EpIG-DB V 1.0
The current version of EpiG-DB consists of 463,196 individuals from 3005 species (including 960 morphospecies) belonging to 60 families and 411 genera, collected from 18148 relevés (17762 trees and 386 understory plots) with 76% of trees sampled within 687 forest plots (Fig. 1).
Figure 1: Spatial distribution of 80 datasets integrated in EpIG‐DB 1.0 across the Neotropic WWF biomes.
Datasets' spatial distribution indicates data deficient areas; for instance, the Amazon & the Caribbean, where data needs to be urgently collected.
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Figure 2: Climate envelope of EpIG‐DB 1.0 data across Whittaker biomes.
Data are distributed along a broad climate range, covering tropical and subtropical biomes where epiphytes typically grow, but also cold & very rainy tropical regions.
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All in all, 80 datasets have been gathered from 9 countries, 6 biomes and 45 ecoregions in the Neotropics (Fig. 1). More than half of the relevés (66%) are distributed in natural ecosystems, only 19% are found in anthropogenic areas and 15% in semi-natural ecosystems. We need more data from disturbed or regenerating sites!
We are currently developing EpIG-DB 2.0
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For the second version, we are working to provide:
1. Taxonomical backbone: We're using a modified version of EpiList 1.0 (Zotz et al. 2021), updating the taxonomy with the June 2024 version of the World Flora Online (WFO) to offer comprehensive taxonomic harmonization. We aim to maintain each species' taxonomic concept, including all possible synonyms. 2. Data types: - Community Data: We are expanding plot- and tree-based sampling to include studies that use the tree section as a sampling unit. The new version has grown by 7,836 relevés, increasing from 18,148 to 25,984. The datasets using trees as a sampling unit rose to 25,482 (up from 17,762 in EpIG1.0), while plot-based datasets increased to 502 (from 386 in EpIG-DB 1.0, Figure 3). We are still integrating studies that sample tree sections, adding ecological measurements like height, length, and diameter to standardize sampled areas across all schemes. This may allow us to establish a function relating area to species richness or abundance. - Occurrence Data: We've gathered data from free-access web repositories, specialized epiphytic plant databases, unpublished databases (e.g., South American Dry Diagonal Forest Epiphytes, neotropical Araceae, Polypodiaceae of America, Marie Selby Botanical Gardens herbarium), & EpIG-DB. Details in Carmona-Higuita et al. (2023). - Functional Traits & Checklists: These are currently in progress. The functional trait data will complement the unpublished version after Hietz et al. (2022), which includes 66 traits and 128,171 individual functional trait values—40% more than the published dataset. We will also add data extracted from research papers. 3. Data Integration: To integrate these diverse data types, we are using the latest version of TurboVeg 3.0 (Hennekes 2015), which can handle community, trait, checklist, & occurrence data. |
Figure 3. Blue dots belong to datasets from the first version and the red ones are those in the current version (sample-based community data), currently being updated.
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Suggested tools for cooperation:
(a) Resources and tools for effective collaboration: Researchers are encouraged to use collaborative tools and resources to facilitate open and reproducible research. Recommended tools include the use in combination of R Projects, RStudio, and GitHub to collaborate and develop reproducible results such as this one (here in GitHub). All co-authors of a project should be guaranteed access to these tools. For those interested in further resources or facing paywalls, contact the custodian for alternative options.
Here, we provide some resources that may be helpful for collaborative work:
(b) Engagement of early career researchers (ECRs): To promote the involvement of ECRs, conduct targeted workshops or joint meetings to discuss analytical solutions and assign specific tasks. When engaging with collaborators, be mindful of their current institutional and economic support.
(c) CRediT author statement and roles: The use of the CRediT author statement is encouraged to define and acknowledge the roles and contributions of all co-authors clearly. Lead authors must use this statement to ensure that all contributions are appropriately recognized and to facilitate transparency in authorship. It can be generated using the R programming language, as shown here. Researchers who cannot provide specific feedback may participate differently, by offering technical expertise, or assisting in data analysis and interpretation. This engagement ensures meaningful collaboration and leverages the expertise of existing data providers.
(a) Resources and tools for effective collaboration: Researchers are encouraged to use collaborative tools and resources to facilitate open and reproducible research. Recommended tools include the use in combination of R Projects, RStudio, and GitHub to collaborate and develop reproducible results such as this one (here in GitHub). All co-authors of a project should be guaranteed access to these tools. For those interested in further resources or facing paywalls, contact the custodian for alternative options.
Here, we provide some resources that may be helpful for collaborative work:
- Contribute to working groups and build strong networks.
- To optimise the benefits of interdisciplinary collaborations for early career researchers.
- Diversity and interpersonal skills to maintain collaborative research teams.
- A guide on data management.
(b) Engagement of early career researchers (ECRs): To promote the involvement of ECRs, conduct targeted workshops or joint meetings to discuss analytical solutions and assign specific tasks. When engaging with collaborators, be mindful of their current institutional and economic support.
(c) CRediT author statement and roles: The use of the CRediT author statement is encouraged to define and acknowledge the roles and contributions of all co-authors clearly. Lead authors must use this statement to ensure that all contributions are appropriately recognized and to facilitate transparency in authorship. It can be generated using the R programming language, as shown here. Researchers who cannot provide specific feedback may participate differently, by offering technical expertise, or assisting in data analysis and interpretation. This engagement ensures meaningful collaboration and leverages the expertise of existing data providers.
If you wish to be part of EpIG or contribute to EpIG-DB, please get in touch with us by filling this form, we will get in touch ASAP.
EpIG is funded by:
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Design and content by Glenda Mendieta-Leiva and Derio Antonio Jiménez López and contributors (previously: Rosa María Villanueva Espinoza & Helena Einzmann).