This metadata corresponds to the EUNIS forest and other wooded land habitat types, distribution based on vegetation plot data dataset.

The forest and other wooded land habitat type is the land where the dominant vegetation is, or was until very recently, trees with a canopy cover of at least 10%. It includes temporarily unstocked areas due to clear-cutting as part of a forest management practice or natural disasters which are expected to be regenerated within 5 years but does not include land that is predominantly under agricultural or urban land use. Trees are defined as woody plants, typically single-stemmed, that can reach a height of at least 5 m at maturity unless stunted by poor climate or soil. Includes Alnus and Populus swamp forest and riverine Salix forest. Excludes Corylus avellana scrub and Salix and Frangula carrs. Excludes lines of trees, coppices, regularly tilled tree nurseries. Excludes stands of climatically-limited dwarf trees (krummholz) < 3m high, such as occur at the arctic or alpine tree limit which are considered scrub (section S) . Excludes tree stands in agricultural production systems, such as fruit tree plantations, olive orchards and agroforestry systems (dehesa and montado) where crops are grown under tree cover - canopy less than 10%, which are listed under sparsely wooded grasslands. Old plantations which have many of the characteristics of natural or semi-natural forests are included, more intensively managed, and less natural, forests are included in vegetated man-made habitats.

The verified forest and other wooded land habitat samples used are derived from the Braun-Blanquet database (http://www.sci.muni.cz/botany/vegsci/braun_blanquet.php?lang=en) which is a centralised database of vegetation plots and comprises copies of national and regional databases using a unified taxonomic reference database. The geographic extent of the distribution data are all European countries except Armenia and Azerbaijan. The dataset is provided both in Geodatabase and Geopackage formats.

The Farm Business Survey (FBS) is conducted annually to collect business information from about 2,100 farms in England and Wales. The survey provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of the impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers.

The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer, etc. Up to and including the 2001/02 survey, FBS estimates were based on matching the sample between two adjacent years and farm weights. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample.

The typology used to determine the FBS farm type classification has been revised from 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification was based on 2007 standard output (SO) coefficients. From 2012, the farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classification was based on 2013 SO coefficients. The coefficients have been revised again for 2023/24 and are based on 2017 coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available on the GOV.UK FBS documents web page.

The Farm Business Survey is available from UKDS under Special Licence access conditions. See the' Access data' section for further details on how to apply for access to the data.

For the second edition (January 2003), data and documentation were resupplied by DEFRA. The new data excludes sections I, J and Y of the questionnaire (noted in the User Guide).

The Farm Business Survey (FBS) is conducted annually to collect business information from about 2,100 farms in England and Wales. The survey provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of the impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers.

The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer, etc. Up to and including the 2001/02 survey, FBS estimates were based on matching the sample between two adjacent years and farm weights. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample.

The typology used to determine the FBS farm type classification has been revised from 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification was based on 2007 standard output (SO) coefficients. From 2012, the farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classification was based on 2013 SO coefficients. The coefficients have been revised again for 2023/24 and are based on 2017 coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available on the GOV.UK FBS documents web page.

The Farm Business Survey is available from UKDS under Special Licence access conditions. See the' Access data' section for further details on how to apply for access to the data.

This metadata describes the EUNIS forest and other wooded land habitat types probability maps distribution datasets created from habitat probability mapping. Probability maps are refined versions of habitat suitability maps. Suitability maps model areas where the environment is suitable for a certain habitat type. Probability maps include information on existing land cover and narrow the modelled suitable distribution to areas where the habitat type is probable to occur.

The forest and other wooded land habitat type is the land where the dominant vegetation is, or was until very recently, trees with a canopy cover of at least 10%. It includes temporarily unstocked areas due to clear-cutting as part of a forest management practice or natural disasters which are expected to be regenerated within 5 years but does not include land that is predominantly under agricultural or urban land use. Trees are defined as woody plants, typically single-stemmed, that can reach a height of at least 5 m at maturity unless stunted by poor climate or soil. Includes Alnus and Populus swamp forest and riverine Salix forest. Excludes Corylus avellana scrub and Salix and Frangula carrs. Excludes lines of trees, coppices, regularly tilled tree nurseries. Excludes stands of climatically-limited dwarf trees (krummholz) < 3m high, such as occur at the arctic or alpine tree limit which are considered scrub (section S) . Excludes tree stands in agricultural production systems, such as fruit tree plantations, olive orchards and agroforestry systems (dehesa and montado) where crops are grown under tree cover - canopy less than 10%, which are listed under sparsely wooded grasslands. Old plantations which have many of the characteristics of natural or semi-natural forests are included, more intensively managed, and less natural, forests are included in vegetated man-made habitats.

The modelled probability for EUNIS forest and other wooded land habitat types is an indication of where conditions are favourable for the habitat type based on sample plot data (Braun-Blanquet database) and the Maxent software package. The modelled probability map may be used as a proxy for the geographical distribution of the habitat type. Note however that it is not representing the actual distribution of the habitat type. As predictors for the probability modelling not only climate and soil parameters have been taken into account, but also so-called RS-EVB's, Remote Sensing-enabled Essential Biodiversity Variables, like land use, vegetation height, phenology, and LAI (Leaf Area Index). Because the EBV's are restricted by the extent of the remote sensing data (EEA38 countries and the United Kingdom) the modelling result does also not go beyond this boundary.

1. The changes to physical and chemical ecosystem characteristics as a response to pervasive and intensifying land use have the potential to alter the consumer-resource interactions and to rewire the flow of energy through entire food webs. 2. We investigated these structural and functional properties of food webs in stream ecosystems distributed across woodland, agricultural and urban areas in the Zagreb region of Croatia. We compared resource availability and consumer diet composition using stable isotope mixing models and tested how the isotopic variance of basal resources, primary consumers, macroinvertebrate predators, and other food-web characteristics change with different land use types. 3. Combination of increased loading and altered composition of nutrients, lower water discharge and higher light availability at urban sites likely promoted the contribution of aquatic macrophytes to diets of primary consumers. Macroinvertebrate predators shifted their diet, relying more on active filterers at urban sites relative to woodland and agricultural sites. Urban food webs also had lower trophic redundancy (i.e. fewer species at each trophic level) and a more homogenised energy flow from lower to higher trophic levels. There was no effect of land use on isotopic variation of basal resources, primary consumers or macroinvertebrate predators, but all these trophic groups at urban and agricultural sites were 15N-enriched relative to their counterparts in woodland stream food webs. 4. The physical and chemical ecosystem characteristics associated with intensive land use altered the resource availability, trophic redundancy and the flow of energy to other trophic levels, with potentially negative consequences for community dynamics and ecosystem functioning. These empirical findings indicate that reducing nutrient pollution, agricultural runoffs and maintaining riparian vegetation can mitigate the impacts of land use on structure and function of stream ecosystems.

The widespread influence of land use and natural disturbance on population, community, and landscape dynamics and the long-term legacy of disturbance on modern ecosystems requires that a historical, broad-scale perspective become an integral part of modern ecological studies and conservation assessment and planning. In previous studies, the Harvard Forest Long Term Ecological Research (LTER) program has developed an integrated approach of paleoecological and historical reconstruction, meteorological modeling, air photo interpretation, GIS analyses, and field studies of vegetation and soils, to address fundamental ecological questions concerning the rates, direction, and causes of vegetation change, to evaluate controls over modern species and community distributions and landscape patterns, and to provide critical background for conservation and restoration planning. In the current study, we extend this approach to investigate the link between landscape history and the abundance, distribution, and dynamics of species, communities and landscapes of the Cape Cod to Long Island coastal region, including the islands of Martha's Vineyard, Nantucket, and Block Island. The study region includes many areas of high conservation priority that are linked geographically, historically, and ecologically. This data package includes GIS layers digitized by Harvard Forest researchers from copies of the US Coastal Survey “T-Sheet” maps available from the National Archives in College Park, Maryland. The US Coastal Survey, and then the US Coast and Geodetic Survey mapped the region, or specific parts of it, several times between 1832 and the 1960s. In this project we digitized the earliest T-Sheet available for each location. The original maps were surveyed between 1832 and 1886, with most of them made between 1835 to 1855. The original maps showed features such as roads, farm walls, railroads, buildings, some industrial buildings, saltworks, wharfs, and land cover including woodlands, sandplains, grasslands, open agricultural fields, cultivated areas, fruit tree orchards, wetlands, etc. Many sheets had symbols which differentiated conifer trees from hardwoods. There were some inconsistencies in what features were mapped or how they were drawn between the original T-Sheets. Since we digitized the maps over the course of several different research projects, we did not always digitize all of the same features in each geographic area, therefore users of this data are encouraged to look at scans of the original T-Sheets for their specific areas of interest (links below). We always digitized land cover and roads and occasionally buildings and fences as mentioned in the datasets below.

This paper presents a phytosociological study on the forest vegetation of Olea europaea var. sylvestris of Sicily and of the smaller minor islands. In Sicily, Oleaster formations show considerable climacic potentiality in the bioclimactic belts between the infra- and the thermomediterranean with single edapho-climacic penetrations that are also in the mesomediterranean; however, these were largely destroyed by man in order to make room for crops. Furthermore, the residual expressions of the Oleaster forests are limited, and often exist as regenerated woodland made possible by the abandonment of agricultural land; they are often small forest nuclei – high maquis, woods and micro-woods – with a more or less discontinuous distribution within the agricultural landscape. The fieldwork is supported by 120 phytosociological surveys carried out in various areas of Sicily, which were statistically analyzed and compared with other published data from surveys of other communities that have been described for the Tyrrhenian (Sardinia and Corsica) and Balearic Islands up until now. The phytosociological and statistical analysis has led to the description of three new associations two of which are classified in the Oleo-Ceratonion alliance (Ruto chalepensis-Oleetum sylvestris and Chamaeropo humilis-Oleetum sylvestris), the third in the Erico-Quercion ilicis alliance (Calicotomo infestae-Oleetum sylvestris).

The 2005 land use boundary data layer is an integral part of the planning data in the Lexington-Fayette-Urban County Government Geographic Information System. This information is used by the Division of Planning in case review, enforcement, and long range planning. GIS data layers are accessed by personnel in most LFUCG divisions for basic applications such as viewing, querying, and map output production.This dataset is designed to represent the existing land use during 2005 within Lexington-Fayette County. The land use in the county is surveyed by the Lexington-Fayette Urban County Government Division of Planning as an initial step in reviewing the comprehensive plan. The dataset is created by dissolving parcels with same land use and utilization of street centerlines as edges.The data is in ESRI feature class format, but can be exported to any number of supported formats, including shapefile and dxf. The native projection for the data is Kentucky State Plane North (NAD83), but may have been reprojected for use in other applications. Please check metadata to determine current projection.Code Values Provided by the LFUCG Division of Planning• DUP: Duplex housing - Two dwelling units sharing a common wall on one lot • TH: Townhomes - Attached dwelling units sharing a common wall, but not a floor to ceiling, with one dwelling unit per lot. Duplexes on separate lots are townhomes. • MFH: Multi-family housing - Three or more attached dwelling units on one lot. Trailor Parks • COM: Commercial: Retail/Restaurant/Personal Services - Commercial: Retail (food, non-food, including gas and alcohol), Restaurants, Entertainment, Applebees Park, Red Mile, Rupp Arena, Personal Services such as Hair and Nail Salons, Tax Preparation, Dry Cleaners, and Athletic Clubs. • OFF: Professional Office - All types of offices including Medical, Engineering/Architectural, Law, Consulting, Real Estate, and Research and Development. • GRQ: Lodging/Group Quarters - Dormitories, Hotels/Motels, Fraternities and Sororities, Nursing Homes/Assisted Living Facilities. • AG: Agricultural - Livestock, Crops, or Woodlands • CON: Construction - Contractor Yards, Concrete Mixing, Building Supplies, Lumber Yards • LI: Light Industry/Manufacturing/Warehouse - All industrial uses that are non-HI and non Construction. Outdoor storage • HI: Heavy Industry - Quarry, Chemical Processing, Stockyards, Gas Tank Farms, junk yards,towing• WHS: Warehousing - Warehouses & storage facilities • TR: Transportation - Airport, Bus Depots/Transit Center, Truck Freight Terminals, Distribution Facilities, Rail yards. • GS: Green Space - Undevelopable areas • P/SP: Public/Semi-public Use - Universities, Colleges, Cemeteries, Libraries, Corrections, Institutions, Museums, Cultural Facilities, Social Services, Fire Stations, Civic Clubs, Government Offices, Public work facilities, Utilities • HLC: Healthcare - Hospitals, Outpatient Surgery Centers, and Office Parks for medical, dental, and pharmaceutical uses exclusively. • REC: Recreation - Parks (private/public), Golf Courses (private/public), Skating Rinks, Neighborhood Recreation Centers, and Multipurpose Indoor Recreation (like the Stadium), Community Centers, Senior Centers • SCH: Schools - Verify coverage on maps. • REL: Places of Worship - Churches, Synagogues, Mosques. Verify coverage on maps. • PL: Parking Lot - Parking as a Principle Use • VAC: Vacant Lot - Non-greenway, Non-park, no structures • UUT: Underutilized Candidates - Vacant Buildings, Dilapidated Buildings.

The Farm Business Survey (FBS) is conducted annually to collect business information from about 2,100 farms in England and Wales. The survey provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of the impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers.

The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer, etc. Up to and including the 2001/02 survey, FBS estimates were based on matching the sample between two adjacent years and farm weights. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample.

The typology used to determine the FBS farm type classification has been revised from 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification was based on 2007 standard output (SO) coefficients. From 2012, the farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classification was based on 2013 SO coefficients. The coefficients have been revised again for 2023/24 and are based on 2017 coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available on the GOV.UK FBS documents web page.

The Farm Business Survey is available from UKDS under Special Licence access conditions. See the' Access data' section for further details on how to apply for access to the data.

For the third edition (February 2010), the data format and documentation have been updated. This study was previously held in the form of SPSS files generated from a SIR database, which covered two survey years. The depositors have recently undergone a program to compile older data in the FBS series into Access databases, both to improve usability of the older data and compatibility with the later FBS waves, that are already available in Access format. Documentation has also been provided in similar format to current FBS surveys.

The Farm Business Survey (FBS) is conducted annually to collect business information from about 2,100 farms in England and Wales. The survey provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of the impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers.

The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer, etc. Up to and including the 2001/02 survey, FBS estimates were based on matching the sample between two adjacent years and farm weights. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample.

The typology used to determine the FBS farm type classification has been revised from 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification was based on 2007 standard output (SO) coefficients. From 2012, the farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classification was based on 2013 SO coefficients. The coefficients have been revised again for 2023/24 and are based on 2017 coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available on the GOV.UK FBS documents web page.

The Farm Business Survey is available from UKDS under Special Licence access conditions. See the' Access data' section for further details on how to apply for access to the data.

For the second edition (February 2010), the data format and documentation have been updated. This study was previously held in the form of SPSS files generated from a SIR database, which covered two survey years. The depositors have recently undergone a program to compile older data in the FBS series into Access databases, both to improve usability of the older data and compatibility with the later FBS waves, that are already available in Access format. Documentation has also been provided in similar format to current FBS surveys.

The Farm Business Survey (FBS) is conducted annually to collect business information from about 2,100 farms in England and Wales. The survey provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of the impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers.

The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer, etc. Up to and including the 2001/02 survey, FBS estimates were based on matching the sample between two adjacent years and farm weights. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample.

The typology used to determine the FBS farm type classification has been revised from 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification was based on 2007 standard output (SO) coefficients. From 2012, the farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classification was based on 2013 SO coefficients. The coefficients have been revised again for 2023/24 and are based on 2017 coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available on the GOV.UK FBS documents web page.

The Farm Business Survey is available from UKDS under Special Licence access conditions. See the' Access data' section for further details on how to apply for access to the data.

For the fourth edition (September 2019), a revised database has been deposited.

The Farm Business Survey (FBS) is conducted annually to collect business information from about 2,100 farms in England and Wales. The survey provides information on the financial position and physical and economic performance of farm businesses, to inform policy decisions on matters affecting farm businesses and to enable analysis of the impacts of policy options. It is intended to serve the needs of farmers, farming and land management interest groups, government (both national and European), government partners, and researchers.

The primary objective of survey results is to contrast the performance or other business characteristics of different groupings of farm, such as between regions or other geographical or environmental designations, farm types, farm size, age or education of farmer, etc. Up to and including the 2001/02 survey, FBS estimates were based on matching the sample between two adjacent years and farm weights. Farm weights were still calculated to present a matched sample however. From the 2002/03 survey onwards, matching between adjacent years was dropped altogether, and weights are now calculated for the full sample.

The typology used to determine the FBS farm type classification has been revised from 2009 onwards. The FBS typology is now based on standard outputs expressed in euros, with a minimum threshold of 25,000 euro (irrespective of the SLR) for FBS eligibility. Between 2009 and 2011, FBS farm type classification was based on 2007 standard output (SO) coefficients. From 2012, the farm type classification was based on 2010 SO coefficients, and from 2017 the FBS farm type classification was based on 2013 SO coefficients. The coefficients have been revised again for 2023/24 and are based on 2017 coefficients. The change in typology has had an effect on the distribution of farms by farm type and income averages. Further information regarding the change in typology is available on the GOV.UK FBS documents web page.

The Farm Business Survey is available from UKDS under Special Licence access conditions. See the' Access data' section for further details on how to apply for access to the data.

The 2019/20 and 2020/21 FBS survey samples are slightly smaller than normal, as Covid-19 restrictions impacted data collection.

For the second edition (December 2022) data for sections F3, K, N and R have been added to the study, and the documentation has been updated.