The Important Bird and Biodiveristy Areas (IBA) Programme is a BirdLife International Programme to conserve habitats that are important for birds. These areas are defined according to a strict set of guidelines and criteria based on the species that occur in the area. The Important Bird Areas of Southern Africa directory was first published 1998 and identified within South Africa 122 IBAs. In September 2015 a revised IBA Directory was published by BirdLife South Africa. All these IBAs were objectively determined using established and globally accepted criteria. An IBA is selected on the presence of the following bird species in a geographic area: • Bird species of global or regional conservation concern; • Assemblages of restricted-range bird species; \ • Assemblages of biome-restricted bird species; and • Concentrations of numbers of congregatory bird species. For more information see: http://www.birdlife.org.za/conservation/importantbird-areas/documents-and-downloads

The updated layers can been accessed directly from the source from the 'Important Bird Areas Canada', official website. They have an interactive map, the IBA Database, downloadable resources, in KMZ format. At this location you can also help monitor bird populations at IBAs and contribute to ongoing research and maintenance of these valuable areas. Bird Studies Canada. (n.d.). Important Bird and Biodiversity Areas in Canada. Available at: https://www.ibacanada.ca/index.jsp?lang=en Russell J. and D. Fifield. (2001). Marine bird Important Bird Areas in northern Labrador: conservation concerns and potential strategies. Canadian Nature Federation, Bird Studies Canada, Natural History Society of Newfoundland and Labrador, 134pp

Data Updated: Feb 2020For commercial use of this dataset please visit https://www.ibat-alliance.org/ (note that IBAs are a subset of KBAs)The most important sites for birds are known as Important Bird Areas (IBAs). The IBA Programme of BirdLife International is a worldwide initiative aimed at identifying and protecting a network of sites, critical for the conservation of the world's birds. These sites were selected on the basis of the bird numbers and species complements they hold. IBAs are particularly important for species that congregate in large numbers, such as wintering and passage waterbirds and breeding seabirds. Many sites have also been identified for species of global, and European/EU conservation concern. This dataset contains IBA boundaries from Great Britain, Northern Ireland, the Isle of Man, and the Channel Islands. It is projected in WGS 1984. The dataset was updated in Feb 2020 to resolve a projection issue. More info

This is a GIS dataset containing spatial boundaries of Important Bird Areas (IBAs) in Wales. IBAs are a world-wide network of areas, identified using internationally agreed set of criteria, highlighting defined areas an being important globally for the conservation of bird populations. Important Bird and Biodiversity Areas (IBAs) are: - Places of international significance for the conservation of birds and other biodiversity - Recognised world-wide as practical tools for conservation - Distinct areas amenable to practical conservation action - Identified using robust, standardised criteria - Sites that together form part of a wider integrated approach to the conservation and sustainable use of the natural environment. This is an external third party dataset owned in the UK by the Royal Society for the Protection of Birds (RSPB) and licensed to NRW for internal use only.

Effective seabird conservation requires management of key locations for nesting, foraging, and migration. The identification of critical marine bird colonies and pelagic concentration areas has a varied history with many definitions applied. Important Bird Areas (IBAs) are based on an established program that uses standardized criteria to identify essential habitats, which are areas that hold a significant proportion of the population of one or more bird species. BirdLife International, in partnership with the National Audubon Society, developed standardized criteria defining Important Bird Areas, establishing a global “currency” for bird conservation. To qualify as a globally significant IBA, a proposed site must hold a significant number of a globally threatened species, or a significant percentage of a global population, as evidenced by documented, repeated observation of substantial congregations in an area. For full details on the methods used, please see our IBA report.This file has been updated to take into account additional species of significance within each IBA: for example, an IBA nominated at the global level for one species, but encompassing populations of continental or state importance for other species.

CDFW BIOS GIS Dataset, Contact: Andrea Jones, Description: Important Bird Areas (IBA) as described and analyzed by Cooper (2004), Important Bird Areas of California. Each IBA entry supports a list of sensitive species reported to occur within the IBA and a ranking score based on criteria described in the metadata.

Le Important Bird Areas o IBA, sono delle aree che rivestono un ruolo chiave per la salvaguardia degli uccelli e della biodiversità, la cui identificazione è parte di un progetto a carattere mondiale, curato da BirdLife International. Il progetto IBA nasce dalla necessità di individuare dei criteri omogenei e standardizzati per la designazione delle ZPS. Le IBA sono state utilizzate per valutare l adeguatezza delle reti nazionali di ZPS designate negli Stati membri. Per essere riconosciuto come IBA, un sito deve possedere almeno una delle seguenti caratteristiche: ospitare un numero significativo di individui di una o più specie minacciate a livello globale; fare parte di una tipologia di aree importante per la conservazione di particolari specie (es. zone umide); essere una zona in cui si concentra un numero particolarmente alto di uccelli in migrazione. La risorsa comprende l'inventario del 2002 delle IBA terrestri, aggiornato ?nel 2016 in base agli studi sulla Berta Maggiore portati avanti tra il 2008 e il 2014 che hanno condotto alla individuazione di 4 nuove IBA Marine e successivamente nel 2019, al fine di risolvere alcune discrepanze con i confini delle ZPS e con gli elementi naturali ed antropici del paesaggio.

Urban green spaces are closely related to the abundance and biodiversity of birds by providing important habitats and together contribute to ecosystem health. This project aims to guide the University of British Columbia Botanical Garden to create Bird-friendly green spaces by using LiDAR data to analyze and map UBCBG's bird habitat suitability and create a 3D digital twin model of UBCBG in the open source game engine Minetest to increase 3D visualization and aid in landscape planning. By extracting the Canopy Height Model (CHM) using LiDAR data and performing individual tree segmentation, the derived metrics were used to identify trees with the highest bird habitat suitability index. The results showed that the suitability index ranges from -0.0016 to 0.5187, with a mean value of 0.2051. There are 68 trees with high suitability above the 0.4 intervals which have significance to bird populations and are worthy of being protected, accounting for only 3.38% of the total trees. They usually have a low vertical complexity index and foliage height diversity but are characterized by very tall trees with relatively large tree crowns. The Digital Elevation Model (DEM), Canopy Height Model (CHM) generated by LiDAR data were visualized in Minetest's UBCBG's 3D digital twin model using real terrain mod as topography and vegetation layers, while bird habitat suitability was used to symbolize the tree canopy layer. This study is highly relevant for landscape adaptation and planning in conjunction with other management considerations to support bird-friendly green spaces. The digital twin model can be used for educational and promotional purposes, and for landscape planning and aesthetic design with the consideration of bird conservation.

Original provider: Caroline Fox, Dalhousie University and Raincoast Conservation Foundation

Dataset credits: Caroline Fox, Dalhousie University and Raincoast Conservation Foundation

Abstract: Associated publication abstract: Increasingly disrupted and altered, the world’s oceans are subject to immense and intensifying anthropogenic pressures. Of the biota inhabiting these ecosystems, marine birds are among the most threatened. For conservation efforts targeting marine birds to be effective, quantitative information relating to their at-sea density and distribution is typically a crucial knowledge component. In this study, we generated predictive machine learning ensemble models for 13 marine bird species and 7 groups (representing 24 additional species) in Canada’s Pacific coast waters, including several species listed under Canada’s Species at Risk Act. Predictive models were based on systematic marine bird line transect survey information collected in spring, summer, and fall on Canada’s Pacific coast (2005−2008). Multiple Covariate Distance Sampling (MCDS) was used to estimate marine bird density along transect segments. Spatial and temporal environmental predictors, including remote sensing information, were used in model ensembles, which were constructed using 4 machine learning algorithms in Salford Systems Predictive Modeler v7.0 (SPM7): Random Forests, TreeNet, Multivariate Adaptive Regression Splines, and Classification and Regression Trees. Predictive models were subsequently combined to generate seasonal and overall predictions of areas important to marine birds based on normalized marine bird species or group richness and densities. Our results employ open access data sharing and are intended to better inform marine bird conservation efforts and management planning on Canada’s Pacific coast and for broader-scale geographic initiatives across North America and elsewhere.

Supplemental information: Marine bird line-transect survey information collected using Distance Sampling in coastal British Columbia, Canada (2005-2008) is provided in three forms: (1) raw, unadjusted marine bird sightings; (2) for a subset of species, marine bird density estimates along 1km transect segments using Multiple Covariates Distance Sampling (MCDS), and; (3) for a subset of species, surface density estimates per ~14km2 hexagon using machine learning ensemble modeling. For data products 2 and 3, the marine bird subsets were restricted to species sighted in sufficient numbers for analysis. Surveys were completed by Raincoast Conservation Foundation.

1. Raw data: raw, unadjusted sighting of marine bird species on water and in flight. Attributes such as column labels are included in the attributes definition section.

Note that several species alpha codes are non-standard, due to grouping of species identifications (e.g., large gulls and dark shearwaters).

ANMU = Ancient Murrelet ANMUf = Ancient Murrelet family (varying #s of parents and chicks, or just chicks) BAEA = Bald Eagle BEKI = Belted Kingfisher BFAL = Black-footed Albatross BLKI = Black-legged Kittiwake BLOY = Black Oystercatcher BLSC = Black Scoter BLTU = Black Turnstone BOGU = Bonaparte's Gull BRAC = Brandt's Cormorant BRAN = Brant Goose BUFF = Bufflehead Duck BULS = Buller's Shearwater CAAU = Cassin's Auklet CAGU = California Gull CANG = Canada Goose COLO = Common Loon COME = Common Merganser COMU = Common Murre COMUf = Common Murre family (parent with chick, or just chicks) CORA = Common Raven DARK = Sooty Shearwater, Short-tailed Shearwater, Flesh-footed Shearwater DCCO = Double-crested Cormorant DEJU = Dark-eyed Junco DUNL = Dunlin FTSP = Fork-tailed Storm Petrel GBHE = Great Blue Heron GWGU = Glaucous-winged Gull HADU = Harlequin Duck HETHGU = Herring Gull/Thayer's Gull HOGR = Horned Grebe HOPU = Horned Puffin LAAL = Laysan Albatross LEFTSP = mixed flock Fork-tailed and Leach's Storm-petrels LESP = Leach's Storm Petrel LTDU = Longtail Duck LTJA = Long-tailed Jaeger MALL = Mallard Duck MAMU = Marbled Murrelet MEGU = Mew Gull NOCR = Northwestern Crow NOFU = Northern Fulmar NSHO = Northern Shoveler OSPR = Osprey PAJA = Parasitic Jaeger PALO = Pacific Loon PECO = Pelagic Cormorant PFSH = Pink-footed Shearwater PIGU = Pigeon Guillemot POJA = Pomarine Jaeger RBME = Red-breasted Merganser RHAU = Rhinoceros Auklet RNGR = Red-necked Grebe RNPH = Red-necked Phalarope RTLO = Red-throated Loon RUHU = Rufous Hummingbird SAGU = Sabine's Gull SNGO = Snow Goose STAL = Short-tailed Albatross SUSC = Surf Scoter THGU = Thayer's Gull TOWA = Townsend's Warbler TRES = Tree Swallow TUPU = Tufted Puffin TUPUf = Tufted Puffin family (parent with chick) WEGR = Western Grebe WEGU = Western Gull WHIM = Whimbrel WWSC = White-winged Scoter YBLO = Yellow-billed Loon

UNAL = Unidentified Alcid
UNCO = Unidentified cormorant
UNDU = Unidentified ducks in the distance
UNGE = Unidentified Geese in the distance
UNGO = Unidentified Goldeneye
UNGR = Unidentified Grebe
ULGU = Unidentified Larus Gull
UNJA = Unidentified Jaeger
UNLO = Unidentified Loon
UNSO = Unidentified Scoter
UNSW = Unidentified Shearwater
UNSH = Unidentified Shorebirds
UNST = Unidentified Storm-petrel
UNTE = Unidentified Tern
UNTU = Unidentified Turnstone

1. Marine bird density estimates along 1km transect segments using Multiple Covariates Distance Sampling (MCDS).

Note that several species alpha codes are non-standard, due to grouping of species identifications (e.g., large gulls and dark shearwaters).

ANMU = Ancient Murrelet BFAL = Black-footed Albatross CAAU = Cassin's Auklet COMU = Common Murre CORM = Cormorants (Brandt's, Double-crested, Pelagic) DARK = Dark shearwaters (Flesh-footed, Short-tailed, Sooty) FTSP = Fork-tailed Storm-petrel GREB = Grebes (Horned, Red-necked, Western) LESP = Leach's Storm-petrel lgGULL = large Larus spp. gulls (California, Glaucous-winged, American, Thayer's) LOON = Loons (Yellow-billed, Common, Red-throated, Pacific) MAMU = Marbled Murrelet NOFU = Northern Fulmar PFSH = Pink-footed Shearwater PIGU = Pigeon Guillemot RHAU = Rhinoceros Auklet RNPH = Red=necked Phalarope SCOT = Scoters (Black, White-winged, Surf) smGULL = small gulls (Black-legged Kittiwake, Bonaparte's, Mew, Sabine's) TUPU = Tufted Puffin

Field names represent, using ANMU and BFAL as the examples:

• first few fields represent summary fields (i.e., FID, Shape)
• SEGID = unique line transect segment ID. Can use this field to join across species files.
• VOYAGE = On planned transect (T) or on passage (P), which are unplanned transects.
• SPEED = vessel speed (knts).
• MO = Month, numeric (1-12).
• SegLength = Segment length. Most should = 1 km, but shorter segments have been retained.
• Season = Spring (april, may, june), Summer (August), Fall (October, November).
• Point_X and Point_Y = x and y coordinates using BC Albers.
• Effort = Same as SegLength.
• DATE = year-month-day.
• YEAR = year.
• DAY_YR = Day of the year, beginning with with January 1 = 1.
• AREA = Segment length (km) X perpendicular distance (km) from boat for that particular species (unit = km2; identified using MCDS Distance Analysis).
• ANMUw_D (all other examples BIRDw_D) = estimated density of Ancient Murrelets along the transect segment (including family groups, see below). Lowercase "w" = birds on water.
• ANMUf_D = exception for ANMU family groups. Lowercase "f" = family groups on water (parent(s) with flightless chicks or flightless chicks alone).
• BFALs_D (all other examples BIRDs_D) = estimated density of Black-footed ALbatrosses along the transect segment. Lowercase "s" = birds in flight. Note that flying bird density estimates should be used and interpreted with caution.

1. Density estimations per hexagon (approx. 14km2):

Shape file name represents the bird species (e.g., ANMU = Ancient Murrelet) plus "w" (w = density estimates of birds on water only) or "sw" (sw = density estimates of combination of birds in flight and on water).

Note that several species alpha codes are non-standard, due to grouping of species identifications (e.g., large gulls and dark shearwaters).

ANMU = Ancient Murrelet BFAL = Black-footed Albatross CAAU = Cassin's Auklet COMU = Common Murre CORM = Cormorants (Brandt's, Double-crested, Pelagic) DARK = Dark shearwaters (Flesh-footed, Short-tailed, Sooty) FTSP = Fork-tailed Storm-petrel GREB = Grebes (Horned, Red-necked, Western) LESP = Leach's Storm-petrel lgGULL = large Larus spp. gulls (California, Glaucous-winged, American, Thayer's) LOON = Loons (Yellow-billed, Common, Red-throated, Pacific) MAMU = Marbled Murrelet NOFU = Northern Fulmar PFSH = Pink-footed Shearwater PIGU = Pigeon Guillemot RHAU = Rhinoceros Auklet RNPH = Red=necked Phalarope SCOT = Scoters (Black, White-winged, Surf) smGULL = small gulls (Black-legged Kittiwake, Bonaparte's, Mew, Sabine's) TUPU = Tufted Puffin

Field names represent, using ANMUw as the example:

• first few ields represent summary fields (i.e., FID, Shape and Id)
• HexagonID = unique hexagon cell ID. Can use this field to join across species files.
• X_coord and Y_Coord = should be self explanatory.
• spr_ANMUw = estimated Ancient Murrelet on water density estimates (birds/km2) in spring (April 2007, May 2007, June 2008)
• sum_ANMUw = same as above, except in summer (August 2005, 2006 and 2008)
• fal_ANMUw = same as above, except in fall (October and November 2007)
• ANMUw_AnAv = average across spring, summer, and fall density estimates

description: The adopted Standards for Bird-Safe Buildings explains the documented risks that structures present to birds. Over thirty years of research has proven the risk to be biologically significant for certain bird species. Recent studies have determined that annual bird fatalities in North America from window collisions may be as high as 1 billion birds per year or 1-5% of all birds. While the facts are staggering, the solutions are within reach. The majority of these deaths are foreseeable and avoidable. The document summarizes proven successful remedies such as window treatments, lighting design, and lighting operation. The bird refuge maps out areas of particular risk to birds. These areas are within 300ft of: open water, inland water bodies greater than 2 acres in size, open space greater than 2 acres, the shoreline. For more information visit the Standards for Bird-Safe Buildings web site: http://www.sf-planning.org/index.aspx?page=2506 A PDF map of the refuge is available here: http://www.sf-planning.org/ftp/files/publications_reports/library_of_cartography/Urban_Bird_Refuge_Poster.pdf The data is in zipped GIS shapefile format.; abstract: The adopted Standards for Bird-Safe Buildings explains the documented risks that structures present to birds. Over thirty years of research has proven the risk to be biologically significant for certain bird species. Recent studies have determined that annual bird fatalities in North America from window collisions may be as high as 1 billion birds per year or 1-5% of all birds. While the facts are staggering, the solutions are within reach. The majority of these deaths are foreseeable and avoidable. The document summarizes proven successful remedies such as window treatments, lighting design, and lighting operation. The bird refuge maps out areas of particular risk to birds. These areas are within 300ft of: open water, inland water bodies greater than 2 acres in size, open space greater than 2 acres, the shoreline. For more information visit the Standards for Bird-Safe Buildings web site: http://www.sf-planning.org/index.aspx?page=2506 A PDF map of the refuge is available here: http://www.sf-planning.org/ftp/files/publications_reports/library_of_cartography/Urban_Bird_Refuge_Poster.pdf The data is in zipped GIS shapefile format.

Abstract The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived. This dataset contains the water-dependent asset list for the Clarence Morton bioregion. The Asset list is stored in an MS Access database and there is also an element list ESRI polygon shapefile. The…Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived. This dataset contains the water-dependent asset list for the Clarence Morton bioregion. The Asset list is stored in an MS Access database and there is also an element list ESRI polygon shapefile. The Bioregional Assessments methodology (Barrett et al., 2013) defines a water-dependent asset as a spatially distinct, geo-referenced entity contained within a bioregion with characteristics having a defined cultural indigenous, economic or environmental value, and that can be linked directly or indirectly to a dependency on water quantity and/or quality. Purpose For creation of asset list for bioregional assessment. Dataset History Under the BA program, a first Pass Element List is developed for each defined bioregional assessment area. Water dependent assets identified by Catchment Management Authorities are supplemented with additional elements from datasets held by the Australian and state/territory governments. The element list is a geospatial database that contains spatial data (GIS) files that provide the location for each individual element. The spatial data contains limited attribution but can be joined to the related source data table in the AssetList (joins based on ElementID). This ancillary data is the complete set of data supplied for the element by the source. For more information about field names, values and codes check documents on the _AnR_CurrentData\AnR_Documentation directory. The elements in the database have been sourced through various means. Initially, data was provided by Catchment Management Authorities (CMA) having been funded by the Office of Water Science to identify elements within their catchment areas to use as an input to the Bioregional Assessment Programme. Further specific information about this process can be provided upon request. Additional elements were identified by the Environmental Resources Information Network (ERIN, Australian Government Department of the Environment) based on various datasets held by the Australian Government and relevent state/territory governments and other custodians. After compiling the element list, each element was classified by ERIN into a Group, Subgroup and Class using the classification scheme outlined in the Element and Receptor Methodology (Barrett et al., 2013). This is a preliminary classification and should be checked and updated by the BA project teams. The dataset is comprised of elements that geographically intersect the Gloucester preliminary assessment extent (PAE). Spatial data have not been clipped to the assessment extent. In all cases, the full extent of features that extend beyond the boundaries of the assessment extent have been included. Spatial data have been incorporated into the database as provided. hence a single element may be represented by a single, discrete spatial unit (polygon, line or point), or a number of discrete spatial locations (e.g. as multipart polygons). ERIN has not combined like elements, nor exploded multipart elements. It should be noted that a 'formal' water dependence test has not been undertaken on this data. It is assumed that the NRM regional offices have provided elements only if they are 'water dependent', and that features within the datasets provided by ERIN are also 'water dependent', or potentially water dependent. The history of this dataset: 2/07/2014 Initial database. 15/08/2014 Initial database with new WSP assets Lineage: Compiled for the Office of Water Science (OWS) Bioregional Assessment Programme. Refer to associated documentation: AnR data description 20130925.doc Source datasets: -Compiled for OWS Bioregional Assessments. Refer to associated AnR data description documentation. Source datasets: Source code: WAIT: Northern Rivers, South East Queensland Description: Assets identified by the CMAs/NRMs. Custodian: Northern Rivers; South East Queensland; OWS/ERIN Source code: DIWA Description: Important wetlands from the Directory of Important Wetlands in Australia. Custodian: Department of the Environment Source code:Ramsar Description: Wetlands of International Importance (Ramsar Wetlands) Custodian: Department of the Environment Notes: RESTRICTED FOR USE WITHIN DEPARMENT ONLY for the data in VIC, SA, TAS and ACT Source code: CAPAD Description: Compiled information on protected areas from state and territory Governments and other protected area managers, published in the Collaborative Australian Protected Area Database (CAPAD). Identifies a number of protected areas and their components within the PAE. Custodian: Department of the Environment Notes: RESTRICTED FOR USE WITHIN DEPARMENT ONLY Source code: GDEsub Description: identifies components of ecosystems that may rely on the subsurface presence of groundwater (includes vegetation ecosystems) within the drainage basins occurring within the PAE. Custodian: BoM Notes: Likely to contain spatial overlaps with other assets Source code: GDEsur Description: identifies components of ecosystems that may rely on the surface expression of groundwater witihn the PAE. Custodian: BoM Notes: Likely to contain spatial overlaps with other assets Source code: IBA Description: Important Bird Areas (IBAs) are sites of global bird conservation importance. Each IBA meets one of four global criteria used by BirdLife International. Identifies 13 important bird areas occurring on floodplains and lakes within the PAE. Custodian: Birds Australia Threatened Ecological Communities Source code: TEC Description: Modelled "known" and "likely" distributions of threatened ecological communities listed under the Environment Protection and Biodiversity Conservation (EPBC) Act 1999. TECs within the CLM PAE Custodian: Department of the Environment Notes: RESTRICTED FOR USE WITHIN DEPARMENT ONLY Threatened Species Source code: Species Description: Modelled "known" and "likely" distributions of species of national environmental significance as listed under the EPBC Act. Custodian: Department of the Environment Notes: species habitat distributions provided include only those areas that intersect the PAE, and do not represent the total known/likely distributions of each species. RESTRICTED FOR USE WITHIN DEPARMENT ONLY Natural, Historic and Indigenous Heritage Places A number of different lists andregisters exist of natural, historic and Indigenous heritage places throughout Australia. These are not comprehensive lists of heritage places, but lists of the places that have been identified and recorded up to the present time. The following registers include places which may be considered as assets under the Bioregional Assessments Program: Source code: CHL Description:Commonwealth Heritage List. Natural, historic and Indigenous places of heritage significance owned or controlled by the Australian Government Source code: NatHeritage Description: National Heritage List. Natural, historic and Indigenous places that are of outstanding national heritage value to the Australian nation. Source code: RNE Description: Register of the National Estate Archive of information about more than 13,000 places throughout Australia. Custodian: Department of Environment Source code: WHA Description: World Heritage Areas The World Heritage Convention aims to promote cooperation among nations to protect heritage from around the world that is of such outstanding universal value that its conservation is important for current and future generations. Custodian: Department of Environment Dataset Citation Bioregional Assessment Programme (2014) Version 02 Asset list for Clarence Morton 8/8/2014 - ERIN ORIGINAL DATA. Bioregional Assessment Derived Dataset. Viewed 10 July 2017, http://data.bioregionalassessments.gov.au/dataset/1f9e74b4-a3b7-4693-acb7-ae29653b6392. Dataset Ancestors Derived From Queensland QLD - Regional - NRM - Water Asset Information Tool - WAIT - databases Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013 Derived From Matters of State environmental significance (version 4.1), Queensland Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From National Groundwater Information System (NGIS) v1.1 Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Queensland QLD Regional CMA Water Asset Information WAIT tool databases RESTRICTED Includes ALL Reports Derived From QLD Petroleum Leases, 28/11/2013 Derived From Natural Resource Management (NRM) Regions 2010 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From CLM - 16swo NSW Office of Water Surface Water Offtakes - Clarence Moreton v1 24102013 Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From QLD Dept of Natural Resources and Mines, Surface Water Entitlements 131204 Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From Ramsar Wetlands of Australia Derived From GEODATA TOPO 250K Series 3 Derived From NSW Catchment

Hexagonal framework: There are many datasets that can go into a regional greenprint. In order for them to be used most effectively, they need to be organized in a manner that allows queries across the data. A regular hexagonal grid shapefile was chosen to aggregate the many datasets. The hexagons were 10 hectares (24.7 acres) in extent. Aggregating the data in a shapefile such as this also enables analysis using Marxan optimization software. Ecological and infrastructure data were aggregated as fields within the shapefile.Greenprint data:Greenprint data were identified through team members’ knowledge of available data,internet searches, and discussions with stakeholders and others. These were downloaded or otherwise acquired and added to the hexagonal database (see below for specific sampling methods for each greenprint element).Connectivity. Four connectivity assessments were included in the greenprint: California Essential Habitat Connectivity (CEHC; Spencer et al. 2010), Bay Area Critical Linkages (BACL; Penrod et al. 2013), a Central Coast conservation network design (Thorne; Thorne et al. 2006), and a Central Valley conservation network design (Huber; Huber et al. 2010). CEHC is a relatively coarse‐scale, statewide analysis of important connectivity areas and covers the full study area. BACL used many of the same methodologies as CEHC, but is finer‐scale. However, it does not cover San Luis Obispo and Santa Barbara counties. Thorne uses a different methodology than the previous analysis; it covers the full study area. Finally, Huber only addresses the easternmost sections of the study area. It depicts areas of connectivity potentially linking the Central Coast and Central Valley ecoregions. In addition to these terrestrial datasets, a fish passage barrier was included in order to address aquatic connectivity issues. The California Fish Passage Assessment Database Project (PAD) documents the location and other details of barriers to fish passage on waterways across California. Hexagons were attributed as belonging to these datasets if their centroid was located within the boundary.Critical Habitat. U.S. Fish and Wildlife Service (USFWS) has delineated Critical Habitat for 29 federally‐listed species within the study area (Table 1). This is less than one third of the listed species in the study area. Critical Habitat data are available for both terrestrial and aquatic species, and taxa include plants, mammals, birds, reptiles and amphibians, fish, and invertebrates. Hexagons were attributed as belonging to these datasets if their centroid was located within the boundary.ACE II. California Department of Fish and Wildlife (CDFW) developed a database with numerous biodiversity measurements for the state of California. These Areas of Conservation Emphasis (ACE II) is a hexagonal dataset measuring species richness, rarity, and other biodiversity metrics. Weighted rarity scores from ACE II were assigned to hexagons based on centroid location.Other conservation priorities. The Nature Conservancy’s ecoregional priorities and Audubon society’s Important Bird Areas (IBA) were included as other conservation priorities covering the full study region.Land cover. Unfortunately, there is no single, fine scale land cover dataset covering the full study region. Land cover information was combined from several to create an overall land cover dataset. CalVeg was used as the base layer. In areas that were not covered by CalVeg, we used a combination of Landfire land cover data and FRAP land cover data. In addition, the Nature Conservancy provided fine scale land cover data for the Salinas and San Benito river riparian areas.Habitat models. The team compiled existing spatial data on the locations of state and federally‐listed species (threatened and endangered). These were selected from the statewide California Natural Diversity Database (CNDDB). Points selected were those that were listed as “Presumed Extant” and from 1980‐present. Each of these points was buffered by two and four miles. Using the California Wildlife Habitats Relationships model (CWHR), land cover types scored as “High” for use by that species were selected from within the buffered points.Watersheds. Hexes were attributed for their inclusion in HUC 8, 10, and 12 digit watersheds.Counties. Hexes were attributed for their inclusion in the six counties in the planning region.Existing conservation. California Protected Areas Database (CPAD) and the National Conservation Easement Database (NCED) were used to identify existing conservation lands.Local datasets. A total of 54 local, site‐specific datasets were included in the database. Examples include City of Santa Barbara biological features, Santa Cruz County blueprint, and mountain lion collar data.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains polygon, line shapefiles and point representing thee Hunter terrestrial and riverine Landscape Classes respectively. Between v02 and v03 some reformatting has taken place to make it suitable for use in the BAIP. Namely, the singlepart point landscape class has been re-issued as a mutlipoint shapefile. Also the singlepart versions of the polygon and line landscape classes are omitted in this dataset to avoid confusion. Lastly lc_id fields have been re-numbered so that each landscape class has a uniqiue ID withing the subregion. Note that because of this lc_ids in previous versions are obsolete and should be ingnored. The lc_ids now run as follows: polygon landscape classes 1- 21 line landscape classes 22 - 25 point landscape class 26 This version contains an additional shapefile (HUN_Forested_Wetlands_riverine_only_within_ZoPHC.shp) which represents the Landscape class "Forested Wetlands" extracted for the riverine sections within the Zone of Potential Hydrological Change. Dataset History The terrestrial Landscape class polygons are sourced directly from the from input polygon source datasets and clipped to the Hunter PAE (which is the same as the subregion boundary). GDE landscape classes derive directly from the source NSW OoW GDE layer's Keith Form attribute, though the "Riverine Forests" Keith Forms are combined with the "Forested Wetlands" LC and the "Mangrove Swamps" and "Saltmarshes" Keith Forms are from this source are not used . Rather "Saline Wetlands" and "Seagrass" LCs are sourced from the Marcophytes input source data. The remaining LCs in the River and Estuarine LC_Group are sourced from the NSW_Wetlands 2006 data The Economic Landuse LC_Group terrestrial LC polygons are mainly sourced from the ACLUM catchment landuse from the PRIMARY V7 classification, and retain the source class names except that "1 Conservation and natural environments" is renamed the "non-GDE Native Vegtation" LC. A further exception is the "Plantation and Production Forestry" LC. This is derived from the SECONDARY_V7 classification where: "SecondV7" = '2.2 Production forestry' OR "SecondV7" = '3.1 Plantation forestry' OR "SecondV7" = '4.1 Irrigated plantation forestry' the 4 input polygon layers were formatted and UNIONed. where there was overlap the Landscape class was taken in the following order of precedence: 1 DPI Estuarine Macrophytes (for Saline wetlands and Seagrass LCs) 2 OoW GDE mapping (for GDE group LCs) 3 NSW Wetlands 2006 (for remaining Coastal Lakes and Estuaries Group LCs) 4 ACLUM 2014 (for Plantation and production forestry LC) 5 GHM Vegetation mapping ( for nonGDE Native vegetation LC) 6 ACLUM 2014 (for remaining Economic group LCs) The Riverine LC lines are derived directly from the Perreniality source dataset The point Spring Landscape classes are sourced from the Assets database where the centroids of the 4 Spring Asset polygons were taken. This version contains an additional shapefile (HUN_Forested_Wetlands_riverine_only_within_ZoPHC.shp) which represents the Landscape class "Forested Wetlands" extracted for the riverine sections within the Zone of Potential Hydrological Change. Dataset Citation Bioregional Assessment Programme (2016) HUN Landscape Classification v04. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/38e3e4e1-e2ba-457e-960a-97fed0b716ec. Dataset Ancestors Derived From Bioregional_Assessment_Programme_Catchment Scale Land Use of Australia - 2014 Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013 Derived From HUN Landscape Classification v02 Derived From Travelling Stock Route Conservation Values Derived From NSW Wetlands Derived From Climate Change Corridors Coastal North East NSW Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From Climate Change Corridors for Nandewar and New England Tablelands Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Fauna Corridors for North East NSW Derived From Asset database for the Hunter subregion on 27 August 2015 Derived From Hunter CMA GDEs (DRAFT DPI pre-release) Derived From Estuarine Macrophytes of Hunter Subregion NSW DPI Hunter 2004 Derived From Geofabric Surface Network - V2.1.1 Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Camerons Gorge Grassy White Box Endangered Ecological Community (EEC) 2008 Derived From Spatial Threatened Species and Communities (TESC) NSW 20131129 Derived From Asset database for the Hunter subregion on 24 February 2016 Derived From Natural Resource Management (NRM) Regions 2010 Derived From Gosford Council Endangered Ecological Communities (Umina woodlands) EEC3906 Derived From NSW Office of Water Surface Water Offtakes - Hunter v1 24102013 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From Asset list for Hunter - CURRENT Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From Northern Rivers CMA GDEs (DRAFT DPI pre-release) Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From Ramsar Wetlands of Australia Derived From Native Vegetation Management (NVM) - Manage Benefits Derived From GEODATA TOPO 250K Series 3 Derived From NSW Catchment Management Authority Boundaries 20130917 Derived From Geological Provinces - Full Extent Derived From Hunter subregion boundary Derived From NSW Office of Water Surface Water Licences Processed for Hunter v1 20140516 Derived From Groundwater Economic Elements Hunter NSW 20150520 PersRem v02 Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From Atlas of Living Australia NSW ALA Portal 20140613 Derived From Bioregional Assessment areas v03 Derived From Greater Hunter Native Vegetation Mapping with Classification for Mapping Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From GW Element Bores with Unknown FTYPE Hunter NSW Office of Water 20150514 Derived From Climate Change Corridors (Dry Habitat) for North East NSW Derived From Groundwater Entitlement Hunter NSW Office of Water 20150324 Derived From Asset database for the Hunter subregion on 20 July 2015 Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From NSW Office of Water GW licence extract linked to spatial locations for NorthandSouthSydney v3 13032014 Derived From Asset database for the Hunter subregion on 16 June 2015 Derived From Australia World Heritage Areas Derived From HUN River Perenniality v01 Derived From Lower Hunter Spotted Gum Forest EEC 2010 Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From Greater Hunter Native Vegetation Mapping Derived From Threatened migratory shorebird habitat mapping DECCW May 2006 Derived From NSW Office of Water - GW licence extract linked to spatial locations for North and South Sydney v2 20140228 Derived From HUN AssetList Database v1p2 20150128 Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From Climate Change Corridors (Moist Habitat) for North East NSW Derived From Operating Mines OZMIN Geoscience Australia 20150201 Derived From NSW Office of Water - National Groundwater Information System 20141101v02 Derived From Asset database for the Hunter subregion on 22 September 2015 Derived From Groundwater Economic Assets Hunter NSW 20150331 PersRem Derived From Australia - Species of National Environmental Significance Database Derived From Monitoring Power Generation and Water Supply Bores Hunter NOW 20150514 Derived From Bioregional Assessment areas v01 Derived From Bioregional Assessment areas v02 Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From Asset database for the Hunter subregion on 12 February 2015 Derived From NSW Office of Water Groundwater Entitlements Spatial Locations Derived From NSW Office of Water Groundwater Licence Extract, North and South Sydney - Oct 2013 Derived From Commonwealth Heritage List Spatial Database (CHL) Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release) Derived From Darling River Hardyhead Predicted Distribution in Hunter River Catchment NSW 2015 Derived From Groundwater Dependent Ecosystems supplied by the NSW Office of Water on 13/05/2014

The EU Birds Directive (79/409/EEC) requires designation of SPAs for: listed rare and vulnerable species; regularly occurring migratory species, such as ducks, geese and waders; wetlands, especially those of international importance, which attract large numbers of migratory birds each year. (Internationally important means that 1% of the population of a species uses the site, or more than 20,000 birds regularly use the site.) This is a national dataset.

Agriculture crops in the Imperial Valley of California provide valuable habitat for many resident and migratory birds and are a very important component of the Salton Sea Ecosystem (Patten et. al. 2003), but detailed information regarding avian species use, distribution and abundance is lacking. In 2006 the California Department of Fish and Game, the Salton Sea Authority, and the USGS initiated a monthly survey of birds using Imperial Valley agriculture fields to provide information regarding avian species composition and use. Driving transects were originally delineated on a map as one continuous transect projected to cover a good representation of a subset of the entire Imperial Valley agriculture area. The original transect included mostly highways with heavy traffic and high speeds. It was decided for safety reasons that slower speed limits and lighter traffic areas would be more suitable to this type of survey. The result is two transects of roughly the same distance. The west transect is west of Highway 111, beginning at the junction of Highway 111 and Sinclair Road and ending on Harris Road and Butters Road. The east transect is east of Highway 111, beginning at the junction of Highway 111 and Sinclair Road. and ending on Harris Road and Butters Road. See map for details http://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=4964 . Surveys began 30 minutes after sunrise on the west transect. The east transect was completed on the next consecutive day commencing at 30 minutes after sunrise. Driving speed was limited to 25 miles per hour where possible. When available, the survey was conducted with a driver and an observer. Many times the driver was alone and made all of the observations. As the vehicle followed the transect, fields on either side of the road were scanned for birds. When birds were observed in a field the vehicle was stopped and a GPS location was taken using a Magellan Sport Trac Topo. All bird species were counted using Steiner Merlin 10 x 42 binoculars and/or Nikon Fieldscope ED 20x45 zoom. Passerines were excluded from counts. Bird numbers were estimated by first counting all members of a species within one view of the binoculars or the spotting scope. This was done three to five times and an average number per optic view determined. The average value was then multiplied by the number of optic views required to cover the concentration of birds at that site. The resulting number was recorded as the estimated number of birds present. All members of a species present at an observation site were totaled to provide an estimated number of that species present at that site. Crop information, time of observation, and notes about the condition of the field were recorded for the field in which birds were detected The maximum distance birds were observed varied based on weather, angle of the sun with respect to the observer, or distance to the edge of the field birds were observed in. As a general estimate of mean maximum distance I used 400M. This distance is based on a visual estimate of the distance from a road to the far edge of a field in the Imperial Valley. Many fields had late stage crops that were tall and dense and subsequently could not be accurately surveyed from a vehicle. No effort was used to count and remove these crops from the survey area. If birds were observed flying out of or into these late stage crops information was recorded on only the birds seen in flight.

The SACS Tier 1 Environmental and Cultural Resources Exposure Index is a percentage-based aggregation of three Tier 1 Risk Assessment exposure indices: Environmental Index 30%, Cultural Index 40%, and Habitat Index 30%.

The SACS Tier 1 Environmental Exposure Index depicts a weighted aggregate of national GIS datasets related to administration and identification of high value environmental resources, within the SACS study area. The input datasets were identified during the USACE North Atlantic Coast Comprehensive Study (NACCS), with a few additional datasets in the Audubon Important Bird Areas, and National Estuarine Research Reserves. These data were clipped to the SACS study area and weighted consistently with the NACCS effort: Page 109 https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf. These data were then converted to a uniform grid based on the NACCS weighting, and summed. The resulting raster was then normalized between 0 and 1, with 1 containing the highest value, or the most overlapping datasets. The resulting index is displayed with a stretch symbology, percent clip (min -.5 max .5). This grid resolution is 30m.

The SACS Tier 1 Habitat Exposure Index depicts a weighted aggregate of national GIS datasets related to high value habitat areas, within the SACS study area. The input datasets were identified during the North Atlantic Coastal Comprehensive study, with an addition of the UNEP WCMC identified global seagrass locations. These data were clipped to the SACS study area, weighted consistently with the NACCS effort: Page 109 https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf. These data were then converted to a uniform gird based on the NACCS weighting, and summed. The resulting raster was then normalized between 0 and 1, with 1 containing the highest value, or the most overlapping datasets. The resulting index is displayed with a stretch symbology, percent clip (min -.5 max .5). This grid resolution is 30m.

The SACS Tier Cultural Resources Exposure Index depicts a weighted aggregated of national GIS datasets related to cultural resources within the SACS study areas. The input datasets include the National Register of Historic Places as well as the USGS Protected Areas Database– Historic or Cultural Areas. These national datasets were clipped to the SACS study area and weighted consistently with the USACE NACCS effort: Page 109 https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf. These vector data were then converted to a uniform grid based on the NACCS weighting, and summed. The resulting raster was then normalized between 0 and 1, with 1 containing the highest value, or the most overlapping datasets. The resulting index is displayed with a 4-class equal interval symbology to be able identify point features that have been converted to grid pixels. This grid resolution is 30m.

Input datasets, weighting, and download locations are referenced as follows:

Environmental

USFWS - https://www.fws.gov/GIS/data/national/index.html Coastal Barrier Islands under CBRA – Weight - 91 Rare, Threatened, and Endangered Species – Weight - 86 Refuges – Weight 89

TNC - https://maps.tnc.org/gis_data.html

Conservation Areas – Weight - 73

NOAA - https://coast.noaa.gov/digitalcoast/data/nerr.html National Estuarine Research Reserves – Weight - 75

Audubon- https://www.audubon.org/important-bird-areas Important Bird Areas – Weight 75

DHS City, County, State and Federal Parks > 100 Acres – Weight – 44

Habitat

UNEP WCMC 2018 (https://data.unep-wcmc.org/datasets/7) Seagrass Locations – Weight – 88

NOAA Coastal Change Analysis Program (C-CAP)

·
Estuarine Emergent Marsh – Weight - 96 Forested Wetland – Weight - 80 Scrub – Shrub Wetland – Weight - 73

USFWS – National Wetland Inventory (NWI)

·
Freshwater Forested/Shrub Wetland – Weight - 82 Riverine Wetlands – Weight - 61

NOAA – Environmental Sensitivity Index (ESI)

·
Rocky Shoreline – Weight - 31 Unconsolidated Shore – Mud, Organic, Flat – Weight - 47 Unconsolidated Shore – Sand, Gravel, Cobble – Weight – 66

Cultural Resources

NPS

·
National Register of Historic Places – Weight – 75

USGSProtected Areas Database – Historic or Cultural Areas – Weight - 75This Tier 1 dataset is available for download here:Tier 1 Risk Assessment Download

eBird is a collective enterprise that takes a novel approach to citizen science by developing cooperative partnerships among experts in a wide range of fields: population ecologists, conservation biologists, quantitative ecologists, statisticians, computer scientists, GIS and informatics specialists, application developers, and data administrators. Managed by the Cornell Lab of Ornithology eBird’s goal is to increase data quantity through participant recruitment and engagement globally, but also to quantify and control for data quality issues such as observer variability, imperfect detection of species, and both spatial and temporal bias in data collection. eBird data are openly available and used by a broad spectrum of students, teachers, scientists, NGOs, government agencies, land managers, and policy makers. The result is that eBird has become a major source of biodiversity data, increasing our knowledge of the dynamics of species distributions, and having a direct impact on the conservation of birds and their habitats.

Cette donnée représente les Important Birds Area (IBA) délimitées.

One of the tools being used to foster implementation in Bird Conservation Regions is the concept of focus areas which are geographically explicit areas supporting general habitat characteristics preferred by priority birds. Focus areas are not the only areas within a BCR that provide basic habitat needs for priority species but are geographic areas that have been identified by the bird conservation community as areas of high conservation potential because of their biological attributes at the landscape scale. The New England/Mid-Atlantic bird focus areas were defined by staff of partner agencies and organizations during the BCR 30 all-bird workshop held in December 2004, as well as during other workshops and efforts focused on bird conservation within the region. Criteria developed for designating waterfowl focus areas have been adopted for use in defining other bird focus areas within BCR 30. These are:Areas are regionally important to one or more life history stages or seasonal-use periods.Focus areas are developed within the context of landscape-level conservation and biodiversity.Focus areas are made up of discrete and distinguishable habitats or habitat complexes demonstrating clear ornithological importance. The boundaries are defined using ecological factors such as wetlands and wetland buffers.Focus areas are large enough to supply all the necessary requirements for survival during the season for which it is important, except where small, disjunct areas are critical to survival and a biological connection is made, such as areas used by migrating shorebirds. The focus areas depicted in this plan should be considered an initial draft set for the BCR and will need to be periodically revised as new tools become available to aid in site selection and enhanced through a review process. The process used to generate focus areas has important limitations that should be understood by anyone using the maps or list in this plan. The list of focus areas is biased in terms of taxonomic groups, habitats, jurisdictions, and existing knowledge. Not all bird experts in the region attended BCR 30 workshops where lines were drawn on maps, and some geographic areas and species groups were better represented than others. In the spirit of consensus, we tended to be inclusive with focus areas suggested. No attempt was made to verify the importance of each focus area identified or to rank them or quantify their relative contributions to different bird species or groups. It is important to consider that due to differences in their ecology, some avian taxa lend themselves to the concept of focus areas better than others. Species that tend to occur in large congregations and/or in relatively open habitats that are easily observed (e.g., shorebirds at beaches or waterfowl in bays) are likely covered more completely by current focus areas than are species that are secretive, widely dispersed, typically occur in small numbers, or use habitats that are difficult to observe (e.g., secretive marsh birds). Over the long-term, model-based approaches should be used for widely distributed species to determine the most suitable habitats across the landscape to focus conservation efforts on (see conservation design discussion in Chapter 6). In this draft, maps of focus areas for each bird group have been created and illustrate where overlap occurs in areas considered to be important for the different taxonomic groups and where conservation efforts can benefit multiple groups of birds. Focus areas targeted for one taxonomic group are not necessarily less important than focus areas supporting multiple group of birds, because they might be extremely important for some of the highest priority species in that single bird group. Statistics for individual focus areas (e.g., acres/hectares, acres protected, etc.) can be found in Appendix A of the BCR 30 Plan at http://www.acjv.org/bcr30.htm.References for process of delineating focus areas for BCR 30: 1) BCR 30 Implementation Plan - http://www.acjv.org/bcr30.htm2) ACJV Waterfowl Implementation Plan Focus Areas – Guidelines for delineation of waterfowl focus areas can be found in the ACJV WIP section titled The Plan, http://acjv.org/wip/acjv_wip_main.pdf, page 66, Item 7.2, Important Geographic Areas for Waterfowl Habitat Conservation in the Atlantic Coast Joint Venture.Additional Guidance on delineation of BCR focus areas:1) White paper titled “SUGGESTIONS FOR A SUCCESSFUL BIRD CONSERVATION REGION WORKSHOP” by David Pashley – U.S. NABCI Coordinator, Art Martell – Canadian NABCI Coordinator, Craig Watson – U.S. Fish and Wildlife Service, Kevin Loftus – Ontario Ministry of Natural Resources, and Andrew Milliken - U.S. Fish and Wildlife Service. 2001. These suggestions are derived from successful Bird Conservation Region (BCR) meetings for the South Atlantic Migratory Bird Initiative (part of BCR 27) and the Lower Great Lakes/St. Lawrence Plain (BCR 13). Available upon request.2) BCR Coordinators Workshop, Patuxent National Wildlife Research Center, October 13, 2004. Minutes include discussion of core elements for BCR planning, of which one component is the delineation of focus areas. Available upon request.

This Conservation Site shapefile contains spatial and other information of over 750 sites of conservation, scientific, and ecological interest distributed across all of Idaho’s landscapes. Sites represent a variety of ecosystems and typically have intact ecological processes, exemplary native plant communities, unique geologic processes, or important habitat for species (e.g., Important Bird Areas). Conservation site boundaries often include most of the land area necessary to maintain the ecological processes of interest. For most areas, site boundaries also include a variable width buffer, but do not necessarily include an entire watershed. In some situations site boundaries nearly match those of a special management area, such as IDFG Wildlife Management Area (WMA), Research Natural Area (RNA), or USFWS National Wildlife Refuge (NWR). Corresponding descriptions for each site polygon in this shapefile describe the site, its location, size, design considerations, biological or other natural significance, ecological processes and functions, ecological condition and integrity, conservation or protection status, stewardship concerns, and known occurrences of communities and rare species. Approximately 475 of the sites in the Conservation Site shapefile contain significant wetland or riparian habitat. The majority of these sites were identified between 1996 and 2007, when IDFG completed wetland inventories across approximately two-thirds of Idaho’s river basins (wetland conservation strategies for most basins are available at https://fishandgame.idaho.gov/content/page/wetlands-publications-idaho-natural-heritage-program). These projects involved field surveys of wetland and riparian areas to document their condition, function, and biodiversity value. Field surveys were supplemented with interpretation of aerial imagery and National Wetland Inventory maps. Wetland sites were mapped relatively broadly, but typically finer than a HUC 12 scale (i.e., they include adjacent upland buffers). Wetland sites were typically classifiedaccording to habitat diversity, biodiversity significance, condition, and landscape context or viability into these conservation priority categories: Class I—highest priority; relatively undisturbed; often support unique or rare wetland types that are very sensitive to disturbance; often supports high concentrations of globally and state rare plant or animal species, and high diversity of common plant associations in excellent ecological condition; provide a high level of diverse wetland functions (i.e., hydrologic processes, water quality, etc. are intact); impacts should be avoided as these sites may be impossible to replace within a human lifetime; alteration may result in significant degradation that is not easily mitigated or restored; conservation efforts should focus on full protection including maintenance of hydrologic regimes. Class II—second highest priority; differentiated from Class I sites based on condition or biological significance; often support globally or state rare plant or animal species and/or contains rare or unique wetland types; human influences are apparent (i.e., portions of wetland are in excellent condition, however drier, accessible sites are impacted); moderate to high diversity of common plant associations in good to excellent ecological condition; wetland functions are intact; impacts and hydrologic modification should be avoided; mitigation and restoration may be possible, but may involve significant investments to be successful; improved stewardship may be necessary to alleviate low level impacts (e.g., improper livestock grazing).Reference—support common plant associations in good ecological condition, contain rare or unique wetland types in fair condition, and/or support state rare plant or animal species; human impacts are present, but functions are mostly intact; these wetlands may be the best remaining examples in areas of relatively high human influence and are therefore sometimes useful for monitoring the progress of restoration or enhancement of similar wetland types; they may also serve as donor sites for plant material used in restoration or enhancement; improved stewardship is often needed to maintain or improve function and condition.Habitat—provide moderate to outstanding wetland functions, such as food chain support, maintenance of important (and scarce) plant and wildlife habitat, or water quality support; provide numerous ecological services, although ecological condition is often impaired due to human activities; restoration, enhancement, and/or management may be necessary to improve or maintain wetland functions and condition; may have high potential for designation as, or expansion of, existing wildlife refuges or publically managed areas.Restoration Opportunity—After 2005, the Restoration Opportunity classification was added; currently support, or has the high likelihood of supporting, at least several important or rare (at local watershed scale) wetland functions and values, such as habitat for common and/or rare species, unique wetland types, or other locally important functions (e.g., water quality), but where human disturbance has notably decreased all functions and ecological condition; however, functions and condition are restorable with moderate levels of investment and coordination and a mix of public and private ownership (with willing landowners); often in areas with completed watershed or water quality management or improvement plans.PurposeConservation Sites are intended to help guide conservation planning, restoration, and implementation of protective measures using the full range of existing conservation tools, ranging from governmental programs to voluntary incentives. For example, Class I and Class II federal lands could be designated as RNAs, Special Interest Areas, Areas of Critical Environmental Concern, or Wildlife Refuge. Class I and Class II private lands could be conserved establishment of conservation easements, acquisition, and/or legal management agreements. Avoidance of impacts to these wetlands (and mitigation if unavoidable) may be required when obtaining permits under S. 404 of the Clean Water Act. Reference sites may rely on voluntary conservation, legal protection, avoidance of impacts, and/or application of Best Management Practices by the current landowner or manager. Habitat sites can be targeted for voluntary protection through incentives to private landowners. Reference and Habitat sites often benefit from improved stewardship, active enhancement, and application of Best Management Practices by landowners.