This web map provides a detailed vector basemap for the world symbolized with a light gray, neutral background style with minimal colors, labels, and features that is designed to draw attention to your thematic content. The web map includes vector tile layers that are similar in content and style to the popular Light Gray Canvas map, which is delivered as two layers with raster fused map cache. This map includes a vector tile layer that provides unique capabilities for customization and high-resolution display. The map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. The layers in this map are built using the same data sources used for the Light Gray Canvas and other Esri basemaps.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map.Customize this MapBecause this map includes a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers, switch to alternate local language (in some areas), and refine the treatment of disputed boundaries. See the Vector Basemap group for other vector web maps. For details on how to customize this map, please refer to these articles on the ArcGIS Online Blog.

MD/PA Sandy Supplemental Lidar Data Acquisition and Processing Production Task USGS Contract No. G10PC00057 Task Order No. G14PD00397 Woolpert Order No. 74333 CONTRACTOR: Woolpert, Inc. This task is for a high resolution data set of lidar covering approximately 1,845 square miles. The lidar data was acquired and processed under the requirements identified in this task order. Lidar data is a remotely sensed high resolution elevation data collected by an airborne platform. The lidar sensor uses a combination of laser range finding, GPS positioning, and inertial measurement technologies. The lidar systems collect data point clouds that are used to produce highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures, and vegetation. The task required the LiDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. The final products include classified LAS, one (1) meter pixel raster DEMs of the bare-earth surface in ERDAS IMG Format, and 8-bit intensity images. Each LAS file contains lidar point information, which has been calibrated, controlled, and classified. Additional deliverables include hydrologic breakline data, control data, tile index, lidar processing and survey reports in PDF format, FGDC metadata files for each data deliverable in .xml format, and LAS swath data. Ground conditions: Water at normal levels; no unusual inundation; no snow; leaf off. Coastal tiles 18SVH065720 and 8SVH095690 contain no lidar points as they exist completely in water. A DEM IMG was generated for these two tiles as the digitized hydro breakline assumed the data extent in the area. As such only 2568 LAS and Intensity files will be delivered along with 2570 DEM IMG's.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Image Service Link: https://mdgeodata.md.gov/lidar/rest/services/Kent/MD_kent_hillshade_m/ImageServer

This crash dataset does include crashes from 2023 up until near the middle of July that have been reviewed and loaded into the Maine DOT Asset Warehouse. This crash dataset is static and was put together as an example showing the clustering functionality in ArcGIS Online. In addition the dataset was designed with columns that include data items at the Unit and Persons levels of a crash. The feature layer visualization by default will show the crashes aggregated by the predominant crash type along the corridor. The aggregation settings can be toggled off if desired and crashes can be viewed by the type of crash. Both the aggregation and standard Feature Layer configurations do include popup settings that have been configured.As mentioned above, the Feature Layer itself has been configured to include a standard unique value renderer based on Crash Type and the layer also includes clustering aggregation configurations that could be toggled on or off if the user were to add this layer to a new ArcGIS Online Map. Clustering and aggregation options in ArcGIS Online provide functionality that is not yet available in the latest version of ArcGIS Pro (<=3.1). This additional configuration includes how to show the popup content for the cluster of crashes. Users interested in learning more about clustering and aggregation in ArcGIS Online and some more advanced options should see the following ESRI article (https://www.esri.com/arcgis-blog/products/arcgis-online/mapping/summarize-and-explore-point-clusters-with-arcade-in-popups/).Popups have been configured for both the clusters and the individual crashes. The individual crashes themselves do include multiple tables within a single text element. The bottom table does include data items that pertain to at a maximum of three units for a crash. If a crash includes just one unit then this bottom table will include only 2 columns. For each additional unit involved in a crash an additional column will appear listing out those data items that pertain to that unit up to a maximum of 3 units. There are crashes that do include more than 3 units and information for these additional units is not currently included in the dataset at the moment. The crash data items available in this Feature Layer representation includes many of the same data items from the Crash Layer (10 Years) that are available for use in Maine DOT's Public Map Viewer Application that can be accessed from the following link(https://www.maine.gov/mdot/mapviewer/?added=Crashes%20-%2010%20Years). However this crash data includes data items that are not yet available in other GIS Crash Departments used in visualizations by the department currently. These additional data items can be aggregated using other presentation types such as a Chart, but could also be filtered in the map. Users should refer to the unit count associated to each crash and be aware when a units information may not be visible in those situations where there are four or more units involved in a crash.

Looking for information on a construction project near you? Project Portal offers a comprehensive view of all current, funded, and planned projects occurring across the State of Maryland. You can quickly and easily access specific project information, including a general overview, interactive map, news, schedule, pictures and video, supporting documents, and upcoming public meetings. It’s easy to search by location for a specific project, or by county for a list of all projects in your jurisdiction.(MDOT SHA Project Portal Individual Project Page Web Map)MDOT SHA WebsiteContact Us

This layer shows the existing water mains prior to installation of new municipal water mains for the North Kent Study Area. This data is used in the North Kent Disposal Area PFAS web map.The fields found in this dataset are:Field NameDescriptionLocationLocations of water main: City of Rockford or Plainfield TownshipYou can find more information about the North Kent Study Area by visiting the House Street Disposal Area webpage or the Rockford Tannery webpage on the Michigan PFAS Action Response Team (MPART) website. For questions about this content, reach out to Leah Gies, GiesL1@Michigan.gov.This data was provided to the Michigan Department of Environment, Great Lakes, and Energy (EGLE) by the consulting firm AECOM.

This dataset identifies areas where the distribution of Great Crested Newts (GCN) has been categorised into district zones relating to GCN occurrence and the level of impact development is likely to have on this species. Red zones contain key populations of GCN, which are important on a regional, national or international scale and include designated Sites of Special Scientific Interest for GCN. Amber zones contain main population centres for GCN and comprise important connecting habitat that aids natural dispersal. Green zones contain sparsely distributed GCN and are less likely to contain important pathways of connecting habitat for this species. White zones contain no GCN. However, as most of England forms the natural range of GCN, white zones are rare and will only be used when it is certain that there are no GCN.Full metadata can be viewed on data.gov.uk.

This interactive World Feature Service (WFS) displays geographically maintained or monitored areas in the borough of Ashford, Kent. ArcGIS polygon layers have been created for the map display and represent the extents of certain environmental, historical and regulated areas managed by Ashford Borough Council.

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer called the Sea Level Rise and Coastal Flooding Impacts Viewer. It depicts potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at: https://coast.noaa.gov/slr. This metadata record describes the Maryland, North digital elevation model (DEM), which is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Sea Level Rise and Coastal Flooding Impacts Viewer described above. This DEM includes the best available lidar known to exist at the time of DEM creation that met project specifications. This DEM includes data for Cecil, Harford, and Kent Counties. The DEM was produced from the following lidar data sets: 1. 2014 Cecil County, MD Lidar 2. 2013 Harford County, MD Lidar 3. 2015 Sandy Supplemental MD/PA QL2 Lidar (Kent County, MD) The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88, Geoid12B) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 3 meters.

Maryland Department of Planning (MDP) maps annexations from municipalities. This dataset is created and maintained by the Maryland Department of Planning. These boundaries are not intended to serve as a legal description. Fields:Municipality Name: Name of Municipality located in Maryland.Jurisdiction Code – Four letter county code: ALLE (Allegany), ANNE (Anne Arundel), BACI (Baltimore City), BACO (Baltimore County), CALV (Calvert), CARO (Caroline), CARR (Carroll), CECI (Cecil), CHAR (Charles), DORC (Dorchester), FRED (Frederick), GARR (Garrett), HARF (Harford), HOWA (Howard), KENT (Kent), MONT (Montgomery), PRIN (Prince George’s) QUEE (Queen Anne’s), SOME (Somerset), STMA (St. Mary’s), TALB (Talbot), WASH (Washington), WICO (Wicomico), WORC (Worcester). Last updated: 9/5/2024This is an MD iMAP hosted service. Find more information at https://imap.maryland.gov.Feature Service Link:https://mdgeodata.md.gov/imap/rest/services/Boundaries/MD_PoliticalBoundaries/FeatureServer/2

State of Maryland municipality boundary dataset.Maryland Department of Planning (MDP) maps annexations from municipalities. This dataset is created and maintained by the Maryland Department of Planning. These boundaries are not intended to serve as a legal description. Fields:MUN_NAME (Municipality Name): Name of Municipality located in Maryland.RESOLUTION_NUMBER (Resolution Number): Local Municipality Annexation Resolution Number.ANNEXATION_DATE (Annexation Date) (DD/MM/YYYY): The Annexation Date field shows when there's been a change in the boundary. This date is known as the “Effective Date” from the municipality. The date 1/1/1997 is used as a default date of when annexation changes were first indicated in the GIS layer and not necessarily of when it was actually annexed. If there's a date of 1/1/1997, it can be assumed that the annexation occurred on this date or before. For example, for Baltimore City, the city boundary was determined hundreds of years ago. Other than that default date, the date will show when the property was annexed. ACRES (GIS Acres): GIS calculated acres.JURSCODE (Jurisdiction Code) – Four letter county code: ALLE (Allegany), ANNE (Anne Arundel), BACI (Baltimore City), BACO (Baltimore County), CALV (Calvert), CARO (Caroline), CARR (Carroll), CECI (Cecil), CHAR (Charles), DORC (Dorchester), FRED (Frederick), GARR (Garrett), HARF (Harford), HOWA (Howard), KENT (Kent), MONT (Montgomery), PRIN (Prince George’s) QUEE (Queen Anne’s), SOME (Somerset), STMA (St. Mary’s), TALB (Talbot), WASH (Washington), WICO (Wicomico), WORC (Worcester).This is a MD iMAP hosted service. Find more information on https://imap.maryland.govhttps://mdgeodata.md.gov/imap/rest/services/Boundaries/MD_PoliticalBoundaries/FeatureServer/5

Abstract

Regular assessments of species’ status are an essential component of conservation planning and adaptive management. They allow the progress of past or ongoing conservation actions to be evaluated and can be used to redirect and prioritize future conservation actions. Most countries perform periodic assessments for their own national adaptive management procedures or national red lists. Furthermore, the countries of the European Union have to report on the status of all species listed on the directives of the Habitats Directive every 6 years as part of their obligations under Article 17. However, these national level assessments are often made using non-standardized procedures and do not always adequately reflect the biological units (i.e., the populations) which are needed for ecologically meaningful assessments.

Since the early 2000’s the Large Carnivore Initiative for Europe (a Specialist Group of the IUCN’s Species Survival Commission) has been coordinating periodic surveys of the status of large carnivores across Europe (e.g., von Arx et al. 2004; Salvatori & Linnell 2005, Kaczensky et al. 2013). These have covered the Eurasian lynx (Lynx lynx), the wolf (Canis lupus), the brown bear (Ursus arctos) and the wolverine (Gulo gulo). The golden jackal (Canis aureus) has been added to the LCIE prerogatives in 2014. The species is rapidly expanding in Europe (Trouwborst et al. 2015; Männil & Ranc 2022), a large-scale phenomenon that resembles that of the other large carnivores. Golden jackals are thriving in human-dominated landscapes (Ćirović et al. 2016; Lanszki et al. 2018; Fenton et al. 2021), where they are often functioning as the top predators, despite having smaller body size that is typical for large carnivores. The expansion of the species triggers many questions among scientists, stakeholders, and policy makers (Trouwborst et al. 2015; Hatlauf et al. 2021), that are closely connected to those raised by the other large carnivores (e.g., potential conflicts with livestock or hunting). In this context, monitoring the species’ expansion, delineating populations, assessing the species' legal and protection status, and addressing the concerns raised by this rapidly expanding carnivore requires a high level of coordination among regional experts.

These surveys involve the contributions of the best available experts and sources of information. While the underlying data quality and field methodology varies widely across Europe, these coordinated assessments do their best to integrate the diverse data in a comparable manner and make the differences transparent. They also endeavor to conduct the assessments on the most important scales. This includes the continental scale (all countries except for Russia, Belarus, Moldova and the parts of Ukraine outside the Carpathian Mountain range), the scale of the EU 28 (where the Habitats Directive operates) and of the biological populations which reflect the scale at which ecological processes occur (Linnell et al. 2008). In this way, the independent LCIE assessments provide a valuable complement to the ongoing national processes.

Our last assessments covered the period 2006-2011 (Kaczensky et al. 2013; Chapron et al. 2014) but, at the time, did not include golden jackals. The current assessment is based on the period 2012-2016 and broadly follows the same methodology. Explicit distinctions are made between classification based on empirical data and expert opinion. The population definitions used in this report follow those proposed in (Ranc et al. 2018); areas whose presence category was defined by expert opinion were not assigned to a specific population, though.

Methods

The mapping approach follows the methods described in Chapron et al. (2014) and Kaczensky et al. (2013). It updates the published Species Online Layers (SPOIS) to the period 2012-2016.

In short, large carnivore presence was mapped at a 10x10 km ETRS89-LAEA Europe grid scale. This grid is widely used for the Flora-Fauna-Habitat reporting by the European Union (EU) and can be downloaded at: http://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2

The map encompasses the EU countries plus the non-EU Balkan states, Switzerland, Norway, and the Carpathian region of Ukraine. Presence in a grid cell was ideally mapped based on carnivore presence and frequency in a cell resulting in:

1 = Permanent (presence confirmed in >= 3 years in the last 5 years OR in >50% of the time OR reproduction confirmed within the last 3 years)

3 = Sporadic (highly fluctuating presence) (presence confirmed in <3 years in the last 5 years OR in <50% of the time)

5 = Expert-based presence (high confidence) (expert-based opinion; very suitable habitat near permanent presence areas)

6 = Expert-based presence (low confidence or unconfirmed records) (expert-based opinion; suitable habitat near presence areas or unconfirmed C3 records of jackal presence)

7 = Expert-based absence (high confidence) (jackal presence according to coarse-resolution hunting bag data but experts think, with high confidence, the species is not present)

8 = Expert-based absence (low confidence) (jackal presence according to coarse-resolution hunting bag data but experts think the species is not present)

Where grid cells were assigned different values between neighboring countries; the “disputed” cells were given the “higher” presence values e.g., a cell categorized as “sporadic” by one country and “permanent” by another was categorized as “permanent”. Data-based categories (1,3) were given priority over expert-based categories (5 through 8).

To assess the quality of carnivore signs we used the SCALP criteria developed for the standardized monitoring of Eurasian lynx (Lynx lynx) in the Alps (Molinari-Jobin et al. 2012):

Category 1 (C1): “Hard facts”, verified and unchallenged large carnivore presence signs (e.g., dead animals, DNA, verified camera trap images);

Category 2 (C2): Large carnivore presence signs controlled and confirmed by a large carnivore expert (e.g., trained member of the network), which requires documentation of large carnivore signs; and

Category 3 (C3): Unconfirmed category 2 large carnivore presence signs and all presence signs such as sightings and calls which, if not additionally documented, cannot be verified.

See Hatlauf and Böcker (2022) for best practices regarding golden jackal records.

Usage Notes

The data available consists of a shapefile at a 10 x 10 km resolution compiled for the period 2012-2016 for the Large Carnivore Initiative of Europe IUCN Specialist Group and for the IUCN Red List Assessment.

References

Boitani, L., F. Alvarez, O. Anders, H. Andren, E. Avanzinelli, V. Balys, J. C. Blanco, U. Breitenmoser, G. Chapron, P. Ciucci, A. Dutsov, C. Groff, D. Huber, O. Ionescu, F. Knauer, I. Kojola, J. Kubala, M. Kutal, J. Linnell, A. Majic, P. Mannil, R. Manz, F. Marucco, D. Melovski, A. Molinari, H. Norberg, S. Nowak, J. Ozolins, S. Palazon, H. Potocnik, P.-Y. Quenette, I. Reinhardt, R. Rigg, N. Selva, A. Sergiel, M. Shkvyria, J. Swenson, A. Trajce, M. Von Arx, M. Wolfl, U. Wotschikowsky and D. Zlatanova. 2015. Key actions for Large Carnivore populations in Europe. Institute of Applied Ecology (Rome, Italy). Report to DG Environment, European Commission, Bruxelles. Contract no. 07.0307/2013/654446/SER/B3

Ćirović, D., A. Penezić and M. Krofel. 2016. Jackals as cleaners: Ecosystem services provided by a mesocarnivore in human-dominated landscapes. Biological Conservation, 199: 51–55.

Chapron, G., Kaczensky, P., Linnell, J.D.C., von Arx, M., Huber, D., Andrén, H., López-Bao, J.V., Adamec, M., Álvares, F., Anders, O., Balčiauskas, L., Balys, V., Bedő, P., Bego, F., Blanco, J.C., Breitenmoser, U., Brøseth, H., Bufka, L., Bunikyte, R., Ciucci, P., Dutsov, A., Engleder, T., Fuxjäger, C., Groff, C., Holmala, K., Hoxha, B., Iliopoulos, Y., Ionescu, O., Jeremić, J., Jerina, K., Kluth, G., Knauer, F., Kojola, I., Kos, I., Krofel, M., Kubala, J., Kunovac, S., Kusak, J., Kutal, M., Liberg, O., Majić, A., Männil, P., Manz, R., Marboutin, E., Marucco, F., Melovski, D., Mersini, K., Mertzanis, Y., Mysłajek, R.W., Nowak, S., Odden, J., Ozolins, J., Palomero, G., Paunović, M., Persson, J., Potočnik, H., Quenette, P.-Y., Rauer, G., Reinhardt, I., Rigg, R., Ryser, A., Salvatori, V., Skrbinšek, T., Stojanov, A., Swenson, J.E., Szemethy, L., Trajçe, A., Tsingarska[1]Sedefcheva, E., Váňa, M., Veeroja, R., Wabakken, P., Wölfl, M., Wölfl, S., Zimmermann, F., Zlatanova, D. and Boitani, L. 2014. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science 346: 1517-1519.

Fenton, S., Moorcroft, P.R., Ćirović, D., Lanszki, J., Heltai, M., Cagnacci, F., Breck, S., Bogdanović, N., Pantelić, I., Ács, K. and Ranc, N. 2021. Movement, space-use and resource preferences of European golden jackals in human-dominated landscapes: insights from a telemetry study. Mammalian Biology, 101: 619–630.

Hatlauf, J. and Böcker, F. 2022. Recommendations for the documentation and assessment of golden jackal (Canis aureus) records in Europe. BOKU reports on wildlife research and willdife management 27. Ed: Institute of Wildlife Biology and Game Management (IWJ), University of Natural Resources and Life Sciences, Vienna. ISBN: 978-3-900932-94-7

Hatlauf, J., Bayer, K., Trouwborst, A. and Hackländer, K. 2021. New rules or old concepts? The golden jackal (Canis aureus) and its legal status

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.