Skip to main content
NCBI home page
Scientific Data logoLink to Scientific Data
. 2024 Nov 22;11:1269. doi: 10.1038/s41597-024-04125-y

Global planted forest data for timber species

Sarah B Richardson 1,✉, Lauralee An 2, Sarah E Pollack 3,4, Hemalatha Velappan 4, Ruth Nogueron 1, Jessica Richter 1, Shelley L Gardner 5, Karen L Williams 2, John C Hermanson 4, Elizabeth Dow Goldman 1, Suzanne M Peyer 4,✉
PMCID: PMC11584855  PMID: 39578486

Abstract

Discerning whether certain timber species were harvested from natural forests versus often less restricted planted forests can help ascertain the legality of wood products that enter the global market. However, readily available global planted forest data to the species level have been scarce. We confronted the need for such data by developing a two-pronged dataset, consisting of ‘polygon’ and ‘non-polygon’ location-based data, collectively, Planted Forest Timber Data. We obtained the polygon data from the World Resources Institute’s Spatial Database of Planted Trees v2.0, extracting data specific to traded timber species. We derived the non-polygon data from peer-reviewed literature and government documents. The polygon dataset encompasses 27 countries and 253 species and the non-polygon dataset spans 91 countries and 447 species. We envision that the more these two living datasets grow, the more they will mutually benefit from one another for data cross-validation. This assembled information is meant to equip global leaders in forest governance, policy, enforcement, and research with vetted data for promoting legal timber trade and protecting biodiversity.

Subject terms: Environmental impact, Forestry

Background & Summary

Degradation of our world’s forests expedites biodiversity loss and threatens the overall health of our planet1. The drivers of forest degradation include, but are not limited to, the global demand for timber1,2 and the clearing of land to produce agricultural commodities such as oil palm and beef1–3. Consequently, the forests themselves and tree species within are at risk of being overharvested. In addition, some of the more sought-after, often threatened, tree species are being logged from protected forests, including indigenous lands, putting into question the legality of the wood that enters the global markets4. Thus, documentation of the world’s forest types and their harvesting restrictions is important for monitoring their degradation and ultimate deforestation.

Implementation and enforcement of international regulatory frameworks offer a means to counter forest loss5–7. Effectively implementing such frameworks requires tailored reference data relevant to timber harvest and trade. For example, in the United States, the Lacey Act specifies that it is unlawful to import wildlife, including animal and plant products, harvested in violation of laws in the place of origin5,6. Therefore, implementation of the Lacey Act requires knowing the country from which a species was harvested and assurance that it was harvested in compliance with relevant laws. Similarly, under the European Union (EU) Deforestation-Free Products Regulation (EUDR), commodities linked to deforestation or forest degradation in the source country are prohibited on the EU market6,7. In effect, the success of each framework relies on supply chain transparency, which can be partially evaluated with species occurrence data for the source country.

The Lacey Act and EUDR adhere to the treaty put forth by the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and local government export restrictions. CITES is an international agreement designed to protect species at risk of overexploitation8. Species listed by CITES fall into one of three categories, Appendix I, II, or III, ranging from the most (I) to least restricted (III) in terms of trade8. Hardwoods, especially throughout the tropics, are commonly covered by CITES, as are softwoods to a lesser degree9. In addition to CITES, export restrictions can also be applied locally, by country governments, to limit the trade of particular species or products, including bans on logs or sawnwood for all species (https://www.forest-trends.org/known-forest-product-export-restrictions/). The legality of trade-restricted species can also depend on the forest management system from which the tree was harvested. Notably, harvesting a given species from a natural forest can have greater restrictions than harvesting the same species from a planted forest, which the United Nations Food and Agriculture Organization (FAO) defines as “trees established through planting or deliberate seeding”10. For example, the species Swietenia macrophylla (big leaf mahogany) has fallen under the above legal structure in Brazil, where its removal from natural, primitive, or regenerated forests is prohibited, but logging from planted forests is permitted11,12. As such, adhering to CITES and country export restrictions not only requires species- and country-specific information, but also whether the timber was extracted from natural versus planted forests.

While crucial to forest legality, discerning whether a timber species originated from a natural or planted forest is challenging, because cohesive and easily accessible planted forest data to the species level have been sparse. Yet, different groups have worked toward meeting this need13–19. In 2000, the FAO collected data on planted forests, including limited species composition and annual planting, to create the Planted Forests DataBase (PFDB)14. Although the information within the PFDB was verified with references on planted forests, including FAO datasets, it is not regularly updated. However, the FAO continues to publish reports on planted forest locations, plot areas, and other data20. More recently, the World Resources Institute produced the Spatial Database of Planted Trees (SDPT)15, a collection of geospatial planted forest data for over 80 countries that is gaining recognition and use among the scientific community17,18,21. However, only roughly one-third of the data records in the SDPT v1.0 are specific to timber species production, with the remaining focused on tree crops or unspecified uses (see Methods, Polygon dataset)15. Of the data records for timber that are present, their collective plot areas constitute the majority of the SDPT15. As with the PFDB, the SDPT v1.0 also has taxonomic gaps at the species level14,15. While both databases provide invaluable information, species data remain a critical need.

The goal of this project was to develop a substantive set of planted forest data for timber, prioritizing species- and country-level information. To accomplish this goal, we created a two-pronged living dataset, called Planted Forest Timber Data (PFTD), which includes polygon and non-polygon data (Fig. 1). Our intent for these two datasets was that they would complement, inform, and continue to build off of one another into the future for data supplementation and validation purposes. The polygon data (Fig. 1A) were extracted from the SDPT v2.019, updated from SDPT v1.015. This update focused on adding additional location and species data by conducting a literature review and outreach to organizations (Fig. 1A; see Methods, Polygon dataset). The extracted data included only those data in v2.0 most relevant to wood species in trade. Simultaneously, we created the non-polygon dataset (Fig. 1B) by conducting literature- and government-report-based searches for location-based planted forest data. This dataset includes important species- and country-level information, but does not have precise polygon boundaries. However, the location data in the non-polygon dataset include geographic information at different granularity levels ranging from country to point coordinates.

Fig. 1.

Fig. 1

Open in a new tab

Data assembly workflow. The ‘Sources’ were used to generate the ‘Initial Datasets’ which underwent ‘Curation’ to produce the ‘Final Datasets’. The final datasets consisted of the (A) polygon dataset and (B) non-polygon dataset (right panel).

To our knowledge, this project is the first to compile global planted forest data for timber species in trade. Creating the PFTD resulted in increased taxonomic and geographic resolution and coverage for nearly 600 timber species that are important for trade. Working toward refined and comprehensive information on planted forests could support analytics for the regulatory government agencies tasked with implementing the Lacey Act, EUDR, and others, while assisting industry with their compliance. The data could also aid in the development and testing of models, such as those that predict species range distributions or estimate forest cover and other land-use changes.

Methods

Data collection

The PFTD are composed of two types of information, polygon and non-polygon data, divided into two distinct living datasets (Fig. 1A,B)22,23. Between the two datasets, the planted forests range from small experimental plots to large commercial operations. The polygon dataset (Fig. 1A)22 includes visual delineations of the planted forest boundaries. These data are organized into GeoPackages with an accompanying summary table that links the collective data together. In contrast to the polygon data, the planted forest plots in the non-polygon dataset (Fig. 1B)23 do not have delineated boundaries, but still have species information at least to the country level, if not more detailed. These data could be used in applications where the distribution of species across political boundaries is important to know.

In developing both datasets, we followed the Darwin Core (DwC) standard as closely as possible. The DwC is a glossary of terms for cataloging biodiversity information to facilitate the sharing of data across different organizations24. To each column of data in the PFTD, we assigned a field name with a description that most closely corresponded with a DwC term (Fig. 1, Curation, Standardize). For instances in which we could not identify a matching term, we created our own custom terms. In the polygon dataset, we limited the field names to ten characters to prevent their automatic truncation, which could occur when the data are exported from a Geographic Information System (GIS) platform. For example, we shortened the DwC term vernacularName to vernacName, preserving the structure of the term as much as possible. Keys for all terms are included in the datasets themselves (see Data Records)22,23.

Polygon dataset

To increase species-level information for the polygon dataset, we updated SDPT v1.015 to SDPT v2.019. We searched for polygon data by directly contacting government and non-governmental organizations (NGOs; Fig. 1, Sources)19. We also searched for publicly available maps, such as those provided on government websites or published in scientific journals (Fig. 1, Sources)19. The SDPT update (Fig. 1, Initial Datasets) resulted in an increase in the coverage of countries, from 82 in v1.0 to 158 in v2.0. Also, the number of countries with specific epithet information increased from 52 in v1.0, with 53 scientific names, to 82 in v2.0, with 287 scientific names19.

Our polygon dataset is composed of a subset of the SDPT v2.0, specifically the portion of data in the SDPT that pertained to tree species commonly associated with timber trade (Fig. 1A)22. In its original form, the SDPT v2.0 contains data on both timber and tree crops that are distributed among the categories of ‘wood fiber or timber’, ‘fruit’, ‘oil palm’, and ‘rubber’, or are unspecified (‘other’ or ‘unknown’). However, traded timber species could be found in multiple categories, not just ‘wood fiber or timber’. For instance, some tree species that are categorized as ‘fruit’, such as Juglans regia (English walnut), are also used widely in the wood industry for furniture or handicrafts. To determine what qualifies as a timber species, we used a set of scientific names from Lacey Act declaration data (unpublished confidential document, 2023) for comparison to the full list of scientific names in the SDPT v2.0. The scientific names in the Lacey Act declaration data are filed by importers upon the entry of their wood products into the United States’ market. Specifically, we used a sample of three years of Lacey Act declaration data, from 2020 to 2023, which altogether contained over one million records. Species names in the un-filtered polygon dataset that did not match those from the Lacey Act data were excluded from the final polygon dataset (Fig. 1, Curation, Filter). Prior to filtering, we verified the spelling of the scientific names from the Lacey Act data, which are not always correctly spelled upon filing of the declarations (see Technical Validation, Taxonomic names). Additionally, we used the Botanic Gardens Conservation International’s Working List of Commercial Timber Species25 as a secondary source to ensure that all species in the PFTD were timber species.

We also calculated the area of each planted forest plot. These areas were of interest because they could help infer the volume of wood produced per plot, which might be important for estimating the likelihood that a species was harvested from a planted forest as opposed to a natural forest setting. We imported the polygons from the SDPT v2.0 into QGIS 3.32 Lima (http://qgis.org) and used the area calculation function (ellipsoid WGS84) in the attribute table field calculator to obtain their areas (Fig. 1, Curation). In the SDPT, each unique feature is assigned a final_id, for which each final_id could consist of many individual polygons or multi-polygons. For the summary table (see Data Records), we summed the areas by final_id.

Non-polygon dataset

As a complement to the polygon dataset, we assembled the non-polygon dataset (Fig. 1B)23. To locate these data, we queried scientific databases (e.g., Scopus, Web of Science, Science Direct) and library catalogs for primary and secondary literature, respectively, and performed internet searches for government reports and national databases (Fig. 1, Sources). Our search criteria for data focused on a particular tree species, including both the genus and specific epithet, growing within a planted forest in a particular country. For instance, a typical search string could be “Dalbergia nigra” AND “Mexico” AND “planted forest” OR “plantation”. Although, in rare instances, we obtained sources within which only the genus was provided. Similar to the SDPT v2.0, some of the national databases for the non-polygon dataset included non-timber species. We used the same filtering process as described above to focus our data on timber species (see Polygon dataset; Fig. 1, Curation, Filter).

In addition to country-level species data, we extracted other relevant information from the sources for inclusion into the non-polygon dataset. Although an initial query began with one scientific name, sometimes a single publication contained data on other species as well, including their scientific names and the geographical locations of the planted forests16. While our queries tended to focus at the country-level, oftentimes we obtained more detailed geographical data, such as regional, state, and county information. Although most publications were limited in detailed geospatial data, sometimes point coordinates were reported. When available, we included information about the planted forests, such as plot size, tree spacing, vernacular names of species, and whether the plots were of mixed species versus monocultures.

We indicated in our non-polygon dataset the absence of planted forests for a given species, either within a specific country or worldwide. Therefore, some data records could lack the country if a planted forest for a species is not known to exist worldwide (i.e., specified as ‘Absent’ under ‘occurrenceStatus’). Although we did not specifically search for data on the absence of planted forests, such information could still be important for evaluating import declarations. For example, if it has been confirmed there are no planted forests of a species in a particular country, then any declarations (e.g., Lacey Act) indicating the presence of that species in the country might be flagged for further investigation if other distribution data are also lacking.

Data Records

Data file descriptions and repositories

All files associated with the PFTD (Table 1) are available through Dryad23 and Zenodo22. The polygon data are stored in Zenodo among 27 GeoPackages, one for each country (Table 1: A1)22. Each summarized row of polygon data contains up to 13 possible fields (Table 1: A2, A3; Table 2A). The non-polygon data are housed in Dryad within one main file, with each row of data including up to 27 possible fields (Table 1: B1, B2; Table 2B)23. The PFTD also include summaries of independent evidence for a species growing in the specified countries (Table 1: A4, A5, B3, B4; see Technical Validation). Additionally, the PFTD include an accompanying ‘README’ in Dryad with data descriptions and file structure (Table 1: C1)23. The field names for the polygon and non-polygon datasets are grouped under the categories taxonomy, geography, plot details, and sources (Table 2: A, B). The definitions for each field are provided in the data field key files (Table 1: A3, B2).

Table 1.

Files associated with the PFTD, including the (A) polygon data, (B) non-polygon data, and (C) information about the data, files, and accompanying code.

Dataset File Name File Format File Contents
A. Polygon (1) PFTD_[ISO 3166 alpha-2 country code]_Pol (e.g. PFTD_AR_Pol) .gpkg 27 GeoPackages containing polygon information
(2) PFTD_PolSum .tsv Polygon data summary
(3) PFTD_PolSumKey .tsv Data field key
(4) PFTD_PolSumVal .tsv Validation data
(5) PFTD_PolSumValKey .tsv Validation data field key
B. Non-polygon (1) PFTD_NonPol .tsv Non-polygon data
(2) PFTD_NonPolKey .tsv Data field key
(3) PFTD_NonPolVal .tsv Validation data
(4) PFTD_NonPolValKey .tsv Validation data field key
C. Both A & B (1) README .md Data description and file structure for PFTD
Open in a new tab

Table 2.

Data categories and field names associated with the PFTD, including the (A) polygon and (B) non-polygon data.

Dataset Data Category Field Name
A. Polygon Taxonomy genus, specificEpithet, canonicalName, verbatimCanonicalName, vernacularName
Geography countryCode
Plot Details plantTypeareaSqKm
Sources bibliographicCitation, imageryYear, method, final_id, finalCode
B. Non-polygon Taxonomy genus, specificEpithet, canonicalName, verbatimCanonicalName, vernacularName
Geography countryCode, island, islandGroup, stateProvince, county, locality, verbatimLocality, verbatimLatitude, verbatimLongitude, decimalLatitude, decimalLongitude, georeferenceProtocol, geodeticDatum
Plot Details occurrenceStatus, establishmentMeans, plantType, standAgeYears, yearPlanted, harvestCycleYears, areaSqKm, standSpacingMeters
Sources bibliographicCitation
Open in a new tab

Technical Validation

Taxonomic names

We verified the taxonomic information in the PFTD to ensure that our data conformed to the most recently accepted species classifications. Specifically, we used the Royal Botanic Gardens, Kew’s World Checklist of Vascular Plants (WCVP)26 and its sister database, Plants of the World Online (POWO, https://powo.science.kew.org/; Fig. 1, Curation, Validate), to verify the acceptability and spelling of the scientific names, using customized taxonomic name resolution code (see Code Availability)27. We used the WCVP to automatically flag the unaccepted names (e.g., synonyms, misspellings) and then used POWO to manually correct those names that were not accepted. In cases where the scientific name was deemed to be incorrectly spelled, the code identified the closest matching accepted name, assigning a score ranging from zero (no match) to one (perfect match; see Code Availability). For example, an input of Theobroma grandiflora would correct to Theobroma grandiflorum, with a match of 0.95 or 95%. For reference purposes, we retained within the PFTD the original names indicated within the source (verbatimCanonicalName) and also included the accepted name (canonicalName). In addition to correcting the scientific names in the PFTD, we also used the refined data as feedback to correct the scientific names in the original SDPT v2.0.

Geographic hierarchy

As recommended by the DwC (see Methods, Data Collection), we also validated the geographic locations referenced within our sources for the non-polygon dataset (Fig. 1, Curation, Validate). Such validation was required because the different levels of geographic hierarchy can be complex and geographic boundaries and names are known to change over time. We used the Getty Thesaurus of Geographic Names Online (The Getty Research Institute, https://www.getty.edu/research/tools/vocabularies/tgn/) to validate the geographic locations. We were also able to derive higher-level geographic information from the locations provided by the source. For example, if the source provided a city, we could then derive the county, state or province, island, island group, and country.

Species-country pairs

Additionally, we evaluated if there was existing evidence for each species to grow within the specified country, whether in a natural or planted forest setting (Fig. 1, Curation, Validate). For each unique species-country pair in the PFTD (Fig. 2) we searched for at least one additional independent source to validate the pair (see Code Availability)27. We searched among the following seven publicly available databases, which offer distribution information: (1) International Union for the Conservation of Nature and Natural Resources Red List of Threatened Species (https://www.iucnredlist.org); (2) Global Naturalized Alien Flora (GLONAF)28; (3) WCVP26; (4) EU-Forest29; (5) Centre for Agriculture and Bioscience International Compendium (CABI, https://www.cabidigitallibrary.org/journal/cabicompendium); (6) Global Biodiversity Information Facility (GBIF, occurrence)30; and (7) GBIF (human observation, https://www.gbif.org). We distinguished between the two GBIF datasets, because occurrence data (6) might have higher quality control standards than the human observation data (7), which are often collected through citizen science efforts (e.g., iNaturalist).

Fig. 2.

Fig. 2

Open in a new tab

Number of unique species planted by country. Both polygon and non-polygon data are represented together. Darker green color indicates a higher number of unique species in the given country.

We automated the process of validating the species-country pairs for all datasets (see Code Availability), except for CABI and GBIF human observation data. For CABI, we obtained the distribution data from the CABI web pages for each species. We queried the GBIF human observation data manually and then indicated the number of occurrences for each species-country pair. A higher number of occurrences might confer greater confidence in the likelihood of the existing pair. For the remaining species-country pairs for which there were no matches found within any of the above databases (1–7), we searched for primary literature that supported the presence of the given species in the specified country, indicating up to five positive sources. We also cross-checked whether the species-country pairs in the polygon dataset were present in the non-polygon dataset and vice versa (Table 1: A4, A5, B3, B4; Table 3). From the collective analyses above, we found additional, independent support for 86% and 91% of the unique species-country pairings in the polygon and non-polygon datasets, respectively. Lack of such support for a species-country pair does not necessarily imply erroneous data, as these instances could be indicative of new introductions or previously unknown occurrences.

Table 3.

Number of unique genera, species, CITES-listed species, countries, and species-country pairs within the (A) polygon and (B) non-polygon datasets, (C) the information that these two datasets have in common, and (D) the total for the polygon and non-polygon data (A+B-C).

A. Polygon B. Non-polygon C. In common D. Total
Genera 163 253 105 311
Species 253 447 104 596
CITES species 13 37 10 40
Countries 27 91 24 94
Species-country pairs 326 730 39 1,017
Open in a new tab

Polygon and non-polygon data overlap

Finally, we examined the extent to which the polygon and non-polygon data could be used reciprocally to validate the planted forest locations and species within (Fig. 1, Curation, Validate). As noted above, some of the sources that we used to create the non-polygon dataset contained point coordinates (see Methods, Non-polygon dataset). We were specifically interested in whether these point coordinates fell within any polygon boundaries from the polygon data. In QGIS, we overlaid the non-polygon point coordinates (N = 198) with the entire set of polygons. Of the 198 total point coordinates, 14 fell within the polygons22,23. Thus, we were able to cross-validate the existence of planted forests for these 14 locations between the polygon and non-polygon datasets. However, none of these 14 locations had matching species between the two datasets.

The presence of mixed planted forests and land-use change might account for the lack of direct species matches between the two datasets for these 14 locations. In the non-polygon dataset, three independent sources indicated Dalbergia odorifera and Santalum album as growing at a given location in China31–33. Conversely, in one source referenced in the polygon dataset, supervised classification was used to predict the presence of the most dominant genus, Eucalyptus, at this same location18. However, all four sources could be correct, because high-value timber species (e.g. D. odorifera, S. album) have been replacing the more common Eucalyptus species, which are under increasing government restrictions in China due to environmental issues33. In another, similar example, two sources in the non-polygon dataset reported D. odorifera, Quercus ilex, Quercus petraea, and Quercus suber as species planted at the same location in China34,35 where Cunninghamia species were predicted by supervised classification, as referenced in the polygon dataset18. Interestingly, the plot with D. odorifera, Q. heiferiana, Q. petraea, and Q. suber was surrounded by a mixed forest that included Cunninghamia lanceolata33. Lastly, one source in the non-polygon dataset focused on plots of Copaifera langsdorffii, Dalbergia nigra, Pterocarpus rohrii, Schizolobium parahyba, and Senna multijuga growing within a reserve in Brazil surrounded by Eucalyptus-dominated planted forest36. The source referenced in the polygon dataset also predicted Eucalyptus species at this same location by manual polygon delineation and supervised classification37. The above comparisons among sources support the need for our polygon and non-polygon datasets and the value of such cross-validation between the two.

Usage Notes

General information

The polygon dataset is in a GeoPackage format, which can be opened and visualized with GIS software, such as QGIS and ArcGIS. Other software environments, R and Python for example, can access GeoPackage format as well. In both the polygon and non-polygon datasets, we used the International Organization for Standardization (ISO) 3166 alpha-2 country codes. The ISO online browsing platform provides the country codes and the corresponding country names (https://www.iso.org/obp/ui).

Either the polygon or non-polygon dataset can stand alone, or the two datasets could complement one another. For example, the non-polygon data could help fill gaps in the polygon data, such as the assignment of likely scientific names that correspond with the vernacular names of species provided by the source. Further, the point coordinate data included in the non-polygon data could validate the polygon data, or vice versa, where there is overlap (see Technical Validation).

For mixed planted forests, which are in both the polygon and non-polygon datasets, the plot area is for the entire planted forest, including all species. Thus, the specific area for an individual species is not known.

As the polygon data were collected from various sources with differing methods, the resolutions also vary19.

The list of species in the database is not exhaustive. Lack of species information from a certain country does not indicate the absence of a species, unless specifically noted within the datasets (e.g., see Methods, Non-polygon dataset).

Uncertainties

Together, the polygon and non-polygon datasets were assembled from many sources that have different methodologies for identifying planted forests. Such methods fall into one or more of the following categories: supervised machine learning classification (polygon dataset), manual polygon delineation (polygon dataset), and ground-truthed observation (polygon and non-polygon datasets). These methods are likely to differ in their accuracy. Machine learning classification models are limited by the quality and size of the training dataset and thus, are most likely to identify planted forests within the scope of that training dataset38. Manual polygon delineation and ground-truth data could also suffer from inaccuracies due to differences in imagery date, class assignment (e.g., land, species), and various human-related errors39. With these possible inaccuracies, one method should not be considered superior over the others, but their differences should be noted.

Additionally, the sources within the PFTD cover a broad range of publication years. Depending on the planting year, the species composition of the forests could potentially have changed with time, leading to some degree of uncertainty in the data. As such, these data should be considered as a starting point for further analyses, as land-use conversions are in continual flux.

It is also important to note that planted forests are defined differently between sources19. We chose to use FAO’s definition of a planted forest (see Background & Summary)10 as it broadly encompasses the data of interest.

Data sources

The original sources are provided within the polygon and non-polygon datasets (Table 1: A2, B1; Table 2: A, B, Sources)22,23 and below (see References)16,18,19,31–37,40–144.

Acknowledgements

Funding for this project was provided by the US Department of Agriculture, Animal and Plant Health Inspection Service under awards 21-CA-11132762-186, 20-DG-11132762-228, and 22-CA-11132762-413. The findings and conclusions in this publication are those of the authors and should not be construed to represent any USDA or US Government determination or policy.

Author contributions

S.B.R. writing original draft, editing, methodology, data collection, data curation, data validation, formal analysis. L.A. writing original draft, methodology, data collection, formal analysis. S.E.P. methodology, data collection, data curation, formal analysis, editing, data validation. H.V. methodology, data collection, data curation, formal analysis, editing, data validation. R.N. data collection. J.R. data collection, data curation, formal analysis, data validation. S.L.G. conceptualization, data collection, funding acquisition. K.L.W. conceptualization, funding acquisition. J.C.H. data validation, funding acquisition. E.D.G. conceptualization, data collection, data curation, funding acquisition. S.M.P. conceptualization, data collection, data curation, data validation, writing original draft, editing, funding acquisition.

Code availability

Code is available (Python version 3.11.9) for the resolution of taxonomic names and for the validation of species-country pairs in Zenodo, hosted by Dryad (10.5281/zenodo.13000336; see Technical Validation)27.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Sarah B. Richardson, Email: richardson.sarahbaker@gmail.com

Suzanne M. Peyer, Email: smpeyer@uw.edu

References

  • 1.Lapola, D. M. et al. The drivers and impacts of Amazon forest degradation. Science379, 349 https://www.science.org/doi/10.1126/science.abp8622 (2023). [DOI] [PubMed]
  • 2.Juniyanti, L., Purnomo, H., Kartodihardjo, H. & Prasetyo, L. B. Understanding the driving forces and actors of land change due to forestry and agricultural practices in Sumatra and Kalimantan: A systematic review. Land10, 463, 10.3390/land10050463 (2021). [Google Scholar]
  • 3.Casagranda, Y. G. et al. Emergent research themes on sustainability in the beef cattle industry in Brazil: An integrative literature review. Sustainability15, 4670, 10.3390/su15054670 (2023). [Google Scholar]
  • 4.Urrunaga, J. M. et al. The laundering machine: How fraud and corruption in Peru’s concession system are destroying the future of its forests. https://eia-international.org/wp-content/uploads/The-Laundering-Machine.pdf (Environmental Investigation Agency, 2012).
  • 5.Department of Agriculture, Animal and Plant Health Inspection Service. Lacey Act implementation plan; definitions for exempt and regulated articles. 7 CFR Part 357, Docket No. APHIS-2009-0018, RIN 0579-AD11 (Federal Register, 2013).
  • 6.Gan, J., Cashore, B. & Stone, M. W. Impacts of the Lacey Act Amendment and the Voluntary Partnership Agreements on illegal logging: Implications for global forest governance. Journal of Natural Resources Policy Research5, 209–226, 10.1080/19390459.2013.832479 (2013). [Google Scholar]
  • 7.European Union. Regulation (EU) 2023/1115 of the European Parliament and of the Council of 31 May 2023 on the making available on the Union market and the export from the Union of certain commodities and products associated with deforestation and forest degradation and repealing Regulation (EU) No 995/2010. Official Journal of the European Unionhttp://data.europa.eu/eli/reg/2023/1115/oj (2023).
  • 8.Convention on International Trade in Endangered Species of Wild Fauna and Flora. Text of the Convention. https://cites.org/sites/default/files/eng/disc/CITES-Convention-EN.pdf (1983). [DOI] [PubMed]
  • 9.Gasson, P. How precise can wood identification be? Wood anatomy’s role in support of the legal timber trade, especially CITES. IAWA journal32(2), 137–154 (2011). [Google Scholar]
  • 10.Food and Agriculture Organization of the United Nations. Global forest resources assessment 2020: Main report. 10.4060/ca9825en (2020).
  • 11.da Silva, L. I. L. & Sliva, M. Estabelece critérios para exploração da espécie Swietenia macrophylla King (mogno), e dá outras providênciashttps://faolex.fao.org/docs/pdf/bra112587.pdf (2003).
  • 12.da Silva, L. I. L., Dimarzio, J. A. & Silva, M. Suspende a exploração da espécie Mogno (Swietenia macrophylla King) no Território Nacional, pelo período de cento e cinqüenta dias, e dá outras providênciashttps://www.planalto.gov.br/ccivil_03/decreto/2003/d4593.htm (2003).
  • 13.Carle, J., Del Lungo, A. & Varmola, M. The need for improved forest plantation data. XII World Forestry Congresshttps://www.fao.org/4/XII/0985-B4.htm (2003).
  • 14.Del Lungo, A. Planted forest database: Analysis of annual planting trends and silvicultural parameters for commonly planted species. Working Paper FP/26 (Food and Agriculture Organization, 2003).
  • 15.Harris, N., Goldman, E. D. & Gibbes, S. Spatial database of planted trees (SDPT VERSION 1.0)https://www.wri.org/publication/planted-trees (2019).
  • 16.Cubbage, F. et al. Global timber investments, 2005 to 2017. Forest Policy and Economics112, 102082, 10.1016/j.forpol.2019.102082 (2020). [Google Scholar]
  • 17.Du, Z. et al. A global map of planting years of plantations. Scientific Data9, 141, 10.1038/s41597-022-01260-2 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Abbasi, A. O. et al. Spatial database of planted forests in East Asia. Scientific Data10, 480, 10.1038/s41597-023-02383-w (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Richter, J. et al. Spatial database of planted trees (SDPT VERSION 2.0)10.46830/writn.23.00073 (2024).
  • 20.Food and Agriculture Organization of the United Nations. Global forest resources assessment 2020: Terms and Definitions. Forest Resources Assessment Working Paper 188 (2020).
  • 21.Lesiv, M. et al. Global forest management data for 2015 at a 100 m resolution. Scientific Data9, 199, 10.1038/s41597-022-01332-3 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Richardson, S. B. et al. Global planted forest data for timber species. Zenodo10.5281/zenodo.14010483 (2024). [DOI] [PubMed]
  • 23.Richardson, S. B. et al. Global planted forest data for timber species. Dryad10.5061/dryad.2280gb626 (2024). [DOI] [PubMed]
  • 24.Wieczorek, J. et al. Darwin Core: An evolving community-developed biodiversity data standard. PLOS ONE7, e29715, 10.1371/journal.pone.0029715 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Mark, J., Newton, A. C., Oldfield, S. & Rivers, M. The international timber trade: A working list of commercial timber tree species. https://www.bgci.org/resources/bgci-tools-and-resources/a-working-list-of-commercial-timber-tree-species/ (Botanic Gardens Conservation International, 2014).
  • 26.Govaerts, R., Nic Lughadha, E., Black, N., Turner, R. & Paton, A. The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. Scientific Data8, 215, 10.1038/s41597-021-00997-6 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Richardson, S. B. et al. Global planted forest data for timber species. Zenodo10.5281/zenodo.14057500 (2024). [DOI] [PubMed]
  • 28.van Kleunen, M. et al. The Global Naturalized Alien Flora (GloNAF) database. Ecology100, 1–2, 10.1002/ecy.2542 (2019). [DOI] [PubMed] [Google Scholar]
  • 29.Mauri, A., Strona, G. & San-Miguel-Ayanz, J. EU-Forest, a high-resolution tree occurrence dataset for Europe. Scientific Data4, 160123, 10.1038/sdata.2016.123 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Global Biodiversity Information Facility10.15468/dl.uefn8h (2024).
  • 31.Lu, J. K. et al. Concurrent carbon and nitrogen transfer between hemiparasite Santalum album and two N2-fixing hosts in a sandalwood plantation. Forest Ecology and Management464, 118060, 10.1016/j.foreco.2020.118060 (2020). [Google Scholar]
  • 32.Meng, S. et al. Impacts of nitrogen on physiological interactions of the hemiparasitic Santalum album and its N2-fixing host Dalbergia odorifera. Trees35, 1039–1051, 10.1007/s00468-021-02103-0 (2021). [Google Scholar]
  • 33.Zhang, P. et al. Effects of weeding and fertilization on soil biology and biochemical processes and tree growth in a mixed stand of Dalbergia odorifera and Santalum album. Journal of Forestry Research32, 2633–2644, 10.1007/s11676-020-01286-5 (2021). [Google Scholar]
  • 34.Vettraino, A. et al. Sentinel trees as a tool to forecast invasions of alien plant pathogens. PLOS ONE10, e0120571, 10.1371/journal.pone.0120571 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Cisse, E. H. M. et al. Responses of woody plant Dalbergia odorifera treated with glycine betaine to drought and cold stresses: Involvement of the alternative oxidase. Biologia Plantarum66, 56–66, 10.32615/bp.2021.062 (2022). [Google Scholar]
  • 36.Costa, M. S., Ferreira, K. E. B., Botosso, P. C. & Callado, C. H. Growth analysis of five Leguminosae native tree species from a seasonal semidecidual lowland forest in Brazil. Dendrochronologia36, 23–32, 10.1016/j.dendro.2015.08.004 (2015). [Google Scholar]
  • 37.Petersen, R. et al. Mapping tree plantations with multispectral imagery: preliminary results for seven tropical countrieshttps://www.wri.org/research/mapping-tree-plantations-multispectral-imagery-preliminary-results-seven-tropical (2016).
  • 38.Schmitt, M. & Wu, Y. L. Remote sensing image classification with the SEN12MS dataset. International Society for Photogrammetry and Remote Sensing Congress10.48550/arXiv.2104.00704 (2021).
  • 39.Carlotto, M. J. Effect of errors in ground truth on classification accuracy. International Journal of Remote Sensing30, 4831–4849, 10.1080/01431160802672864 (2009). [Google Scholar]
  • 40.Amougou, A. et al. Preliminary Report on Sustainable Harvesting of Prunus africana (Rosaseae) in the Mount Cameroon. (National Forestry Development Agency, 2011).
  • 41.Amusant, N. et al. Planting rosewood for sustainable essential oil production: Influence of surrounding forest and seed provenance on tree growth and essential oil yields. Bois et Forêts Des Tropiques326, 57 (2015). [Google Scholar]
  • 42.Andersen, U. S. et al. Conservation through utilization: A case study of the vulnerable Abies guatemalensis in Guatemala. Oryx, 4210.1017/S0030605308007588 (2008).
  • 43.Arunkumar, A. & Joshi, G. Pterocarpus santalinus (red sanders) an endemic, endangered tree of India: Current status, improvement and the future. Journal of Tropical Forestry and Environment4, 1–10, 10.31357/jtfe.v4i2.2063 (2014). [Google Scholar]
  • 44.Averos, J. B. et al. Fluctuación poblacional de Premnobius cavipennis (Coleoptera: Curculionidae: Scolytinae) en plantaciones de balso (Ochroma pyramidale) en la zona central del litoral ecuatoria. Revista Colombiana de Entomología47, e9279, 10.25100/socolen.v47i1.9279 (2021). [Google Scholar]
  • 45.Bevacqua, R. F., Sayama, J. N. & Miller, R. H. Mahogany in Micronesia (University of Guam, College of Natural & Applied Sciences, 2021).
  • 46.Blanchette, R. A., Jurgens, J. A. & Heuveling van Beek, H. Growing Aquilaria and Production of Agarwood in Hill Agro-Ecosystems, in Integrated Land Use Management in the Eastern Himalayas (eds. Eckman, K. & Ralte, L.) 66-82 (Akansha Publishing House Delhi, 2015).
  • 47.Bodeker, G., van ‘t Klooster, C. & Weisbord, E. Prunus africana (Hook.f.) Kalkman: The overexploitation of a medicinal plant species and Its legal context. The Journal of Alternative and Complementary Medicine20, 810–822, 10.1089/acm.2013.0459 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Boldt, C. E. Jack pine plantations in the Nebraska sand hills. Journal of Forestry67, 96–100 (1969). [Google Scholar]
  • 49.Booth, T. H. & Jovanovic, T. Climate change impacts on species planting domains: A preliminary assessment for selected plantation forests in Fiji, Papua New Guinea and the Solomon Islands. International Forestry Review16, 191–198, 10.1505/146554814811724775 (2014). [Google Scholar]
  • 50.Burns, R. M. & Honkala, B. H. (tech. coords.). Silvics of North America: Volume 1. Conifers, Agriculture Handbook 654. (United States Department of Agriculture, Forest Service, 1990).
  • 51.Burns, R. M. & Honkala, B. H. (tech. coords.). Silvics of North America: Volume 2. Hardwoods, Agriculture Handbook 654. (United States Department of Agriculture, Forest Service, 1990).
  • 52.Cañadas-López, Á. et al. Growth and yield models for balsa wood plantations in the coastal lowlands of Ecuador. Forests10, 733, 10.3390/f10090733 (2019). [Google Scholar]
  • 53.Cao, Y. & Chen, Y. Ecosystem C:N:P stoichiometry and carbon storage in plantations and a secondary forest on the Loess Plateau, China. Ecological Engineering105, 125–132, 10.1016/j.ecoleng.2017.04.024 (2017). [Google Scholar]
  • 54.Castro, J. et al. Life cycle and development of Coptoborus ochromactonus (Coleoptera: Curculionidae: Scolytinae), a pest of balsa. Journal of Economic Entomology112, 729–735, 10.1093/jee/toy403 (2019). [DOI] [PubMed] [Google Scholar]
  • 55.Cedeño, P. E. & Flowers, R. W. Heilipodus unifasciatus (Champion) (Coleoptera: Curculionidae: Molytinae: Hylobiini) attacking plantations of Ochroma pyramidale (Cavanilles Ex Lamarck) urban (Malvaceae) in Ecuador. The Coleopterists Bulletin66, 344–346, 10.1649/072.066.0408 (2012). [Google Scholar]
  • 56.Cheboiwo, J. K., Mugabe, R. & Langat, D. Review of conservation of Prunus africana and international trade opportunities for its bark in Kenya. Journal of Emerging Trends in Engineering and Applied Sciences5, 372–377 (2014). [Google Scholar]
  • 57.Chen, G. et al. Soil aggregate characteristics and stability of soil carbon stocks in a Pinus tabulaeformis plantation. New Forests48, 837–853, 10.1007/s11056-017-9600-x (2017). [Google Scholar]
  • 58.Chen et al. Recovery time of soil carbon pools of conversional Chinese fir plantations from broadleaved forests in subtropical regions, China. Science of The Total Environment587–588, 296–304, 10.1016/j.scitotenv.2017.02.140 (2017). [DOI] [PubMed] [Google Scholar]
  • 59.Chen, Y. T. et al. Variation and stability of seed yield, fall-off process, seed size and form in live oak. Forest Research28, 524–530 (2015). [Google Scholar]
  • 60.Chen, Y. T., Chen, Y. C., Huang, Y. Q., Sun, H. J. & Chen, D. F. Preliminary study on Quercus virginiana introduction in eastern China. Forest Research20, 542–546 (2007). [Google Scholar]
  • 61.Chen, Y. T., Sun, H. J., Wang, S. F. & Shi, X. Growth performances of five North American oak species in Yangzi River delta of China. Forest Research26, 344–351 (2013). [Google Scholar]
  • 62.Choosa-Nga, P., Sangwanit, U. & Kaewgrajang, T. The arbuscular mycorrhizal fungi’s diversity in fabaceous trees species of northeastern Thailand. Biodiversitas Journal of Biological Diversity20, 405–412, 10.13057/biodiv/d200214 (2019). [Google Scholar]
  • 63.Condro, A. A., Setiawan, Y., Prasetyo, L. B., Pramulya, R. & Siahaan, L. Retrieving the national main commodity maps in Indonesia based on high-resolution remotely sensed data using cloud computing platform. Land9, 377, 10.3390/land9100377 (2020). [Google Scholar]
  • 64.Convention on International Trade in Endangered Species of Wild Fauna and Flora. Asia Regional Workshop on Agarwood: Management of Wild and Plantation-Grown Agarwood Trees. Twentieth meeting of the Plants Committee, Dublin, Ireland (2012).
  • 65.Convention on International Trade in Endangered Species of Wild Fauna and Flora. Results and Analysis of the Questionnaire on Production Systems of Tree Species, Plantations and Definitions of Artificial Propagation. Twenty-third meeting of the Plants Committee, Geneva, Switzerland (2017).
  • 66.Debonne, N., van Vliet, J. & Verburg, P. Future governance options for large-scale land acquisition in Cambodia: impacts on tree cover and tiger landscapes. Environmental Science and Policy94, 9–19, 10.1016/j.envsci.2018.12.031 (2019). [Google Scholar]
  • 67.Dieste, A., Cabrera, M. N., Clavijo, L. & Cassella, N. Analysis of wood products from an added value perspective: The Uruguayan forestry case. Maderas. Ciencia y Tecnología21, 305–316, 10.4067/S0718-221X2019005000303 (2019). [Google Scholar]
  • 68.Dong, L., Liu, Z., Li, F. & Jiang, L. Modelling primary branch growth based on a multilevel nonlinear mixed-effects model: A Pinus koraiensis plantation case study in north-east China. Southern Forests: a Journal of Forest Science77, 179–190, 10.2989/20702620.2014.1001676 (2015). [Google Scholar]
  • 69.Doucet, J. L. et al. Enrichment of Central African logged forests with high-value tree species: Testing a new approach to regenerating degraded forests. International Journal of Biodiversity Science, Ecosystem Services & Management12, 83–95, 10.1080/21513732.2016.1168868 (2016). [Google Scholar]
  • 70.Estrada-Contreras, I., Equihua, M., Laborde, J., Martínez Meyer, E. & Sánchez-Velásquez, L. R. Current and future distribution of the tropical tree Cedrela odorata L. in Mexico under climate change scenarios using MaxLike. PLOS ONE11, e0164178, 10.1371/journal.pone.0164178 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Fierros, M. Programa de desarrollo de plantaciones forestales comerciales a 15 años de su creación. Comisión Nacional Forestalhttp://www.conafor.gob.mx:8080/biblioteca/ver.aspx?articulo=493 (2012).
  • 72.Food and Agriculture Organization. Global Forest Resources Assessment 2010: Main Report. FAO Forestry Paper 163 (2010).
  • 73.Forest Department, Government of Belize. Belize collect Earth/open foris land use and land use change assessment protocol. https://forest.gov.bz/wp-content/uploads/2023/09/Resource-Belize-Collect-Earth-Protocol-2019_v1.pdf (2019).
  • 74.Francis, J. K. Mahogany Planting and Research in Puerto Rico and the US Virgin Islands in Big-Leaf Mahogany: Genetics, Ecology, and Management (Springer, 2003).
  • 75.Gei, M. G. & Powers, J. S. The influence of seasonality and species effects on surface fine roots and nodulation in tropical legume tree plantations. Plant and Soil388, 187–196, 10.1007/s11104-014-2324-1 (2014). [Google Scholar]
  • 76.Goulet, E., Rueda, A. & Shelton, A. Management of the mahogany shoot borer, Hypsipyla grandella (Zeller) (Lepidoptera:Pyralidae), through weed management and insecticidal sprays in 1- and 2-year-old Swietenia humilis Zucc. plantations. Crop Protection24, 821–828, 10.1016/j.cropro.2005.01.007 (2005). [Google Scholar]
  • 77.Government of Vietnam. Forest Resource Data. http://maps.vnforest.gov.vn/help/en/DataSets.htm (2016).
  • 78.Groves, M. & Rutherford, C. CITES and Timber–A Guide to CITES-Listed Tree Species. (Kew Publishing, Royal Botanic Gardens, Kew, 2015).
  • 79.Han-ying, S. & Dai-bin, L. Influence of main site factors on Fraxinus mandshurica (Oleaceae) plantation. Journal of Forestry Research14, 83–86, 10.1007/BF02856770 (2003). [Google Scholar]
  • 80.Haro-Carrión, X., Loiselle, B. & Putz, F. E. Tree species diversity, composition and aboveground biomass across dry forest land-cover types in coastal Ecuador. Tropical Conservation Science14, 194008292199541, 10.1177/1940082921995415 (2021). [Google Scholar]
  • 81.Heilmayr, R., Echeverría, C., Fuentes, R. & Lambin, E. F. A plantation-dominated forest transition in Chile. Applied Geography75, 71–82, 10.1016/j.apgeog.2016.07.014 (2016). [Google Scholar]
  • 82.Helmer, E. H. et al. Detailed maps of tropical forest types are within reach: forest tree communities for Trinidad and Tobago mapped with multiseason landsat and multiseas fine-resolution imagery. Forest Ecology and Management279, 147–166, 10.1016/j.foreco.2012.05.016 (2012). [Google Scholar]
  • 83.Ingram, V., Awono, A., Schure, J. & Ndam, N. National Prunus africana Management Plan for Cameroon. https://pure.uva.nl/ws/files/4205179/76923_ingram01.pdf (Center for International Forestry Research, 2009).
  • 84.Instituto Forestal de Chile. Los recursos forestales en Chile, Inventario Forestal Nacional. http://mapaforestal.infor.cl/flexviewers/ifc_2016 (2023).
  • 85.Kerdraon, D. et al. Litter traits of native and non-native tropical trees influence soil carbon dynamics in timber plantations in Panama. Forests10, 209, 10.3390/f10030209 (2019). [Google Scholar]
  • 86.Md. Khan, N. I. et al. Allometric relationships of stem volume and stand level carbon stocks at varying stand density in Swietenia macrophylla King plantations, Bangladesh. Forest Ecology and Management430, 639–648, 10.1016/j.foreco.2018.09.002 (2018). [Google Scholar]
  • 87.Kim, K. M., Kim, C. M. & Jun, E. J. Study on the standard for 1:25,000 scale digital forest type map production in Korea. Journal of the Korean Association of Geographic Information Studies12, 143–151 (2009). [Google Scholar]
  • 88.Kriebel, H. B. et al. Geographic variation in Quercus rubra in north central United States plantations. Silvae Genetica25, 118–122 (1976). [Google Scholar]
  • 89.Lee, S. K. & Seo, S. T. First report of Armillaria root disease caused by Armillaria tabescens on Carpinus tschonoskii in South Korea. Plant Disease100, 213–213, 10.1094/PDIS-06-15-0651-PDN (2016). [Google Scholar]
  • 90.Lee, Y. K. et al. Differences of tree species composition and microclimate between a mahogany (Swietenia macrophylla King) plantation and a secondary forest in Mt. Makiling, Philippines. Forest Science and Technology2, 1–12, 10.1080/21580103.2006.9656293 (2006). [Google Scholar]
  • 91.Li, M. et al. Mixture of N2-fixing tree species promotes organic phosphorus accumulation and transformation in topsoil aggregates in a degraded karst region of subtropical China. Geoderma413, 115752, 10.1016/j.geoderma.2022.115752 (2022). [Google Scholar]
  • 92.Lim, G. T. et al. Mahogany shoot borer control in Malaysia and prospects for biocontrol using weaver ants. Journal of Tropical Forest Science20, 147–155 (2008). [Google Scholar]
  • 93.Lugo, A. E. Comparison of tropical tree plantations with secondary forests of similar age. Ecological Monographs62, 1–41, 10.2307/2937169 (1992). [Google Scholar]
  • 94.Marshall, A., Nelson, C. R. & Hall, J. S. Species selection and plantation management in enrichment planting with native timber species in the Panama Canal watershed. Frontiers in Forests and Global Change5, 925877, 10.3389/ffgc.2022.925877 (2022). [Google Scholar]
  • 95.Martínez, M., Cognato, A. I., Guachambala, M. & Boivin, T. Bark and ambrosia beetle (Coleoptera: Curculionidae: Scolytinae) diversity in natural and plantation forests in Ecuador. Environmental Entomology48, 603–613, 10.1093/ee/nvz037 (2019). [DOI] [PubMed] [Google Scholar]
  • 96.Mayaka, T. B. A family of segmented polynomial functions for modelling the border effect on the diameter growth of Ayous (Triplochiton scleroxylon K. Schum). Forest Ecology and Management70, 275–283, 10.1016/0378-1127(94)90093-0 (1994). [Google Scholar]
  • 97.Mayhew, J. E. & Newton, A. C. The Silviculture of Mahogany (Swietenia macrophylla)10.1079/9780851993072.0000 (CABI Publishing, 1998).
  • 98.Mayhew, J. E., Andrew, M., Sandom, J. H., Thayaparan, S. & Newton, A. C. Silvicultural Systems for Big-Leaf Mahogany Plantations in Big-Leaf Mahogany: Genetics, Ecology, and Management (Springer, 2003).
  • 99.Mead, D. J. Sustainable management of Pinus radiata plantations. FAO Forestry Paper 170 (Food and Agriculture Organization, 2013).
  • 100.Meunpong, P., Penboon, C., Kuasakun, N. & Wachrinrat, C. Tree dimension and environmental correlates of heartwood content in Siamese rosewood (Dalbergia cochinchinensis). Biodiversitas Journal of Biological Diversity22, 3297–3303, 10.13057/biodiv/d220635 (2021). [Google Scholar]
  • 101.Midgley, S., Blyth, M., Howcroft, N., Midgley, D. & Brown, A. Balsa: Biology, Production and Economics in Papua New Guinea. ACIAR Technical Reports 73 (Australian Government, Australian Centre for International Agricultural Research, 2010).
  • 102.Ministerio de Agricultura y Desarrollo Rural, Dirección de Cadenas Agrícolas y Forestales. Base de datos relacionada con área plantada con Plantaciones Forestales Comerciales: Datos Abiertos Colombia. https://www.datos.gov.co/en/Agricultura-y-Desarrollo-Rural/Base-de-datos-relacionada-con-rea-plantada-con-Pla/h3uz-jvkj (2022).
  • 103.Ministère de Forêts et de la Faune & World Resources Institute. Atlas forestier du Cameroun. https://cmr.forest-atlas.org/pages/domaine-forestier (n.d.).
  • 104.Ministerio de Ganadería, Agricultura y Pesca. Cartografía Forestal 2021. https://www.gub.uy/ministerio-ganaderia-agricultura-pesca/datos-y-estadisticas/datos/resultados-cartografia-forestal-2021 (2021).
  • 105.Ministerio del Ambiente, Agua y Transición Ecológica, Ecuador. Mapa interactivo. http://ide.ambiente.gob.ec:8080/mapainteractivo/ (2018).
  • 106.Mohammad, N. et al. Spacing, pit size and irrigation influence early growth performances of forest tree species. Journal of Tropical Forest Science33, 69–76 (2021). [Google Scholar]
  • 107.Mohd-Jamil, A. W. & Khairul, M. Variations of mechanical properties in plantation timbers of jelutong (Dyera costulata) and khaya (Khaya ivorensis) along the radial and vertical positions. Journal of Tropical Forest Science29, 114–120 (2017). [Google Scholar]
  • 108.Montagnini, F., Cusack, D., Petit, B. & Kanninen, M. Environmental services of native tree plantations and agroforestry systems in Central America. Journal of Sustainable Forestry21, 51–67, 10.1300/J091v21n01_03 (2004). [Google Scholar]
  • 109.Montagnini, F. & Sancho, F. Net nitrogen mineralization in soils under six indigenous tree species, an abandoned pasture and a secondary forest in the atlantic lowlands of Costa Rica. Plant and Soil162, 117–124, 10.1007/BF01416097 (1994). [Google Scholar]
  • 110.Nath, C. D., Boura, A., De Franceschi, D. & Pélissier, R. Assessing the utility of direct and indirect methods for estimating tropical tree age in the Western Ghats, India. Trees26, 1017–1029, 10.1007/s00468-012-0679-6 (2012). [Google Scholar]
  • 111.Nduwamungu, J. et al. Rwanda forest cover mapping using high resolution aerial photographs. Global Geospatial Conference, (2013).
  • 112.Negreros-Castillo, P. & Mize, C. W. Regeneration of mahogany and Spanish cedar in gaps created by railroad tie extraction in Quintana Roo, Mexico. Forest Ecology and Management255, 308–312, 10.1016/j.foreco.2007.09.052 (2008). [Google Scholar]
  • 113.Ngueguim, J. R. et al. Growth and productivity of Pericopsis elata (Harms) Meeuwen in some forest plantations of Cameroon. Forest Science and Technology8, 1–10, 10.1080/21580103.2012.658234 (2012). [Google Scholar]
  • 114.Niu, X., Gao, P., Wang, B. & Liu, Y. Fractal characteristics of soil retention curve and particle size distribution with different vegetation types in mountain areas of northern China. International Journal of Environmental Research and Public Health12, 15379–15389, 10.3390/ijerph121214978 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 115.Nölte, A. et al. Broad-scale and long-term forest growth predictions and management for native, mixed species plantations and teak in Costa Rica and Panama. SSRN Electronic Journal10.2139/ssrn.3987790 (2021).
  • 116.Ohashi, O. S. et al. Management of Hypsipyla grandella in Swietenia macrophylla King Plantations in Pará and São Paulo States, Brazil. Technical Report Final Project (Universidade Federal Rural De Amazônia, 2011).
  • 117.Paul, G. S. et al. Foliar herbivory and leaf traits of five native tree species in a young plantation of central Panama. New Forests43, 69–87, 10.1007/s11056-011-9267-7 (2011). [Google Scholar]
  • 118.Petit, B. & Montagnini, F. Growth in pure and mixed plantations of tree species used in reforesting rural areas of the humid region of Costa Rica, Central America. Forest Ecology and Management233, 338–343, 10.1016/j.foreco.2006.05.030 (2006). [Google Scholar]
  • 119.Piotto, D., Montagnini, F., Ugalde, L. & Kanninen, M. Performance of forest plantations in small and medium-sized farms in the Atlantic lowlands of Costa Rica. Forest Ecology and Management175, 195–204, 10.1016/S0378-1127(02)00127-5 (2003). [Google Scholar]
  • 120.Piotto, D., Víquez, E., Montagnini, F. & Kanninen, M. Pure and mixed forest plantations with native species of the dry tropics of Costa Rica: A comparison of growth and productivity. Forest Ecology and Management190, 359–372, 10.1016/j.foreco.2003.11.005 (2004). [Google Scholar]
  • 121.Md. Pitol, N. S. & Md. Mian, B. High carbon storage and oxygen (O2) release potential of mahagony (Swietenia macrophylla) woodlot plantation in Bangladesh. Saudi Journal of Biological Sciences30, 103498, 10.1016/j.sjbs.2022.103498 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 122.Roy, P. S. et al. New vegetation type map of India prepared using satellite remote sensing: comparison with global vegetation maps and utilities. International Journal of Applied Earth Observation and Geoinformation39, 142–159 (2015). [Google Scholar]
  • 123.Schimleck, L. R. et al. Examination of wood properties of plantation-grown pernambuco (Caesalpinia echinata). IAWA Journal34, 34–48, 10.1163/22941932-00000004 (2013). [Google Scholar]
  • 124.Semwal, R. L., Nautiyal, S., Maikhuri, R. K., Rao, K. S. & Saxena, K. G. Growth and carbon stocks of multipurpose tree species plantations in degraded lands in central Himalaya, India. Forest Ecology and Management310, 450–459, 10.1016/j.foreco.2013.08.023 (2013). [Google Scholar]
  • 125.Servicio Nacional Forestal y de Fauna Silvestre. Registros Nacionales, Plantaciones Forestales. https://sniffs.serfor.gob.pe/estadistica/es/tableros/registros-nacionales/plantaciones (2024).
  • 126.Singh, A. & Raizada, P. Seed germination of selected dry deciduous trees in response to fire and smoke. Journal of Tropical Forest Science22, 465–468 (2010). [Google Scholar]
  • 127.Siqueira, D. P., Riter Netto, A. F., Freire, J. M. & Barroso, D. G. Natural mycorrhizal association of two tropical N-fixing monospecific plantations Southeastern Brazil. Rhizosphere19, 100377 (2021). [Google Scholar]
  • 128.Smith, C. K., Gholz, H. L. & de Assis Oliveira, F. Fine litter chemistry, early-stage decay, and nitrogen dynamics under plantations and primary forest in lowland Amazonia. Soil Biology and Biochemistry30, 2159–2169, 10.1016/S0038-0717(98)00099-6 (1998). [Google Scholar]
  • 129.Smith, C. K., de Assis Oliveira, F., Gholz, H. L. & Baima, A. Soil carbon stocks after forest conversion to tree plantations in lowland Amazonia, Brazil. Forest Ecology and Management164, 257–263, 10.1016/S0378-1127(01)00599-0 (2002). [Google Scholar]
  • 130.Smith, C. K., Gholz, H. L. & de Assis Oliveira, F. Soil nitrogen dynamics and plant-induced soil changes under plantations and primary forest in lowland Amazonia, Brazil. Plant and Soil200, 193–204, 10.1023/A:1004348116786 (1998). [Google Scholar]
  • 131.Smith, K., Gholz, H. L. & de Assis Oliveira, F. Litterfall and nitrogen-use efficiency of plantations and primary forest in the eastern Brazilian Amazon. Forest Ecology and Management109, 209–220, 10.1016/S0378-1127(98)00247-3 (1998). [Google Scholar]
  • 132.Suresh, K. et al. Variation in Heartwood Formation and Wood Density in Plantation-Grown Red Sanders (Pterocarpus santalinus), Wood is Good, 139-151 10.1007/978-981-10-3115-1_14 (Springer, 2017).
  • 133.Tamil Nadu Forest Plantation Corporation Limited. Information Hand Book of Tamil Nadu Forest Plantation Corporation Limited (TAFCORN) (Government of Tamil Nadu, 2005).
  • 134.Tewari, V. P. & Kumar, V. S. K. Growth and yield functions for Dalbergia sissoo plantations in the hot desert of India grown under irrigated conditions. Journal of Tropical Forest Science17, 87–103 (2005). [Google Scholar]
  • 135.Thu, P. Q., Quang, D. N. & Dell, B. Threat to cedar, Cedrela odorata, plantations in Vietnam by the weevil, Aclees sp. Journal of Insect Science10, 1–9, 10.1673/031.010.19201 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 136.United Nations Environment Programme World Conservation Monitoring Centre. Review of Prunus africana from Cameroon. http://elibrary.unep-wcmc.org/Document/GetDocument/16239/Review_of_Prunus_africana_from_Cameroon (2008).
  • 137.United States Department of Agriculture, National Agricultural Statistics Service. CropScape: Cropland Data Layer. Center for Spatial Information Science and Systemshttps://nassgeodata.gmu.edu/CropScape (2023).
  • 138.Vargas, I. C. & Sandoval, R. N. Appreciation of the Chilean Forest Resource: Plantations of Pinus radiata and Eucalyptus Sp.: 1985–1996. (Planning and Statistics Branch, Policy and Planning Division, Forestry Department, Food and Agriculture Organization, 1998).
  • 139.Wadsworth, F. H. & González, E. Sustained mahogany (Swietenia macrophylla) plantation heartwood increment. Forest Ecology and Management255, 320–323, 10.1016/j.foreco.2007.09.053 (2008). [Google Scholar]
  • 140.Wang, S. L. & Fan, N. N. Effects of management measures on soil properties of Pinus koraiensis plantations. Advanced Materials Research356–360, 2758–2762, 10.4028/www.scientific.net/AMR.356-360.2758 (2011). [Google Scholar]
  • 141.Webb, E. L. & Md. Yousuf Hossain, S. Dalbergia sissoo mortality in Bangladesh plantations: Correlations with environmental and management parameters. Forest Ecology and Management206, 61–69, 10.1016/j.foreco.2004.10.055 (2005). [Google Scholar]
  • 142.Xiao, C. et al. Latest 30-m map of mature rubber plantations in mainland southeast Asia and Yunnan province of China: Spatial patterns and geographical characteristics. Progress in Physical Geography: Earth and Environment45, 736–756, 10.1177/0309133320983746 (2021). [Google Scholar]
  • 143.Zavitkovski, J. & Dawson, D. H. Intensively cultured plantations: Structure and biomass production of 1 to 7-year-old tamarack in Wisconsin. TAPPI61, 68–70 (1978). [Google Scholar]
  • 144.Zhao, Y. et al. Detailed dynamic land cover mapping of Chile: Accuracy improvement by integrating multi-temporal data. Remote Sensing of Environment183, 170–185, 10.1016/j.rse.2016.05.016 (2016). [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Richardson, S. B. et al. Global planted forest data for timber species. Zenodo10.5281/zenodo.14010483 (2024). [DOI] [PubMed]
  2. Richardson, S. B. et al. Global planted forest data for timber species. Dryad10.5061/dryad.2280gb626 (2024). [DOI] [PubMed]
  3. Richardson, S. B. et al. Global planted forest data for timber species. Zenodo10.5281/zenodo.14057500 (2024). [DOI] [PubMed]

Data Availability Statement

Code is available (Python version 3.11.9) for the resolution of taxonomic names and for the validation of species-country pairs in Zenodo, hosted by Dryad (10.5281/zenodo.13000336; see Technical Validation)27.

Articles from Scientific Data are provided here courtesy of Nature Publishing Group

RESOURCES