MARTEX-BR: A high-resolution geospatial data record of MARine Temperature EXtremes across BRazil's coastal foundation habitats

About this dataset MARTEX-BR is an open-source, high-resolution data record on the occurrence of marine heatwaves (MHWs) and marine cold spells (MCSs) across major coastal habitats of Brazil. Here we present the occurrence, attributes, and trends of MHW and MCS events over 119,227 analysed sites, following a 1 km grid over a GIS-based habitat map conceptualised by Magris et al. (2021). The current assessed habitats are: (i) coral reefs, (ii) mesophotic reefs, (iii) bryozoan reefs, (iv) rhodolith beds, (v) Halimeda banks, (vi) seagrass meadows, (vii) kelp forests, and (viii) rocky shores. File description and structure The latest MARTEX-BR Version 2025.12 refers to data collected from April 1st, 1985 to December 31st, 2025. Files are separated into two classes: WT (With Trend): refers to the standard fixed baseline approach, where MHWs and MCSs are detected from the original SST time series without any trend removal. WT data are suited for investigating total thermal exposure relative to historically adapted conditions, which is the ecologically relevant metric for assessing cumulative heat stress on marine habitats. DT (Detrended): also known as shifting baseline approach, where the warming linear trend was removed from the SST time series prior to event detection. DT data are suited for isolating changes in episodic SST variability from the underlying long-term warming signal, helping to disentangle whether extreme event patterns are driven by chronic warming or by shifts in short-term variability. A set of three datasets is available for each extreme event group (MHW and MCS), as described below. mhws_dataframe and csps_dataframe: a data record of each individual marine heatwave or marine cold spell event and its attributes, recorded per single assessed pixel. Variables: Variable Description Site_ID Unique numeric identifier for each assessed pixel Latitude Latitude of the site centroid (decimal degrees) Longitude Longitude of the site centroid (decimal degrees) Ecoregion Marine ecoregion following Spalding et al. (2007) Habitat Habitat type following Magris et al. (2021) MHW_ID / CSP_ID Sequential event identifier per site Start_date Event start date (YYYY-MM-DD) End_date Event end date (YYYY-MM-DD) Duration Event duration (days) Average_intensity Mean SST anomaly over the event duration (°C) Maximum_intensity Peak SST anomaly over the event duration (°C) Cumulative_intensity Sum of daily SST anomalies over the event duration (°C days) Intensity_variability Standard deviation of daily SST anomalies over the event duration (°C) Onset_rate Rate of SST change from event start to peak intensity (°C/day) Decline_rate Rate of SST change from peak intensity to event end (°C/day) Category Severity category following Hobday et al. (2018): I — Moderate, II — Strong, III — Severe, IV — Extreme block_mhws_dataframe and block_csps_dataframe: an annual block-average summary of the event-level attributes described above, computed per site per year. Variables: Variable Description Site_ID Unique numeric identifier for each assessed pixel Latitude Latitude of the site centroid (decimal degrees) Longitude Longitude of the site centroid (decimal degrees) Ecoregion Marine ecoregion following Spalding et al. (2007) Habitat Habitat type following Magris et al. (2021) Year Centre year of the annual block Number_of_MHWs / Number_of_CSPs Number of events in the year Duration Mean event duration in the year (days) Average_intensity Mean average intensity across events in the year (°C) Maximum_intensity Mean maximum intensity across events in the year (°C) Cumulative_intensity Mean cumulative intensity across events in the year (°C days) Intensity_variability Mean intensity variability across events in the year (°C) Onset_rate Mean onset rate across events in the year (°C/day) Decline_rate Mean decline rate across events in the year (°C/day) MHWs_total_days / CSPs_total_days Total number of event days in the year MHWs_total_cum / CSPs_total_cum Total cumulative intensity in the year (°C days) Moderate_MHWs_days / Moderate_CSPs_days Number of days classified as Category I Strong_MHWs_days / Strong_CSPs_days Number of days classified as Category II Severe_MHWs_days / Severe_CSPs_days Number of days classified as Category III Extreme_MHWs_days / Extreme_CSPs_days Number of days classified as Category IV trend_mhws_dataframe and trend_csps_dataframe: a trend analysis summary per site, reporting the long-term mean, linear trend per decade, and statistical significance (p ≤ 0.05) for key event metrics. Variables: Variable Description Site_ID Unique numeric identifier for each assessed pixel Latitude Latitude of the site centroid (decimal degrees) Longitude Longitude of the site centroid (decimal degrees) Ecoregion Marine ecoregion following Spalding et al. (2007) Habitat Habitat type following Magris et al. (2021) MHWs_per_year / CSPs_per_year Long-term mean number of events per year MHWs_linear_trend_per_decade / CSPs_linear_trend_per_decade Linear trend in event frequency (events per decade) MHWs_trend_is_significant / CSPs_trend_is_significant Whether the frequency trend is statistically significant (True/False) Duration_per_year Long-term mean event duration (days) Duration_linear_trend_per_decade Linear trend in duration (days per decade) Duration_trend_is_significant Whether the duration trend is statistically significant (True/False) Max_intensity_per_year Long-term mean maximum intensity (°C) Max_intensity_linear_trend_per_decade Linear trend in maximum intensity (°C per decade) Max_intensity_trend_is_significant Whether the intensity trend is statistically significant (True/False) Cum_intensity_per_year Long-term mean cumulative intensity (°C days) Cum_intensity_linear_trend_per_decade Linear trend in cumulative intensity (°C days per decade) Cum_intensity_trend_is_significant Whether the cumulative intensity trend is statistically significant (True/False) Intensity_var_per_year Long-term mean intensity variability (°C) Intensity_var_linear_trend_per_decade Linear trend in intensity variability (°C per decade) Intensity_var_trend_is_significant Whether the intensity variability trend is statistically significant (True/False) Onset_rate_per_year Long-term mean onset rate (°C/day) Onset_rate_linear_trend_per_decade Linear trend in onset rate (°C/day per decade) Onset_rate_trend_is_significant Whether the onset rate trend is statistically significant (True/False) Decline_rate_per_year Long-term mean decline rate (°C/day) Decline_rate_linear_trend_per_decade Linear trend in decline rate (°C/day per decade) Decline_rate_trend_is_significant Whether the decline rate trend is statistically significant (True/False) How were MHWs and MCSs computed? In brief, a long-term, daily 1 km composite SST product was developed by combining two datasets: the GHRSST Multi-scale Ultra-high Resolution (MUR) Global Foundation Sea Surface Temperature Analysis (v4.1) (Chin et al., 2017) and the NOAA Coral Reef Watch (CRW) v3.1 product, also known as CoralTemp (Skirving et al., 2020). This composite SST series was validated against in situ sea surface temperature observations from the Brazilian National Buoy Programme (PNBOIA). Extreme events were then identified from the composite SST series using the standard methodology proposed by Hobday et al. (2016) and Schlegel et al. (2021), in which periods of anomalous SST conditions exceeding/below a seasonally varying 90th/10th percentile threshold for five or more consecutive days are classified as MHW/MCS events. As mentioned earlier, here we present the extreme data records identified following both WT (With Trend) and DT (Detrended) SST series approaches. For detailed information on MHWs and MCSs computation, please refer to the original article: "(Under review) A multi-decadal baseline of marine temperature extremes across Brazil's coastal foundation habitats, 2026". To whom it can be useful? MARTEX-BR can be useful for researchers, conservation practitioners, and environmental managers working on marine ecology, climate impacts, spatial conservation planning, and environmental risk assessment along the Brazilian coast. The dataset is particularly suited for studies on thermal stress exposure across habitats and ecoregions, long-term trends in extreme SST events, marine protected area effectiveness under climate change, and ecological vulnerability assessments. The data can also serve as input for species distribution models, cumulative impact analyses, and early-warning frameworks for marine temperature extremes. How can I collaborate? This data record is envisioned as a dynamic and evolving resource. Considering the rapid redistribution of sensitive coastal habitats, ongoing updates and regional collaborations will be essential to improve spatial coverage and ensure long‐term accuracy. We therefore welcome contributions from researchers and institutions interested in expanding and refining this collective effort. If you are interested in collaborating, please contact us directly via the corresponding author email provided. MARTEX-BR Web Application As part of our outreach efforts, MARTEX-BR is also presented as an interactive web application deployed at https://martex.coletivo.eco.br. The application allows users to visualise and retrieve the main MHW and MCS metrics at site-specific level across all 119,227 analysed habitat sites. The application interface was built using the React JavaScript framework and Mapbox GL JS. *Due to operational reasons, the data available in the MARTEX-BR Web Application are WT-type data only. We plan to incorporate the DT records in future updates. References Chin, T.M., Vazquez-Cuervo, J., & Armstrong, E.M. (2017). A multi-scale high-resolution analysis of global sea surface temperature. Remote Sensing of Environment, 200, 154–169. Hobday, A.J., Alexander, L.V., Perkins, S.E., Smale, D.A., Straub, S.C., Oliver, E.C.J., ... & Wernberg, T. (2016). A hierarchical approach to defining marine heatwaves. Progress in Oceanography, 141, 227–238. Hobday, A.J., Oliver, E.C.J., Sen Gupta, A., Benthuysen, J.A., Burrows, M.T., Donat, M.G., ... & Smale, D.A. (2018). Categorizing and naming marine heatwaves. Oceanography, 31(2), 162–173. Magris, R.A., Cavalcante, G.H., Gurjão, L.M., Soares, M.O., & Santos, B.A. (2021). A blueprint for securing Brazil's marine biodiversity. Diversity and Distributions, 27(2), 198–215. Schlegel, R.W., Oliver, E.C.J., Wernberg, T., & Smale, D.A. (2021). Nearshore and offshore co-occurrence of marine heatwaves and cold-spells. Progress in Oceanography, 151, 189–205. Skirving, W.J., Marsh, B.L., De La Cour, J.L., Liu, G., Harris, A., Maturi, E., ... & Eakin, C.M. (2020). CoralTemp and the Coral Reef Watch Coral Bleaching Heat Stress Product Suite Version 3.1. Remote Sensing, 12(23), 3856. 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), 573–583.