The Ancient Woodland Inventory identifies over 52,000 ancient woodland sites in England. Ancient woodland is identified using presence or absence of woods from old maps, information about the wood's name, shape, internal boundaries, location relative to other features, ground survey, and aerial photography. The information recorded about each wood and stored on the Inventory Database includes its grid reference, its area in hectares and how much is semi-natural or replanted. Guidance document can be found on our Amazon Cloud Service Prior to the digitisation of the boundaries, only paper maps depicting each ancient wood at 1:50 000 scale were available.Full metadata can be viewed on data.gov.uk.

Mapping of ancient forests, developed by IGN, based on a comparison of current forest cover with that of State-Major maps, dating from the mid-19th century (period of forest minimum in France).

The El Pilar Project has been conducting research at El Pilar, Belize and Guatemala since 1993, and was founded on a base of survey work that goes back to 1983. This unusual archaeological program recognizes the present environment as a part of the ancient Maya past. Our mission is the preservation and conservation of endangered resources through local and international education. Addressing tensions between culture and nature, we use the past as a reference to build a responsible future. Weaving together traditional knowledge and practice with scientific inquiry and interpretation, we promote a deeper awareness of heritage through local partnership.

The University of California Santa Barbara (UCSB) Maya Forest GIS is an essential tool to organize and use the numerous geographic resources involved in our studies, and provide reliable datasets for the project.

NOTE: Harvard Forest did not provide written metadata. Rather, this metadata was written by the Martha's Vineyard Commission based on a phone conversation with Harvard Forest staff in Dec. 2023.Utilizing Harvard Forest's "Ancient Woodlands" data of 2019, the Martha's Vineyard Commission ran the Identity geoprocessing tool with that dataset and the MVC's 12/22/2023 version of the Open Space Conservation data for Martha's Vineyard. This process retained all of the Ancient Woodlands data and superimposed the attributes of [Level of Protection] and [OS_ID] into the Ancient Woodlands polygons. This permits one to readily analyze the data to see where Ancient Forests are currently protected in perpetuity, etc. Please see the Description section for background about Harvard Forest's delineation of the Ancient Woodlands.A domain table is provided which explains the Level of Protection codes. The [OS_ID] is a numeric ID which links back to the MVC's Open Space Conservation dataset. OS_ID is a unique identifier within that dataset. The numbers before the hyphen is the Town ID. 62 = Chilmark, 89 = Edgartown, 104 = Aquinnah, 221 = Oak Bluffs, 296 = Tisbury, 327 = West Tisbury. The Ancient Woodlands data were produced by Harvard Forest in Dec. 2019. Data for Martha's Vineyard were received from Harvard Forest in November of 2023. Looking at historic maps and older aerial photographs, Harvard Forest noted for 4 time periods when forest was present on the map or photo. Locations where forest was present for all 4 time periods, were regarded as 'Ancient Woodlands'. The 4 time periods reviewed were: circa 1850, circa 1890, 1938, 1993. Then the data were updated, based on Google's November 2018 photos, for recent development or new agricultural areas. Areas where development or agricultural fields existed were removed from the Ancient Woodlands dataset. The areas delineated in this dataset, represent Ancient Woodlands present as of year-end 2018. Please note: Ancient Woodlands are not the same as "Old Growth Forest".The specific citation for the 1850 and 1890 map are unknown. Harvard Forest georeferenced those maps in ArcGIS software. The 1938 black and white aerial photographs were georeferenced by a consultant hired by Harvard Forest. As an aside FYI, the 1993 photographs (color infrared? black & white?) is the same year of photography which was used for The Nature Conservancy's vegetation delineation of Martha's Vineyard. Not sure if Harvard Forest used the exact same photos as the TNC project but the time period is the same.Harvard Forest is in the process of analyzing the potential ecological benefit of these Ancient Woodlands. Preliminary results show these area have more huckleberry than non-Ancient Woodland areas.The Martha's Vineyard Commission did convert the Harvard Forest Ancient Woodlands data from multi-part polygons to single-part polygons prior to running the Identity analysis.

The age of a forest is defined by the maintenance of a wooded canopy from the middle of the 19th century until today. The digitisation of old forests was carried out by the Morvan Regional Natural Park in 2012 using the product IGN SCAN State-Major® (1:40,000) on the 117 communes of the Charter 2008-2019. In 2016, the evolution of the land use of ancient forests on the Park is informed by the IPAMAC (Inter PArc MAssif Central) by comparing the old forests with the forests listed in the BD Forests of IGN. There are therefore 3 datasets related to the evolution of ancient forests: Ancient Forests (FA): major map forests (1818-1866) still present on the ground on the BD ForestsDeforestation (DEB): forests present on the State Major’s map (1818-1866) but disappeared today on the BD Forests Recent Forests (FR): forests not present on the map of the State Major (1818-1866) but present on the BD forests in 2019, the team of the Regional Natural Park of Morvan completed this data according to the same process by integrating the areas of old forests and their evolution on Natura 2000 sites outside the Park and the new adhering or study municipalities of the Park for the 2020-2035 Charter.

The forest plan provides information on: Who owns the forest? What function does it perform? Or what is legally part of the forest? The inventory map shows the current forest structure: Where is a young or old forest and where can coniferous, deciduous or mixed forest be found?

This data set provides the distribution of young forests (forests less than 27 years of age) and their estimated stand ages across the full extent of Russia at 500-m resolution for the year 2012. The distribution of young forests was modeled with MODIS 500-m records for 12- to 27-year-old forests and augmented with the 0- to 11-year-old forest distribution as aggregated from 30 m resolution contemporary Landsat imagery.

Abstract

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied.

Abstract: Old growth forest mapping from aerial photograph interpretation of canopy species regeneration and senescent growth stages. Scale 1:25,000. Bounded by NSW Morriset Forestry District. Boundaries include the New England Highway and Hunter River in the North,the Blue mountains and Wollemi National Park in the west and the Illawarra highway in the south. VIS_ID 4122

Purpose

To map old growth forest in the Morriset area.

Dataset History

This data and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are represented here as originally supplied.

Digitised on screen over 1:250,000 scale topographic maps. Attributes were verified in the field and by NSW state forests. Stereoscopic interpretation using a range of stereoscopes with a variety of magnifications (eg. Topcon and Abrams stereoscopes with x10 magnification). Attributes table & codes - Information contained in the attribute table is: regrowth-juvenile and sapling, regrowth-pole, Mature-early mature & mature, senescent-late mature and overmature, disturbance code. The project was only concerned with pyrophytic vegetation consequently, vegetation that was <10% pyrophytic was coded with an O and rainforest was coded with an R. Mapping pathway - An api pathway was developed specifically for the BOGMP. O =0%.if vegetation obviously rainforest= R 1 =0-10%,if vegetetation obviously rainforest=R 2 =11-20% and 3 = 21-30% If understorey rainforest rather than grass or heath. On ecological grounds this should be called rainforest but there might be some debate over the upper part of class 3.= R 4 = 31-50% difficult to see the understorey, would require ground truthing. Could have rainforest in which case there would be an argument about whether to be treated as a separate vegetation type or as a serial stage of rainforest Discretionary(presence/absence of rainforest understorey) 5 =51-60% Could have rainforest elements but difficult to determine as rainforest. Eucalypt = 81-100% unlikely to be rainforest in understorey. Eucalypt The mapping pathway specified that in eucalypt forest, primary polygon primary polygon delineation was based on floristics then split firstly on structural differences, structure (% regrowth and senescence), secondly on height classes then regrowth size class, and tagged for relative stand density and disturbance indicators. No senescence was recorded for polygons outside of State Forest with >30% regrowth. Eucalyptus-dominated vegetation with <20% ccp was not delineated. Data recording. The aerial photographs were pre-prepared and supplied to interpreters with Effective areas and land tenure boundaries marked directly on to the photographs. The effective area of a photograph included all images closer to the centre of the photograph than to the centre of any other. The central old growth study area used recent logging disturbance maps provided by state forests. Land Cover - Vegetation Cover greater than 20% canopy cover Floristics - Classification into pyrophytic vegetation, <10% pyrophytic vegetation, and rainforest Strata - Mapping pathway delineates a code of rainforest or eucalypt according to understorey type in areas with discrepencies Growth Stage - Regeneration and senescence Multi-attribute Mapping - Native vegetation greater than 20% ccp delineated. Relative stand density for the regrowth component of the vegetation also identified. Special features identified (eg. exotic pine plantation). Land use / cover not identified. Survey Type point to plant transects. Inaccessible areas were assessed using aircraft. Information Collected Growth stage, disturbance and vegetation assessment Date of surveys -1996? Minimum Polygon Size -25 hectares Edge Matching - Not assessed Polygon Attribution - Comparison of the growth stage polygon codes and linework against a hard copy map and against the original linework on the aerial photographs.. Both a 10% random sample of the photographs,and all the photographs in a specific area were checked for coding and linework errors. Custodian - NPWS Date of map product -1996 Strengths - A validation process was implemented. Detailed growth stage information and disturbance information. Field checking was undertaken. Multi-attribute mapping with broad geographic coverage, relatively high quality data capture techniques, Weaknesses - The difference in ability of the interpreters (eg moist, high site quality forest types were more reliably mapped than other forest types).Field work was insufficient, confined to state forest tenure. High possibility of post mapping logging and disturbance.

Dataset Citation

NSW Office of Environment and Heritage (2015) Old Growth Forest Mapping Broad, Central, 1996. VIS_ID 4122 2015 20150116. Bioregional Assessment Source Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/85a296b9-0c03-4dec-a0c1-cb22debbdbd1.

Map of the distribution of old-growth forest ecosystems across extant forest in the Lower North East CRA region. Three separate classifications and mapping techniques were used to derive the ecosystems in three distinct biogeographic regions and these classifications and maps were then expertly integrated and merged to create a full coverage across the region. The ecosystems were mapped for application in the Comprehensive Regional Assessment process. They were then clipped to candidate old growth, component of the BOGMP successional forest growth stage mapping. The 100m modelled grid data is to be used in a regional context and not for fine scale interpretation. For areas without detailed vegetation mapping (western portions of the UNE and LNE regions, and the southern portion of the LNE region) the modelled distributions were used to predict the proportion of a modelled ecosystem only. As a result, the exact spatial representation of the data is not designed to be accurate. VIS_ID 5060

Forests where there was also forest according to the Science Society map around the 1780s. In these areas there may have been forest for more than 200 years. Due to the potentially long forest continuity, these areas are included in the assessment of forest sensitivity.

This data publication includes data used in "Spatial aspects of structural complexity in Sitka spruce – western hemlock forests, including evaluation of a new canopy gap delineation method" by Schneider and Larson (2017). These data represent trees and plots from a study led by Vernon LaBau for his M.S. Thesis at Oregon State University, which he completed in 1967. Data were collected in 1964 on ten, 1.42 hectare plots (laid out as 5 by 7 chains). Data include tree location within subplots, tree species, diameter at breast height, and height in logs.Data were originally collected to assess the utility of clustered point (prism) sampling to quantify old-growth forest structure in the unique rainforests of southeast Alaska. LaBau wrote: "This study was a test of eight basal area factors and five point sampling cluster patterns in a computer oriented sampling study of coastal Alaska old-growth spruce-hemlock stands. It was an attempt to learn which basal area factor and which type of point sample cluster pattern should be used in such stands. A test of the effect of stand density on point sampling was also made." This data continues to be valuable for research and has been most recently used to assess spatial pattern of old-growth forests in southeast Alaska.These data were originally published on 04/01/2020. Minor metadata updates were made on 07/21/2022.

Maps that represent the relative probability of fire refugia generated from an empirical model trained on the aggregate history (1985-2019) of wildfire severity, topographical features, and climate normal data with the Random Forest modeling algorithm. Areas identified as having a higher probability for fire refugia are those that burn less severely than the surrounding landscape. Map values at the other end of the spectrum represent areas that will likely burn at higher severity. Values in the future map represent output from application of the model with projections of large wildfire suitability based on future predictions for temperature and precipitation.

These maps can be used to examine where forests, including old forests, are likely to survive a wildfire. Refugia models can be used to evaluate the outcomes of different types of past management and wildfire that influence the probability of fire refugia. Refugia products can be used to evaluate old forest dynamics by intersecting refugia probability maps with maps of old forest or spotted owl habitat, to evaluate the degree of overlap under different fire weather conditions and through time.

Aligning forest/fuels/fire management with topography (conditioned on current or future climate) that relates to normal wildfire severity (low to high). The future model provides forest managers, fire protection agencies, and policy- makers empirical estimates of how much and where climate change might affect the landscape patterns.

The modeled output covers the entire range of Northern Spotted Owls: Washington, Oregon, and northern California within the Northwest Forest Plan (NWFP) boundary. This subspecies is found in the Northern California Region.

The forest fire map shows forest fires that occurred mainly in the territory of southern Quebec, i.e. the area located south of the territorial limit of attributable forests. This map data makes it possible to improve knowledge about fire regimes and to meet the specific needs of special management plans following forest fires. They can also be used to meet a variety of study and research needs, such as analyzing the impact of climate change, modeling post-fire regeneration, and studying ecosystem dynamics. This information is obtained from and produced from a variety of sources, including satellite images, aerial photographs, field or aerial surveys, fire scar dating, and archival documents. This data contains four types of mapping as well as fire regime mapping: • Detailed fire mapping, from 1976 to the present. This mapping includes burn types, total burn and partial burn, when information is available. In addition, for fires that have been characterized, information on the classes of burning patterns is added. The minimum mapping area can be up to 0.1 ha, depending on the source products used. This map is partially available for areas located in the north of southern Quebec. • Mapping the simplified contours of fires, from 1972 to today. This map shows the external contours of fires (without fragmentation), in order to represent them globally in a product that is easily usable and can be integrated into current information systems, GPS or others. Resulting from the fusion of detailed fire mapping, this product was designed to meet various customer needs. This map is partially available for the sectors located in the north of southern Quebec. • The mapping of the origin of fires having been listed by the protection organizations (e.g.: SOPFEU) for the period from 1972 to today. This mapping includes the date, the source of ignition (human or lightning) and the protection zone. It is available for all of Quebec. • The mapping of ancient fires concerns fires that occurred between the very end of the 19th century and 1975. This mapping comes from the information present on the forest maps of the first and second inventories, as well as from the information contained on the ecoforest maps of the third and fourth inventories. The dating of these fires is done using various methods, including the analysis of study trees bearing fire scars and the consultation of archival documents. This data is available for the following regions: Saguenay-Lac-Saint-Jean (02), Bas-Saint-Laurent (02), Bas-Saint-Laurent (01), Gaspésie-Îles-de-la-Madeleine (11), Abitibi-Témiscamingue (08), Mauricie-Centre-du-Québec (04-17), and Lanaudière-du-Québec (04-17), and Lanaudière-Laurentides (14-15). • Mapping fire regimes in southern Quebec. This map shows 13 zones with distinct fire regimes. These areas were delineated based on available information on the areas burned during the period 1890-2020 and other potentially decisive environmental variables, such as physiography, the abundance of different tree species known to be dependent on fire as well as the location of natural and anthropogenic ignitions. Fire regime mapping covers all forest areas under management as well as a more northern portion that is not managed. The detailed methodology is presented in Forest Research Paper no. 189 “Zoning fire regimes in southern Quebec” (coming soon). This zoning may be useful to ensure better consideration of the risk of fire in a forest management context. It can also serve as a territorial basis for projecting future fire activity taking into account various factors, such as climate change, fire suppression as well as changes in the types of fuels and their distribution on the territory.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Saarland landscape program refers to areas that have existed for several centuries (as far as map records are available) as forest areas. In these areas, it can be assumed that soil development under the forest umbrella (of course under the influence of the different forest use phases with Rottwald, Lower and Middle Forests as well as age-class forests with coniferous wood cultivation) has still taken place in the closest way for Central Europe. See Saarland Landscape Programme, Chapter 9.6.1. (As of June 2009)

Map of the distribution of old-growth forest ecosystems across extant forest in the Upper North East CRA region. Two separate classifications and mapping techniques were used to derive the ecosystems in two distinct biogeographic regions and these classifications and maps were then expertly integrated and merged to create a full coverage across the region. They were then clipped to candidate old growth, component of the CRAFTI successional forest growth stage mapping. The 100m modelled grid data is to be used in a regional context and not for fine scale interpretation. For areas without detailed vegetation mapping (western portions of the UNE and LNE regions, and the southern portion of the LNE region) the modelled distributions were used to predict the proportion of a modelled ecosystem only. As a result, the exact spatial representation of the data is not designed to be accurate. VIS_ID 5058

Primary forest is natural forest of any age. Primary forest includes forest disturbed naturally, for example by wildfire, insects or wind, but excludes forest that has been degraded by logging, roads, or other industrial human activities. Not all primary forest is old, but all old growth in BC is primary forest.Park and protected area boundaries are black.We used the Vegetation Resource Inventory data to define the forested landscape as lands having a site index of over 5. We defined lower productivity primary forest as having a site index of 5 to 10, and higher productivity primary forest as having a site index of 10 +. The industrially disturbed lands layer includes existing and approved cutblocks, roads, pipeline right of ways, major mines, transmission lines, agricultural land, and private land.Data were downloaded in February, March and November 2020 from the BC Data Catalogue, the BC Oil and Gas Commission Centre portal, and the federal NFIS. Layers used are listed below. Provincial forest inventory data are coarse and this map may not be accurate at fine scales, or accurately depict logging prior to 1985.Visit us at https://conservationnorth.org/ to find out how you can help protect BC's remaining primary and old growth forests.Data used:Harvesting:Harvested Areas of BC (Consolidated Cutblocks) (2023, Febuary)RESULTS - Openings svw (2023, January)Forest Tenure Cutblock Polygons (FTA 4.0) (2023, March)NFIS Harvest Year / Mask 1985 -2015 (2023, December)NFIS CA Forest Harvest 1985 -2020 (2023, Febuary 2023)Forested AreasVRI - 2019 - Forest Vegetation Composite Rank 1 Layer (R1) (2023, May)Non Forest excluded-Query: Site Index under 4.9 Low Productivity-Query: Site Index Between 5.0 and 10Primary forest -Query:Site Index above 10Additional DataParcelMap BC Parcel Fabric (2020, May)Digital Road Atlas (DRA) - Master Partially-Attributed Roads (2020, May)Permitted Mine Areas - Major MineBC (2020 March)Transmission Lines (2020, May)Pipeline Segments (Permitted) (2020, Oct)Pipeline Rights of Way (Permitted) (2020, Oct)BC Parks, Ecological Reserves, and Protected Areas (2020, May)National Parks of Canada within British Columbia (2020, March)

The link: Access the data directory is available in the section*Dataset Description Sheets; Additional Information*. The forest fire map shows forest fires that occurred mainly in the territory of southern Quebec, i.e. the area located south of the territorial limit of attributable forests. This map data makes it possible to improve knowledge about fire regimes and to meet the specific needs of special management plans following forest fires. They can also be used to meet a variety of study and research needs, such as analyzing the impact of climate change, modeling post-fire regeneration, and studying ecosystem dynamics. This information is obtained from and produced from a variety of sources, including satellite images, aerial photographs, field or aerial surveys, fire scar dating, and archival documents. This data contains four types of mapping as well as fire regime mapping: • Detailed fire mapping, from 1976 to today. This mapping includes burn types, total burn and partial burn, when information is available. In addition, for fires that have been characterized, information on the classes of burning patterns is added. The minimum mapping area can be up to 0.1 ha, depending on the source products used. This map is partially available for sectors located in the north of southern Quebec. • Mapping the simplified contours of fires, from 1972 to today. This map shows the external contours of fires (without fragmentation), in order to represent them globally in a product that can be easily used and integrated into current information systems, GPS or others. Resulting from the fusion of detailed fire mapping, this product was designed to meet various customer needs. This map is partially available for sectors located in the north of southern Quebec. • The mapping of the points of origin of fires having been listed by the protection organizations (e.g.: SOPFEU) for the period from 1972 to the present. This mapping includes the date, the source of ignition (human or lightning) and the protection zone. It is available for the whole of Quebec. • The mapping of ancient fires concerns fires that occurred between the very end of the 19th century and 1975. This mapping comes from the information present on the forest maps of the first and second inventories, as well as from the information contained on the ecoforest maps of the third and fourth inventories. The dating of these fires is done using various methods, including the analysis of study trees bearing fire scars and the consultation of archival documents. These data are available for the following regions: Saguenay-Lac-Saint-Jean (02), Bas-Saint-Laurent (02), Bas-Saint-Laurent (01), Gaspésie-Îles-de-la-Madeleine (11), Abitibi-Témiscamingue (08), Mauricie-Centre-du-Québec (02), Mauricie-Centre-du-Québec (02), Bas-Saint-Laurent (01), and Lanaudière-Laurentides (14-15). • Mapping fire regimes in southern Québec. This map shows 13 zones with distinct fire regimes. These areas were delineated based on available information on the areas burned during the period 1890-2020 and other potentially decisive environmental variables, such as physiography, the abundance of different tree species known to be dependent on fire as well as the location of natural and anthropogenic ignitions. Fire regime mapping covers all forest areas under management as well as a more northern portion that is not managed. The detailed methodology is presented in Forest Research Paper no. 189 “Zoning fire regimes in southern Quebec” (coming soon). This zoning may be useful to ensure better consideration of the risk of fire in a forest management context. It can also serve as a territorial basis for projecting future fire activity taking into account various factors, such as climate change, fire suppression as well as changes in the types of fuels and their distribution on the territory. This third party metadata element was translated using an automated translation tool (Amazon Translate).

We reconstructed a historical forest cover dataset for China from 1900 to 2020 at five-year intervals and 1 km spatial resolution (Fig. 1). Evaluation of the Mixed-Cell Cellular Automata (MCCA) model indicated an overall accuracy of 0.97, F1-score of 0.91, and Kappa coefficient of 0.89 for the 2000s (SI Appendix, Table S1). Subsequently, we used historical topographic maps surveyed in the 1950s to further validate the dataset, which yielded an overall accuracy of 0.97, F1-score of 0.82, and Kappa coefficient of 0.81 (SI Appendix, Fig. S1, Table S2). To assess historical accuracy, we used the locations of 283,707 georeferenced old trees, each over 100 years old, as indicators of early 20th-century forest fragments (SI Appendix, Fig. S2). To validate the forest cover maps, we identified a 1 km2 pixel as a ‘hit’ if it contained both at least one old tree and a forested area ≥1 hectare (i.e., ≥1% forest cover). This threshold provides a conservative yet ecologically justified benchmark for forest presence, capturing the minimal extent of a forest patch likely to contain ancient trees, given their average density (~0.36 trees/km²) (39), while reducing misclassification risks from isolated pixels or geolocation uncertainty. Based on this criterion, 93.86% (n = 266,294) of old trees in 2020 and 78.66% (n = 223,152) of those estimated to be present in 1900 fell within qualifying pixels. Additionally, when using a stricter 10-hectare threshold, 84.21% (n = 238,912) in 2020 and 70.05% (n = 198,742) in 1900 still met the criterion. These high concordance rates across spatial thresholds and time periods underscore the reliability of our forest reconstruction and the robustness of the multi-source validation strategy.

Map of the distribution of old-growth forest ecosystems across extant forest in the Lower North East CRA region. Three separate classifications and mapping techniques were used to derive the ecosystems in three distinct biogeographic regions and these classifications and maps were then expertly integrated and merged to create a full coverage across the region. The ecosystems were mapped for application in the Comprehensive Regional Assessment process. They were then clipped to candidate old growth, component of the BOGMP successional forest growth stage mapping.

The 100m modelled grid data is to be used in a regional context and not for fine scale interpretation. For areas without detailed vegetation mapping (western portions of the UNE and LNE regions, and the southern portion of the LNE region) the modelled distributions were used to predict the proportion of a modelled ecosystem only. As a result, the exact spatial representation of the data is not designed to be accurate.

VIS_ID 5060

This is a data product of the estimated composition of tree taxa at the time of European and Euro-American settlement of the northeastern United States. Composition is defined as the proportion of stems larger than approximately 20 cm diameter at breast height in 22 taxonomic groupings, generally at the genus level. The data come from settlement survey records that provide raw data that are transcribed and then aggregated spatially, giving count data. The domain is divided into two regions, eastern (Maine to Ohio) and western (Indiana to Minnesota). Public Land Survey point data in the western region are aggregated to a regular 8 km grid, while data in the eastern region, from Town Proprietor Surveys, are aggregated at the township level in irregularly-shaped local administrative units. The product is based on a Bayesian statistical model fit to the count data that estimates composition on a regular 8 km grid. The statistical model allows us to estimate composition at locations with no data and to smooth over noise caused by limited counts in locations with data. Critically, it also allows us to quantify uncertainty in our composition estimates. We expect this data product to be useful for understanding the state of vegetation in the northeastern United States prior to large-scale European settlement. In addition to specific regional questions, the data product can also serve as a baseline against which to investigate how forests and ecosystems change after intensive settlement. This material is based upon work supported by the National Science Foundation under grants #DEB-1241874, 1241868, 1241870, 1241851, 1241891, 1241846, 1241856, 1241930.