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Assessment of heat stress exposure in the wider Caribbean coral reefs through the regional delineation of degree heating week data from 1985-01-01 to 2017-12-31 (NCEI Accession 0205786)

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This data package presents a three-decade (1985-2017) assessment of heat stress exposure in the wider Caribbean coral reefs at the ecoregional and local scales.

The main heat stress indicator used was the Degree Heating Weeks (DHW) calculated from daily Sea Surface Temperature "CoralTemp" data from CRW-NOAA available from 1985 to the present and from the maximum monthly mean (MMM) version 3.1 at 5 km of the CRW-NOAA program. Different metrics were calculated based on daily DHW and are available in this dataset:

a) the maximum value of DHW per pixel for the entire time series
b) the frequency of the annual maximum values of DHW ≥ 4 °C-
weeks (a predictor of coral "bleaching risk") per pixel
c) the frequency of the annual maximum values of DHW ≥ 8 °C-
weeks (a predictor of bleach-induced mortality or "mortality risk") per pixel
d) the year in which the maximum of DHW occurred
e) the trend of the annual maximum values of DHW per pixel.

Based on the spatiotemporal annual maximum DHW, a new regionalization of heat stress was performed by cluster analysis with the K-means algorithm through the unsupervised classification, this new regionalization delimits the Caribbean in 8 Heat Stress Regions (HSR). We summarized spatiotemporal daily data to describe the temporal patterns at an ecoregional scale by calculating the descriptive statistics of the regional DHW on a given day. This dataset represents a new baseline and regionalization of heat stress in the wider Caribbean coral reefs that will enhance conservation and planning efforts underway.
  • Cite as: Muñiz-Castillo, Aarón Israel; Rivera-Sosa, Andrea; Chollett, Iliana; Eakin, C. Mark; Andrade-Gómez, Luisa; McField, Melanie; Arias-González, Jesús Ernesto (2019). Assessment of heat stress exposure in the wider Caribbean coral reefs through the regional delineation of degree heating week data from 1985-01-01 to 2017-12-31 (NCEI Accession 0205786). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0205786. Accessed [date].
gov.noaa.nodc:0205786
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    • File Specification: NCEI_NetCDF_Grid_Template_v2.0, ACDD-1.3, CF-1.6
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Distributor NOAA National Centers for Environmental Information
+1-301-713-3277
NCEI.Info@noaa.gov
Dataset Point of Contact NOAA National Centers for Environmental Information
ncei.info@noaa.gov
Time Period 1985-01-01 to 2017-12-31
Spatial Bounding Box Coordinates
West: -97
East: -59.2
South: 8.35
North: 32.7
Spatial Coverage Map
General Documentation
Associated Resources
Publication Dates
  • publication: 2019-11-15
Data Presentation Form Digital table - digital representation of facts or figures systematically displayed, especially in columns
Dataset Progress Status Complete - production of the data has been completed
Historical archive - data has been stored in an offline storage facility
Data Update Frequency As needed
Supplemental Information
Submission Package ID: 2JHFMG
Purpose This data package presents a three-decade (1985-2017) assessment of heat stress exposure in the wider Caribbean coral reefs at the ecoregional and local scales. This database is part of the doctoral thesis of A. Israel Muñiz Castillo carried out at CINVESTAV Unidad Mérida with a CONACyT doctoral grant #340074. The study presented in Muñiz-Castillo et al. (2019) related to this dataset aimed to: (a) Characterize the geographical extent and variability of heat stress in the Caribbean ecoregions, (b) classify the wider Caribbean into new heat-stress regions based on historical heat stress, and (c) assess the temporal variability of heat stress in the Caribbean and its relation to past ENSO events.
Use Limitations
  • accessLevel: Public
  • Distribution liability: NOAA and NCEI make no warranty, expressed or implied, regarding these data, nor does the fact of distribution constitute such a warranty. NOAA and NCEI cannot assume liability for any damages caused by any errors or omissions in these data. If appropriate, NCEI can only certify that the data it distributes are an authentic copy of the records that were accepted for inclusion in the NCEI archives.
Dataset Citation
  • Cite as: Muñiz-Castillo, Aarón Israel; Rivera-Sosa, Andrea; Chollett, Iliana; Eakin, C. Mark; Andrade-Gómez, Luisa; McField, Melanie; Arias-González, Jesús Ernesto (2019). Assessment of heat stress exposure in the wider Caribbean coral reefs through the regional delineation of degree heating week data from 1985-01-01 to 2017-12-31 (NCEI Accession 0205786). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0205786. Accessed [date].
Cited Authors
Principal Investigators
  • Aarón Muñiz-Castillo
    LEEAC-CINVESTAV (Centro de Investigación y de Estudios Avanzados del I.P.N.)
  • Jesús Arias-González
    LEEAC-CINVESTAV (Centro de Investigación y de Estudios Avanzados del I.P.N.)
Contributors
Resource Providers
Publishers
Acknowledgments
  • Related Funding Agency: CINVESTAV; FOMIX; CONACYT
Theme keywords NODC DATA TYPES THESAURUS NODC OBSERVATION TYPES THESAURUS WMO_CategoryCode
  • oceanography
CoRIS Discovery Thesaurus
  • Geographic Information > Climate
  • Geographic Information > Sea Surface Temperature
CoRIS Theme Thesaurus
  • EARTH SCIENCE > Biosphere > Zoology > Corals > Coral Diseases > Bleaching > Degree Heating Week (DHW)
  • EARTH SCIENCE > Biosphere > Zoology > Corals > Coral Diseases > Bleaching > Sea Surface Temperature (SST)
Global Change Master Directory (GCMD) Science Keywords Provider Keywords
  • Annual maximum DHW valye from 1985 to 2017
  • Frequency of annual maximum DH values > = 4 degree Celsius weeks
  • Frequency of annual maximum DH values > = 8 degree Celsius weeks
  • Heat-stress regions
  • Maximum DHW value per pixel from 1987 to 2017
  • Median of the regional DHW values on a given day
  • Reef location mask
  • Trend of annual maximum DHW
  • Year in which the maximum DHW occurred
Data Center keywords NODC COLLECTING INSTITUTION NAMES THESAURUS NODC SUBMITTING INSTITUTION NAMES THESAURUS
Instrument keywords NODC INSTRUMENT TYPES THESAURUS Provider Instruments
  • Mixed spatial information
  • Satellite sensor
Place keywords NODC SEA AREA NAMES THESAURUS CoRIS Place Thesaurus
  • COUNTRY/TERRITORY > Bahamas > Bahamas (24N078W0000)
  • COUNTRY/TERRITORY > Cuba > Cuba (22N079W0000)
  • COUNTRY/TERRITORY > Dominican Republic > Dominican Republic (19N070W0000)
  • COUNTRY/TERRITORY > Haiti > Haiti (19N072W0000)
  • COUNTRY/TERRITORY > Mexico > Mexico (20N100W0000)
  • COUNTRY/TERRITORY > United States of America > Florida > Florida Reef Tract (26N080W0007)
  • COUNTRY/TERRITORY > United States of America > Puerto Rico > Puerto Rico (18N066W0000)
  • OCEAN BASIN > Atlantic Ocean > Caribbean Sea /North Atlantic Ocean > Cuba > Cuba (22N079W0000)
  • OCEAN BASIN > Atlantic Ocean > Caribbean Sea /North Atlantic Ocean > Dominican Republic > Dominican Republic (19N070W0000)
  • OCEAN BASIN > Atlantic Ocean > Caribbean Sea /North Atlantic Ocean > Greater Antilles > Haiti (19N072W0000)
  • OCEAN BASIN > Atlantic Ocean > Caribbean Sea /North Atlantic Ocean > Puerto Rico > Puerto Rico (18N066W0000)
  • OCEAN BASIN > Atlantic Ocean > Gulf of Mexico > Mexico > Mexico (20N100W0000)
  • OCEAN BASIN > Atlantic Ocean > North Atlantic Ocean > Bahamas > Bahamas (24N078W0000)
  • OCEAN BASIN > Atlantic Ocean > North Atlantic Ocean > Florida Reef Tract (26N080W0007)
Global Change Master Directory (GCMD) Location Keywords
Project keywords NODC PROJECT NAMES THESAURUS Provider Project Names
  • Biodiversided y funcionalidad de los arrecifes coralinos del Caribe Mexicano
  • Healthy Reefs for Healthy People (Smithsonian Institution and Mesoamerican Reef)
  • Red Oceano, Clima y Cambio Global
  • Vulnerabilidad ecologica del Sistema Arrecifal Mesoamericano
Keywords NCEI ACCESSION NUMBER
Use Constraints
  • Cite as: Muñiz-Castillo, Aarón Israel; Rivera-Sosa, Andrea; Chollett, Iliana; Eakin, C. Mark; Andrade-Gómez, Luisa; McField, Melanie; Arias-González, Jesús Ernesto (2019). Assessment of heat stress exposure in the wider Caribbean coral reefs through the regional delineation of degree heating week data from 1985-01-01 to 2017-12-31 (NCEI Accession 0205786). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0205786. Accessed [date].
Access Constraints
  • Use liability: NOAA and NCEI cannot provide any warranty as to the accuracy, reliability, or completeness of furnished data. Users assume responsibility to determine the usability of these data. The user is responsible for the results of any application of this data for other than its intended purpose.
Fees
  • In most cases, electronic downloads of the data are free. However, fees may apply for custom orders, data certifications, copies of analog materials, and data distribution on physical media.
Lineage information for: dataset
Processing Steps
  • 2019-11-15T21:59:30Z - NCEI Accession 0205786 v1.1 was published.
Output Datasets
Lineage information for: dataset
Processing Steps
  • Parameter or Variable: Maximum DHW value per pixel from 1987 to 2017 (calculated); Units: °C-weeks; Observation Category: satellite; Sampling Instrument: satellite sensor; Sampling and Analyzing Method: The main heat stress indicator used was the Degree Heating Weeks (DHW) (Liu et al., 2014) calculated from daily Sea Surface Temperature "CoralTemp" data from CRW-NOAA available from 1985 to the present (https://coralreefwatch.noaa.gov/product/5km/index.php) and from the maximum monthly mean (MMM) version 3.1 at 5 km of the CRW-NOAA program (https://coralreefwatch.noaa.gov/satellite/bleaching5km/index.php). The maximum DHW value for the entire time series from 1985 to 2017 for the Caribbean reefs was obtained (Muñiz-Castillo et al., 2019). This raster shows the greatest magnitude of exposure to heat stress presented in the entire time series, it was obtained from simple functions performed in R version 3.4.1 (R Core Team, 2017) using the “raster” (Hijmans, 2017) and “sp” (Bivand, R.S. et al., 2013; Pebesma & Bivand, 2005) libraries. References: Bivand, R.S., Pebesma, E.J., & Gomez-Rubio, V. (2013). Applied spatial data analysis with R; Second edition. New York: Springer. Hijmans, R. J. (2017). Raster: Geographic Data Analysis and Modeling. R package version 2.6-7. Liu, G., Heron, S. F., Mark Eakin, C., Muller-Karger, F. E., Vega-Rodriguez, M., Guild, L. S., … Lynds, S. (2014). Reef-scale thermal stress monitoring of coral ecosystems: New 5-km global products from NOAA coral reef watch. Remote Sensing, 6(11), 11579–11606. http://doi.org/10.3390/rs61111579 Muñiz-Castillo, A. I., Rivera-Sosa, A., Chollett, I., Eakin, C. M., Andrade-Gómez, L., McField, M., & Arias-González, J. E. (2019). Three decades of heat stress exposure in Caribbean coral reefs: a new regional delineation to enhance conservation. Scientific Reports. http://doi.org/10.1038/s41598-019-47307-0 Pebesma, E. J., & Bivand, R. S. (2005). Classes and methods for spatial data in R. R News 5. R Core Team. R: A language and environment for statistical computing. (2017). Vienna, Austria.: R Foundation for Statistical Computing..
  • Parameter or Variable: Annual maximum DHW value from 1985 to 2017 (calculated); Units: °C-weeks; Observation Category: satellite; Sampling Instrument: satellite sensor; Sampling and Analyzing Method: The main heat stress indicator used was the Degree Heating Weeks (DHW) (Liu et al., 2014) calculated from daily Sea Surface Temperature "CoralTemp" data from CRW-NOAA available from 1985 to the present (https://coralreefwatch.noaa.gov/product/5km/index.php) and from the maximum monthly mean (MMM) version 3.1 at 5 km of the CRW-NOAA program (https://coralreefwatch.noaa.gov/satellite/bleaching5km/index.php). The annual DHW maximum was the main indicator used to evaluate the exposure to heat stress and represents the maximum heat stress accumulated in the year (Muñiz-Castillo et al., 2019). This multi-layer raster was produced with functions conducted in R version 3.4.1 ( R Core Team, 2017) using the “raster” (Hijmans, 2017) and “sp” (Bivand, R.S. et al., 2013; Pebesma & Bivand, 2005) libraries. References: Bivand, R.S., Pebesma, E.J., & Gomez-Rubio, V. (2013). Applied spatial data analysis with R; Second edition. New York: Springer. Hijmans, R. J. (2017). Raster: Geographic Data Analysis and Modeling. R package version 2.6-7. Liu, G., Heron, S. F., Mark Eakin, C., Muller-Karger, F. E., Vega-Rodriguez, M., Guild, L. S., … Lynds, S. (2014). Reef-scale thermal stress monitoring of coral ecosystems: New 5-km global products from NOAA coral reef watch. Remote Sensing, 6(11), 11579–11606. http://doi.org/10.3390/rs61111579 Muñiz-Castillo, A. I., Rivera-Sosa, A., Chollett, I., Eakin, C. M., Andrade-Gómez, L., McField, M., & Arias-González, J. E. (2019). Three decades of heat stress exposure in Caribbean coral reefs: a new regional delineation to enhance conservation. Scientific Reports. http://doi.org/10.1038/s41598-019-47307-0 Pebesma, E. J., & Bivand, R. S. (2005). Classes and methods for spatial data in R. R News 5. R Core Team. R: A language and environment for statistical computing. (2017). Vienna, Austria.: R Foundation for Statistical Computing..
  • Parameter or Variable: Frequency of annual maximum DHW values ≥ 4 ºC-weeks (calculated); Units: N° of events; Observation Category: satellite; Sampling Instrument: satellite sensor; Sampling and Analyzing Method: The frequency of annual maximum DHW values ≥ 4 °C-weeks (a predictor of coral “bleaching risk”) per pixel based on the annual DHW maxima was calculated (Muñiz-Castillo et al., 2019). This indicator represents the frequency at which annual maxima have exceeded the recognized threshold at which coral bleaching can occur (Beyer et al., 2018; Eakin et al., 2010; Heron, Maynard, van Hooidonk, & Eakin, 2016). The resulting raster presenting the frequency of events was obtained from simple functions performed in R version 3.4.1( R Core Team, 2017) using the “raster” (Hijmans, 2017) and “sp” (Bivand, R.S. et al., 2013; Pebesma & Bivand, 2005) libraries. References: Beyer, H. L., Kennedy, E. V, Beger, M., Chen, C. A., Cinner, J. E., Darling, E. S., … Hoegh-Guldberg, O. (2018). Risk-sensitive planning for conserving coral reefs under rapid climate change. Conservation Letters, (May), e12587. http://doi.org/10.1111/conl.12587 Bivand, R.S., Pebesma, E.J., & Gomez-Rubio, V. (2013). Applied spatial data analysis with R; Second edition. New York: Springer. Eakin, C. M., Morgan, J. A., Heron, S. F., Smith, T. B., Liu, G., Alvarez-Filip, L., … Yusuf, Y. (2010). Caribbean corals in crisis: Record thermal stress, bleaching, and mortality in 2005. PLoS ONE, 5(11). http://doi.org/10.1371/journal.pone.0013969 Heron, S. F., Maynard, J. A., van Hooidonk, R., & Eakin, C. M. (2016). Warming Trends and Bleaching Stress of the World’s Coral Reefs 1985–2012. Scientific Reports, 6(1), 38402. http://doi.org/10.1038/srep38402 Hijmans, R. J. (2017). Raster: Geographic Data Analysis and Modeling. R package version 2.6-7. Muñiz-Castillo, A. I., Rivera-Sosa, A., Chollett, I., Eakin, C. M., Andrade-Gómez, L., McField, M., & Arias-González, J. E. (2019). Three decades of heat stress exposure in Caribbean coral reefs: a new regional delineation to enhance conservation. Scientific Reports. http://doi.org/10.1038/s41598-019-47307-0 Pebesma, E. J., & Bivand, R. S. (2005). Classes and methods for spatial data in R. R News 5. R Core Team. R: A language and environment for statistical computing. (2017). Vienna, Austria.: R Foundation for Statistical Computing..
  • Parameter or Variable: Frequency of annual maximum DHW values ≥ 8 ºC-weeks (calculated); Units: N° of events; Observation Category: satellite; Sampling Instrument: satellite sensor; Sampling and Analyzing Method: The frequency of annual maximum DHW values ≥ 8 °C-weeks (a predictor of bleaching-induced mortality or “mortality risk”) per pixel based on the annual DHW maxima was calculated (Muñiz-Castillo et al., 2019). This indicator represents the frequency at which annual maxima have exceeded the recognized threshold at which mass coral mortality due to bleaching may occur (Beyer et al., 2018; Eakin et al., 2010; Heron et al., 2016). The resulting raster presenting the frequency of events was obtained from simple functions performed in R version 3.4.1( R Core Team, 2017) using the “raster” (Hijmans, 2017) and “sp” (Bivand, R.S. et al., 2013; Pebesma & Bivand, 2005) libraries. References: Beyer, H. L., Kennedy, E. V, Beger, M., Chen, C. A., Cinner, J. E., Darling, E. S., … Hoegh-Guldberg, O. (2018). Risk-sensitive planning for conserving coral reefs under rapid climate change. Conservation Letters, (May), e12587. http://doi.org/10.1111/conl.12587 Bivand, R.S., Pebesma, E.J., & Gomez-Rubio, V. (2013). Applied spatial data analysis with R; Second edition. New York: Springer. Eakin, C. M., Morgan, J. A., Heron, S. F., Smith, T. B., Liu, G., Alvarez-Filip, L., … Yusuf, Y. (2010). Caribbean corals in crisis: Record thermal stress, bleaching, and mortality in 2005. PLoS ONE, 5(11). http://doi.org/10.1371/journal.pone.0013969 Heron, S. F., Maynard, J. A., van Hooidonk, R., & Eakin, C. M. (2016). Warming Trends and Bleaching Stress of the World’s Coral Reefs 1985–2012. Scientific Reports, 6(1), 38402. http://doi.org/10.1038/srep38402 Hijmans, R. J. (2017). Raster: Geographic Data Analysis and Modeling. R package version 2.6-7. Muñiz-Castillo, A. I., Rivera-Sosa, A., Chollett, I., Eakin, C. M., Andrade-Gómez, L., McField, M., & Arias-González, J. E. (2019). Three decades of heat stress exposure in Caribbean coral reefs: a new regional delineation to enhance conservation. Scientific Reports. http://doi.org/10.1038/s41598-019-47307-0 Pebesma, E. J., & Bivand, R. S. (2005). Classes and methods for spatial data in R. R News 5. R Core Team. R: A language and environment for statistical computing. (2017). Vienna, Austria.: R Foundation for Statistical Computing..
  • Parameter or Variable: Trend of annual maximum DHW (calculated); Units: °C-weeks per year; Observation Category: model output; Sampling Instrument: satellite sensor; Sampling and Analyzing Method: The trend of annual maximum DHW was calculated with a Generalized Least Squares model (GLS) (Muñiz-Castillo et al., 2019), introducing to the regression a structure of temporal autocorrelation (AR1, which represents the covariance of order 1 considering the temporal similarity between the nearest years; Weatherhead et al., 1998). Because we calculated the trend from annual values, the GLS model did not consider seasonality. Once the slope of the regression was obtained, we calculated the significance of the slope at a 95% confidence, considering as a null hypothesis that the tendency was equal to zero. In all pixels in which the slope was not significant, the value of zero was set to represent a null slope. The analyses were performed from the functions available in the “nlme” library (Pinheiro J, Bates D, DebRoy S, 2017) of program R (R Core Team, 2017). *References: Muñiz-Castillo, A. I., Rivera-Sosa, A., Chollett, I., Eakin, C. M., Andrade-Gómez, L., McField, M., & Arias-González, J. E. (2019). Three decades of heat stress exposure in Caribbean coral reefs: a new regional delineation to enhance conservation. Scientific Reports. http://doi.org/10.1038/s41598-019-47307-0 Pinheiro J, Bates D, DebRoy S, S. D. and R. C. T. (2017). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-131, https://CRAN.R-project.org/package=nlme. R Package Version 3.1-131, Https://CRAN.R-Project.Org/Package=nlme. http://doi.org/10.1016/j.tibs.2011.05.003 R Core Team. R: A language and environment for statistical computing. (2017). Vienna, Austria.: R Foundation for Statistical Computing. Weatherhead, E. C., Reinsel, G. C., Tiao, G. C., Meng, X.-L., Choi, D., Cheang, W.-K., … Frederick, J. E. (1998). Factors affecting the detection of trends: Statistical considerations and applications to environmental data. Journal of Geophysical Research: Atmospheres, 103(D14), 17149–17161. http://doi.org/10.1029/98JD00995.
  • Parameter or Variable: Year in which the maximum DHW occurred (calculated); Units: Year; Observation Category: satellite; Sampling Instrument: satellite sensor; Sampling and Analyzing Method: The year in wich the maximum DHW was recorded in each pixel was calculated (Muñiz-Castillo et al., 2019). The resulting raster presenting the year with the maximum DHW in the entire time series and was obtained from simple functions performed in R version 3.4.1( R Core Team, 2017) using the “raster” (Hijmans, 2017) and “sp” (Bivand, R.S. et al., 2013; Pebesma & Bivand, 2005) libraries. *References: Bivand, R.S., Pebesma, E.J., & Gomez-Rubio, V. (2013). Applied spatial data analysis with R; Second edition. New York: Springer. Muñiz-Castillo, A. I., Rivera-Sosa, A., Chollett, I., Eakin, C. M., Andrade-Gómez, L., McField, M., & Arias-González, J. E. (2019). Three decades of heat stress exposure in Caribbean coral reefs: a new regional delineation to enhance conservation. Scientific Reports. http://doi.org/10.1038/s41598-019-47307-0 Pebesma, E. J., & Bivand, R. S. (2005). Classes and methods for spatial data in R. R News 5. R Core Team. R: A language and environment for statistical computing. (2017). Vienna, Austria.: R Foundation for Statistical Computing..
  • Parameter or Variable: Heat-Stress Regions (calculated); Units: Heat-Stress Regions ID; Observation Category: model output; Sampling Instrument: satellite sensor; Sampling and Analyzing Method: The regionalization of heat stress was performed by a clustering analysis with the K-means algorithm through the unsupervised classification function present in the “RStoolbox” library (Leutner, Horning, Schwalb-Willmann, & Hijmans, 2018). The maximum annual DHWs during the years 1985-2017 were used as input to the clustering procedure. To identify the optimal number of groups, we used the graphic elbow criterion. This evaluation illustrated a curve of the remaining variation from the addition of each given number of groups, revealing a relationship of the variance among added groups and the total variance. The spatiotemporal variation of heat stress (cluster analysis using K-means and eight optimal regions obtained using elbow criteria) yielded eight spatially distinct heat-stress regions (HSR) characterized by different time patterns of exposure levels (Muñiz-Castillo et al., 2019). *References: Leutner, B., Horning, N., Schwalb-Willmann, J., & Hijmans, R. J. (2018). RStoolbox: Tools for Remote Sensing Data Analysis. R package version 0.2.3. Muñiz-Castillo, A. I., Rivera-Sosa, A., Chollett, I., Eakin, C. M., Andrade-Gómez, L., McField, M., & Arias-González, J. E. (2019). Three decades of heat stress exposure in Caribbean coral reefs: a new regional delineation to enhance conservation. Scientific Reports. http://doi.org/10.1038/s41598-019-47307-0.
  • Parameter or Variable: Median of the regional DHW values on a given day (calculated); Units: °C-weeks; Observation Category: satellite; Sampling Instrument: satellite sensor; Sampling and Analyzing Method: Based on the spatiotemporal information of the daily DHW in the mask of coral reefs, time series that present the main descriptive statistical indicators at the ecoregion scale (Spalding et al., 2007) on a given day was made. For this purpose, the statistical descriptors were obtained summarizing the information of all the pixels of each ecoregion in each of the days considered (Muñiz-Castillo et al., 2019). *References: Muñiz-Castillo, A. I., Rivera-Sosa, A., Chollett, I., Eakin, C. M., Andrade-Gómez, L., McField, M., & Arias-González, J. E. (2019). Three decades of heat stress exposure in Caribbean coral reefs: a new regional delineation to enhance conservation. Scientific Reports. http://doi.org/10.1038/s41598-019-47307-0 Spalding, M. D., Fox, H. E., Allen, G. R., Davidson, N., Ferdaña, Z. a., Finlayson, M., … Robertson, J. (2007). Marine Ecoregions of the World: A Bioregionalization of Coastal and Shelf Areas. BioScience, 57(7), 573. http://doi.org/10.1641/B570707.
  • Parameter or Variable: Reef locations mask (calculated); Units: Mask; Observation Category: other; Sampling Instrument: Based on mixed spatial information; Sampling and Analyzing Method: Heat stress on coral reefs was characterized by analyzing the pixels within 20 km of reef locations within the wider Caribbean (32.7°N-8.4°N, 59.2°-97.0°W). We used a buffer to include nearby adjacent areas with the presence of coral reefs and improving visualization and reef scale. Reef locations were obtained from the Global Distribution of Coral Reefs (UNEP-WCMC & Centre, 2010). This raster represents the mask of the coral reefs for the subsequent analysis presented in Muñiz-Castillo et al. (2019) and in this dataset. *References: Muñiz-Castillo, A. I., Rivera-Sosa, A., Chollett, I., Eakin, C. M., Andrade-Gómez, L., McField, M., & Arias-González, J. E. (2019). Three decades of heat stress exposure in Caribbean coral reefs: a new regional delineation to enhance conservation. Scientific Reports. http://doi.org/10.1038/s41598-019-47307-0 UNEP-WCMC & Centre, W. F. Global Distribution of Coral Reefs. World Wide Web Electron. Publ. http//data.unep-wcmc.org/datasets 1–4 (2010)..
Acquisition Information (collection)
Instrument
  • laboratory analysis
Last Modified: 2024-02-21T13:44:27Z
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