup>2</sup> belt (5-m x 100-m) transect (McClanahan
 2019). One to 9 belt
 transect replicates were completed per
 study site between 1991 and 2020. Individual fish were counted in 9 preselected families that included the Acanthuridae,
 Balistidae, Chaetodontidae, Diodontidae, Labridae, Monacanthidae,
 Pomacanthidae, Pomacentridae, and Scaridae. These 9 families contain a mix
 of life histories and fished and unfished species and were chosen for their high
 number of species and as indicators of the total species richness in a
 location or region (Allen and Werner 2002). Individuals from the different
 families were counted during subsequent passes of the same belt
 transect and combined to create a
 metric of richness as the number of species per 500 m<sup>2</sup> in these 9
 families. The number of coral and fish
 species were counted in 657 site x transect x time
 replications. Subsequently, replicated
 transects within sites were pooled into unique 346 sites of which 2 were
 outliers. Following outlier and pooling procedure, the
 final dataset of 344 unique
 sites had 286
 coral and 320 fish replicates. Taxonomic richness was normalized to z-scores (i.e., -3 to +3 SDs)
 using the residuals of the
 best-fit species-coral
 cover and fish species-biomass relationships. The residuals were derived from a best-fit to a
 logistic model where
 residuals were extracted and normalized. Residuals are hereafter referred to as
 residual richness. Standardization was also applied to
 the GSM and site susceptibility metrics. The normalization reduced sampling bias that might influence testing for
 associations with environmental factors.
description: # Western Indian Ocean coral and fish normalized site richness
 This readme file was
 generated on 2022-07-17 by Tim McClanahan GENERAL INFORMATION   1.    
 Title of Dataset: WIO Coral and fish normalized site richness collected
 from 1991 to 2020 2.     Author Information  Name: Tim McClanahan ORCID:
 Institution: Wildlife
 Conservation Society Address: 2300 Southern Boulevard, Bronx, New York,
 10460, United States Email:   3.    
 Geographic location of data collection: Kenya, Tanzania, Mozambique,
 Madagascar, Mauritius, Mayotte and, Comoros 4.     Funding sources that
 supported the collection of the data: Marine Science for Management
 (MASMA) programme of the Western Indian Ocean Marine Science Association
 (WIOMSA), United States Agency for International Development (USAID),
 World-Wide Fund for Nature (WWF) Mozambique country program, Sustainable
 Poverty Alleviation from Coastal Ecosystem Services (SPACES) project
 NE-K010484-1′, funded by the Ecosystem Services for Poverty Alleviation
 (ESPA) program (ESPA) of the Department for International Development
 (DFID), the Economic and Social Research Council (ESRC) and the Natural
 Environment Research Council (NERC), the John D. and Catherine T.
 MacArthur Foundation, the Bloomberg Foundations, and Synchronicity  
 SHARING/ACCESS INFORMATION 1.     Contact author:
 2.      Dryad data storage uses the CC0
 license, which enables users to distribute, remix, adapt, and build upon
 the material in any medium or format, with no conditions. 3.    
 Recommended citation for this dataset: McClanahan 2022. Western Indian
 Ocean Normalized Number of Taxa Data   DATA & FILE OVERVIEW   1.    
 Description of dataset The data includes the coral reef locations and site
 descriptors, residuals of numbers of fish and coral taxa, environmental
 variables, water quality and human influence variables averaged over
 1991-2019 sampling period. 2.     File List:   METHODOLOGICAL INFORMATION
   Fish families were sampled by snorkel and scuba diving using two
 separate belt transect methods. The first method estimated the number of
 species and biomass for the same 500 m2 belt (5-m x 100-m) transect. One
 to nine belt transect replicates were completed per study site between
 1991 and 2020. Individual fish were counted in nine preselected families
 that in- cluded the Acanthuridae, Balistidae, Chaetodontidae, Diodonti-
 dae, Labridae, Monacanthidae, Pomacanthidae, Pomacentridae and Scaridae.
 These nine families contain a mix of life histories and fished and
 unfished species and were chosen for their high number of species and as
 indicators of the total species richness in a location. Individuals from
 the different families were counted during subsequent passes of the same
 belt transect and combined to create a metric of richness as the number of
 species per 500 m2 in these nine families. Biomass was estimated by
 counting and sizing individuals in 24 families and an ‘others’ category.
 These 25 groups contributed to most of the total biomass and catch from
 coral reefs (i.e., &gt;95%). Individual fish sizes were estimated in
 10-cm-size interval classes with a minimum cut-off size of 3 cm.
 Length–weight relationships per family taken from Kenyan fisheries
 landings were used to calculate by the by-family and total biomass.
 Environmental data compilations accessed several sources from satellite
 and shipboard measurements. Environmental layers included those expected
 to influence marine organisms including oceanographic layers such as wave
 energy, photosynthetic active radiation (PAR), and chlorophyll-a.
 Additionally, several water temperature or thermal stress metrics known to
 influence chronic and acute stress on marine organisms were calculated
 including SST mean, skewness, rate of rise, kurtosis, and cumulative
 degree-heating weeks. Distance to 50m depth and distance to shore were
 included. Human influence variables included travel time to nearest
 population and market and gravity to the nearest city and population. Fish
 census observers also recorded local site metrics including depth and
 habitats, recorded as reef edge, reef crest, reef flat, or reef lagoon.
  DATA-SPECIFIC INFORMATION FOR: WIO Coral and fish normalized site
 richness.csv   1.     Number of variables: 26 2.     Number of cases/rows:
 344 3.     Variable List: * Latitude, decimal degrees: Geographic latitude
 location * Longitude, decimal degrees: Geographic longitude location *
 Depth, m: Depth of surveyed location in meters * Coral residuals
 (asymptote model), taxa: Residuals of numbers of coral taxa and percentage
 coral cover from asymptote model * Fish residuals (asymptote model), taxa:
 Residuals of numbers of fish and fish biomass from asymptote model * Coral
 community bleaching susceptibility, % bleached: Percentage of bleached
 coral * Standardized fish biomass, z score: standardized or z-score of
 fish biomass * Standardized number of fish, z score: standardized or
 z-score of numbers of fish * Standardized coral community bleaching
 susceptibility, z score: standardized or z-score of coral community
 bleaching susceptibility * Global stress model, 0 to 1: multivariate index
 of coral reef exposure to climatic stress * TSA_minimum, oC: Minimum
 thermal stress anomaly calculated from 1985 and 2015 * TSA_maximum, oC:
 Maximum thermal stress anomaly calculated from 1985 and 2015 * SST_mean,
 oC: Mean of sea surface temperatures calculated from 1985 - 2020 *
 SST_skewness: skewness of sea surface temperature distributions calculated
 from 1985 - 2020. * SST_Rate of rise, oC/y: the slope of the Kendall trend
 test on mean annual SST values for the 1985 to 2020 period * SST_kurtosis:
 kurtosis of sea surface temperature distributions calculated from 1985 -
 2020 * cummulative DHW, oC-weeks: Cumulative degree heating weeks (ºC-
 weeks), estimated as the sum of annual maximum DHW for each reef cell from
 1985 – 2020. * PAR_maximum, E/m2/day: Photosynthetically active radiation
 (maximum) in Einstein’s/m2/day * mean_NPP, g C/m2/yr: mean net primary
 productivity (g C/m2/yr) * Mean wave energy, kW/m: Mean wave energy
 (kW.m-1) * Travel time_population, minutes: Travel time to nearest
 population (minutes) * Travel time_market, minutes: Travel time to market
 (minutes) * Gravity_nearest city/numbers per time travel2: Gravity to
 nearest city or market (population/travel time (hrs)2) * Gravity_nearest
 population, population/travel time2: Gravity to nearest population
 (population/travel time (hrs)2) * Distance to 50m depth, km: Distance to
 50m depth or contour from sampling site * Distance to shore, km: Distance
 to shoreline from sampling site * Chlorophyll_mean, mg m–3: Mean
 chlorophyll a (mg/m3) * Chlorophyll_median, mg m–3: Median chlorophyll a
 (mg/m3) 4.     Missing data codes: * blank cells = Missing 5.    
 Specialized formats or other abbreviations used: * DHW = Degree heating
 weeks * SST = Sea surface temperature * TSA = Thermal stress anomaly