DOI: 10.5061/dryad.w3r22811m
title: Mapping the Azores Marine Park vulnerability to temperature changes

description: Aim: Identifying highly vulnerable regions to climate change is
 increasingly incorporated in marine management planning given the
 redistribution of species in the three-dimensional space following
 temperature changes. Here, we developed a spatially explicit vulnerability
 framework incorporating sensitivity, exposure, and adaptive capacity of
 species living within the Azores Marine Park (AMP). Location: Azores
 Marine Park, North Atlantic. Methods: We quantified species sensitivity to
 temperature changes based on thermal affinity and georeferenced their
 distribution with quality-controlled records from various data
 compilators. To assess their degree of exposure we extracted historical
 (1995 - 2020) environmental temperature across latitudes, longitudes, and
 depths and calculated mean interannual temperature change (i.e., increase
 or decrease) and temperature variability. We estimated the adaptive
 capacity of species based on two traits representing the ability to
 relocate at adult and early life-stage (i.e., “Motility” and
 “Developmental Mechanism”) using the FUN Azores trait database. To map the
 results, we pooled the species into 3D-regions representing cubes of 0.25º
 x 0.25º resolution and 50 and 500 m depth bands at shallow and deep areas,
 respectively. We then assigned a sensitivity, exposure, and adaptive
 capacity score to each spatial unit based on species scores and
 occurrences, combined them to a final vulnerability class (i.e., “Highly
 Vulnerable”, “Advisable Monitoring”, “Expected Relocation”, and “Least
 Concern”). Results: “Highly Vulnerable” and “Advisable Monitoring” regions
 exist only in the benthic environment across various MPAs and depths. The
 increased mobility of benthopelagic and pelagic species explain the
 absence of the most vulnerable categories in these environments. Main
 conclusions: We advise strong conservations measures in “Highly
 Vulnerable” areas and monitoring of environmental variables and
 populations in areas classified as “Advisable Monitoring” and “Expected
 Relocation”, respectively. Our results suggests that the Azores deep-sea
 benthos is highly vulnerable to both warming and wide temperature
 variations.
description: <strong>Spatio-temporal resolution</strong>
 We created a three-dimensional grid with fixed coordinates (0.25
 x 0.25 degrees) and depths where species data and environmental data were
 grouped together. We created specific depth bands for every 50 m. above
 200 m. (i.e., 0 – 50 m., 50 – 100 m., 100 – 150 m., 150 – 200 m.) and for
 every 500 m. below 200 m. down to 3500 m. (i.e., 200 – 500 m., 500 – 1000
 m., 1000 – 1500 m., 1500 – 2000 m., 2000 – 2500 m., 2500 – 3000 m., 3000 –
 3500 m.). We obtained a community baseline by pooling together data from
 all recorded years (1905 – 2018), because data were scarce and unbalanced
 through time. <strong>Temperature exposure based
 on historical data</strong> We extracted
 historical temperature (i.e., 1995 - 2020) datapoints in the 4D domain
 (i.e., Latitude, Longitude, Depth, and Time) from the Copernicus data
 portal<sup>7</sup> using Python. We imported the net.cdf files
 into R using the package
 <em>“</em>ncdf4<em>”</em>. We assigned each
 datapoint into a cube of the original three-dimensional mesh according to
 its four dimensions and calculated the mean interannual temperature change
 per cube to inform magnitude of change and its associated standard
 deviation to estimate temperature variation within each 3D-region across
 years. We classified each region into general increasing temperature if
 mean interannual change per region was positive and into high or low
 variability in relation to 50<sup>th</sup> percentile of
 temperature variability across years.
 <strong>Final vulnerability
 score</strong> Vulnerability of each 3D-region
 is determined by the region’s exposure to temperature change and the
 sensitivity and adaptive capacity of its component species. A final
 vulnerability category for each 3D-region was based on the combination of
 the three elements. Therefore, exposed regions where most species are
 sensitive and lack adaptive capacity classify into the “Highly Vulnerable”
 category. Regions where most species are sensitive and have a low adaptive
 capacity but are not exposed classify as “Advisable Monitoring”. We
 classified regions as “Expected Relocation”, if they are exposed and most
 of its inhabiting species are sensitive but adaptive. Regions of “Least
 Concern” were reserved for cases where species were not sensitive at all.
 We applied the same procedure to assess vulnerability to temperature
 change (i.e., increase tendency) and temperature variation producing two
 scores for each 3D-region.
 <strong>Biogeography</strong> We
 mapped the final derived vulnerability 3D-region classifications in R
 using the package <em>“</em>terra<em>”</em>
 (Hijmans, 2023) and <em>“</em>ggplot2<em>”</em>.
 Geographic coordinate system used for all the mappings correspond to WGS
 84 (World Geodetic System 1984 - EPSG 4326) and for map projections WGS
 84/ UTM Zone 26N (EPSG:32626 code; https://epsg.io/). For each
 environment (i.e., benthic, benthopelagic, and pelagic), we mapped
 vulnerability scores to temperature increases and vulnerability scores to
 temperature variation, resulting in six vulnerability maps.
 <strong>Correlations between temperature and species
 traits</strong> To assess the relationship
 between species characteristics and species temperature tolerances (i.e.,
 temperature maximum, minimum, and range) we ran spearman correlations with
 the function <em>cor </em>from the “stats” package in R
 programming software. We selected traits that define a species ecological
 niche form the “FUN Azores” trait database (i.e., “Maximum Body Size”,
 “Motility”, “Environmental Position”, “Trophic Position”) or new traits
 adapted from the “FUN Azores” trait database (i.e., “Depth Ranges”,
 “Substrate Type”).
description: # Mapping the Azores Marine Park vulnerability to temperature changes
 [https://doi.org/10.5061/dryad.w3r22811m](https://doi.org/10.5061/dryad.w3r22811m) ## Description of the data and file structure This dataset contains code and data files that should allow replication of the workflow that created a biogeographic vulnerability assessment to temperature changes in the Azores Marine Park. Identifying highly vulnerable regions to climate change is increasingly incorporated in marine management planning given the redistribution of species in the three-dimensional space following temperature changes. Here, we developed a spatially explicit vulnerability framework incorporating sensitivity, exposure, and adaptive capacity of species living within the Azores Marine Park (AMP). We adapt this framework across diverse ecosystems, from benthic, benthopelagic, and pelagic domains adopting a trait biogeography perspective. The data provided responds to the following methodology: * subdividing the Azores Marine Park into three-dimensional spatial units (i.e., cubes with specific latitude, longitude, and depths); * identifying historical temperature mean and variation at each 3D-region and potential climate refugia; * assigning a vulnerability class (i.e., “Highly Vulnerable”, “Advisable Monitoring”, “Expected Relocation”, and “Least Concern”) to each 3D-region for each temperature metric: mean interannual temperature change (i.e., temperature tendency) and temperature variability within the study timeframe;  * mapping the four different vulnerability classes in the Azores Marine Park. ### Files and variables #### File: RCode_data_DDI2.zip **Description:**  Functions&geographicdata (folder):  * amp_coo3.csv: Azores Marine Park coordinates. Latitude and longitude in decimal degrees. * amp*_*coo3_rep.csv: Azores Marine Park coordinates repeated for graphs. Latitude and longitude in decimal degrees. * amp_pol.shp: shape file with Azores Marine Park coordinates (.cpg, .dpf, .shx files accompany the .shp files in this folder). * dep_ban_function.r: r function to assign a particular depth band to multiple datapoints according to its recorded depth. Exposure (folder): * copernicus-tmp-data (folder): includes all the downloaded netcdf files from Copernicus site ([https://www.copernicus.eu/pt-pt](https://www.copernicus.eu/pt-pt)). Temperature units: ºC. File naming convention: model - type of variable_variable - year (e.g., CMEMS-GLOBAL_001_030-bottomT_thetao-2000). * faz_envrionmental_data_fig10&11.r: includes the code to process environmental data extracted from Copernicus. Output: maps with temperature increases and variability. VulnerabilityScores (folder):    Latitudes and longitudes in decimal degrees. * cha_tt_025deg_msd.csv: temperature change per spatial unit * all_map*_*benthic9.csv: adaptive capacity scores for the benthic environment * all_map*_*benthopel4.csv: adaptive capacity scores for the benthopelagic environment * all_map*_*pelagic8.csv: adaptive capacity scores for the pelagic environment * temp_map_ben.csv: temperature range scores for the benthic environment in ºC * temp_map_benpel2.csv: temperature range scores for the benthopelagic environment in ºC * temp_map_pel2.csv: temperature range scores for the pelagic environment in ºC * temp_max_map_ben2.csv: temperature max. scores for the benthic environment in ºC * temp_max_map_benpel2.csv: temperature max. scores for the benthopelagic environment in ºC * temp_max_map_pel2.csv: temperature max. scores for the pelagic environment in ºC * faz_vulnerability_score_fig4&9.r: R code to score and map vulnerability classes within the Azores Marine Park. faz_data_processing_fig2.r: Code to summarise the data processing methods. Correlations (folder): * temp*cor.csv: raw data.* Units: Max*_*bod_siz in cm.; Dep in m.; Tmax and Tmin in ºC; tem_ran in ºC. * faz_temp_trait_correlations_figA2.r: R code to apply correlations between traits. ## Code/software RStudio and .csv file reader such as excel.  .shp files can be opened with RStudio using the *terra* package. .ncdf files can be opened with RStudio using the *ncdf4* package and using the following function: nc_open() Neus Campanyà-Llovet. (2024). neuscamllo/multiple_netcdf_read: v1.0.0 (v1.0.0). Zenodo. [https://doi.org/10.5281/zenodo.13992594](https://doi.org/10.5281/zenodo.13992594)