Western Indian Ocean coral and fish normalized site richness collected between 1991 to 2000
Data files
Dec 11, 2023 version files 120.47 KB
Abstract
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.
README: Western Indian Ocean coral and fish normalized site richness
https://doi.org/10.5061/dryad.jdfn2z3dd
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: https://orcid.org/0000-0001-5821-3584
Institution: Wildlife Conservation Society
Address: 2300 Southern Boulevard, Bronx, New York, 10460, United States
Email: tmcclanahan@wcs.org
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: tmmcalanahan@wcs.org
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., >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
Methods
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 m2 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 m2), such that richness was the number of taxa encountered per ~40 m2 (McClanahan et al. 2007). Within each quadrat, hard coral colonies (>5 cm) were identified to genus and the coverage of hard coral was estimated to the nearest 5%. One identification exception was Porites, which was classified as either branching, massive, or Porites rus. A second was Galaxea, which was classified as G. astreata or G. fasciscularis. 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 m2 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 m2 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.