This geographic information system (GIS) data layer shows the dominant lithology and geochemical, termed lithogeochemical, character of near-surface bedrock in the New England region covering the states of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. The bedrock units in the map are generalized into groups based on their lithological composition and, for granites, geochemistry. Geologic provinces are defined as time-stratigraphic groups that share common features of age of formation, geologic setting, tectonic history, and lithology. This data set incorporates data from digital maps of two NAWQA study areas, the New England Coastal Basin (NECB) and the Connecticut, Housatonic, and Thames River Basins (CONN) areas and extends data to cover the states of Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. The result is a regional dataset for the lithogeochemical characterization of New England (the layer named NE_LITH). Polygons in the final coverage are attributed according to state, drainage area, geologic province, general rock type, lithogeochemical characteristics, and specific bedrock map unit.

Combined New England City and Town Areas; 2020 Census - January 1, 2020 vintage

A regression model that estimates monthly temperature and precipitation as a function of latitude, longitude, and elevation for the New England area was used to estimate annual growing degree days and precipitation for the state of Massachusetts. For details of the regression model please see the published paper (Ollinger, S.V., Aber, J.D., Federer, C.A., Lovett, G.M., Ellis, J.M., 1995. Modeling Physical and Chemical Climate of the Northeastern United States for a Geographic Information System. US Dept of Agriculture, Forest Service, Radnor, PA, USA).

Background and Data Limitations The Massachusetts 1830 map series represents a unique data source that depicts land cover and cultural features during the historical period of widespread land clearing for agricultural. To our knowledge, Massachusetts is the only state in the US where detailed land cover information was comprehensively mapped at such an early date. As a result, these maps provide unusual insight into land cover and cultural patterns in 19th century New England. However, as with any historical data, the limitations and appropriate uses of these data must be recognized: (1) These maps were originally developed by many different surveyors across the state, with varying levels of effort and accuracy. (2) It is apparent that original mapping did not follow consistent surveying or drafting protocols; for instance, no consistent minimum mapping unit was identified or used by different surveyors; as a result, whereas some maps depict only large forest blocks, others also depict small wooded areas, suggesting that numerous smaller woodlands may have gone unmapped in many towns. Surveyors also were apparently not consistent in what they mapped as ‘woodlands’: comparison with independently collected tax valuation data from the same time period indicates substantial lack of consistency among towns in the relative amounts of ‘woodlands’, ‘unimproved’ lands, and ‘unimproveable’ lands that were mapped as ‘woodlands’ on the 1830 maps. In some instances, the lack of consistent mapping protocols resulted in substantially different patterns of forest cover being depicted on maps from adjoining towns that may in fact have had relatively similar forest patterns or in woodlands that ‘end’ at a town boundary. (3) The degree to which these maps represent approximations of ‘primary’ woodlands (i.e., areas that were never cleared for agriculture during the historical period, but were generally logged for wood products) varies considerably from town to town, depending on whether agricultural land clearing peaked prior to, during, or substantially after 1830. (4) Despite our efforts to accurately geo-reference and digitize these maps, a variety of additional sources of error were introduced in converting the mapped information to electronic data files (see detailed methods below). Thus, we urge considerable caution in interpreting these maps. Despite these limitations, the 1830 maps present an incredible wealth of information about land cover patterns and cultural features during the early 19th century, a period that continues to exert strong influence on the natural and cultural landscapes of the region. For users without access to GIS software, the data are available for viewing at: http://harvardforest.fas.harvard.edu/research/1830instructions.html Acknowledgements Financial support for this project was provided by the BioMap Project of the Massachusetts Natural Heritage and Endangered Species Program, the National Science Foundation, and the Andrew Mellon Foundation. This project is a contribution of the Harvard Forest Long Term Ecological Research Program.

Wildlands in New England is the first U.S. study to map and characterize within one region all conserved lands that, by design, allow natural processes to unfold with no active management or intervention. These “forever wild lands” include federal Wilderness areas along with diverse public and private natural areas and reserves. Knowing the precise locations of Wildlands, their characteristics, and their protection status is important as both a baseline for advancing conservation initiatives and an urgent call to action for supporting nature and society. Wildlands play a unique role in the integrated approach to conservation and land planning advanced by the Wildlands, Woodlands, Farmlands & Communities (WWF&C) initiative, which calls for: at least 70 percent of the region to be protected forest; Wildlands to occupy at least 10 percent of the land; and all existing farmland to be permanently conserved. This research was conducted by WWF&C partners Harvard Forest (Harvard University), Highstead Foundation, and Northeast Wilderness Trust, in collaboration with over one hundred conservation organizations and municipal, state, and federal agencies. This dataset contains the Geographical Information System (GIS) polygon layer of Wildlands created by this project and used in all analyses for the 2023 report. Another GIS layer will be updated as new Wildlands are brought to our attention or created and will be available at https://wildlandsandwoodlands.org/ for researchers.

Landscape connectivity is integral to the persistence of metapopulations of wide ranging carnivores and other terrestrial species. The objectives of this research were to investigate the landscape characteristics essential to use of areas by lynx and bobcats in northern New England, map a habitat availability model for each species, and explore connectivity across areas of the region likely to experience future development pressure. A Mahalanobis distance analysis was conducted on location data collected between 2005 and 2010 from 16 bobcats in western Vermont and 31 lynx in northern Maine to determine which variables were most consistent across all locations for each species using three scales based on average 1) local (15 minute) movement, 2) linear distance between daily locations, and 3) female home range size. The bobcat model providing the widest separation between used locations and random study area locations suggests that they cue into landscape features such as edge, availability of cover, and development density at different scales. The lynx model with the widest separation between random and used locations contained five variables including natural habitat, cover, and elevation—all at different scales. Shrub scrub habitat—where lynx’s preferred prey is most abundant—was represented at the daily distance moved scale. Cross validation indicated that outliers had little effect on models for either species. A habitat suitability value was calculated for each 30 m2 pixel across Vermont, New Hampshire, and Maine for each species and used to map connectivity between conserved lands within selected areas across the region. Projections of future landscape change illustrated potential impacts of anthropogenic development on areas lynx and bobcat may use, and indicated where connectivity for bobcats and lynx may be lost. These projections provided a guide for conservation of landscape permeability for lynx, bobcat, and species relying on similar habitats in the region.

This is a raster map of bobcat (Lynx rufus) distribution in the New England region of the northeastern United States. The value of each pixel in the map is an estimate of the species probability of occurrence. Occurrence estimates were calculated from species-specific distribution models fit using expert-opinion data and generalized linear mixed modeling. For details on the collection of expert opinion data, the modeling process, and the development of the distribution maps, please see: Pearman-Gillman, S, J. E. Katz, R. Mickey, J. Murdoch, and T. Donovan. 2020. Predicting wildlife distribution patterns in New England USA with expert elicitation techniques. Global Ecology and Conservation 21:e00853.

Understanding the impacts of landscape change on species distributions can help inform decision-making and conservation planning. Unfortunately, empirical data that span large spatial extents across multiple taxa are limited. In this study, we used expert elicitation techniques to develop species distribution models (SDMs) for harvested wildlife species (n = 10) in the New England region of the northeastern United States. We administered an online survey that elicited opinions from wildlife experts on the probability of species occurrence throughout the study region. We collected 3396 probability of occurrence estimates from 46 experts, and used linear mixed-effects methods and landcover variables at multiple spatial extents to develop SDMs. We applied models to rasters (30 × 30 m pixles) of the New England region to map each species’ distribution. Details of the project can be found in the following publication: Pearman-Gillman SB, Katz JE, Mickey R, Murdoch JD, and Donovan TM. 2020. Predicting wildlife distribution patterns in New England USA with expert elicitation techniques. Global Ecology and Conservation 21:e00853. doi:10.1016/j.gecco.2019.e00853

I. SNEP HRU Project Background The Southeast New England Program (SNEP) region consists of watersheds in Massachusetts and Rhode Island that primarily drain into Narragansett Bay, Buzzards Bay, or Nantucket Sound. It encompasses all or portions of 134 municipalities many of which are highly developed. The region faces multiple water quality issues with stormwater being previously identified a major contributor. These maps have been generated for all 134 Municipalities including 81 subwatersheds in the SNEP region to provide organizations and municipalities a way to understand where significant stormwater pollution may be originating. For organizations or municipalities with GIS capabilities the data that created these maps is available as well. II. What are HRUs? Hydrologic Response Units (HRUs) describe a landscape through unique combinations of land use and land cover (residential, commercial, forest, etc.), soil types (A, B, C, D), and additional characteristics such as slope, and impervious cover. These landscape characteristics, or HRUs, provide the building block to quantify stormwater pollutant loads (nitrogen, phosphorus, and total suspended solids (TSS)) originating from a given land area. The HRUs and nutrient pollutant loads in stormwater provides a baseline from which reduction targets can be created. III. How can HRUs be used? These maps and their underlying data can provide critical information to municipalities, watershed organizations, EPA, and others to assess stormwater pollutant loads in SNEP watersheds. EPA expects that this information will facilitate further understanding of the distribution of stormwater pollutant load source areas throughout the watersheds. This information serves to advance a broader understanding of stormwater impacts and potential management options by the public and direct stakeholders. Consistent HRUs may help municipalities implement MS4 permitting requirements and facilitate stormwater management strategies, such as land use conversion, stormwater Control Measure (SCM) siting, and targeting areas for conservation. HRU mapping can identify best locations for SCMs and can be utilized with additional stormwater planning tools (such as EPA’s Opti-Tool) to develop a cost-effective stormwater management plan. By providing a consistent HRU map for the SNEP region, practitioners can focus their efforts on implementation of SCM strategies rather than mapping their landscape. Hotspot mapping is a tool that integrates the HRU analysis and stormwater runoff pollutant load outputs to indicate areas where pollutant loads are highest and areas that stormwater controls may be best implemented. The HRUs and pollutant loads can be overlayed with parcel analysis to determine which parcels have high loads/areas of large impervious cover. The parcel data can help towns prioritize their efforts by determining the properties with highest potential to reduce pollutant loads through stormwater controls. Similarly, it can help determine which properties have large stormwater pollutant loads. IV. Other Resources HRUs That have been completed by EPA - Taunton River Watershed FDC Project and Tisbury, MA IC Disconnection Project The Cape Cod Commission developed HRUs for Barnstable County (CCC: Barnstable County HRUs). The UNH Stormwater Center developed parcel level hotspot mapping in New Hampshire for municipalities to prioritize where new BMPs should be placed (UNHSC: NH Hotspot Mapping).

This data set contains the 1995-era or early-date classifications of US coastal zone 65 and can be used to analyze change. This imagery was collected as part of the Multi-Resolution Land Characteristics program in a multi-agency effort to provide baseline multi-scale environmental characteristics and to monitor environmental change. This data set utilized 10 full or partial Landsat scenes which were analyzed according to the Coastal Change Analysis Program (C-CAP) protocol to determine land cover. Note: These data were reprojected from their native projection into North American Datum 1983 (NAD83) / Massachusetts State Plane coordinate system, Mainland Zone (Fipszone 2001) meters by the Massachusetts Office of Coastal Zone Management on Oct. 12, 2006.

SUPPLEMENTAL INFORMATION: This Classification and change analysis is based on Landsat TM scenes: p11r30 (08/14/1995), p11r31 (09/12/1994), p12r30 (07/04/1995), p12r31 (08/21/1995), p12r32 (06/15/1994), p13r30 (07/29/1996), p13r31 (08/09/1994), p13r32 (08/09/1994), p14r29 (05/31/1995)

Eelgrass Beds 2009 Set:

This data layer was created by the Conservation Management Institute, Virginia Tech University for the USFWS National Wetlands Inventory, Region 5. The project area encompasses the eastern end of Long Island Sound, including Fishers Island and the North Fork of Long Island. It includes all coastal embayments and nearshore waters (i.e., to a depth of -15 feet at mean low water) bordering the Sound from Clinton Harbor in the west to the Rhode Island border in the east and including Fishers Island and the North Shore of Long Island from Southold to Orient Point and Plum Island. The study area includes the tidal zone of 18 sub-basins in Connecticut: Little Narragansett Bay, Stonington Harbor, Quiambog Cove, Mystic Harbor, Palmer-West Cove, Mumford Cove, Paquonock River, New London Harbor, Goshen Cove, Jordan Cove, Niantic Bay, Rocky Neck State Park, Old Lyme Shores, Connecticut River, Willard Bay, Westbrook Harbor, Duck Island Roads, and Clinton Harbor, and two areas in New York: Fishers Island and a portion of the North Shore of Long Island. Delineations of 2009 eelgrass beds were completed using 1:20,000 true color aerial photography flown at low tide on 7/14/2009 and 7/15/2009. Extensive field work was conducted by the USFWS Region 5 Southern New England-New York Bight Coastal Program Office in October, November, and December 2009 with 193 field sites checked. The 2009 photography was scanned and geo-rectified using 2006 NAIP 1 meter true color imagery. Data have been summarized in a technical report: Tiner, R., K. McGuckin, M. Fields, N. Fuhrman, T. Halavik, and A. MacLachlan. 2010. 2009 Eelgrass Survey for Eastern Long Island Sound, Connecticut and New York. U.S. Fish and Wildlife Service, National Wetlands Inventory Program, Northeast Region, Hadley, MA. National Wetlands Inventory report. 16 pp. plus Appendix.

This data layer was created by the Conservation Management Institute, Virginia Tech University for the USFWS National Wetlands Inventory, Region 5. The project area encompasses the eastern end of Long Island Sound, including Fishers Island and the North Fork of Long Island. It includes all coastal embayments and nearshore waters (i.e., to a depth of -15 feet at mean low water) bordering the Sound from Clinton Harbor in the west to the Rhode Island border in the east and including Fishers Island and the North Shore of Long Island from Southold to Orient Point and Plum Island. The study area includes the tidal zone of 18 sub-basins in Connecticut: Little Narragansett Bay, Stonington Harbor, Quiambog Cove, Mystic Harbor, Palmer-West Cove, Mumford Cove, Paquonock River, New London Harbor, Goshen Cove, Jordan Cove, Niantic Bay, Rocky Neck State Park, Old Lyme Shores, Connecticut River, Willard Bay, Westbrook Harbor, Duck Island Roads, and Clinton Harbor, and two areas in New York: Fishers Island and a portion of the North Shore of Long Island. Delineations of 2009 eelgrass beds were completed using 1:20,000 true color aerial photography flown at low tide on 7/14/2009 and 7/15/2009. Extensive field work was conducted by the USFWS Region 5 Southern New England-New York Bight Coastal Program Office in October, November, and December 2009 with 193 field sites checked. The 2009 photography was scanned and geo-rectified using 2006 NAIP 1 meter true color imagery.

Map layers.

Conserved core areas and connective habitat.

This is a raster map of wild turkey (Meleagris gallopavo) distribution in the New England region of the northeastern United States. The value of each pixel in the map is an estimate of the species probability of occurrence. Occurrence estimates were calculated from species-specific distribution models fit using expert-opinion data and generalized linear mixed modeling. For details on the collection of expert opinion data, the modeling process, and the development of the distribution maps, please see: Pearman-Gillman, S, J. E. Katz, R. Mickey, J. Murdoch, and T. Donovan. 2020. Predicting wildlife distribution patterns in New England USA with expert elicitation techniques. Global Ecology and Conservation 21:e00853.

This data release provides a generalized lithology look-up table for the lithogeochemical classification of Vermont's bedrock geologic map units. The table is defined from the mapped bedrock geologic units published by Ratcliffe and others (2011) and the generalized lithology of rock group A and rock group B for lithogeochemical classification as defined by Robinson and Kapo (2003). The 2003 classification was created fro all six New England states and Vermont's geologic units were based on an older, less detailed, bedrock map of Vermont by Doll and others (1961). The new data table in this data release is designed to be joined with the published attribute table from the 2011 map database, as part of the bedrock geologic map unit polygons. The join attribute is the item called "Lith" in the 2011 map database. The data table is non-interpretive and the 2011 map data were not modified. The data release contains two files, including one metadata file and one comma-delimited (CSV) file: VTcontax_attrib_lithology.csv. References: Doll, C.G., Cady, W.M., Thompson, J.B., and Billings, M.P., 1961, Centennial geologic map of Vermont: Vermont Geological Survey, Miscellaneous Map MISCMAP-01, scale 1:250,000. Ratcliffe, N.M., Stanley, R.S., Gale, M.H., Thompson, P.J., and Walsh, G.J., 2011, Bedrock geologic map of Vermont: U.S. Geological Survey Scientific Investigations Map 3184, 3 sheets, scale 1:100,000, https://pubs.usgs.gov/sim/3184/ Robinson, G.R., Jr., and Kapo, K.E., 2003, Generalized lithology and lithogeochemical character of near-surface bedrock in the New England region: U.S. Geological Survey Open-File Report 03-225, https://pubs.usgs.gov/of/2003/of03-225/

The Historic Environment Opportunity Map for New Woodland dataset identifies areas in England that may be suitable for new woodland, based solely on available Historic Environment data. The dataset categorises land by different opportunity ratings to reflect the potential suitability of land for woodland creation while acknowledging areas of uncertainty due to data availability.

The purpose of this dataset is to guide landowners, planners, and decision-makers in considering woodland creation from a historic environment perspective. It should be noted that this dataset only considers the Historic Environment and therefore the opportunity ratings do not guarantee or preclude approval for woodland creation proposals.

As any forestry proposal could have the potential to affect the Historic Environment you should contact your local historic environment service. The local historic environment service can provide further data to support woodland creation proposals.

NHLE is the official, up to date register of all nationally protected historic buildings and sites in England.

SHINE is a single, nationally consistent dataset of non-designated historic and archaeological features from across England that could benefit from land management schemes.

The opportunity ratings are as defined:

· Favourable - Areas deemed suitable for new woodland on consideration of available Historic Environment data.

· Neutral - Areas deemed neither favourable nor unfavourable for new woodland on consideration of available Historic Environment data. Proposals in these areas will require additional consideration of the Historic Environment on a case-by-case basis.

· Unclassified - Areas, where SHINE data has been supplied, with no assigned opportunity rating. This illustrates a current absence of recorded data from a Historic Environment perspective. However, as SHINE data is included in the dataset for this area, a degree of confidence may be inferred when considering the absence of historic environment features.

· Unclassified (No SHINE supplied) - Areas, where SHINE data has not been supplied, with no assigned opportunity rating. This illustrates a current absence of recorded data from a Historic Environment perspective.

· Unsuitable - Areas deemed unsuitable for new woodland on consideration of available Historic Environment data.

Unclassified areas may be suitable or unsuitable for new woodland. To better understand these areas, contact the local historic environment service in accordance with the UKFS and Historic Environment Guidance for Forestry in England - GOV.UK

The datasets included in each opportunity rating are as follows:

Favourable

· Lost Historic Woodlands (ArchAI/Forestry Commission) – An A.I. dataset that identifies areas of woodland depicted on early 20th Century Ordnance Survey mapping which have since been lost.

Neutral

· Historic Parklands (Zulu Ecosystems) – an A.I. dataset that identifies areas of parkland depicted on early 20th Century Ordnance Survey mapping.

· World Heritage Site Core data (Historic England) – Core areas of World Heritage Sites, as designated by UNESCO.

· World Heritage Site Buffer (Historic England) – Buffer zones surrounding World Heritage Sites, as designated by UNESCO.

· Ridge and Furrow (Low) (ArchAI) – an A.I. dataset that identifies areas of less well-preserved historic ridge and furrow derived from LiDAR data.

Unclassified

· HER Boundaries (SHINE supplied) – Geographic areas covered by local historic environment services, where SHINE data has been supplied to the Forestry Commission.

· HER Boundaries (No SHINE supplied) - Geographic areas covered by local historic environment services where SHINE data has not been supplied to the Forestry Commission.

Unsuitable

· Historic Landscape Characterisation (HLC) (local historic environment services) – regional datasets that provide information on the historic character of the landscape.

· Scheduled Monuments (Historic England) – Protected archaeological sites of national importance.

· Scheduled Monuments Buffer – A 20 metre buffer surrounding Scheduled Monuments in-line with UKFS.

· Selected Heritage Inventory for Natural England (SHINE)(local historic environment services) – National dataset of non-designated heritage assets.

· Registered Parks and Gardens (Historic England) – Parks and Gardens designated as being of national significance.

· Registered Battlefields (Historic England) – Battlefields designated as being of national significance.

· Ridge and Furrow (High) (ArchAI) – an A.I. dataset that identifies areas of well-preserved historic ridge and furrow derived from LiDAR data.

This map shows the boundaries of regions, shires and municipalities as from 1 January 1967. The map is annotated to show the areas transferred from the Namoi to the New England Region.

The scale is 48 miles = 7/8 inch.


(SR Map No.52734). 1 map.

Note:
This description is extracted from Concise Guide to the State Archives of New South Wales, 3rd Edition 2000.

The New England Fishery Management Council protected a set of seven habitat closure areas in 2004, and Lydonia and Oceanographer Canyons in 2005. The Northeast Canyons and Seamounts Marine National Monument was established in 2016.Note that this map is not an authoritative source for protected area boundaries, and that web maps within this story map are not intended to be used in isolation. The authoritative source is the NOAA Greater Atlantic Regional Fisheries Office GIS data page.

This data package contains 3 GIS layers showing generalized forest types across New England as delineated in older forestry publications. These were digitized so that they can be used to illustrate broad vegetation patterns across the region in modern publications. These GIS layers include maps drawn by Hawley and Hawes (1912), RT Fisher (1933), and Westveld and the Committee on Silviculture, New England Section, Society of American Foresters (1956).

Light detection and ranging (LiDAR) has become a common tool for generating remotely sensed forest inventories. However, regional modeling of forest attributes using LiDAR has remained challenging due to varying parameters between LiDAR datasets, such as pulse density. Here we develop a regional model using a three dimensional convolutional neural network (CNN). We then apply our model to publicly available data over New England, generating maps of fourteen forest attributes at a 10 m resolution over 85 % of the region. Attributes include aboveground biomass (kg), total biomass (kg), tree count (#), percent conifer (%), basal area (m^2), mean height (m), quadratic mean diameter (cm), percent spruce/fir (%), percent white pine (%), inner bark volume (m^3), merchantable volume (m^3), and spruce/fir volume (m^3. All values correspond to the amount per pixel cell (I.E. kg of biomass found within that pixel). Map/model performance was assessed using the USFS’s FIA inventory, which constituted an independent dataset free from spatial autocorrelation. More data can be found in the following pre-print:

Ayrey, E., Hayes, D. J., Kilbride, J. B., Fraver, S., Kershaw, J.
A., Cook, B. D., & Weiskittel, A. R. (2019). Synthesizing
Disparate LiDAR and Satellite Datasets through Deep Learning to
Generate Wall-to-Wall Regional Forest
Inventories. bioRxiv, 580514.