Find research datasets worth reusing

opencc-by-4.0Dec 2017View details →

Рис. 9. Частотно-раЗмерное распределение створок устрицы (Crassostrea gigas) иЗ раковинной кучи (все выборки). Fig. 9. Size-frequency distribution of valves of the giant oyster (Crassostrea gigas) from the shell-midden (all samples). in Mollusks from the shell-midden of the Telyakovskogo 2 site in southern Primorye (Yankovskaya culture), their paleoecology and role in paleoeconomy

Рис. 9. Частотно-раЗмерное распределение створок устрицы (Crassostrea gigas) иЗ раковинной кучи (все выборки). Fig. 9. Size-frequency distribution of valves of the giant oyster (Crassostrea gigas) from the shell-midden (all samples).

Table 2 in The dung beetles of Venezuela (Coleoptera: Scarabaeidae: Scarabaeinae): catalogue and updated distribution

Table 2. Comparison of catalogues and checklists recording dung beetles (Coleoptera: Scarabaeinae) species for Venezuela.<table><tbody><tr><th>Autor</th><th>Year of publication Geographical coverage</th><th>Number of species recorded from Venezuela</th></tr></tbody><tbody><tr><th>Harold Gillet</th><td>1869 1911</td><td>world world</td><td>5 15</td></tr><tr><th>Blackwelder</th><td>1944</td><td>Latin America</td><td>49</td></tr><tr><th>Roze</th><td>1955</td><td>Venezuela</td><td>73</td></tr><tr><th>Krajcik</th><td>2012</td><td>world</td><td>40</td></tr></tbody></table>

Table 1 in The dung beetles of Venezuela (Coleoptera: Scarabaeidae: Scarabaeinae): catalogue and updated distribution

Table 1. Number of species by genera of dung beetles (Coleoptera: Scarabaeinae) recorded for Venezuela and the world.<table><tbody><tr><th>Dung beetle genera present in Venezuela</th><th>Species in Venezuela (Roze 1955)</th><th>Species in Venezuela (current work)</th><th>Species in the world (Cupello et al. 2023b; Schoolmeesters 2023)</th></tr></tbody><tbody><tr><th>Agamopus</th><td>–</td><td>1</td><td>5</td></tr><tr><th>Anisocanthon</th><td>–</td><td>–</td><td>4</td></tr><tr><th>Anomiopus</th><td>–</td><td>8</td><td>63</td></tr><tr><th>Ateuchus</th><td>4</td><td>10</td><td>102</td></tr><tr><th>Bdelyropsis</th><td>–</td><td>1</td><td>3</td></tr><tr><th>Bdelyrus</th><td>–</td><td>1</td><td>27</td></tr><tr><th>Bradypodidium</th><td>–</td><td>1</td><td>3</td></tr><tr><th>Canthidium</th><td>2</td><td>1</td><td>178</td></tr><tr><th>Canthon</th><td>29</td><td>13</td><td>163</td></tr><tr><th>Canthonella</th><td>–</td><td>1</td><td>17</td></tr><tr><th>Copris</th><td>1</td><td>–</td><td>280</td></tr><tr><th>Coprophanaeus</th><td>–</td><td>9</td><td>50</td></tr><tr><th>Cryptocanthon</th><td>–</td><td>4</td><td>43</td></tr><tr><th>Deltochilum</th><td>3</td><td>10</td><td>114</td></tr><tr><th>Dendropaemon</th><td>1</td><td>4</td><td>41</td></tr><tr><th>Diabroctis</th><td>2</td><td>2</td><td>5</td></tr><tr><th>Dichotomius</th><td>13</td><td>20</td><td>200</td></tr><tr><th>Digitonthophagus</th><td>–</td><td>1</td><td>16</td></tr><tr><th>Eurysternus</th><td>3</td><td>15</td><td>53</td></tr><tr><th>Genieridium</th><td>–</td><td>1</td><td>7</td></tr><tr><th>Gromphas</th><td>1</td><td>1</td><td>6</td></tr><tr><th>Hansreia</th><td>–</td><td>1</td><td>6</td></tr><tr><th>Malagoniella</th><td>–</td><td>1</td><td>9</td></tr><tr><th>Ontherus</th><td>2</td><td>8</td><td>60</td></tr><tr><th>Onthophagus</th><td>5</td><td>8</td><td>2257</td></tr><tr><th>Oxysternon</th><td>–</td><td>5</td><td>11</td></tr><tr><th>Phanaeus</th><td>4</td><td>6</td><td>83</td></tr><tr><th>Pseudocanthon</th><td>–</td><td>2</td><td>11</td></tr><tr><th>Scatimus</th><td>–</td><td>2</td><td>13</td></tr><tr><th>Scybalocanthon</th><td>–</td><td>4</td><td>24</td></tr><tr><th>Sulcophanaeus</th><td>–</td><td>4</td><td>15</td></tr><tr><th>Sylvicanthon</th><td>–</td><td>1</td><td>15</td></tr><tr><th>Tetraechma</th><td>–</td><td>1</td><td>5</td></tr><tr><th>Uroxys</th><td>2</td><td>2</td><td>59</td></tr></tbody></table>

Pan-European temperature distribution at depth - GeoDH project

The dataset includes two shapefiles showing the temperature distribution at depth in Europe, specifically areas with temperatures exceeding 50°C at 1000m depth and 90°C at 2000m depth. This dataset was developed for assessing the potential of Geothermal District Heating in Europe as part of the GeoDH project (<a href="http://geodh.eu/" target="_new" rel="noopener">http://geodh.eu/</a>). Please note that this represents the state of the art as of 2014 and that geological, technological, and regulatory developments may have occurred since its creation, and users should verify if more recent data is available for their purposes.

Dataset: Osmoregulation and hypoxia tolerance in the cenote isopod Creaseriella anops: Insights into its distribution in Karst Subterranean Estuaries

This data set contains the information supporting the research article  "Osmoregulation and hypoxia tolerance in the cenote isopod Creaseriella anops: Insights into its distribution in Karst Subterranean Estuaries"  It contains Respirometry, indicators of cellular damage and Antioxidant system, critical temperatures, and Temperature induced metabolic rates of the isopod Creaseriella anops, and endemic species of the Karst Subterranean Estuaries from the Yucatan Peninsula Mexico.

TreeGOER Köppen-Geiger Zone Distributions: Observations for 48,129 tree species across the 30 climate zones for 1931-1960, 1961-1990 and 1991-2020 climates

TreeGOER (Tree Globally Observed Environmental Ranges) is a database that documents the environmental ranges (minimum, maximum, median, mean and 5%, 25%, 75% and 95% quantiles) for 48,129 tree species and for 51 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. TreeGOER is available from the following Zenodo archives: <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a> The TreeGOER ranges were calculated after cleaning occurrence records and standardizing species names with the <a href="https://bsapubs.onlinelibrary.wiley.com/doi/10.1002/aps3.11388">WorldFlora</a> R package to <a href="https://onlinelibrary.wiley.com/doi/10.1002/tax.12373">World Flora Online</a> or the <a href="https://www.nature.com/articles/s41597-021-00997-6">World Checklist of Vascular Plants</a> for a global GBIF occurrence download of 44,267,164 occurrences (GBIF.org 2021 GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.77gcvq">https://doi.org/10.15468/dl.77gcvq</a>). The process of compilation of TreeGOER with 30 arc-seconds global grid layers, two examples of BIOCLIM applications that investigated the effects of climate change on global tree diversity patterns and R scripts to repeat these analyses have been described by Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology 29: 6303–6318. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>. This Zenodo archive documents the occurrence of the same previously compiled and cleaned observations for the TreeGOER across global raster layers that document the 1931-1960, 1961-1990 and 1991-2020 Köppen-Geiger climate zones. These global raster layers were created for the following article: <ul> <li>Beck, H. E., T. R. McVicar, N. Vergopolan, A. Berg, N. J. Lutsko, A. Dufour, Z. Zeng, X. Jiang, A. I. J. M. van Dijk, and D. G. Miralles. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections, Scientific Data 10, 724 (2023). <a href="https://doi.org/10.1038/s41597-023-02549-6">https://doi.org/10.1038/s41597-023-02549-6</a>. The Köppen-Geiger classifcation maps, associated confidence maps, and underpinning monthly near-surface air temperature and precipitation climatologies in netCDF format can all be downloaded <a href="https://doi.org/10.6084/m9.figshare.21789074.v1">here</a>.</li> </ul>   For each of the 48,129 tree species, the distribution is given for <ul> <li>Historical climates: number of observations in the 1931-1960, 1961-1990 and 1991-2020 Köppen-Geiger Zone climate zone</li> <li>Mixed climate: number of observations in the 1931-1960 Köppen-Geiger Zone climate zones if observations were before 1961, in the 1961-1990 Köppen-Geiger Zone climate zones if observations were between 1961 and 1990, and in the 1991-2020 Köppen-Geiger Zone climate zones if observations were after 1990</li> <li>Static climate: number of observations if those observations remained in the same Köppen-Geiger Zone climate zones</li> </ul>   The development of this data set archive supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation and through the Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso projects, by the Bezos Earth Fund to the Quality Tree Seed for Africa in Kenya and Rwanda project and by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa.

Vertical distribution of heterotrophic nanoflagellates in the Baltic Proper

This dataset contains data on the abundance of prokaryotes, heterotrophic nanoflagellates (HNF), specific lineages of HNF and environmental factors in the Baltic Sea collected during four cruises of r/v Baltica (National Fisheries Research Institute) in 2021. The Excel file includes six sheets: <ol> <li>The "Parameters-Data" sheet lists all parameters for data presented in the "Data" sheet. Column A (Name) contains the variables names, column B (Unit) contains units in which they were measured, column C (Method/Device) contains information on the methodology, and column D (Comments) contains additional information</li> <li>The "Data" sheet contains data in a wide format for all variables listed in the "Parameters-Data" sheet measured at sampling depths. The first row contains variable names (listed in Column A of the Parameters-Data sheet) with units in square brackets</li> <li>The "Parameter-Size" sheet lists parameters for data presented in the "Size" sheet in the same format as described for the "Parameters-Data" sheet. Starting from row 5 in columns A and B, the number of measured HNF cells for each sample is given </li> <li>The "Size" sheet contains size measurements of HNF in the samples in a long format. The number of cells measured in each sample is provided in the "Parameter-Size" sheet</li> <li>The "Parameters-CTD depth profiles" sheet lists parameters for data presented in the "CTD depth profiles" sheet in the same format as described for the "Parameters-Data" sheet.</li> <li>The "CTD depth profiles" sheet contains full-depth profiles of variables measured with a CTD probe with 1 m resolution.</li> </ol>

opencc-by-4.0Apr 2024View details →

Spatiotemporal distribution of global peatland area during the Holocene

The global peatland area dataset comprises netCDF files, which consist of 13 sets of maps showing the global extent of peatlands at a spatial resolution of 0.5° × 0.5°. All maps are provided at 1,000-year time intervals between 12 and 0 ka BP. The peatland area files named “Global_peatland_area_BA_*” were reconstructed using the BA method, and the files named “Global_peatland_area_IDW_*” were reconstructed using the IDW method. The global peatland records included data on location, latitude, longitude, peat type, basal ages, and end ages. The global pollen of Sphagnum  records included latitude, longitude, Sphagnum content, and peat basal and end ages.

CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction

<div> <div> Application of artificial intelligence (AI) has been ubiquitous in the growth of research in the areas of basic sciences. Frequent use of machine learning (ML) and deep learning (DL) based methodologies by researchers has resulted in significant advancements in the last decade. These techniques led to notable performance enhancements in different tasks such as protein structure prediction, drug-target binding affinity prediction, and molecular property prediction. In material science literature, it is well-known that crystalline materials exhibit topological structures. Such topological structures may be represented as graphs and utilization of graph neural network (GNN) based approaches could help encoding them into an augmented representation space. Primarily, such frameworks adopt supervised learning techniques targeted towards downstream property prediction tasks on the basis of electronic properties (formation energy, bandgap, total energy, etc.) and crystalline structures. Generally, such type of frameworks rely highly on the handcrafted atom feature representations along with the structural representations. In this paper, we propose an unsupervised framework namely, CrysAtom, using untagged crystal data to generate dense vector representation of atoms, which can be utilized in existing GNN-based property predictor models to accurately predict important properties of crystals. Empirical results show that our dense representation embeds chemical properties of atoms and enhance the performance of the baseline property predictor models significantly. </div> </div>

Distributing quantum correlations through local operations and classical resources

Text files containing data of the figures shown in "Distributing quantum correlations through local operations and classical resources". Where some variables do not affect the plot value, for instance the values of φ in Figures 4a and 4b, fewer plot points of these variables are used in the final heatmaps to allow more detail in the other variables which do affect the function values. https://arxiv.org/abs/2408.05490.

opencc-by-4.0Dec 2023View details →

Results Data: Understanding the distributional effects of recurrent floods in the Philippines

This dataset provides all the files required to visualize the main Figures of the paper: Sauer, Inga and Walsh, Brian James and Frieler, Katja and Bresch, David N. and Otto, Christian, Understanding the Distributional Effects of Recurrent Floods in the Philippines (May 27, 2024). The filenames indicate the figures for which the dataset serve as input. The name "haz" indicates a file that provides flooded areas, the final number indicates the admin1 region. keff-> capital stock damage cons_sm-> consumption loss smoothed with savings wb_sm-> well-being loss smoothed with savings

Inferring cosmology from gravitational waves using non-parametric detector-frame mass distribution: Data Release

Dataset release accompanying Inferring cosmology from gravitational waves using non-parametric detector-frame mass distribution.

Dataset for Integrated Species Distribution Model for pikeperch larvae in the Porvoo-Sipoo archipelago

This record contains the data required to run the code for fitting the Integrated Species Distribution Model described in <a href="https://arxiv.org/abs/2206.08817">arXiv:2206.08817 [stat.ME].</a> <h1>Files in this record</h1> <ul> <li>transect_data.csv Line transect observations from Porvoo-Sipoo archipelago, Finland on June 2017.</li> <li>expert_assessments.tif Rasterized, anonymous expert assessments. Categorical values denoting how likely a given location is to be a spawning location for pikeperch. 4 categories, with smaller values corresponding to higher probabilities.</li> <li>covariate_raster_example.tif Rasterized example environmental covariate values. These are similarly structured as the covariate data used in the study and compatible with the analysis code. However, since we do not have the permission to release the original data set, these values are instead generated based on the projected planar coordinates such that they have roughly similar spatial gradients as the original covariates.</li> </ul> <h1>Detailed descriptions</h1> <h2>Transect data</h2> <h3>Location and replicate identifiers</h3> <ul> <li> id : transect identifier. Replicates of the same transect have the same identifier. </li> <li> id2 : alternate transect identifier, unique for each transect. </li> <li> repeated : whether transect was replicated or not. </li> <li> X_euref : easting coordinate, EUREF_FIN_TM35FIN, for the transect starting location in [meters] </li> <li> Y_euref : northing coordinate, EUREF_FIN_TM35FIN, for the transect starting location in [meters] </li> <li>date : date of the measurement, DD/MM/YYYY</li> <li>week : week number of the measurement date</li> </ul> <h3>In situ measurements</h3> <ul> <li> volume : Transect water volume [m^3]. Transect length (500m) multiplied by sampler surface area. Used as survey effort. </li> <li> heading : compass heading (direction) for the transect, in [degrees]. </li> <li> SumKUHA : total pikeperch (Sander lucioperca, kuha in Finnish) larvae count in each transect [scalar] </li> </ul> <h2>Expert assessments</h2> The raster contains assessments from 10 local experts encoded as separate raster layers (Expert_1, Expert_2, ..., Expert_10). Raster resolution is 50m x 50m and the planar coordinates are based on the same coordinate reference system as the transect observations (UTM zone 35). The assessments are coded as integers with values between 1 and 4, with smaller values corresponding to higher probabilities. <h2>Covariate raster example</h2> This raster has the same spatial dimensions and uses the same coordinate reference system as the expert assessment raster and has three layers, one for each covariate. The covariate values are generated based on the spatial coordinates such that each covariate has similar spatial gradient as the original covariate. The covarites have the same names as in the original covariate data (dptLUKE, dist10m and lined3km). <h1>Creators</h1> Transect data collected and curated by Sanna Kuningas. Original covariate rasters curated by Sanna Kuningas from data sets collected by the Finnish Environment Institute and the Natural Resources Institute Finland. Expert assessments originally digitized and rasterized by Jussi Mäkinen.  Additional refinement to assessment rasters by Karel Kaurila. Preparation for publishing on Zenodo for all of the data sets  by Karel Kaurila. <h2>Change log</h2> <ul> <li> 2025 Jan 31: Included columns date and week for transect_data.csv.</li> </ul>

opencc-by-nc-sa-4.0Aug 2024View details →

Supplementary material for the article "Reaching Meaning through Language: What can Children Tell Us about Distributivity?"

<div> <div>This data set includes the supplementary material for the article "Reaching Meaning through Language: What can Children Tell Us about Distributivity?". All content is documented in the README.md file.</div> <div>Abstract: Sentences with a plural subject receive a distributive reading if the predicate refers to the atomic members or a collective one if it relates to the whole group. Previous accounts suggest that the distributive representation includes an additional semantic operator, and comprehension experiments show that adults interpret an ambiguous sentence as collective. However, children accept distributive readings more often, questioning their presumed greater difficulty. The current study investigates these interpretations in a novel way through a production study. Italian adults and preschoolers described distributive and collective pictures. We found that adults produced more distributive expressions, in line with semantic theories and psycholinguistic findings. Children were not fully sensitive to the need to express markers disambiguating the two readings. However, when they recognised the difference between pictures, they produced more collective markers, different from adults. We discussed our results at the intersection of language acquisition, semantic theories, and cognitive development.</div> </div>

opencc-by-4.0Feb 2020View details →

Distribution of the Natura 2000 habitat type 7220 (Cratoneurion) in Flanders and Brussels Capital Region, Belgium (version 2025)

The dataset is a geospatial collection of points that correspond with the presence or absence of the Natura 2000 habitat type <code>7220</code> (Petrifying springs with tufa formation (Cratoneurion)) in springs and streaming water segments in the Flemish and Brussels Capital Region, Belgium. The dataset also contains  a number of locations that were visited during mapping projects but where <code>7220</code> was found absent, or where additional survey is needed to decide on presence/absence of the habittype. The file is a GeoJSON format RFC7946 (WGS84). The data source is produced, owned and administered by the Research Institute for Nature and Forest (INBO, Department of Environment of the Flemish government). Headers are:  <ul> <li><code>id</code>; </li> <li><code>source</code>: original data source;</li> <li><code>validity status</code>: field inventory carried out (<code>gecontroleerd</code>) or not (<code>niet gecontroleerd</code>);</li> <li><code>name</code>: unique name of the site;</li> <li><code>system_type</code>: stream type (<code>rivulet</code>), mire type (<code>mire</code>), unknown (<code>unknown</code>) or na (<code>NA</code>);</li> <li><code>habitattype</code>: <code>7220</code>, no Natura 2000 type <code>(gh)</code>, unconfirmed <code>7220</code> <code>(7220, gh)</code>, alkaline fen<code>(7230)</code>;</li> <li><code>unit_id</code>: spatially related sites are identified by a common identifier</li> <li><code>area_m2</code>: area in square meters;</li> <li><code>year</code>: year of field inventory;</li> <li><code>sbz</code>: inside (1) or outside (0) special area of conservation;</li> <li><code>geometry</code>: latitude, longitude in decimal degrees.</li> </ul>

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Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management

1. Species' ranges are changing at accelerating rates. Species distribution models (SDMs) are powerful tools that help rangers and decision-makers prepare for reintroductions, range shifts, reductions, and/or expansions by predicting habitat suitability across landscapes. Yet, range-expanding or -shifting species in particular face other challenges that traditional SDM procedures cannot quantify, due to large differences between a species' currently-occupied range and potential future range. The realism of SDMs is thus lost and not as useful for conservation management in practice. Here, we address these challenges with an extended assessment of habitat suitability through an integrated SDM database (iSDMdb). 2. The iSDMdb is a spatial database of predicted sites in a species' prediction range, derived from SDM results, and is a single spatial feature that contains additional, user-friendly data fields that synthesise and summarise SDM predictions and uncertainty, human impacts, restoration features, novel preferences in novel spaces, and management priorities. To illustrate its utility, we used the endangered New Zealand sea lion (Phocarctos hookeri). We consulted with wildlife rangers, decision-makers, and sea lion experts to supplement SDM predictions with additional, more realistic, and applicable information for management. 3. Almost half the data fields included in this database resulted from engaging with these end-users during our study. The SDM found 395 predicted sites. However, the iSDMdb's additional assessments showed that the actual suitability of most sites (90%) was questionable due to human impacts. >50% of sites contained unnatural barriers (fences, grazing grasslands), and 75% of sites had roads located within the species' range of inland movement. Just 5% of the predicted sites were mostly (>80%) protected. 4. Integrating SDM results with supplemental assessments provides a way to address SDM limitations, especially for range-expanding or -shifting species. SDM products for conservation applications have been critiqued for lacking transparency and interpretation support, and ineffectively communicating uncertainty. The iSDMdb addresses these issues and enhances the practical relevance and utility of SDMs for stakeholders, rangers, and decision-makers. We exemplify how to build an iSDMdb using open-source tools, and how to make diverse, complex assessments more accessible for end-users.

opencc-zeroOct 2021View details →

opencc-by-4.0Oct 2021View details →

Fig. 1 in A new species of Prionoglaris Enderlein (Psocodea: 'Psocoptera': Prionoglarididae) from an Armenian cave, with an account of the distribution of the genus

Fig. 1. Prionoglaris kapralovi sp. nov. (A) Habitus of male, dorsal view of holotype, in alcohol (body length 3.3 mm). (B) Habitus of female, lateral view of paratype, in alcohol (body length 4.1 mm). (C) Male terminalia, ventral view of holotype, in alcohol. Photographs by S. A. Kapralov.

opencc-by-4.0Oct 2021View details →

Fig. 3 in A new species of Prionoglaris Enderlein (Psocodea: 'Psocoptera': Prionoglarididae) from an Armenian cave, with an account of the distribution of the genus

Fig. 3. Prionoglaris spp.: P. stygia males from Friouato Cave, Morocco (A-F), P. stygia male from Pierre à Perret Cave, Switzerland (G), P. stygia male from W of Atzeneta del Maestrat, Spain (H), P. stygia male from the type locality, Compagnaga Cave, French Pyrenees (I), P. dactyloides, male holotype (J), P. stygia female from Friouato Cave, Morocco (K-L), P. stygia female from the type locality, Compagnaga Cave, French Pyrenees (M). – (A) Phallosome, ventral view. (B) Anterior claw of hind pretarsus. (C) Posterior claw of hind pretarsus. (D) Left forewing. (E-J) Dorso-lateral appendages and medio-internal appendage of phallosome, not in situ (E same male as in A). (K-M) Spermapore sclerite and distal part of spermathecal duct (K and M ventral view; L optical longitudinal section, lateral view).

opencc-by-4.0Aug 2021View details →

Data and Scripts for Schweiger et al. (2021) "Chemical properties of key metabolites determine the global distribution of lichens"

Data and Scripts for Schweiger et al. (2021) "Chemical properties of key metabolites determine  the global distribution of lichens". A detailed description of the individual files is provided in the "Schweiger-et-al-DataPublication-Index-Submissionfiles.txt" file. Summary of the study: In lichen symbioses, fungal secondary metabolites provide UV protection on which certain lichen algae such as trebouxioid green algae sensitively depend. These metabolites differ in their UV absorbance capability and solvability, and thus vary in their propensity of being leached from the lichen body by high precipitation and temperatures, with still unknown implications for the global distribution of lichens. In this global study, we show that the occurrence and chemical properties of fungal-derived metabolites are of eco-evolutionary significance for the global, latitudinal distribution of lichenized Trebouxiophyceae. This might represent an indirect environmental adaptation in which the mycobiont invests to protect the trebouxioid photobiont from harsh environmental conditions and, by doing this, secures its efficient source of photosynthetic carbon.