• Energy Research
• 2021-2025close
• CN close
• US close
• GB close
• EU close

Το σχέδιο ανέμου της Comunitat Valenciana (PECV) αναπτύσσει περιβαλλοντική ανάλυση στους χώρους στους οποίους υπάρχει χρησιμοποιήσιμος αιολικός πόρος. Η περιβαλλοντική ανάλυση καθορίζει την καταλληλότητα του εδάφους καθορίζοντας τρεις περιπτώσεις: Ακατάλληλες ζώνες, κατάλληλες ζώνες με συμμόρφωση με τις προδιαγραφές και τις κατάλληλες ζώνες. Εκπροσώπηση για ενημερωτικούς σκοπούς της χαρτογραφίας σχεδιασμού του σχεδίου ανέμου της κοινότητας της Βαλένθια. Σύνδεσμος προς τους εκτελεστικούς κανονισμούς: https://cindi.gva.es/es/web/energia/pla-eolic-de-la-comunitat-valenciana Le plan éolien du Comunitat Valenciana (PECV) développe une analyse environnementale dans les espaces où il y a une ressource éolienne utilisable. L’analyse environnementale détermine l’aptitude du territoire en définissant trois situations: Zones inadaptées, zones appropriées conformes aux prescriptions et zones appropriées. Représentation à des fins INFORMATIVEs de la cartographie de la planification du plan éolien de la Communauté valencienne. Lien vers les règlements d’application: https://cindi.gva.es/es/web/energia/pla-eolic-de-la-comunitat-valenciana Il-Pjan tar-Riħ tal-Comunitat Valenciana (PECV) jiżviluppa analiżi ambjentali fl-ispazji fejn hemm riżorsa tar-riħ li tista’ tintuża. L-analiżi ambjentali tiddetermina l-idoneità tat-territorju billi tiddefinixxi tliet sitwazzjonijiet: Żoni mhux xierqa, Żoni Adattati mal-konformità mal-preskrizzjonijiet u Żoni Adattat. Rappreżentazzjoni għall-finijiet INFORMATIVI tal-ippjanar tal-kartografija tal-Pjan tar-Riħ tal-Komunità ta’ Valencia. Link għar-regolamenti ta’ implimentazzjoni: https://cindi.gva.es/es/web/energia/pla-eolic-de-la-comunitat-valenciana The Wind Power Plan in the Valencian Community (PECV) develops environmental analysis in spaces where there is a usable wind resource. The environmental analysis determines the suitability of the territory defining three situations: Unsuitable Zones, Suitable Zones with compliance with prescriptions and Suitable Zones. Representation for INFORMATION purposes of the management cartography of the Wind Energy Plan of the Valencian Community. Link to the applicable regulations:https://cindi.gva.es/es/web/energia/pla-eolic-de-la-comunitat-valenciana Het windplan van de Comunitat Valenciana (PECV) ontwikkelt milieuanalyses in de ruimtes waar een bruikbare windbron aanwezig is. Milieuanalyse bepaalt de geschiktheid van het grondgebied door het definiëren van drie situaties: Ongeschikte Zones, Geschikte Zones met naleving van voorschriften en Geschikte Zones. Vertegenwoordiging voor INFORMATIEve doeleinden van de planning cartografie van het Windplan van de Valenciaanse Gemeenschap. Link naar de uitvoeringsverordeningen: https://cindi.gva.es/es/web/energia/pla-eolic-de-la-comunitat-valenciana Der Windplan der Comunitat Valenciana (PECV) entwickelt Umweltanalysen in den Räumen, in denen es eine nutzbare Windressource gibt. Die Umweltanalyse bestimmt die Eignung des Gebiets, indem drei Situationen definiert werden: Ungeeignete Zonen, geeignete Zonen mit Einhaltung von Vorschriften und geeigneten Zonen. Vertretung für INFORMATIVE Zwecke der Planung der Kartographie des Windplans der valencianischen Gemeinschaft. Link zu den Durchführungsverordnungen: https://cindi.gva.es/es/web/energia/pla-eolic-de-la-comunitat-valenciana Planul eolian al Comunitat Valenciana (PECV) dezvoltă analize de mediu în spațiile în care există o resursă eoliană utilizabilă. Analiza de mediu determină aptitudinea teritoriului prin definirea a trei situații: Zone nepotrivite, zone potrivite cu respectarea prescripțiilor și a zonelor potrivite. Reprezentarea în scopuri informative a cartografiei de planificare a planului eolian al Comunității Valenciane. Link către regulamentele de punere în aplicare: https://cindi.gva.es/es/web/energia/pla-eolic-de-la-comunitat-valenciana El Plan Eólico de la Comunitat Valenciana (PECV) desarrolla análisis ambiental en los espacios en los que existe recurso eólico aprovechable. El análisis ambiental determina la aptitud del territorio definiendo tres situaciones: Zonas No Aptas, Zonas Aptas con cumplimiento de prescripciones y Zonas Aptas. Representación a efectos INFORMATIVOS de la cartografía de ordenación del Plan Eólico de la Comunitat Valenciana. Enlace a la normativa de aplicación: https://cindi.gva.es/es/web/energia/pla-eolic-de-la-comunitat-valenciana O Plano Eólico da Comunitat Valenciana (PECV) desenvolve análises ambientais nos espaços em que existe um recurso eólico utilizável. A análise ambiental determina a adequação do território, definindo três situações: Zonas inadequadas, zonas adequadas com cumprimento das prescrições e zonas adequadas. Representação para efeitos INFORMATIVOS da cartografia de planeamento do Plano Eólico da Comunidade Valenciana. Ligação para os regulamentos de execução: https://cindi.gva.es/es/web/energia/pla-eolic-de-la-comunitat-valenciana Forbraíonn Plean Gaoithe Comunitat Valenciana (PECV) anailís chomhshaoil sna spásanna ina bhfuil acmhainn gaoithe inúsáidte. Cinntear le hanailís chomhshaoil oiriúnacht na críche trí thrí chás a shainiú: Criosanna Míoiriúnacha, Criosanna Oiriúnacha maidir le comhlíonadh oideas agus Criosanna Oiriúnacha. Ionadaíocht chun críocha faisnéise a bhaineann le cartagrafaíocht pleanála Phlean Gaoithe Phobal Valencian. Nasc chuig na rialacháin cur chun feidhme: https://cindi.gva.es/es/web/energia/pla-eolic-de-la-comunitat-valenciana

Survey data used in a perception study of stalked barnacle harvesters on the effectiveness of fisheries management practices in Spain, Portugal and France. Harvesters from the following six regions along the Atlantic Arc participated: Morbihan in Brittany (France), Asturias-East, Asturias-West and Galicia (Spain), the Reserva Natural das Berlengas (RNB; Portugal) and the Parque Natural do Sudoeste Alentejano e Costa Vicentina (PNSACV; Portugal). We administered 184 surveys from October 2019 to September 2020 and each region was treated as an independent population. The data includes: general demographic data (Region, Age, Gender, Level of Education, Main income source, Years of Experience); perception data of the effectiveness of the currently implemented management strategies in each region (coded: e_name_of_strategy – using Likert Scale with scores ranging from 1 = completely ineffective to 5 = very effective); data of the willingness for change of the currently implemented management (Yes, No, NA); and data of harvesters’ perceptions regarding the most important strategy to achieve sustainability in the fishery. Because the surveys were conducted both before and during the Covid-19 pandemic (the column Covid indicates whether the data was collected before or during the pandemic), we had to make adjustments in our data collection methods. We provided the following options for survey completion (see the Recollection_of_data column): by hand in a written format, online, or via an oral interview conducted with the assistance of a scientist per telephone. Our results indicate that the majority of harvesters in the regions in Portugal and France were willing to make changes to current management strategies, reflecting their awareness of the need for improvement. Based on the AIC model selection analysis results, the model with the single variable region explained 83% of the cumulative model weight. The variable region was the best predictor of the trends in management strategy preferences, and presented a highly significant goodness-of-fit result (p<0.001), suggesting that regional differences play a significant role in shaping these preferences. No clear trend emerged regarding a single "optimal" management strategy preferred by harvesters across regions. Harvesters in less developed co-management systems favored general input and output restrictions and expressed a desire for greater involvement in co-management processes. Conversely, harvesters in highly developed co-management systems with Territorial User Rights for Fishers (TURFs) preferred the most restrictive and spatially explicit management strategies, such as implementing harvest bans and establishing marine reserves. Our findings emphasise that management strategies do not only need to be tailored to each region's particular practices, needs, and characteristics, but that resource users’ readiness for specific strategies also needs to be considered.

These data include population fitness measurements collected for Acartia hudsonica during multigenerational exposure to ocean warming (OW), ocean acidification (OA), and combined ocean warming and acidification (OWA) including a benign ambient condition temperature and CO2 control (AM).

From September of 2017, till August of 2019, water temperatures and A. aegypti larval presence was recorded in nine 19 liter buckets placed in the backyard of Jon Harrison’s home in Tempe, Arizona (33.339, -111.924), as it was known to experience high abundances of A. aegypti. Buckets were 5 – 10 m apart, and so should not be considered ecologically independent. Onset HOBO Pendant® UA-002-08 data loggers (Bourne, Massachusetts) were used to record temperature levels, and larval presence was observed every 1-10 days depending on season (frequently in the summer, less so in winter). If mosquito larvae were observed, they were collected from the bucket with a net and their species identity confirmed with a dissection scope. The data set labeled Figure 2 data provides the water temperatures in one representative bucket from 2017-2019 as shown in Fig. 2 of the manuscript. Larval rearing for mesocosm experiments The parents of larvae used in the mesocosm overwintering experiments were reared from Maricopa County, AZ, origin eggs collected by Maricopa County Vector Control from September to November of 2019. These eggs were placed in a 500 ml beaker, submerged, and hatched in a solution of 0.25 g/L baker’s yeast (Byttebier et al. 2014). As the 1st instar larvae emerged, they were fed TetraMin fish flakes every 1-2 days, making sure that an excess amount of food was visible in the container. The rearing density for the larvae was maintained at fewer than 500 animals per liter of water. As pupae began to appear, the beaker of larvae was placed in a 95-liter polymer-screened cage to contain the expected adults. Cotton balls saturated with 10% sucrose solution were made available for the adults as they began to emerge; these were taken away two days prior to blood feeding. One week after emerging, the adults were blood-fed using mice (IACUC protocol: 18-1662R). After a three-day gestation period, the females were supplied with moist seed-germinating paper to encourage oviposition. Once the females had finished ovipositing, the eggs were kept moist for an additional 48 hours before being dried, and placed in open zip lock sandwich bags which were stored at 100% humidity and 24°C. High humidity in the egg storage containers was achieved by storing damp paper towels along with the opened egg bags within a larger 3.8L bag. These eggs were kept for less than one month before the hatching procedure was repeated to produce the larvae for the experiment. In the lab, across all life stages, the mosquitoes were exposed to a 12:12 L/D photoperiod at 24°C. After hatching, the 2nd instar larvae were moved to their outdoor experimental mesocosms. The larvae were randomly distributed with 20 larvae supplied per each of three ambient mesocosms (Amb1, Amb2, Amb3) and six to warmed mesocosms (W1 – W6), which were warmed by varying amounts (W1 = least warmed, W6 = most warmed). The goal was to achieve a range of warming from very small warming (1-2°C in the least-warmed mesocosm (W1), to near-summer conditions in the most-warmed mesocosm (W6). Each mesocosm was a 150 ml clear plastic container, filled with 125 mL dechlorinated tap water. TetraMin fish flakes were supplied to each mesocosm, with more added every three days or when food was completely consumed. Although the mesocosms were open, we observed no mosquitoes flying in the field, and none were captured in local water buckets, and all A.a. in the mesocosms were of uniform stage, so we believe that this experiment was not affected by oviposition from wild mosquitoes. Manipulation of thermal conditions for larval outdoor rearing All mesocosms were placed on a table one meter above the ground and protected from rain, wind, and sunlight by a roof. The mesocosms were placed within individual lidless pine boxes (10x10x14 cm, 0.95 cm thick walls), and so were exposed to normal fluctuations in air temperature. Each warmed mesocosm was placed on 40mm2 thermoelectric plates with 40mm2 aluminum heatsinks attached using thermally conductive adhesive on each side. The warming orientation of the thermoelectric plate was positioned upwards, towards the mesocosms, to ensure adequate energy transfer from the heating units to the water. Each thermoelectric device was powered by two KORAD KD3005D 30V, 5A power supplies (Shenzhen, China). The thermoelectric plates were wired in parallel. Variable warming was produced by changing the supplied voltage. Temperatures were measured in the cups using HOBO Pendant® UA-002-08 data loggers submerged in the center of each cup. We did not measure temperature gradients within the mesocosms, but believe that they are likely to be small except possibly in the mesocosms that were maximally-warmed, as the mesocosms were small and mostly not strongly warmed above air temperature. Temperatures were logged each hour in each warmed mesocosm, and in one ambient treatment mesocosm. The data file labeled Figure 3 data provides the wate temperatures at hourly intervals during the experiment for one mesocosm at ambient temperature, mesocosm W1 (the least warmed mesocosm) and mesocosm W6 (the most warmed mesocosm) as shown in Fig. 3 of the manuscript. Global warming trends, human-assisted transport, and urbanization have allowed poleward expansion of many tropical vector species, but the specific mechanisms responsible for thermal mediation of range changes and ecological success of invaders remain poorly understood. Aedes aegypti (Diptera: Culicidae) is a tropical mosquito currently expanding into many higher-latitude regions including the urban desert region of Maricopa County, Arizona. Here, adult populations virtually disappear in winter and spring, and then increase exponentially through summer and fall, indicating that winter conditions remain a barrier to development of A. aegypti. To determine whether cold limits the winter development of A. aegypti larvae in Maricopa County, we surveyed for larval abundance, and tested their capacity to develop in ambient and warmed conditions. Aedes aegypti larvae were not observed in artificial aquatic habitats in winter and spring but were abundant in summer and fall, suggesting winter suppression of adults, larvae or both. Water temperatures in winter months fluctuated strongly; larvae were usually cold-paralyzed at night but active during the day. Despite daytime temperatures that allowed activity, larvae reared under ambient winter conditions were unable to develop to adulthood, perhaps due to repetitive cold damage. However, warming average temperature by 1.7°C allowed many larvae to successfully develop to adults. Because daytime highs in winter will often allow adult flight, it is possible that relatively minor additional winter warming may allow A. aegypti populations to develop and reproduce year-round in Maricopa County. # Data for Mesocosm studies suggest climate change may release Aedes aegypti (Diptera:Culicidae) larvae from cold-inhibition and enable year-round development in a desert city [https://doi.org/10.5061/dryad.nzs7h44z7](https://doi.org/10.5061/dryad.nzs7h44z7) Most of the data for this study are provided as supplementary files in the submitted manuscript. Here we provide representative thermal data. One file (Figure 2 data) contains the temperature data for the bucket kept under ambient conditions as shown in Figure 2, which also shows when Aedes aegypti larvae were found in the bucket. From to October 18 -November 29 2017, water temperatures were recorded every 6 minutes. Thereafter, water temperatures were recorded hourly until August 2, 2019. Another file (Figure 3 data) contains water temperatures for three of the mesocosms used in this study, as shown in the manuscript figure 3. This experiment ran from Jan 31, 2020 - March 1, 2020. One column sW1 was and ## Description of the data and file structure Figure 2 data has two columns, column A gives the date and column B the temperature of the ambient bucket in degrees Centigrade. Figure 3 data has four columns; column A gives the hours since the start of the experiment. Column B shows temperatures for an unheated mesocosm kept at ambient conditions. Column C shows temperatures for W6, the most warmed mesocosm (mean temperature 12C higher than the ambient mesocosm, to represent near-summer conditions). Column D shows temperatures for the least-warmed mesocosm (W1, mean temperature 1.8C higher than the ambient mesocosm). All temperatures are in degrees Centigrade.

The atmosphere concentration of CO2 is steadily increasing and causing climate change. To achieve the Paris 1.5 or 2 oC target, negative emissions technologies must be deployed in addition to reducing carbon emissions. The ocean is a large carbon sink but the potential of marine primary producers to contribute to carbon neutrality remains unclear. Here we review the alterations to carbon capture and sequestration of marine primary producers (including traditional ‘blue carbon’ plants, microalgae, and macroalgae) in the Anthropocene, and, for the first time, assess and compare the potential of various marine primary producers to carbon neutrality and climate change mitigation via biogeoengineering approaches. The contributions of marine primary producers to carbon sequestration have been decreasing in the Anthropocene due to the decrease in biomass driven by direct anthropogenic activities and climate change. The potential of blue carbon plants (mangroves, saltmarshes, and seagrasses) is limited by the available areas for their revegetation. Microalgae appear to have a large potential due to their ubiquity but how to enhance their carbon sequestration efficiency is very complex and uncertain. On the other hand, macroalgae can play an essential role in mitigating climate change through extensive offshore cultivation due to higher carbon sequestration capacity and substantial available areas. This approach seems both technically and economically feasible due to the development of offshore aquaculture and a well-established market for macroalgal products. Synthesis and applications: This paper provides new insights and suggests promising directions for utilizing marine primary producers to achieve the Paris temperature target. We propose that macroalgae cultivation can play an essential role in attaining carbon neutrality and climate change mitigation, although its ecological impacts need to be assessed further. To calculate the parameters presented in Table 1, the relevant keywords "mangroves, salt marshes, macroalgae, microalgae, global area, net primary productivity, CO2 sequestration" were searched through the ISI Web of Science and Google Scholar in July 2021. Recent data published after 2010 were collected and used since area and productivity of plants change with decade. For data with limited availability, such as net primary productivity (NPP) of seagrasses and global area and NPP of wild macroalgae, data collection was extended back to 1980. Total NPP and CO2 sequestration for mangroves, salt marshes, seagrasses and wild macroalgae were obtained by the multiplication of area and NPP/CO2 sequestration density and subjected to error propagation analysis. Data were expressed as means ± standard error.

# Data from: Eocene Shark Teeth from Peninsular Antarctica: Windows to Habitat Use and Paleoceanography. [https://doi.org/10.5061/dryad.qz612jmq2](https://doi.org/10.5061/dryad.qz612jmq2) The repository folder includes scripts and spreadsheets for phosphate oxygen stable isotope (δ18Op) analysis measured from shark tooth biogenic apatite collected from the Eocene deposits of the La Meseta and Submeseta formations (West Antarctica, Seymour Island). It also contains Fourier-Transform Infrared Spectroscopy (FTIR) analysis, a Bayesian model for temperature estimates, and model output extraction scripts from the iCESM simulation for the Early Eocene (Zhu et al., 2020). Scripts and data are stored in specific folders on the type of analysis. All scripts are in R or Python language. **Usage notes** **1 "iCESM modeling scripts" directory** The folder includes scripts in Jupiter Notebook format for extracting and plotting iCESM seawater outputs for the Eocene. The folder includes two files: 1) “d18Ow Analysis Script.ipynb” - This is a Python script primarily using the XArray library, to import iCESM output from Zhu et al. (2020), calculating δ18Ow, and reorganizing the output into monthly time intervals along 25 m and 115 m depth slices, while also averaging output down to these depths; 2) “NetCDF Plotting.ipynb” - this is a Python script primarily using the XArray, Matplotlib, and Cartopy libraries. The script writes a single callable function that creates Matplotlib contour plots from iCESM history output. Variables include temperature, salinity, ideal age, oxygen isotopes, and neodymium isotopes, and map projections include Plate Carree, Mollweide, and orthographic (centering on the Drake Passage). Options are built to enable scale normalization or to set maximum and minimum values for data and select colormaps from a predefined selection of Matplotlib’s “Spectral”, “Viridis”, “Coolwarm”, “GNUplot2”, “PiYG”, “RdYlBu”, and “RdYlGn”. For further questions on model output scripts, please email Adam Aleksinski at [aaleksin@purdue.edu](https://datadryad.org/stash/dataset/doi:10.5061/aaleksin@purdue.edu). **2 "d18O data and maps" directory** The folder includes δ18Op of shark tooth bioapatite and other datasets to interpret shark paleoecology. These datasets include: · δ18Op of shark tooth bioapatite (“shark FEST d18Op.csv”). Isotope measurements were run at the Stable Isotope Ecosystem Laboratory of (SIELO) University of California, Merced (California, USA). · Reference silver phosphate material δ18Op for analytical accuracy and precision (“TCEA reference materials.csv"). Isotope measurements were run at the Stable Isotope Ecosystem Laboratory of (SIELO) University of California, Merced (California, USA). · Bulk and serially sampled δ18Oc data of co-occurring bivalves (Ivany et al., 2008; Judd et al., 2019) (“Ivany et al. 2008_bulk.csv” and “Judd et al., 2019_serial sampling.csv"). · iCESM model temperature and δ18Ow outputs at 3x and 6x pre-industrial CO2 levels for the Early Eocene (Zhu et al., 2020) (“SpinupX3_25m_Mean_Monthly.nc”, “SpinupX6_25m_Mean_Monthly.nc.”, and “CA_x3CO2.csv”). Simulations are integrated from the surface to 25 m. · δ18O values of invertebrate species published in Longinelli (1965) and Longinelli & Nuti (1973), used to convert bulk δ18Oc (V-SMOW) data of bivalves into δ18Op (V-SMOW) values after δ18Oc (V-PDB) - δ18Oc (V-SMOW) conversion found in Kim et al. (2015) (“d18O carbonate and phosphate references.csv”). · R script for data analysis ("d18O data and maps.Rmd”). The script provides annotation through libraries, instrumental accuracy and precision tests, tables, statistical analysis, figures, and model output extractions. . ("TELM_diversity.csv") displays diversity trends of chondrichthyans across TELMs in one of the main figures of the manuscript. **2.1 Dataset description** **shark FEST d18Op.csv** · *Sample_ID*: Identification number of tooth specimens. · *Other_ID*: Temporary identification number of tooth specimens. · *Taxon*: Species assigned to shark tooth specimens. · *TELM*: Stratigraphic units of La Meseta (TELM 2-5; ~45 to ~37 Ma) and Submeseta formations (TELMs 6 and 7; ~37 to ~34 Ma) (Amenábar et al., 2020; Douglas et al., 2014; Montes et al., 2013). · *d18Op*: Mean δ18Op values of silver phosphate crystals precipitated from shark tooth bioapatite. Specimens were run in triplicates, corrected, and standardized on the V-SMOW scale. · *sd*: Standard deviation of silver phosphate triplicate samples per specimen. · *Protocol*: Silver phosphate protocols used to precipitate crystals from shark tooth bioapatite. We adopted the Rapid UC (“UC_Rapid”) and the SPORA (“SPORA”) protocols after Mine et al. and (2017) Larocca Conte et al. (2024) based on the tooth specimen size and sampling strategy. Descriptions of the methods are included in the main manuscript. · *Environment*: Inferred shark habitat based on taxonomy classified as benthic or pelagic environment. · *Collection*: Institutional abbreviations of museum collections from which shark tooth specimens are housed. NRM-PZ is the abbreviation for the Swedish Natural History Museum (Stockholm, Sweden), PRI is the abbreviation for the Paleontological Research Institute (Ithaca, New York, United States), and UCMP is the University of California Museum of Paleontology (Berkeley, California, United States). **TCEA reference materials.csv** · *Identifier_1*: unique identifier number per sample. · *sample*: reference silver phosphate materials (USGS 80 and USGS 81). · *amount*: weight of samples in mg. · *Area 28*: peak area of mass 28 (12C16O). · *Area 30*: peak area of mass 30 (12C18O). · *d18O_corrected*: corrected δ18Op value of reference materials following drift correction, linearity correction, and 2-point calibration to report values on the V-SMOW scale. **Ivany et al. 2008_bulk.csv** · *Telm*: Stratigraphic units of La Meseta (TELM 2-5; ~45 to ~37 Ma) and Submeseta formations (TELMs 6 and 7; ~37 to ~34 Ma) (Amenábar et al., 2020; Douglas et al., 2014; Montes et al., 2013). · *Locality*: Locality code from which bivalves were collected. · *Genus*: Genera of bivalves. Specimens are assigned to *Cucullaea* and *Eurhomalea* genera. · *Line*: Sampling areas of specimens. The sampling strategy is described in Ivany et al. (2008). · *d13C*: δ13C values of specimens from sampled lines. Values are reported in the V-PDB scale. · *d18Oc_PDB*: δ18Oc values of specimens from sampled lines. Values are reported in the V-PDB scale. **Judd et al., 2019_serial sampling.csv** · *Horizon:* horizons of the TELM 5 unit (La Meseta Formation) from which bivalves were collected. Horizon 1 is stratigraphically the lowest, while horizon 4 is the highest (Judd et al., 2019). · *ID*: Identification number of specimens. · *Latitude*: Geographic coordinate where bivalve specimens were collected. · *Longitude*: Geographic coordinate where bivalve specimens were collected. · *Surface sampled*: Specific sampling area, indicating whether sampling occurred in the interior or exterior portion of shells. · *distance*: The distance from the umbo in mm from which sampling occurred along a single shell. · *d18Oc_PDB*: δ18Oc values of specimens from sampled areas of shells. Values are reported on the V-PDB scale. **SpinupX3_25m_Mean_Monthly.nc** See section 1 ("iCESM modeling scripts" directory, “d18Ow Analysis Script.ipynb” script) for a full description of the iCESM model output extraction. **SpinupX6_25m_Mean_Monthly.nc** See section 1 ("iCESM modeling scripts" directory, “d18Ow Analysis Script.ipynb” script) for a full description of the iCESM model output extraction. **CA_x3CO2.csv** · *lat*: Geographic coordinate where temperature and δ18Ow model values are extracted from the iCESM simulation scaled at 3x preindustrial CO2 levels (values averaged within a seawater column depth of 25 m). · *long*: Geographic coordinate where temperature and δ18Ow model values are extracted from the iCESM simulation scaled at 3x preindustrial CO2 levels (values averaged within a seawater column depth of 25 m). · *T_mean*: Simulated seawater temperature values in °C. · *d18Ow*: Simulated seawater δ18Ow values (V-SMOW). · *d18Op*: Simulated seawater δ18Op values (V-SMOW). Values were calculated by using seawater temperature and δ18Ow arrays following the paleothermometer equation after Lécuyer et al. (2013). **d18O carbonate and phosphate references.csv** · *species*: Species of invertebrate taxa. · *type*: Specimen type, including barnacles, brachiopods, crabs, and mollusks. · *depth*: Depth of seawater column where specimens were collected, reported in meters below sea level when specified. · *d18Op*: δ18Op values of invertebrate specimens (V-SMOW). · *d18Oc_PDB*: δ18Oc values of invertebrate specimens (V-PDB). · *Reference*: Citations from which data were taken to build the dataset (Longinelli, 1965; Longinelli & Nuti, 1973). **TELM diversity.csv** · *genus:* genera of sharks and rays compiled from literature (Engelbrecht et al., 2016a, 2016b, 2017a, 2017b, 2019; Kriwet, 2005; Kriwet et al., 2016; Long, 1992; Marramá et al., 2018). · *species*: species of sharks and rays compiled from literature (Engelbrecht et al., 2016a, 2016b, 2017a, 2017b, 2019; Kriwet, 2005; Kriwet et al., 2016; Long, 1992; Marramá et al., 2018). · *Environment*: Inferred shark habitat based on taxonomy classified as benthic or pelagic environment. · *TELM*: Stratigraphic units of La Meseta (TELM 1-5; ~44 to ~37 Ma) and Submeseta formations (TELMs 6 and 7; ~37 to ~34 Ma) (Amenábar et al., 2020; Douglas et al., 2014; Montes et al., 2013). **3 “FTIR data” directory** The folder includes FTIR acquisitions and data analysis scripts on reference materials and shark tooth bioapatite for quality checks to test diagenesis effects on δ18Op of sharks. The folder includes: · The R project file “apatite_ftir.Rproj”. This project file navigates through scripts for raw data processing and data analysis. The background of the raw data was processed following custom R functions from Trayler et al. (2023; [https://github.com/robintrayler/collagen_demineralization](https://github.com/robintrayler/collagen_demineralization)). · The “.Rproj.user” folder includes project-specific temporary files (e.g. auto-saved source documents, window-state, etc.) stored by the R project file “apatite_ftir.Rproj”. The folder may be hidden depending on directory view options. · The “raw data” directory stores spectra acquisitions as .dpt files. Spectra files are stored in the folders “apatite” and “calcite” based on the material type. Spectra were obtained in the 400 – 4000 cm⁻¹ range using a Bruker Vertex 70 Far-Infrared in ATR located at the Nuclear Magnetic Resonance Facility at the University of California Merced (California, USA). · The “processed” directory includes processed spectra stored as .csv files (“apatite_data.csv” and “calcite_data.csv”) following the background correction (Trayler et al., 2023) and processed infrared data from Larocca Conte et al. (2024) (“Larocca Conte et al._SPORA_apatite_data.csv”) from which the NIST SRM 120c spectrum was filtered. Infrared spectra data in “Larocca Conte et al._SPORA_apatite_data.csv” were obtained and corrected following the same methodologies mentioned above. · The “R” directory includes R scripts of customized source functions for background correction (Trayler et al., 2023; inspect the "functions" directory and the R script "0_process_data.R") and data analysis (“data_analysis.R”). The scripts provide annotation through libraries and functions used for data processing and analysis. · Additional datasets. The “data_FTIR_d18O.csv” includes infrared data and δ18Op values of specimens, while the “Grunenwald et al., 2014_CO3.csv” is the dataset after Grunenwald et al. (2014) used to predict carbonate content from the materials featured in this work. **3.1 Dataset description** Spreadsheets included in the “processed” directory The datasets “apatite_data.csv”, “calcite_data.csv”, and “Larocca Conte et al._SPORA_apatite_data.csv” are structured with the following variables: · *wavenumber*: infrared wavenumber in cm-1. · *absorbance*: infrared absorbance value. · *file_name:* .dpt file name from which infrared wavenumber and absorbance values were obtained following the background correction. **data_FTIR_d18O.csv** · *file_name:* .dpt file name from which infrared wavenumber and absorbance values were obtained following the background correction. · *v4PO4_565_wavenumber*: Wavenumber of maximum infrared absorbance around the first νPO4 band, usually at 565 cm-1. · *v4PO4_565*: Peak absorbance value of the first ν4PO4 band (~565 cm-1). · *v4PO4_valley_wavenumber*: Wavenumber of valley between ν4PO4 bands. · *v4PO4_valley*: Absorbance value of the valley between ν4PO4 bands. · *v4PO4_603_wavenumber*: Wavenumber of maximum infrared absorbance around the second ν4PO4 band, usually at 603 cm-1. · *v4PO4_603*: Peak absorbance value of the second ν4PO4 band (~603 cm-1). · *CI*: Crystallinity index calculated after equation provided in (Shemesh, 1990) as (*v4PO4_565* + *v4PO4_603* / *v4PO4_valley*) (i.e., the sum of peak absorbance of νPO4 bands divided by the absorbance value of the valley between peaks). · *material*: Material type of samples (i.e., standard material, enameloid, dentin sampled from the crown or root area of shark teeth, and enameloid mixed with dentin). · *AUC_v3PO4*: Area under the curve of the ν3PO4 and ν1PO4 bands where maximum absorbance is at ~1025 cm-1 and ~960 cm-1, respectively. · *AUC_v3CO3*: Area under the curves of Type-A and Type-B carbonate bands having maximum infrared absorbance at ~1410 (Type-B), ~1456 (Type-B), and ~1545 cm-1 (Type-A). · *v3CO3_v3PO4_ratio*: Ratio between area under the curves of carbonate and phosphate bands (i.e., *AUC_v3CO3* / *AUC_v3PO4*). · *CO3_wt*: Estimated mean carbonate content following the equation in Grunenwald et al. (2014) (i.e. *CO3_wt* = 28.4793 (±1.4803) *v3CO3_v3PO4_ratio* + 0.1808(±0.2710); R2 = 0.985). · *CO3_wt_sd*: Standard deviation of estimated carbonate content calculated by propagating the error around coefficients provided in the Grunenwald et al. (2014) equation (see full equation in *CO3_wt*). · *Taxon*: Species assigned to shark tooth specimens. · *TELM*: Stratigraphic units of La Meseta (TELM 2-5; ~45 to ~37 Ma) and Submeseta formations (TELMs 6 and 7; ~37 to ~34 Ma) (Amenábar et al., 2020; Douglas et al., 2014; Montes et al., 2013). · *d18Op*: Mean δ18Op values of silver phosphate crystals precipitated from shark tooth bioapatite. Specimens were run in triplicates, corrected, and standardized on the V-SMOW scale. · *sd*: Standard deviation of silver phosphate triplicate samples per specimen. · *Collection*: Institutional abbreviations of museum collections where shark tooth specimens are housed. Infrared spectra were obtained from a selected subset of tooth specimens in the care of the Swedish Natural History Museum (NRM-PZ; Stockholm, Sweden). **Grunenwald et al., 2014_CO3.csv** · *sample*: Sample code. · *material*: Material type of samples (i.e., standard material, bone, and enamel). · *v3CO3*: Area under the curves of Type-A and Type-B carbonate bands having maximum infrared absorbance at ~1410 (Type-B), ~1456 (Type-B), and ~1545 cm-1 (Type-A). · *v3PO4*: *AUC_v3PO4*: Area under the curve of the ν3PO4 and ν1PO4 bands where maximum absorbance is at ~1025 cm-1 and ~960 cm-1, respectively. · *v3CO3_v3PO4_ratio*: *v3CO3_v3PO4_ratio*: Ratio between area under the curves of carbonate and phosphate bands (i.e., *v3CO3* /*v3PO4*). · *CO3_wt*: Carbonate content measured via CO2 coulometry. Further details about the analytical measurements are found in Grunenwald et al. (2014). **4 “Bayes_FEST_Temperautre Estimates” directory** The folder includes the Bayesian approach used to estimate posterior seawater temperature, δ18Ow values from δ18Op of sharks bioapatite using a Bayesian approach modified after Griffiths et al. (2023). The original scripts used in Griffiths et al. (2023) are reposited here: [https://github.com/robintrayler/bayesian_phosphate](https://github.com/robintrayler/bayesian_phosphate). The directory includes: · The R project file “Bayes_FEST.Rproj”. This project file navigates through scripts for raw data analysis. · The “.Rproj.user” folder includes project-specific temporary files (e.g. auto-saved source documents, window-state, etc.) stored by the R project file “Bayes_FEST.Rproj”. The folder may be hidden depending on directory view options. · The “data” folder includes the spreadsheets for modeled seawater temperature and δ18Ow values (“CA_x3CO2.csv”) and δ18Op values of shark tooth bioapatite (“shark FEST d18Op.csv”) used as prior information for the Bayesian model. We refer to section 2.1 for the full description of spreadsheets. · The “R” folder includes customized functions for the Bayesian model stored in the “functions” directory and the script for data analysis (“01_model_sharks.R”). The script includes a comparison of paleothermometer equations after Kolodny et al. (1983), Lécuyer et al. (2013), Longinelli & Nuti (1973), and (Pucéat et al. (2010) using the bulk δ18Op shark tooth bioapatite, simulated seawater temperature and δ18Ow values as prior inputs. While all paleothermometers estimate similar posterior bulk δ18Op close to empirical values, temperature estimates using the Pucéat et al. (2010) method are often the highest, generating estimates ~8°C higher than other equations. We therefore used the Lécuyer et al. (2013) paleothermomether for temperature estimates using δ18Op of shark bioapatite grouped by taxa because it: 1\) Provides consistent posterior temperature estimates relative to other equations (Longinelli & Nuti, 1973, Kolodny et al., 1983). 2\) provides temperature values from fish tooth specimens consistent with estimates of co-existing bivalves or brachiopod carbonate shells. The script provides annotation through libraries, statistical analysis, figures, and tables. **4 Software** **4.1 R** R and R Studio (R Development Core Team, 2024; RStudio Team, 2024) are required to run scripts included in the "d18O data and maps", “FTIR data”, and “Bayes_FEST_Temperautre Estimates” directories, which were created using versions 4.4.1 and 2024.04.02, respectively. Install the following libraries before running scripts: “cowplot” (Wilke, 2024), “colorspace” (Zeileis et al., 2020), “DescTools” (Signorell, 2024), “lattice” (Sarkar, 2008), “flextable” (Gohel & Skintzos, 2024), “ggh4x” (van den Brand, 2024), “ggnewscale” (Campitelli, 2024), “ggpubr” (Kassambara, 2023a), “ggspatial” (Dunnington, 2023), “ggstance” (Henry et al., 2024), “ggstar” (Xu, 2022), “greekLetters” (Kévin Allan Sales Rodrigues, 2023), “gridExtra” (Auguie, 2017), “mapdata” (code by Richard A. Becker & version by Ray Brownrigg., 2022); “mapproj” (for R by Ray Brownrigg et al., 2023), “maps” (code by Richard A. Becker et al., 2023), “ncdf4” (Pierce, 2023), “oce” (Kelley & Richards, 2023), “rasterVis” (Oscar Perpiñán & Robert Hijmans, 2023), “RColorBrewer” (Neuwirth, 2022), “rnaturalearth” (Massicotte & South, 2023), “rnaturalearthhires” (South et al., 2024),”rstatix” (Kassambara, 2023b), “scales” (Wickham et al., 2023), “tidyverse” (Wickham et al., 2019), “viridisLite” (Garnier et al., 2023). **4.2 Python** Python scripts, including “d18O Analysis Script.ipynb” and “NetCDF Plotting.ipynb”, utilize the Jupyter Notebook interactive ‘platform and are executed using Python version 3.9.16. Install the following libraries before running scripts: “xarray” (Hoyer & Joseph, 2017), “matplotlib” (Hunter, 2007), “cartopy” (Met Office, 2015). **5 References** Amenábar, C. R., Montes, M., Nozal, F., & Santillana, S. (2020). Dinoflagellate cysts of the la Meseta Formation (middle to late Eocene), Antarctic Peninsula: Implications for biostratigraphy, palaeoceanography and palaeoenvironment. *Geological Magazine*, *157*(3), 351–366. [https://doi.org/10.1017/S0016756819000591](https://doi.org/10.1017/S0016756819000591) Auguie, B. (2017). gridExtra: Miscellaneous Functions for “Grid” Graphics. Retrieved from [https://cran.r-project.org/package=gridExtra](https://cran.r-project.org/package=gridExtra) van den Brand, T. (2024). ggh4x: Hacks for “ggplot2.” Retrieved from [https://cran.r-project.org/package=ggh4x](https://cran.r-project.org/package=ggh4x) Campitelli, E. (2024). ggnewscale: Multiple Fill and Colour Scales in “ggplot2.” Retrieved from [https://cran.r-project.org/package=ggnewscale](https://cran.r-project.org/package=ggnewscale) code by Richard A. Becker, O. S., & version by Ray Brownrigg., A. R. W. R. (2022). mapdata: Extra Map Databases. Retrieved from [https://cran.r-project.org/package=mapdata](https://cran.r-project.org/package=mapdata) code by Richard A. Becker, O. S., version by Ray Brownrigg. Enhancements by Thomas P Minka, A. R. W. R., & Deckmyn., A. (2023). maps: Draw Geographical Maps. Retrieved from [https://cran.r-project.org/package=maps](https://cran.r-project.org/package=maps) Douglas, P. M. J., Affek, H. P., Ivany, L. C., Houben, A. J. P., Sijp, W. P., Sluijs, A., et al. (2014). Pronounced zonal heterogeneity in Eocene southern high-latitude sea surface temperatures. *Proceedings of the National Academy of Sciences of the United States of America*, *111*(18), 6582–6587. [https://doi.org/10.1073/pnas.1321441111](https://doi.org/10.1073/pnas.1321441111) Dunnington, D. (2023). ggspatial: Spatial Data Framework for ggplot2. Retrieved from [https://cran.r-project.org/package=ggspatial](https://cran.r-project.org/package=ggspatial) Engelbrecht, A., Mörs, T., Reguero, M. A., & Kriwet, J. (2016a). A new sawshark, Pristiophorus laevis, from the Eocene of Antarctica with comments on Pristiophorus lanceolatus. *Historical Biology*, *29*(6), 841–853. [https://doi.org/10.1080/08912963.2016.1252761](https://doi.org/10.1080/08912963.2016.1252761) Engelbrecht, A., Mörs, T., Reguero, M. A., & Kriwet, J. (2016b). Revision of Eocene Antarctic carpet sharks (Elasmobranchii, Orectolobiformes) from Seymour Island, Antarctic Peninsula. *Journal of Systematic Palaeontology*, *15*(12), 969–990. [https://doi.org/10.1080/14772019.2016.1266048](https://doi.org/10.1080/14772019.2016.1266048) Engelbrecht, A., Mörs, T., Reguero, M. A., & Kriwet, J. (2017a). Eocene squalomorph sharks (Chondrichthyes, Elasmobranchii) from Antarctica. *Journal of South American Earth Sciences*, *78*, 175–189. [https://doi.org/10.1016/j.jsames.2017.07.006](https://doi.org/10.1016/j.jsames.2017.07.006) Engelbrecht, A., Mörs, T., Reguero, M. A., & Kriwet, J. (2017b). New carcharhiniform sharks (Chondrichthyes, Elasmobranchii) from the early to middle Eocene of Seymour Island, Antarctic Peninsula. *Journal of Vertebrate Paleontology*, *37*(6). [https://doi.org/10.1080/02724634.2017.1371724](https://doi.org/10.1080/02724634.2017.1371724) Engelbrecht, A., Mörs, T., Reguero, M. A., & Kriwet, J. (2019). Skates and rays (Elasmobranchii, Batomorphii) from the Eocene La Meseta and Submeseta formations, Seymour Island, Antarctica. *Historical Biology*, *31*(8), 1028–1044. [https://doi.org/10.1080/08912963.2017.1417403](https://doi.org/10.1080/08912963.2017.1417403) for R by Ray Brownrigg, D. M. P., Minka, T. P., & transition to Plan 9 codebase by Roger Bivand. (2023). mapproj: Map Projections. Retrieved from [https://cran.r-project.org/package=mapproj](https://cran.r-project.org/package=mapproj) Garnier, Simon, Ross, Noam, Rudis, Robert, et al. (2023). {viridis(Lite)} - Colorblind-Friendly Color Maps for R. [https://doi.org/10.5281/zenodo.4678327](https://doi.org/10.5281/zenodo.4678327) Gohel, D., & Skintzos, P. (2024). flextable: Functions for Tabular Reporting. Retrieved from [https://cran.r-project.org/package=flextable](https://cran.r-project.org/package=flextable) Griffiths, M. L., Eagle, R. A., Kim, S. L., Flores, R. J., Becker, M. A., IV, H. M. M., et al. (2023). Endothermic physiology of extinct megatooth sharks. *Proceedings of the National Academy of Sciences*, *120*(27), e2218153120. [https://doi.org/10.1073/PNAS.2218153120](https://doi.org/10.1073/PNAS.2218153120) Grunenwald, A., Keyser, C., Sautereau, A. M., Crubézy, E., Ludes, B., & Drouet, C. (2014). Revisiting carbonate quantification in apatite (bio)minerals: A validated FTIR methodology. *Journal of Archaeological Science*, *49*(1), 134–141. [https://doi.org/10.1016/j.jas.2014.05.004](https://doi.org/10.1016/j.jas.2014.05.004) Henry, L., Wickham, H., & Chang, W. (2024). ggstance: Horizontal “ggplot2” Components. Retrieved from [https://cran.r-project.org/package=ggstance](https://cran.r-project.org/package=ggstance) Hoyer, S., & Joseph, H. (2017). xarray: N-D labeled Arrays and Datasets in Python. *Journal of Open Research Software*, *5*(1), 17. [https://doi.org/10.5334/jors.148](https://doi.org/10.5334/jors.148) Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. *Computing in Science & Engineering*, *9*(3), 90–95. [https://doi.org/10.1109/MCSE.2007.55](https://doi.org/10.1109/MCSE.2007.55) Ivany, L. C., Lohmann, K. C., Hasiuk, F., Blake, D. B., Glass, A., Aronson, R. B., & Moody, R. M. (2008). Eocene climate record of a high southern latitude continental shelf: Seymour Island, Antarctica. *Bulletin of the Geological Society of America*, *120*(5–6), 659–678. [https://doi.org/10.1130/B26269.1](https://doi.org/10.1130/B26269.1) Judd, E. J., Ivany, L. C., DeConto, R. M., Halberstadt, A. R. W., Miklus, N. M., Junium, C. K., & Uveges, B. T. (2019). Seasonally Resolved Proxy Data From the Antarctic Peninsula Support a Heterogeneous Middle Eocene Southern Ocean. *Paleoceanography and Paleoclimatology*, *34*(5), 787–799. [https://doi.org/10.1029/2019PA003581](https://doi.org/10.1029/2019PA003581) Kassambara, A. (2023a). ggpubr: “ggplot2” Based Publication Ready Plots. Retrieved from [https://cran.r-project.org/package=ggpubr](https://cran.r-project.org/package=ggpubr) Kassambara, A. (2023b). rstatix: Pipe-Friendly Framework for Basic Statistical Tests. Retrieved from [https://cran.r-project.org/package=rstatix](https://cran.r-project.org/package=rstatix) Kelley, D., & Richards, C. (2023). oce: Analysis of Oceanographic Data. Retrieved from [https://cran.r-project.org/package=oce](https://cran.r-project.org/package=oce) Kévin Allan Sales Rodrigues. (2023). greekLetters: routines for writing Greek letters and mathematical symbols on the RStudio and RGui. Retrieved from [https://cran.r-project.org/package=greekLetters](https://cran.r-project.org/package=greekLetters) Kolodny, Y., Luz, B., & Navon, O. (1983). Oxygen isotope variations in phosphate of biogenic apatites, I. Fish bone apatite-rechecking the rules of the game. *Earth and Planetary Science Letters*, *64*(3), 398–404. [https://doi.org/10.1016/0012-821X(83)90100-0](https://doi.org/10.1016/0012-821X\(83\)90100-0) Kriwet, J. (2005). Additions to the Eocene selachian fauna of Antarctica with comments on Antarctic selachian diversity. *Journal of Vertebrate Paleontology*, *25*(1), 1–7. [https://doi.org/10.1671/0272-4634(2005)025\[0001:ATTESF\]2.0.CO;2](https://doi.org/10.1671/0272-4634\(2005\)025[0001:ATTESF]2.0.CO;2) Kriwet, J., Engelbrecht, A., Mörs, T., Reguero, M., & Pfaff, C. (2016). Ultimate Eocene (Priabonian) chondrichthyans (Holocephali, Elasmobranchii) of Antarctica. *Journal of Vertebrate Paleontology*, *36*(4). [https://doi.org/10.1080/02724634.2016.1160911](https://doi.org/10.1080/02724634.2016.1160911) Larocca Conte, G., Lopes, L. E., Mine, A. H., Trayler, R. B., & Kim, S. L. (2024). SPORA, a new silver phosphate precipitation protocol for oxygen isotope analysis of small, organic-rich bioapatite samples. *Chemical Geology*, *651*, 122000. [https://doi.org/10.1016/J.CHEMGEO.2024.122000](https://doi.org/10.1016/J.CHEMGEO.2024.122000) Lécuyer, C., Amiot, R., Touzeau, A., & Trotter, J. (2013). Calibration of the phosphate δ18O thermometer with carbonate-water oxygen isotope fractionation equations. *Chemical Geology*, *347*, 217–226. [https://doi.org/10.1016/j.chemgeo.2013.03.008](https://doi.org/10.1016/j.chemgeo.2013.03.008) Long, D. J. (1992). Sharks from the La Meseta Formation (Eocene), Seymour Island, Antarctic Peninsula. *Journal of Vertebrate Paleontology*, *12*(1), 11–32. [https://doi.org/10.1080/02724634.1992.10011428](https://doi.org/10.1080/02724634.1992.10011428) Longinelli, A. (1965). Oxygen isotopic composition of orthophosphate from shells of living marine organisms. *Nature*, *207*(4998), 716–719. [https://doi.org/10.1038/207716a0](https://doi.org/10.1038/207716a0) Longinelli, A., & Nuti, S. (1973). Revised phosphate-water isotopic temperature scale. *Earth and Planetary Science Letters*, *19*(3), 373–376. [https://doi.org/10.1016/0012-821X(73)90088-5](https://doi.org/10.1016/0012-821X\(73\)90088-5) Marramá, G., Engelbrecht, A., Mörs, T., Reguero, M. A., & Kriwet, J. (2018). The southernmost occurrence of Brachycarcharias (Lamniformes, Odontaspididae) from the Eocene of Antarctica provides new information about the paleobiogeography and paleobiology of Paleogene sand tiger sharks. *Rivista Italiana Di Paleontologia e Stratigrafia*, *124*(2), 283–297. Massicotte, P., & South, A. (2023). rnaturalearth: World Map Data from Natural Earth. Retrieved from [https://cran.r-project.org/package=rnaturalearth](https://cran.r-project.org/package=rnaturalearth) Met Office. (2015). Cartopy: a cartographic python library with a Matplotlib interface. Exeter, Devon. Retrieved from [https://scitools.org.uk/cartopy](https://scitools.org.uk/cartopy) Mine, A. H., Waldeck, A., Olack, G., Hoerner, M. E., Alex, S., & Colman, A. S. (2017). Microprecipitation and δ18O analysis of phosphate for paleoclimate and biogeochemistry research. *Chemical Geology*, *460*(March), 1–14. [https://doi.org/10.1016/j.chemgeo.2017.03.032](https://doi.org/10.1016/j.chemgeo.2017.03.032) Montes, M., Nozal, F., Santillana, S., Marenssi, S., & Olivero, E. (2013). Mapa Geológico de Isla Marambio (Seymour), Antártida, escala 1:20,000. *Serie Cartográfica*. Neuwirth, E. (2022). RColorBrewer: ColorBrewer Palettes. Retrieved from [https://cran.r-project.org/package=RColorBrewer](https://cran.r-project.org/package=RColorBrewer) Oscar Perpiñán, & Robert Hijmans. (2023). rasterVis. Retrieved from [https://oscarperpinan.github.io/rastervis/](https://oscarperpinan.github.io/rastervis/) Pierce, D. (2023). ncdf4: Interface to Unidata netCDF (Version 4 or Earlier) Format Data Files. Retrieved from [https://cran.r-project.org/package=ncdf4](https://cran.r-project.org/package=ncdf4) Pucéat, E., Joachimski, M. M., Bouilloux, A., Monna, F., Bonin, A., Motreuil, S., et al. (2010). Revised phosphate-water fractionation equation reassessing paleotemperatures derived from biogenic apatite. *Earth and Planetary Science Letters*, *298*(1–2), 135–142. [https://doi.org/10.1016/j.epsl.2010.07.034](https://doi.org/10.1016/j.epsl.2010.07.034) R Development Core Team. (2024). A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Vienna, Austria. RStudio Team. (2024). RStudio: Integrated Development for R. Boston, MA: RStudio, PBC. Retrieved from [http://www.rstudio.com/](http://www.rstudio.com/). Sarkar, D. (2008). *Lattice: Multivariate Data Visualization with R*. New York: Springer. Retrieved from [http://lmdvr.r-forge.r-project.org](http://lmdvr.r-forge.r-project.org) Shemesh, A. (1990). Crystallinity and diagenesis of sedimentary apatites. *Geochimica et Cosmochimica Acta*, *54*(9), 2433–2438. [https://doi.org/10.1016/0016-7037(90)90230-I](https://doi.org/10.1016/0016-7037\(90\)90230-I) Signorell, A. (2024). DescTools: Tools for Descriptive Statistics. Retrieved from [https://cran.r-project.org/package=DescTools](https://cran.r-project.org/package=DescTools) South, A., Michael, S., & Massicotte, P. (2024). rnaturalearthhires: High Resolution World Vector Map Data from Natural Earth used in rnaturalearth. Retrieved from [https://github.com/ropensci/rnaturalearthhires](https://github.com/ropensci/rnaturalearthhires) Trayler, R. B., Landa, P. V., & Kim, S. L. (2023). Evaluating the efficacy of collagen isolation using stable isotope analysis and infrared spectroscopy. *Journal of Archaeological Science*, *151*, 105727. [https://doi.org/10.1016/j.jas.2023.105727](https://doi.org/10.1016/j.jas.2023.105727) Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., et al. (2019). Welcome to the {tidyverse}. *Journal of Open Source Software*, *4*(43), 1686. [https://doi.org/10.21105/joss.01686](https://doi.org/10.21105/joss.01686) Wickham, H., Pedersen, T. L., & Seidel, D. (2023). scales: Scale Functions for Visualization. Retrieved from [https://cran.r-project.org/package=scales](https://cran.r-project.org/package=scales) Wilke, C. O. (2024). cowplot: Streamlined Plot Theme and Plot Annotations for “ggplot2.” Retrieved from [https://cran.r-project.org/package=cowplot](https://cran.r-project.org/package=cowplot) Xu, S. (2022). ggstar: Multiple Geometric Shape Point Layer for “ggplot2.” Retrieved from [https://cran.r-project.org/package=ggstar](https://cran.r-project.org/package=ggstar) Zeileis, A., Fisher, J. C., Hornik, K., Ihaka, R., McWhite, C. D., Murrell, P., et al. (2020). {colorspace}: A Toolbox for Manipulating and Assessing Colors and Palettes. *Journal of Statistical Software*, *96*(1), 1–49. [https://doi.org/10.18637/jss.v096.i01](https://doi.org/10.18637/jss.v096.i01) Zhu, J., Poulsen, C. J., Otto-Bliesner, B. L., Liu, Z., Brady, E. C., & Noone, D. C. (2020). Simulation of early Eocene water isotopes using an Earth system model and its implication for past climate reconstruction. *Earth and Planetary Science Letters*, *537*, 116164. [https://doi.org/10.1016/j.epsl.2020.116164](https://doi.org/10.1016/j.epsl.2020.116164) Eocene climate cooling, driven by the falling pCO2 and tectonic changes in the Southern Ocean, impacted marine ecosystems. Sharks in high-latitude oceans, sensitive to these changes, offer insights into both environmental shifts and biological responses, yet few paleoecological studies exist. The Middle-to-Late Eocene units on Seymour Island, Antarctica, provide a rich, diverse fossil record, including sharks. We analyzed the oxygen isotope composition of phosphate from shark tooth bioapatite (δ18Op) and compared our results to co-occurring bivalves and predictions from an isotope-enabled global climate model to investigate habitat use and environmental conditions. Bulk δ18Op values (mean 22.0 ± 1.3‰) show no significant changes through the Eocene. Furthermore, the variation in bulk δ18Op values often exceeds that in simulated seasonal and regional values. Pelagic and benthic sharks exhibit similar δ18Op values across units but are offset relative to bivalve and modeled values. Some taxa suggest movements into warmer or more brackish waters (e.g., Striatolamia, Carcharias) or deeper, colder waters (e.g., Pristiophorus). Taxa like Raja and Squalus display no shift, tracking local conditions in Seymour Island. The lack of difference in δ18Op values between pelagic and benthic sharks in the Late Eocene could suggest a poorly stratified water column, inconsistent with a fully opened Drake Passage. Our findings demonstrate that shark tooth bioapatite tracks the preferred habitat conditions for individual taxa rather than recording environmental conditions where they are found. A lack of secular variation in δ18Op values says more about species ecology than the absence of regional or global environmental changes. See methods in Larocca Conte, G., Aleksinski, A., Liao, A., Kriwet, J., Mörs, T., Trayler, R. B., Ivany, L. C., Huber, M., Kim, S. L. (2024). Eocene Shark Teeth From Peninsular Antarctica: Windows to Habitat Use and Paleoceanography. Paleoceanography and Paleoclimatology, 39, e2024PA004965.

This repository contains a series of .csv files developed for the study titled "Plant canopies promote climatic disequilibrium in Mediterranean recruit communities", authored by: Perez-Navarro MA, Lloret F, Molina-Venegas R, Alcántara JM and Verdú M. The author of these files is Perez-Navarro MA. These files are used to characterize species niches, estimate climatic disequilibrium for recruit communities growing under plant canopies and open spaces, and conduct statistical analyses. Variables description of each table is compiled in the METADATA.txt file. Please visit Github readme () to correctly place these files in the folder tree and check for the corresponding scripts where they are required. Please notice that although alternative approaches were calibrated to estimate species niche (accordingly producing multiple niche, distances and disequilibrium dataframes), only niche centroid calibrated discarding 95 percentile of lowest niche density was used for paper results and figures. Also, in case of univariate analyses only bio01, bio06 and bio12 were used in analyses, though species niche and further niche and community estimations were obtained for all 19 variables. This is version 2 (v2) and include extra intermediate .csv required to run all the R scripts included in the abovementioned Github repository. NAs or empty cells present in the .csv files of this repository means no data and do not contribute to the analyses. Visit METADATA.txt file for variables description. These data are under CC0 license. It is possible to share, copy and redistribute the material in any medium or format, and adapt, remix, transform, and build upon the material for any purpose. Studies using R scripts or any data files from these study should cite the abovementioned paper (Perez-Navarro MA, Lloret F, Molina-Venegas R, Alcantara JM, Verdu M. (2024). Plant canopies promote climatic disequilibrium in Mediterranean recruit communities). Please contact m.angeles582@gmail.com in case of having doubts or problems with the existing files and scripts. Current rates of climate change are exceeding the capacity of many plant species to track climate, thus leading communities to be in disequilibrium with climatic conditions. Plant canopies can contribute to this disequilibrium by buffering macro-climatic conditions and sheltering poorly adapted species to the oncoming climate, particularly in their recruitment stages. Here we analyze differences in climatic disequilibrium between understory and open ground woody plant recruits in 28 localities, covering more than 100,000 m2, across an elevation range embedding temperature and aridity gradients in the southern Iberian Peninsula. This study demonstrates higher climatic disequilibrium under canopies compared with open ground, supporting that plant canopies would affect future community climatic lags by allowing the recruitment of less arid-adapted species in warm and dry conditions, but also it endorse that canopies could favor warm-adapted species in extremely cold environments as mountain tops, thus pre-adapting communities living in these habitats to climate change.

Les conditions géothermiques souterraines, quelle que soit la position des aquifères, sont montrées avec des cartes géothermiques appropriées. Cette carte représente les lignes de température attendues à une profondeur de 3 000 m de la carte de la distribution spatiale de la température attendue à une profondeur de 3 000 m (carte géothermique), qui est faite avec des données de 214 forages. Il est fabriqué sur la base des températures mesurées dans des puits accessibles dans tout le pays. Cependant, puisque le champ de température dépend de la composition géologique en profondeur et des caractéristiques tectoniques, le cours des isothermes est le résultat de nombreuses influences telles que la conductivité thermique des roches, la perméabilité et la fissuration des roches, qui se reflètent toutes dans les températures mesurées des puits. À cette profondeur, la chaleur radiogénique dans les roches a également une influence mineure. La répartition des puits, utiles pour les mesures de température, est très inégale et varie en profondeur. Après des températures à une profondeur de 3 000 m, il y a une anomalie positive plus forte dans la partie nord-est de la Slovénie, de la ligne Maribor-Rogatec à l’est, alors qu’il n’y a pas d’anomalie dans la partie orientale du bassin de Krško. Dans la partie nord-est du pays, cela est dû à la croûte terrestre plus mince et au flux de chaleur conductif plus élevé du manteau terrestre. Ailleurs, les températures sont beaucoup plus basses. Las condiciones geotérmicas subterráneas, independientemente de la posición de los acuíferos, se muestran con mapas geotérmicos adecuados. Este mapa representa las líneas de temperatura esperadas a una profundidad de 3 000 m del mapa de la distribución espacial de la temperatura esperada a una profundidad de 3 000 m (Mapa Geotérmico), que se realiza con datos de 214 pozos. Se realiza sobre la base de temperaturas medidas en pozos accesibles en todo el país. Sin embargo, dado que el campo de temperatura depende de la composición geológica en profundidades y características tectónicas, el curso de las isotermas es el resultado de numerosas influencias, como la conductividad térmica de las rocas, la permeabilidad y el agrietamiento de las rocas, todas las cuales se reflejan en temperaturas bien medidas. A esta profundidad, el calor radiogénico en las rocas también tiene una influencia menor. La distribución de los pozos, que fueron útiles para las mediciones de temperatura, es muy desigual y varía en profundidad. Después de temperaturas a una profundidad de 3 000 m, hay una anomalía positiva más fuerte en la parte noreste de Eslovenia, desde la línea Maribor-Rogatec hacia el este, mientras que no hay anomalía en la parte oriental de la cuenca de Krško. En la parte noreste del país, esto se debe a la corteza terrestre más delgada y al mayor flujo de calor conductor del manto de la Tierra. En otros lugares, las temperaturas son mucho más bajas. Die unterirdischen Geothermiebedingungen, unabhängig von der Lage der Grundwasserleiter, werden mit geeigneten geothermischen Karten dargestellt. Diese Karte stellt die erwarteten Temperaturlinien in einer Tiefe von 3 000 m von der Karte der räumlichen Verteilung der erwarteten Temperatur in einer Tiefe von 3 000 m (Geothermiekarte) dar, die mit Daten aus 214 Bohrlöchern erstellt wird. Es wird auf der Grundlage der gemessenen Temperaturen in zugänglichen Brunnen im ganzen Land gemacht. Da das Temperaturfeld jedoch von der geologischen Zusammensetzung in Tiefen und tektonischen Eigenschaften abhängt, ist der Verlauf der Isothermen das Ergebnis zahlreicher Einflüsse wie Wärmeleitfähigkeit von Gesteinen, Durchlässigkeit und Rissbildung von Gesteinen, die alle in gemessenen Brunnentemperaturen reflektiert werden. In dieser Tiefe hat auch radiogene Hitze in Gesteinen einen geringen Einfluss. Die Verteilung der Brunnen, die für Temperaturmessungen nützlich waren, ist sehr ungleichmäßig und variiert in der Tiefe. Nach Temperaturen in einer Tiefe von 3 000 m gibt es eine stärkere positive Anomalie im nordöstlichen Teil Sloweniens, von der Linie Maribor-Rogatec nach Osten, während es im östlichen Teil des Krško-Beckens keine Anomalie gibt. Im Nordosten des Landes ist dies auf die dünnere Erdkruste und die höhere leitfähige Wärmeströmung aus dem Erdmantel zurückzuführen. Anderswo sind die Temperaturen viel niedriger. Le condizioni geotermiche sotterranee, indipendentemente dalla posizione delle falde acquifere, sono mostrate con adeguate mappe geotermiche. Questa mappa rappresenta le linee di temperatura previste ad una profondità di 3 000 m dalla mappa della distribuzione spaziale della temperatura prevista ad una profondità di 3 000 m (Carta geotermica), che è fatta con i dati di 214 pozzi. È realizzato sulla base delle temperature misurate in pozzi accessibili in tutto il paese. Tuttavia, poiché il campo di temperatura dipende dalla composizione geologica in profondità e caratteristiche tettoniche, il decorso delle isoterme è il risultato di numerose influenze come la conducibilità termica delle rocce, la permeabilità e la fessura delle rocce, che si riflettono tutte in temperature misurate bene. A questa profondità, anche il calore radiogenico nelle rocce ha un'influenza minore. La distribuzione dei pozzi, utili per le misurazioni della temperatura, è molto irregolare e varia in profondità. Dopo temperature a una profondità di 3 000 m, c'è un'anomalia positiva più forte nella parte nord-orientale della Slovenia, dalla linea Maribor-Rogatec a est, mentre non vi è alcuna anomalia nella parte orientale del bacino di Krško. Nella parte nord-orientale del paese, questo è dovuto alla crosta terrestre più sottile e al più alto flusso di calore conduttivo dal mantello terrestre. Altrove, le temperature sono molto più basse. De ondergrondse geothermische omstandigheden, ongeacht de positie van de watervoerende lagen, worden weergegeven met geschikte geothermische kaarten. Deze kaart geeft de verwachte temperatuurlijnen weer op een diepte van 3 000 m van de kaart van de ruimtelijke verdeling van de verwachte temperatuur op een diepte van 3 000 m (Geothermiekaart), die wordt gemaakt met gegevens van 214 boorgaten. Het wordt gemaakt op basis van gemeten temperaturen in toegankelijke putten in het hele land. Aangezien het temperatuurveld echter afhankelijk is van de geologische samenstelling in diepten en tektonische kenmerken, is het verloop van isothermen het resultaat van talrijke invloeden zoals thermische geleidbaarheid van gesteenten, doorlaatbaarheid en kraken van gesteenten, die allemaal worden weerspiegeld in gemeten goedtemperaturen. Op deze diepte heeft radiogene warmte in rotsen ook een kleine invloed. De verdeling van putten, die nuttig waren voor temperatuurmetingen, is zeer ongelijk en varieert in diepte. Na temperaturen op een diepte van 3 000 m is er een sterkere positieve anomalie in het noordoosten van Slovenië, van de lijn Maribor-Rogatec naar het oosten, terwijl er geen anomalie is in het oostelijke deel van het Krško-bekken. In het noordoosten van het land is dit te wijten aan de dunnere aardkorst en de hogere geleidende warmtestroom uit de mantel van de aarde. Elders zijn de temperaturen veel lager. Οι υπόγειες γεωθερμικές συνθήκες, ανεξάρτητα από τη θέση των υδροφόρων οριζόντων, παρουσιάζονται με κατάλληλους γεωθερμικούς χάρτες. Ο χάρτης αυτός αναπαριστά τις αναμενόμενες γραμμές θερμοκρασίας σε βάθος 3 000 m από τον χάρτη της χωρικής κατανομής της αναμενόμενης θερμοκρασίας σε βάθος 3 000 m (Γεωθερμικός Χάρτης), ο οποίος γίνεται με δεδομένα από 214 γεωτρήσεις. Γίνεται με βάση τις μετρούμενες θερμοκρασίες σε προσβάσιμα πηγάδια σε όλη τη χώρα. Ωστόσο, δεδομένου ότι το πεδίο θερμοκρασίας εξαρτάται από τη γεωλογική σύνθεση σε βάθη και τεκτονικά χαρακτηριστικά, η πορεία των ισοθερμικών είναι το αποτέλεσμα πολυάριθμων επιδράσεων όπως η θερμική αγωγιμότητα των πετρωμάτων, η διαπερατότητα και η ρωγμή των πετρωμάτων, οι οποίες αντανακλώνται σε μετρημένες θερμοκρασίες φρεατίων. Σε αυτό το βάθος, η ραδιογενής θερμότητα στους βράχους έχει επίσης μια μικρή επιρροή. Η κατανομή των φρεάτων, τα οποία ήταν χρήσιμα για μετρήσεις θερμοκρασίας, είναι πολύ άνιση και ποικίλλει σε βάθος. Μετά από θερμοκρασίες σε βάθος 3000 μέτρων, υπάρχει μια ισχυρότερη θετική ανωμαλία στο βορειοανατολικό τμήμα της Σλοβενίας, από τη γραμμή Maribor-Rogatec προς τα ανατολικά, ενώ δεν υπάρχει ανωμαλία στο ανατολικό τμήμα της λεκάνης Krško. Στο βορειοανατολικό τμήμα της χώρας, αυτό οφείλεται στον λεπτότερο φλοιό της Γης και την υψηλότερη αγώγιμη ροή θερμότητας από τον μανδύα της Γης. Αλλού, οι θερμοκρασίες είναι πολύ χαμηλότερες. The underground geothermal conditions can be presented, irrespective of the aquifers' position, with the appropriate geothermal maps. This map represents the expected temperature lines at a depth of 3000 m and is derived from Geothermal map - Expected temperatures at a depth of 3000 m, which is made with data from 214 boreholes. It is made on the basis of measured temperatures in accessible boreholes throughout the country. However, since the temperature field depends on the geological structure in the depths and tectonic characteristics, the course of the isotherms is a result of many influences, such as thermal conductivity of rocks, permeability and fracturing of rocks, all of which are reflected in the measured temperatures in boreholes. In this depth also a radiogenic heat production in the rocks has smaller influence. The distribution of boreholes, which were useful for the measurement of temperature, is very uneven and different as regard the depths. Following the expected temperatures at a depth of 3000 m a stronger positive anomaly is in the northeastern part of Slovenia, from the line Maribor-Rogatec to the east, while in the eastern part of the Krka basin there is no anomaly any more. In the northeastern part of the country the anomaly is the result of the thinning of the Earth's crust and greater conductive heat flow from the Earth's mantle. Elsewhere temperatures are much lower. Condițiile geotermale subterane, indiferent de poziția acviferelor, sunt afișate cu hărți geotermale adecvate. Această hartă reprezintă liniile de temperatură așteptate la o adâncime de 3 000 m de la harta distribuției spațiale a temperaturii așteptate la o adâncime de 3 000 m (Harta geotermală), care este realizată cu date de la 214 găuri de foraj. Se face pe baza temperaturilor măsurate în puțuri accesibile din întreaga țară. Cu toate acestea, deoarece câmpul de temperatură depinde de compoziția geologică în adâncimi și caracteristici tectonice, cursul izotermelor este rezultatul a numeroase influențe, cum ar fi conductivitatea termică a rocilor, permeabilitatea și fisurarea rocilor, toate acestea fiind reflectate în temperaturile sondei măsurate. La această adâncime, căldura radiogenică din roci are, de asemenea, o influență minoră. Distribuția puțurilor, care au fost utile pentru măsurarea temperaturii, este foarte inegală și variază în profunzime. După temperaturi la o adâncime de 3 000 m, există o anomalie pozitivă mai puternică în partea de nord-est a Sloveniei, de la linia Maribor-Rogatec la est, în timp ce nu există nicio anomalie în partea estică a bazinului Krško. În partea de nord-est a țării, acest lucru se datorează scoarței mai subțiri a Pământului și fluxului de căldură mai mare din mantaua Pământului. În alte părți, temperaturile sunt mult mai scăzute. Il-kundizzjonijiet ġeotermali taħt l-art, irrispettivament mill-pożizzjoni tal-akwiferi, huma murija b’mapep ġeotermali adattati. Din il-mappa tirrappreżenta l-linji tat-temperatura mistennija f’fond ta’ 3 000 m mill-mappa tad-distribuzzjoni spazjali tat-temperatura mistennija f’fond ta’ 3 000 m (Mappa Ġeotermali), li hija magħmula b’data minn 214 boreholes. Dan isir fuq il-bażi ta’ temperaturi mkejla fi bjar aċċessibbli fil-pajjiż kollu. Madankollu, peress li l-kamp tat-temperatura jiddependi fuq il-kompożizzjoni ġeoloġika fil-fond u l-karatteristiċi tettoniċi, il-kors tal-isotermi huwa r-riżultat ta’ bosta influwenzi bħall-konduttività termali tal-blat, il-permeabilità u l-qsim tal-blat, li kollha huma riflessi f’temperaturi mkejla tal-bjar. F’dan il-fond, sħana radjoġenika fil-blat għandha wkoll influwenza minuri. Id-distribuzzjoni tal-bjar, li kienu utli għall-kejl tat-temperatura, hija irregolari ħafna u tvarja fil-fond. Wara temperaturi f’fond ta’ 3 000 m, hemm anomalija pożittiva aktar qawwija fil-parti tal-Grigal tas-Slovenja, mil-linja Maribor-Rogatec lejn il-Lvant, filwaqt li ma hemm l-ebda anomalija fil-parti tal-Lvant tal-baċir ta’ Krško. Fil-parti tal-grigal tal-pajjiż, dan huwa dovut għall-qoxra tad-Dinja irqaq u l-fluss tas-sħana konduttiv ogħla mill-mantell tad-Dinja. Band’oħra, it-temperaturi huma ħafna aktar baxxi. As condições geotérmicas subterrâneas, independentemente da posição dos aquíferos, são mostradas com mapas geotérmicos adequados. Este mapa representa as linhas de temperatura esperadas a uma profundidade de 3 000 m do mapa da distribuição espacial da temperatura esperada a uma profundidade de 3 000 m (Mapa geotérmico), que é feita com dados de 214 furos. É feita com base nas temperaturas medidas em poços acessíveis em todo o país. No entanto, uma vez que o campo de temperatura depende da composição geológica em profundidades e características tectónicas, o curso das isotérmicas é o resultado de inúmeras influências, tais como condutividade térmica das rochas, permeabilidade e rachadura de rochas, todas elas refletidas em temperaturas medidas. A esta profundidade, o calor radiogénico nas rochas também tem uma pequena influência. A distribuição dos poços, que foram úteis para medições de temperatura, é muito desigual e varia em profundidade. Depois de temperaturas a uma profundidade de 3 000 m, há uma anomalia positiva mais forte na parte nordeste da Eslovénia, da linha Maribor-Rogatec ao leste, enquanto não há anomalia na parte oriental da bacia de Krško. Na parte nordeste do país, isto é devido à crosta mais fina da Terra e ao maior fluxo de calor condutor do manto da Terra. Em outros locais, as temperaturas são muito mais baixas.

This dataset contains the underlying data for the following publication: Doukas, H., Spiliotis, E., Jafari, M. A., Giarola, S. & Nikas, A. (2021). Low-cost emissions cuts in container shipping: Thinking inside the box. Transportation Research Part D: Transport and Environment, 94, 102815, https://doi.org/10.1016/j.trd.2021.102815.