A fine-tuned global distribution dataset of marine forests

Species distribution records are a prerequisite to follow climate-induced range shifts across space and time. However, synthesizing information from various sources such as peer-reviewed literature, herbaria, digital repositories and citizen science initiatives is not only costly and time consuming, but also challenging, as data may contain thematic and taxonomic errors and generally lack standardized formats. We address this gap for important marine ecosystem-structuring species of large brown algae and seagrasses. We gathered distribution records from various sources and provide a fine-tuned dataset with ~2.8 million dereplicated records, taxonomically standardized for 682 species, and considering important physiological and biogeographical traits. Specifically, a flagging system was implemented to signal potentially incorrect records reported on land, in regions with limiting light conditions for photosynthesis, and outside the known distribution of species, as inferred from the most recent published literature. We document the procedure and provide a dataset in tabular format based on Darwin Core Standard (DwC), alongside with a set of functions in R language for data management and visualization. Measurement(s) Species • Distribution Technology Type(s) digital curation Factor Type(s) geographic location Sample Characteristic - Organism Fucales • Laminariales • Tilopteridales • Cymodoceaceae • Hydrocharitaceae • Posidoniaceae • Zosteraceae Sample Characteristic - Environment marine biome Sample Characteristic - Location global Measurement(s) Species • Distribution Technology Type(s) digital curation Factor Type(s) geographic location Sample Characteristic - Organism Fucales • Laminariales • Tilopteridales • Cymodoceaceae • Hydrocharitaceae • Posidoniaceae • Zosteraceae Sample Characteristic - Environment marine biome Sample Characteristic - Location global Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12011523


Background & Summary
Bioclimatic modelling 1,2 , macroecology 3 and evolution 4 are fields that have recently seen a boost in broad scale analyses owing to increased accessibility of large scale biodiversity data. Although these can be obtained from digital online databases (e.g., GBIF, the Global Biodiversity Information Facility, www.gbif.org and OBIS, the Ocean Biogeographic Information System, www.obis.org), herbarium (e.g., Macroalgal Herbarium Portal, www.macroalgae.org), museum collections, as well as citizen science initiatives [5][6][7] , they can be very incomplete and contain geographical and taxonomic errors. In particular, studies focused on the impacts of global climate changes 8,9 , or locating evolutionary biodiversity hotspots 10,11 , require complete and extremely accurate baselines on the distribution of species across space and time 12 .
Collating broad-scale biodiversity data from multiple sources is challenged by two major obstacles. First, the lack of complete database compatibility allowing efficient information exchange between distinct sources, alongside with inconsistent file structures 13,14 , leaves data frequently scattered, even for well-known taxa 15 . Second, the quality of several sources has been questioned regarding potential geographical data errors 16 . This is a serious limitation since unreliable biased records can deeply influence the outcomes of research analyses. For instance, distribution models can be strongly influenced by particular marginal records. While records of marine species falling on land (and vice-versa) can be easily identified and dealt with 10 , those distributed in climatically unfavorable regions (i.e., outside species' niche), beyond range margins or dispersal capacities, should be verified and corrected when necessary. Wrong records may be even more likely for rare, elusive, or cryptic species that can be easily confused with others, more common and broadly distributed 17 . An additional problem that is more evident and easier to tackle is related to taxonomic data errors 16 , which can deeply confound the baseline of a species' distribution 18 . When properly reviewed, databases can integrate quality control flags to identify potential data limitations. While some research communities have developed quality control standards on data (e.g., The Ocean Data Standards and Best Practices Project, www.oceandatastandards.org), no implementation has been done so far for the aforementioned data limitations, even in major online data sources providing large scale biodiversity data. Data treatment. The dataset structure was based on Darwin Core Standard (DwC) 32 . This framework for biodiversity data offers a stable and flexible framework to store all fields available in original data sources. Moreover, it provides standard identifiers, labels, and definitions, allowing a full link-back to original data sources.
Taxonomic standardization was performed with the World Register of Marine Species (WoRMS; www. marinespecies.org), a universally authoritative open-access reference system for marine organisms. This tool provides a unique identifier (aphiaID) that enabled to link each taxon originally captured, to an internationally accepted standardized name with associated taxonomic information (including hierarchy, rank, acceptance status and synonymy) that will continue to be updated in the future in case of taxonomic or name changes. In the rare cases of no match with WoRMS (including misspelled entries), or uncertain taxonomic status, the records were removed from the dataset.
Geographical locations were available for most records as coordinates in decimal degrees. For those records missing coordinates, but including information on location, an automatic geocoding procedure was performed with OpenStreetMap 33,34 service (http://planet.openstreetmap.org).
Since unique records may be available across distinct data sources, the final aggregated dataset was subjected to the removal of duplicate records. These were considered when belonging to the same taxon, and recorded in the same exact geographical location (longitude, latitude and depth) and date (year, month and day).

Quality control.
To achieve a fine-tuned dataset, a flagging system was implemented to identify records with doubtful geographical and depth locations. This started by flagging records occurring on land, by using a 1 km threshold from shoreline. This distance represented the lower spatial resolution of the polygon used to define landmass (OpenStreetMap geographic information 33 ). Light availability for photosynthesis was further considered, since it is the main environmental driver restricting the vertical distribution of marine forests 35 . Limiting light was favored in detriment of bathymetry, because it varies with depth throughout the global ocean, particularly in oceanic regions, were it reaches deeper waters 1 . Available light at bottom was extracted from Bio-ORACLE 36 , a dataset providing benthic environmental layers (i.e., along the bottom of the ocean). Because Bio-ORACLE layers are available for 3 different depth ranges, the maximum light value per record was chosen as a conservative approach to estimate the potential depth range for a given location. Records were flagged when light values were below the known limiting threshold of 50 E.m −2 .year −1 for marine forests' photosynthesis 35,37 . This flag was not applied to the brown algae Sargassum fluitans, Sargassum natans 38 and Sargassum pusillum 39 as they can complete a full life cycle floating on the sea surface.
Finally, all records were manually verified to identify potential outliers outside the known distribution of species. This information was based on the most recent published literature and by consulting experts when possible. Because distributional ranges are often documented at an administrative level (e.g., country), the flagging procedure integrated the Marine Ecoregions of the World (MEOW) 40 , a scheme that represents the broad-scale distributional patterns of species/communities in the ocean 40 . Records were flagged when distributed in a MEOW region not considered in the information available in the literature or provided by experts. The MEOW has 3 distinct levels dividing the globe into 12 realms, 62 provinces and 232 ecoregions 40 . We adopted the intermediate level "provinces" to reduce commission errors (cases incorrectly identified as potential outliers) and omission errors (outliers left out, or omitted), potentially arising while considering "realms" and "ecoregions", respectively. Records were removed from the database when no information was available in literature to support the actual distribution of species.
Data collection sources. The dataset gathered information from 18 distinct repositories, 15 herbaria and 569 literature sources. The majority of records resulted from external repositories (82.56% of records), followed by literature (16.07% of records) and herbaria (1.35% of records; Table 1). The main repositories GBIF and OBIS  www.nature.com/scientificdata www.nature.com/scientificdata/ accounted for 52.57% of all records). In terms of species number, the main sources of data were external repositories, followed by herbaria and literature. These covered 96.77%, 61.14% and 13.04% of species, respectively ( Table 2).

technical Validation
The dataset gathered information from multiple sources, some of which may be automatically interoperable, sharing erratic duplicated data, regardless of the credibility of the source. These data can be used in scientific studies, potentially generating misleading results. To address the challenge, we developed a specific quality control data treatment based on automatic and manual pipelines.
The taxonomic standardization using WORMS discarded any misspelled or no-match entries from the dataset, and aggregated 1116 initial taxa into 682 accepted taxa (at the species level). As new taxa are being described and their current status is constantly changing, WoRMS may not yet contain all updated statuses 42 , however, it is continuously being improved and is considered the best available source for marine taxonomic standardization. Together with the identification of duplicate entries, records missing coordinate information or information regarding species' distributional ranges, our approach removed 2,676,350 initial entries from the dataset.
The automatic flagging procedure identified 1.21% of records located on land, and an additional 6.88% records without suitable light conditions for photosynthesis ( Table 1). The manual verification based on published literature and consulting experts flagged 2.74% of records as potential outliers outside the know distribution of species (75,369 records; Table 1). Considering the three flags implemented, literature records appeared the least biased (unique exception of literature records for seagrasses flagged over land; Table 1), followed by digital repositories and herbaria ( Table 2). The number of species flagged by manual verification against known distributional ranges was the lowest for literature (26.96%), followed by repositories (36.96%) and herbaria (60.43%; Table 2).
The flagging system implemented, not available in any of the 33 repositories and herbaria consulted, allowed delivering a fine-tuned dataset of 2,485,534 georeferenced records gathered from multiple sources, with no taxonomic errors (based on the WoRMS current information), no duplicate entries, no records in unsuitable habitats (i.e. land or low light conditions) or too distant from species' biogeographical ranges.
The use of a flagging system allowed retaining valuable data that should not be discarded. For instance, some large brown algae and seagrasses can often be found as rafts 43 , floating on the sea surface, hundreds of kilometers away from their original source 44,45 . While these records are not particularly suitable to build ecological models aimed for benthic species, they are highly valuable to address dispersal ecology. Instead of considering such cases as outliers for exclusion, flagging allows keeping records for users to decide their final use.    www.nature.com/scientificdata www.nature.com/scientificdata/ of occurrence records (e.g., function to export data as geospatial vectors for geographic information systems). All functions are detailed in Table 3 and can be easily installed by entering the following line into the command prompt: source("https://raw.githubusercontent.com/jorgeassis/marineforestsDB/master/sourceMe.R").

Usage Notes
The dataset follows the FAIR principle of Findability, Accessibility, Interoperability and Reusability of data 46 . It is made available as two distinct files in tabular format. The first aggregates all data with no taxonomic errors and no duplicate entries and includes the three fields implemented to flag records. The additional file provides a pruned version of the dataset discarding all potentially biased records. The dataset complies with Darwin Core Standard (DwC) 32 , providing information on taxonomy, geographical location (e.g., coordinates in decimal degrees, depth and uncertainty), reference to original sources (including permanent identifiers; bibliographic Citation DOI), as well as the flagging system implemented ( Table 4).
The integration of the dataset with a set of functions in R language allows easy data acquisition and smooth integration with already available statistical tools, such as those aiming for Ecological Niche Modeling 47,48 . For instance, the dataset can be used to describe the global distribution of species 12,49 , address niche-based questions 3,50,51 , support biodiversity and ecosystem-based conservation 10,52,53 , and to understand correlations between anthropogenic pressures and population extinctions 54 . Additionally, the availability of standard data layers delivering past and future climate change scenarios 36,55 may further expand the applications of this dataset to predict range shifts 9,56,57 or hypothesize important evolutionary scenarios, such as mapping climate-refugia where higher and endemic biodiversity evolved 43,58,59 .
Data transparency and accuracy is a prerequisite for avoiding flawed and/or misleading conclusions, especially when provided to stakeholders and decision makers. The pipelines implemented are explicit, ensuring the clarity and reproducibility of the process and contributing to public data in standard formats (i.e., the Darwin Core Standard). With the flagging system, users can fine-tune the original dataset according to their research needs and boost the quality of their results. Particularly, when requested by decision-makers, more accurate outcomes may provide important climate change-integrated conservation strategies 60 , as well as feed important baseline assessments, like those required in the scope of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES).