AbstractThe clustering of individuals that results from limited dispersal is a double-edged sword: while it allows for local interactions to be mostly among related individuals, it also results in increased local competition. Here I show that, because they mitigate local competition, fitness costs such as reduced fecundity or reduced survival are less costly in spatially structured environments than in non spatial settings. I first present a simple demographic example to illustrate how spatial structure weakens selection against fitness costs. Then, I illustrate the importance of disentangling the evolution of a trait from the evolution of potential associated costs, using an example taken from a recent study investigating the effect of spatial structure on the evolution of host defense. In this example indeed, the differences between spatial and non-spatial selection gradients are due to differences in the fitness costs, thereby undermining interpretations of the results made in terms of the trait only. This illustrates the need to consider fitness costs as proper traits in both theoretical and empirical studies. Usage notesDebarre_2015_EvolutionZipped folder containing the scripts to re-run and plot all the figures presented in the article.

Across the country, public land managers make hundreds of decisions each year that influence landscapes and ecosystems within the lands they manage. Many of these decisions involve vegetation manipulations known as land treatments. Land treatments include activities such as removal or alteration of plant biomass, seeding burned areas, and herbicide applications. Data on these land treatments historically have been stored at local offices and gathering information across large spatial areas was difficult. These valuable data needed to be centralized and stored for Federal agencies involved in land treatments because these data are useful to land managers for policy and management and to scientists for developing sampling designs and studies. In 2008, the Land Treatment Digital Library (LTDL) was created by the U.S. Geological Survey (USGS) to catalog information about land treatments on federal lands in the western United States. The flexible framework of the library allows for the storage of a wide variety of data in different formats. The library contains data in text, tabular, spatial, and image formats. Specific examples include project plans and implementation reports, monitoring data, spatial data files from geographic information systems, digitized paper maps, and digital images of land treatments. The data are entered by USGS employees and are accessible through a searchable website. The LTDL can be used to respond to information requests, conduct analyses and other forms of information syntheses, produce maps, and generate reports for federal managers, scientists, and other authorized users. This data release includes the most up to date data available in the LTDL at the time of release. However, most field offices were last visited to collect their comprehensive treatment data between 2011-2014. Users should be aware that while treatments may exist in some field offices past the date of last collection, it is not a comprehensive representation of land treatments that have occurred on BLM lands during the most recent time span. Offices in southern Idaho and eastern Oregon were revisited in the winter of 2019 and the data collected during those visits are available in this release. Offices in northern Nevada were visited in 2023 and the data collected during those visits are available in this release. Several offices in southern Nevada, Utah, and Wyoming were visited in 2024, and some of the data collected during those visits are available in this release. All available post wildfire emergency stabilization and rehabilitation treatments are included for fires up to 2023.

Geoscience Australia has been deriving raster sediment datasets for the continental Australian Exclusive Economic Zone (AEEZ) using existing marine samples collected by Geoscience Australia and external organisations. Since seabed sediment data are collected at sparsely and unevenly distributed locations, spatial interpolation methods become essential tools for generating spatially continuous information. Previous studies have examined a number of factors that affect the performance of spatial interpolation methods. These factors include sample density, data variation, sampling design, spatial distribution of samples, data quality, correlation of primary and secondary variables, and interaction among some of these factors. Apart from these factors, a spatial reference system used to define sample locations is potentially another factor and is worth investigating. In this study, we aim to examine the degree to which spatial reference systems can affect the predictive accuracy of spatial interpolation methods in predicting marine environmental variables in the continental AEEZ. Firstly, we reviewed spatial reference systems including geographic coordinate systems and projected coordinate systems/map projections, with particular attention paid to map projection classification, distortion and selection schemes; secondly, we selected eight systems that are suitable for the spatial prediction of marine environmental data in the continental AEEZ. These systems include two geographic coordinate systems (WGS84 and GDA94) and six map projections (Lambert Equal-area Azimuthal, Equidistant Azimuthal, Stereographic Conformal Azimuthal, Albers Equal-Area Conic, Equidistant Conic and Lambert Conformal Conic); thirdly, we applied two most commonly used spatial interpolation methods, i.e. inverse distance squared (IDS) and ordinary kriging (OK) to a marine dataset projected using the eight systems. The accuracy of the methods was assessed using leave-one-out cross validation in terms of their predictive errors and, visualization of prediction maps. The difference in the predictive errors between WGS84 and the map projections were compared using paired Mann-Whitney test for both IDW and OK. The data manipulation and modelling work were implemented in ArcGIS and R. The result from this study confirms that the little shift caused by the tectonic movement between WGS84 and GDA94 does not affect the accuracy of the spatial interpolation methods examined (IDS and OK). With respect to whether the unit difference in geographical coordinates or distortions introduced by map projections has more effect on the performance of the spatial interpolation methods, the result shows that the accuracies of the spatial interpolation methods in predicting seabed sediment data in the SW region of AEEZ are similar and the differences are considered negligible, both in terms of predictive errors and prediction map visualisations. Among the six map projections, the slightly better prediction performance from Lambert Equal-Area Azimuthal and Equidistant Azimuthal projections for both IDS and OK indicates that Equal-Area and Equidistant projections with Azimuthal surfaces are more suitable than other projections for spatial predictions of seabed sediment data in the SW region of AEEZ. The outcomes of this study have significant implications for spatial predictions in environmental science. Future spatial prediction work using a data density greater than that in this study may use data based on WGS84 directly and may not have to project the data using certain spatial reference systems. The findings are applicable to spatial predictions of both marine and terrestrial environmental variables.

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

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 database is an initial Asset database for the Central West subregion on 29 April 2015. This dataset contains the spatial and non-spatial (attribute) components of the Central West subregion Asset List as one .mdb files, which is readable as an MS Access database and a personal geodatabase. Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. All reports received associated with the WAIT process for Central West are included in the zip file as part of this dataset. Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Central West subregion are found in the "AssetList" table of the database. In this version of the database only M1 has been assessed. Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "CEN_asset_database_doc_20150429.doc ", located in the zip file as part of this dataset. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document "CEN_asset_database_doc_20150429.doc" located in the zip file. Some of the source data used in the compilation of this dataset is restricted. Dataset History This is initial asset database. 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. Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. Elements are initially included in database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet materiality test 2 (M2) - assets considered to be water dependent. Elements may be represented by a single, discrete spatial unit (polygon, line or point), or a number of spatial units occurring at more than one location (multipart polygons/lines or multipoints). Spatial features representing elements are not clipped to the preliminary assessment extent - features that extend beyond the boundary of the assessment extent have been included in full. To assist with an assessment of the relative importance of elements, area statements have been included as an attribute of the spatial data. Detailed attribute tables contain descriptions of the geographic features at the element level. Tables are organised by data source and can be joined to the spatial data on the "ElementID" field Elements are grouped into Assets, which are the objects used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy. The "Element_to_asset" table contains the relationships and identifies the elements that were grouped to create each asset. Following delivery of the first pass asset list, project teams make a determination as to whether an asset (comprised of one or more elements) is water dependent, as assessed against the materiality tests detailed in the BA Methodology. These decisions are provided to ERIN by the project team leader and incorporated into the Assetlist table in the Asset database. The Asset database is then re-registered into the BA repository. The Asset database dataset (which is registered to the BA repository) contains separate spatial and non-spatial databases. Non-spatial (tabular data) is provided in an ESRI personal geodatabase (.mdb - doubling as a MS Access database) to store, query, and manage non-spatial data. This database can be accessed using either MS Access or ESRI GIS products. Non-spatial data has been provided in the Access database to simplify the querying process for BA project teams. Source datasets are highly variable and have different attributes, so separate tables are maintained in the Access database to enable the querying of thematic source layers. Spatial data is provided as an ESRI file geodatabase (.gdb), and can only be used in an ESRI GIS environment. Spatial data is represented as a series of spatial feature classes (point, line and polygon layers). Non-spatial attribution can be joined from the Access database using the AID and ElementID fields, which are common to both the spatial and non-spatial datasets. Spatial layers containing all the point, line and polygon - derived elements and assets have been created to simplify management of the Elementlist and Assetlist tables, which list all the elements and assets, regardless of the spatial data geometry type. i.e. the total number of features in the combined spatial layers (points, lines, polygons) for assets (and elements) is equal to the total number of non-spatial records of all the individual data sources. Dataset Citation Department of the Environment (2013) Asset database for the Central West subregion on 29 April 2015. Bioregional Assessment Derived Dataset. Viewed 08 February 2017, http://data.bioregionalassessments.gov.au/dataset/5c3f9a56-7a48-4c26-a617-a186c2de5bf7. Dataset Ancestors Derived From Macquarie Marshes Vegetation 1991-2008 VIS_ID 3920 Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014) Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013 Derived From Travelling Stock Route Conservation Values Derived From NSW Wetlands Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only Derived From National Groundwater Dependent Ecosystems (GDE) Atlas Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Environmental Asset Database - Commonwealth Environmental Water Office Derived From NSW Office of Water Surface Water Offtakes - NIC v1 20131024 Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA) Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only) Derived From Ramsar Wetlands of Australia Derived From Native Vegetation Management (NVM) - Manage Benefits Derived From Key Environmental Assets - KEA - of the Murray Darling Basin Derived From National Heritage List Spatial Database (NHL) (v2.1) Derived From Climate Change Corridors (Dry Habitat) for North East NSW Derived From Great Artesian Basin and Laura Basin groundwater recharge areas Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions Derived From New South Wales NSW Regional CMA Water Asset Information WAIT tool databases, RESTRICTED Includes ALL Reports Derived From New South Wales NSW - Regional - CMA - Water Asset Information Tool - WAIT - databases Derived From NSW Office of Water Groundwater licences extract linked to spatial locations NIC v3 (13 March 2014) Derived From Australia - Species of National Environmental Significance Database Derived From NSW Office of Water Groundwater Licence Extract NIC- Oct 2013 Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal Derived From NSW Office of Water Groundwater Entitlements Spatial Locations Derived From NSW Office of Water Surface Water Licences in NIC linked to locations v1 (22 April 2014) Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public) Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release)

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. Hunter Asset Database v2.3 supersedes previous versions of the Hunter Asset database. There are some minor text changes about WD rationale in this V2.3 database: * Changed "Assessment team do not say No" to "All economic assets are by definition water dependent" * Changed "Assessment team say No" to "These are water dependent, but excluded by the project team based on intersection with the PAE is negligible" * Changed "Rivertyles" to "RiverStyles" This dataset contains the Asset database (.mdb), a Geodatabase version for GIS mapping purposes (.gdb), the draft Water Dependent Asset Register spreadsheet, a data dictionary document, and a folder (NRM_DOC) containing documentation associated with the WAIT process as outlined below. The Asset database is registered to the BA repository as an ESRI personal goedatabase (.mdb - doubling as a MS Access database) that can store, query, and manage non-spatial data while the spatial data is in a separate file geodatabase joined by AID/ElementID. Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. A report on the WAIT process for the Hunter is included in the zip file as part of this dataset. Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Hunter subregion are found in the "AssetList" table of the database. Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of " HUN_asset_database_doc_20150922.doc", located in this file. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document " HUN_asset_database_doc_20150922.doc" located in this file. Some of the source data used in the compilation of this dataset is restricted. Dataset History OBJECTID VersionID Date_ Notes 1 1 29/08/2014 Initial database. 3 1.1 16/09/2014 Update the classification for seven identical assets from Gloucester subregion 4 1.2 28/01/2015 Added in NSW GDEs from Hunter - Central Rivers GDE mapping from NSW DPI (50 635 polygons). 5 1.3 12/02/2015 New AIDs assiged to NSW GDE assets (Existing AID + 20000) to avoid duplication of AIDs assigned in other databases. 6 1.4 16/06/2015 "(1) Add 20 additional datasets required by HUN assessment project team after HUN community workshop (2) Turn off previous GW point assets (AIDs from 7717-7810 inclusive) (3) Turn off new GW point asset (AID: 0) (4) Assets (AIDs: 8023-8026) are duplicated to 4 assets (AID: 4747,4745,4744,4743 respectively) in NAM subregion . Their AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using values from that NAM assets. (5) Asset (AID 8595) is duplicated to 1 asset ( AID 57) in GLO subregion . Its AID, Asset Name, Group, SubGroup, Depth, Source, ListDate and Geometry are using values from that GLO assets. (6) 39 assets (AID from 2969 to 5040) are from NAM Asset database and their attributes were updated to use the latest attributes from NAM asset database (7)The databases, especially spatial database, were changed such as duplicated attributes fields in spatial data were removed and only ID field is kept. The user needs to join the Table Assetlist or Elementlist to the spatial data" 7 2 20/07/2015 "(1) Updated 131 new GW point assets with previous AID and some of them may include different element number due to the change of 77 FTypes requested by Hunter assessment project team (2) Added 104 EPBC assets, which were assessed and excluded by ERIN (3) Merged 30 Darling Hardyhead assets to one (asset AID 60140) and deleted another 29 (4) Turned off 5 assets from community workshop (60358 - 60362) as they are duplicated to 5 assets from 104 EPBC excluded assets (5) Updated M2 test results (6)Asset Names (AID: 4743 and 4747) were changed as requested by Hunter assessment project team (4 lower cases to 4 upper case only). Those two assets are from Namoi asset database and their asset names may not match with original names in Namoi asset database. (7)One NSW WSP asset (AID: 60814) was added in as requested by Hunter assessment project team. The process method (without considering 1:M relation) for this asset is not robust and is different to other NSW WSP assets. It should NOT use for other subregions. (8) Queries of Find_All_Used_Assets and Find_All_WD_Assets in the asset database can be used to extract all used assts and all water dependant assts" 8 2.1 27/08/2015 "(1) There are following six assets (in Hun subregion), which is same as 6 assets in GIP subregion. Their AID, Asset Name, Group, SubGroup, Depth, Source and ListDate are using values from GIP assets. You will not see AIDs from AID_from_HUN in whole HUN asset datable and spreadsheet anymore and you only can see AIDs from AID_from_GIP ( Actually (a) AID 11636 is GIP got from MBC (B) only AID, Asset Name and ListDate are different and changed) (2) For BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx, (a) Extracted long ( >255 characters) WD rationale for 19 assets (AIDs: 8682,9065,9073,9087,9088,9100,9102,9103,60000,60001,60792,60793,60801,60713,60739,60751,60764,60774,60812 ) in tab "Water-dependent asset register" and 37 assets (AIDs: 5040,8651,8677,8682,8650,8686,8687,8718,8762,9094,9065,9067,9073,9077,9081,9086,9087,9088,9100,9102,9103,60000,60001,60739,60742,60751,60713,60764,60771,60774,60792,60793,60798,60801,60809,60811,60812) in tab "Asset list" in 1.30 Excel file (b) recreated draft BA-NSB-HUN-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx (3) Modified queries (Find_All_Asset_List and Find_Waterdependent_asset_register) for (2)(a)" 9 2.2 8/09/2015 "(1) Updated M2 results from the internal review for 386 Sociocultural assets (2)Updated the class to Ecological/Vegetation/Habitat (potential species distribution) for assets/elements from sources of WAIT_ALA_ERIN, NSW_TSEC, NSW_DPI_Fisheries_DarlingHardyhead" 10 2.3 22/09/2015 "(1) Updated M2 results from the internal review * Changed "Assessment team do not say No" to "All economic assets are by definition water dependent" * Changed "Assessment team say No" : to "These are water dependent, but excluded by the project team based on intersection with the PAE is negligible" * Changed "Rivertyles" to "RiverStyles"" Dataset Citation Bioregional Assessment Programme (2015) Asset database for the Hunter subregion on 22 September 2015. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/59d91125-2b58-4680-9ef0-645d77a9f76d. Dataset Ancestors Derived From NSW Wetlands Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013 Derived From Travelling Stock Route Conservation Values Derived From Darling River Hardyhead Predicted Distribution in Hunter River Catchment NSW 2015 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 Asset database for the Hunter subregion on 27 August 2015 Derived From Birds Australia - Important Bird Areas (IBA) 2009 Derived From Estuarine Macrophytes of Hunter Subregion NSW DPI Hunter 2004 Derived From Hunter CMA GDEs (DRAFT DPI pre-release) Derived From Camerons Gorge Grassy White Box Endangered Ecological Community (EEC) 2008 Derived From Asset database for the Hunter subregion on 16 June 2015 Derived From Spatial Threatened Species and Communities (TESC) NSW 20131129 Derived From Fauna Corridors for North East NSW 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 Ramsar Wetlands of Australia Derived From Native Vegetation Management (NVM) - Manage Benefits Derived From NSW Office of Water Surface Water Licences Processed for Hunter v1 20140516 Derived From GW Element Bores with Unknown FTYPE Hunter NSW Office of Water 20150514 Derived From Atlas of Living Australia NSW ALA Portal 20140613 Derived From National Heritage List Spatial

This layer provides all linear parcels (e.g. Centreline easements) and their associated descriptive data as a single layer to facilitate their use independently of the all polygon parcels. NOTE: This layer contains primary and non-primary approved, current or historic linear parcels (see status flag descriptions for more information). In conjunction with the Parcels, this layer provides the easiest way to create a relationship with associated tables such as Parcel Title Association List, Parcel Statutory Actions List and Survey Affected Parcels List. This layer contains spatial and non-spatial (without geometry) parcels. The Landonline system which manages the data maintains non-spatial parcels for many different reasons. The non-spatial parcels can only be accessed via WFS or as a full layer file download. No layer clips can be used. The most common reasons for non-spatial parcels are: Flats and unit survey plans will create non-spatial parcels for referencing property rights. This is because the Landonline system has not yet been designed to support the spatial definition of these plans. Titles which were not linked to a spatial parcel during the Landonline title conversion project created non-spatial parcel references. As titles are spatially linked many of these non-spatial parcels will be made historic or will be merged with the associated spatial parcel. Parcels within this layer contain the following status flags: Approved: The definition of a survey-defined parcel that has been processed and authorised as correct in terms of the survey network. Current: A parcels that has been registered or the parcel is made current by a statutory action against a Legalisation plan. Survey Historic: A parcel that has been extinguished from the primary cadastral network but still exists in live Title estates. Historic: A parcel that has been extinguished from the primary cadastral network and no longer exists in live Title estates or has an current recorded statutory action. Typically this happens when a parcel is subdivided or merged, and new titles or actions are registered against the replacing parcels. Note: Only parcel made historic since the beginning of Landonline operations (2002) are included. See this page for the actual dates when Landonline operations started. The Linear parcels within this layer has a nominal accuracy of 0.1-1m in urban areas and 1-100m in rural areas. For more detailed information about parcel accuracies please refer to the Survey Boundary Marks layer which contains accuracies for each parcel node. The originating data for parcel/title associations includes some non-official sources where the official data does not support a link. For more information see.

Over the last 50 years, cue competition phenomena have shaped theoretical developments in animal and human learning. However, recent failures to obtain the well-known blocking effect in standard conditioning procedures, as well as the lengthy and on-going debate surrounding cue competition in the spatial learning literature, have cast doubts on the generality of competition phenomena. In the present study, we manipulated temporal contiguity between predictors and outcomes (Experiments 1-4), and spatial contiguity between landmarks and goals in spatial learning (Experiments 5-7). Across different parametric variations, we observed overshadowing when temporal and spatial contiguity were strong, but no overshadowing when contiguity was weak. Thus, across temporal and spatial domains, we observed that contiguity is necessary for competition to occur, and that competition between cues during learning is absent when cues were either spatially or temporally discontiguous. Consequently, we advance a model in which the contiguity of cues is accounted for, and which can reconcile the previously contradictory findings observed in spatial and non-spatial domains.

It is a classic aim of quantitative and evolutionary biology to infer genetic architecture and potential evolutionary responses to selection from the variance-covariance structure of measured traits. But a meaningful genetic or developmental interpretation of raw covariances is difficult, and classic concepts of morphological integration do not directly apply to modern morphometric data. Here we present a new morphometric strategy based on the comparison of morphological variation across different spatial scales. If anatomical elements vary completely independently, then their variance accumulates at larger scales or for structures composed of multiple elements: morphological variance would be a power function of spatial scale. Deviations from this pattern of `variational self-similarity'' (serving as a null-model of completely uncoordinated growth) indicate genetic or developmental co-regulation of anatomical components. We present biometric strategies and R scripts for identifying patterns of coordination and compensation in the size and shape of composite anatomical structures. In an application to human cranial variation, we found that coordinated variation and positive correlations are prevalent for the size of cranial components, whereas their shape was dominated by compensatory variation, leading to strong canalization of cranial shape at larger scales. We propose that mechanically induced bone formation and remodeling are key mechanisms underlying compensatory variation in cranial shape. Such epigenetic coordination and compensation of growth are indispensable for stable, canalized development and may also foster the evolvability of complex anatomical structures by preserving spatial und functional integrity during genetic responses to selection. Methods Online Appendix of the main mansucript, containing all the formulas and computational details as well as a description of the R package.

Midsagittal landmarks digitized on high-resolution CT scans of a geographically diverse sample of 24 adult human crania (16 females, 8 males). The landmark set comprises 30 anatomical landmarks and 57 semilandmarks.

We provide an R file including functions to compute bending energies, principal warps, partial warp scores, and the non-affine component of shape variation for 2D landmark configurations as well as functions to compute Mardia-Dryden distributions and self-similar distributions of landmarks. We also provide a worked example using the landmark data.

This dataset is a non-spatial table that identifies other recorded traits of NWSS.The aim of the Native Woodland Survey of Scotland (NWSS) was to undertake a baseline survey of all native woodlands, nearly native woodlands and PAWS sites in Scotland in order to create a woodland map linked to a dataset showing type, extent and condition of those woods.

The objectives were to:

Identify the location, type, extent and condition of all native and nearly native woodlands and Plantations on Ancient Woodland Sites (PAWS - as identified from the Ancient Woodland Inventory) in Scotland.Produce a baseline survey map of all native woodland, nearly native woodland and PAWS in Scotland.Collect baseline information to enable future monitoring of the extent and condition of the total Scottish native woodland resource.Provide information to support policy development and the delivery of social, environmental and development forestry.

The following NWSS datasets are available from Scottish Forestry.

Native Woodland Survey of Scotland (base map and polygon level attributes)NWSS Canopy StructureNWSS Habitat ComponentsNWSS Herbivore ImpactNWSS InvasivesNWSS Other TraitsNWSS Species Structures

The following describes the layers available from Scottish Forestry and also gives an indication of the nature of the spatial data and the related component non-spatial data. (N.B. Every table contains a SCPTDATA_I field. This is a unique field which is used to link all other component tables). If you wish to carry out complex analysis, particularly involving elements of the components tables, e.g. species selection, you should do so using GIS software.

NWSS Map:

This is a straightforward view of the data which describes the type of NWSS polygon based on the following categories:

Native woodland: >50% native species in the canopyNearly-native woodland: >=40% and <=50% native species in the canopyOpen land habitat: <20% canopy cover, usually 100% surrounded by woodland and adjoining a native woodlandPAWS: A woodland area wholly or partially identified in the Ancient Semi-natural Woodland Inventory as ancient semi-natural but currently not semi-natural.

NWSS Nativeness:

Displays the percentage share of native species in the total canopy. This ranges from 0% to 100% in 5% classes.

NWSS Habitat:

This view of the data shows the priority woodland type and National Vegetation Classification (NVC) woodland community. Open land habitat is defined by UK Biodiversity Action Plan (BAP) type.

A dominant habitat is recorded for each polygon, however some polygons have habitats of equal dominance. In this case only one of the habitats is recorded in the top level spatial data. To identify all of the habitats in a particular polygon please refer to the NWSS Habitat Components table.

Plantations on Ancient Woodland Sites (PAWS) may not display in the Habitat layer if a surveyor has not recorded a native priority habitat type for the site. This will happen when a site is non-native.

NWSS Canopy Cover:

Displays as a percentage, an assessment of the area covered by trees/shrubs. Values range from 0% to 100% in 10% classes. A minimum of 20% canopy cover is required to define woodland, so the 10% and 20% bands are skewed to allow for this.

NWSS Canopy Structures:

This displays the number of different structures recorded in a polygon (ranging from 0 to 6). The types of recorded structures are veteran, mature, pole immature, shrub, established regeneration or visible regeneration.

A dominant structure is recorded for each polygon, however some polygons have structures of equal dominance. In this case only one of the structures is recorded in the top level spatial data. To identify all of the structures in a particular polygon please refer to the NWSS Canopy Structures.

Information on the species identified in each polygon is also in the NWSS Canopy Structures layer and table.

• indicates a species which is classed as native for the purpose of the survey.

• indicates a species is a shrub not a tree.

NWSS Semi-naturalness:

This view of the data shows the percentage of the polygon that is semi-natural. Values range from 0% to 100% in 10% bands.

NWSS Maturity:

This indicates the approximate stage of woodland development as either: mature, young, regenerating, mixed or shrub. The value is based on the dominance of the structures recorded; a mixed maturity means that none of the others values are dominant.

NWSS Other Traits:

This layer records whether or not there are any other attributes which have been recorded in the polygon. The details of any other traits that have been found can be accessed by viewing the related information attached to a polygon.

NWSS Herbivore Impact:

This view of the data shows the overall impact that herbivores have had on a polygon.

Summary of Attributes

SCPTDATA_I Polygon ID (Unique identifier)PAWS_SURVY Surveyed as PAWSTYPE TypeCANOPY_PCT Canopy cover percentageNATIVE_PCT Native species percentageDOM_HABITA Dominant habitat typeDOM_HB_PCT Dominant habitat type percentageSEMINT_PCT Semi-natural percentageSTRUCT_NUM Number of structuresMATURITY MaturityDOM_STRUCT Dominant structureHERBIVORE Herbivore impactER_NAT_PCT Percentage of establish regeneration of native speciesINVASV_PCT Invasive species percentageINVASV_NUM Number of invasive speciesOTHR_TRAIT Other traits recordedHECTARES Area in hectaresFor more detailed information please see the metadata record on Scotland's SpatialData.gov.scot Metadata Portal

This digital dataset compiles a 3-layer geologic model of the conterminous United States by mapping the altitude of three surfaces: land surface, top of bedrock, and top of basement. These surfaces are mapped through the compilation and synthesis of published stratigraphic horizons from numerous topical studies. The mapped surfaces create a 3-layer geologic model with three geomaterials-based subdivisions: unconsolidated to weakly consolidated sediment; layered consolidated rock strata that constitute bedrock, and crystalline basement, consisting of either igneous, metamorphic, or highly deformed rocks. Compilation of subsurface data from published reports involved standard techniques within a geographic information system (GIS) including digitizing contour lines, gridding the contoured data, sampling the resultant grids at regular intervals, and attribution of the dataset. However, data compilation and synthesis is highly dependent on the definition of the informal terms “bedrock” and “basement”, terms which may describe different ages or types of rock in different places. The digital dataset consists of a single polygon feature class which contains an array of square polygonal cells that are 2.5 km m in x and y dimensions. These polygonal cells multiple attributes including x-y location, altitude of the three mapped layers at each x-y location, the published data source from which each surface altitude was compiled, and an attribute that allows for spatially varying definitions of the bedrock and basement units. The spatial data are linked through unique identifiers to non-spatial tables that describe the sources of geologic information and a glossary of terms used to describe bedrock and basement type.

This dataset is a non-spatial table that identifies the Species Structures of NWSS.The aim of the Native Woodland Survey of Scotland (NWSS) was to undertake a baseline survey of all native woodlands, nearly native woodlands and PAWS sites in Scotland in order to create a woodland map linked to a dataset showing type, extent and condition of those woods.

The objectives were to:

Identify the location, type, extent and condition of all native and nearly native woodlands and Plantations on Ancient Woodland Sites (PAWS - as identified from the Ancient Woodland Inventory) in Scotland.Produce a baseline survey map of all native woodland, nearly native woodland and PAWS in Scotland.Collect baseline information to enable future monitoring of the extent and condition of the total Scottish native woodland resource.Provide information to support policy development and the delivery of social, environmental and development forestry.

The following NWSS datasets are available from Scottish Forestry.

Native Woodland Survey of Scotland (base map and polygon level attributes)NWSS Canopy StructureNWSS Habitat ComponentsNWSS Herbivore ImpactNWSS InvasivesNWSS Other TraitsNWSS Species Structures

The following describes the layers available from Scottish Forestry and also gives an indication of the nature of the spatial data and the related component non-spatial data. (N.B. Every table contains a SCPTDATA_I field. This is a unique field which is used to link all other component tables). If you wish to carry out complex analysis, particularly involving elements of the components tables, e.g. species selection, you should do so using GIS software.

NWSS Map:

This is a straightforward view of the data which describes the type of NWSS polygon based on the following categories:

Native woodland: >50% native species in the canopyNearly-native woodland: >=40% and <=50% native species in the canopyOpen land habitat: <20% canopy cover, usually 100% surrounded by woodland and adjoining a native woodlandPAWS: A woodland area wholly or partially identified in the Ancient Semi-natural Woodland Inventory as ancient semi-natural but currently not semi-natural.

NWSS Nativeness:

Displays the percentage share of native species in the total canopy. This ranges from 0% to 100% in 5% classes.

NWSS Habitat:

This view of the data shows the priority woodland type and National Vegetation Classification (NVC) woodland community. Open land habitat is defined by UK Biodiversity Action Plan (BAP) type.

A dominant habitat is recorded for each polygon, however some polygons have habitats of equal dominance. In this case only one of the habitats is recorded in the top level spatial data. To identify all of the habitats in a particular polygon please refer to the NWSS Habitat Components table.

Plantations on Ancient Woodland Sites (PAWS) may not display in the Habitat layer if a surveyor has not recorded a native priority habitat type for the site. This will happen when a site is non-native.

NWSS Canopy Cover:

Displays as a percentage, an assessment of the area covered by trees/shrubs. Values range from 0% to 100% in 10% classes. A minimum of 20% canopy cover is required to define woodland, so the 10% and 20% bands are skewed to allow for this.

NWSS Canopy Structures:

This displays the number of different structures recorded in a polygon (ranging from 0 to 6). The types of recorded structures are veteran, mature, pole immature, shrub, established regeneration or visible regeneration.

A dominant structure is recorded for each polygon, however some polygons have structures of equal dominance. In this case only one of the structures is recorded in the top level spatial data. To identify all of the structures in a particular polygon please refer to the NWSS Canopy Structures.

Information on the species identified in each polygon is also in the NWSS Canopy Structures layer and table.

• indicates a species which is classed as native for the purpose of the survey.

• indicates a species is a shrub not a tree.

NWSS Semi-naturalness:

This view of the data shows the percentage of the polygon that is semi-natural. Values range from 0% to 100% in 10% bands.

NWSS Maturity:

This indicates the approximate stage of woodland development as either: mature, young, regenerating, mixed or shrub. The value is based on the dominance of the structures recorded; a mixed maturity means that none of the others values are dominant.

NWSS Other Traits:

This layer records whether or not there are any other attributes which have been recorded in the polygon. The details of any other traits that have been found can be accessed by viewing the related information attached to a polygon.

NWSS Herbivore Impact:

This view of the data shows the overall impact that herbivores have had on a polygon.

Summary of Attributes

SCPTDATA_I Polygon ID (Unique identifier)PAWS_SURVY Surveyed as PAWSTYPE TypeCANOPY_PCT Canopy cover percentageNATIVE_PCT Native species percentageDOM_HABITA Dominant habitat typeDOM_HB_PCT Dominant habitat type percentageSEMINT_PCT Semi-natural percentageSTRUCT_NUM Number of structuresMATURITY MaturityDOM_STRUCT Dominant structureHERBIVORE Herbivore impactER_NAT_PCT Percentage of establish regeneration of native speciesINVASV_PCT Invasive species percentageINVASV_NUM Number of invasive speciesOTHR_TRAIT Other traits recordedHECTARES Area in hectaresFor more detailed information please see the metadata record on Scotland's SpatialData.gov.scot Metadata Portal

This layer provides all cadastral parcel polygons and some associated descriptive data that details the appellation (legal description), purpose, size and a list of titles that have an interest in the parcel. NOTE: This layer contains primary and non-primary approved, current or historic linear parcels (see status flag descriptions for more information). In conjunction with the Linear Parcels, this layer provides the easiest way to create a relationship with associated tables such as Parcel Title Association List, Parcel Statutory Actions List and Survey Affected Parcels List. This layer contains spatial and non-spatial (without geometry) parcels. The Landonline system which manages the data maintains non-spatial parcels for many different reasons. The non-spatial parcels can only be accessed via WFS or as a full layer file download. No layer clips can be used. The most common reasons for non-spatial parcels are: Flats and unit survey plans will create non-spatial parcels for referencing property rights. This is because the Landonline system has not yet been designed to support the spatial definition of these plans. Titles which were not linked to a spatial parcel during the Landonline title conversion project created non-spatial parcel references. As titles are spatially linked many of these non-spatial parcels will be made historic or will be merged with the associated spatial parcel. Parcels within this layer contain the following status flags: Approved: The definition of a survey-defined parcel that has been processed and authorised as correct in terms of the survey network. Current: A parcels that has been registered or the parcel is made current by a statutory action against a Legalisation plan. Survey Historic: A parcel that has been extinguished from the primary cadastral network but still exists in live Title estates. Historic: A parcel that has been extinguished from the primary cadastral network and no longer exists in live Title estates or has an current recorded statutory action. Typically this happens when a parcel is subdivided or merged, and new titles or actions are registered against the replacing parcels. Note: Only parcel made historic since the beginning of Landonline operations (2002) are included.See this page for the actual dates when Landonline operations started. Polygons within this layer have a nominal accuracy of 0.1-1m in urban areas and 1-100m in rural areas. For more detailed information about parcel accuracies please refer to the Survey Boundary Marks layer which contains accuracies for each parcel node. The originating data for parcel/title associations includes some non-official sources where the official data does not support a link. For more information see.

Road centerlines with road names and generalized classifications is a snapshot from our spatial roads (addressing) dataset. Maintenance data was pulled from the Borough asset management software, Cartegraph, which is non-spatial. The non-spatial maintenance data was then tied to the spatial roads data through a series of joins and analyses.Roads with multiple maintenance groups listed have shared maintenance responsibilities; for example 1/2 the road may be maintained by the Borough and the other 1/2 maintained by a city. More detailed information regarding the distances each maintenance group is responsible for can be looked up in the Cartegraph database. This more detailed information can not currently be mapped due to differences in design between the spatial roads (911 addressing) dataset and the Cartegraph database.This dataset does not have a scheduled update cycle and should be viewed as just a snapshot in time. It was last updated in Sept 2017.

How can we estimate positions of non-majoritarian institutions in a common policy space? To answer this question, we take highest courts as examples of powerful non-majoritarian institutions and develop a new scaling approach to estimate their position in a common policy space with other political actors. In contrast to previous research, our approach neither relies on individual votes of justices nor assumes that justices ``inherit'' positions from political actors who nominated them. Instead, for each court decision, we use the positions of political actors expressed in written statements as well as the courts' decision outcome to estimate comparable policy positions. In two applications, we position the German Federal Constitutional Court with different German governments and the European Court of Justice with different European governments in common policy spaces and validate them. Finally, we show how our common policy scores can be used to study court--executive relations and inter-institutional interactions.

Many animal species exhibit spatiotemporal synchrony in population fluctuations, which may provide crucial information about ecological processes driving population change. We examined spatial synchrony and concordance among population trajectories of five aerial insectivorous bird species: chimney swift Chaetura pelagica, purple martin Progne subis, barn swallow Hirundo rustica, tree swallow Tachycineta bicolor, and northern rough-winged swallow Stelgidopteryx serripennis. Aerial insectivores have undergone severe guild-wide declines that were considered more prevalent in northeastern North America. Here, we addressed four general questions including spatial synchrony within species, spatial concordance among species, frequency of declining trends among species, and geographic location of declining trends. We used dynamic factor analysis to identify large-scale common trends underlying stratum-specific annual indices for each species, representing population trajectories shared by spatially synchronous populations, from 46 yr of North American Breeding Bird Survey data. Indices were derived from Bayesian hierarchical models with continuous autoregressive spatial structures. Stratum-level spatial concordance among species was assessed using cross-correlation analysis. Probability of long-term declining trends was compared among species using Bayesian generalized linear models. Chimney swifts exhibited declining trends throughout North America, with less severe declines through the industrialized Mid-Atlantic and Great Lakes regions. Northern rough-winged swallows exhibited declining trends throughout the west. Spatial concordance among species was limited, the proportion of declining trends varied among species, and contrary to previous reports, declining trends were not more prevalent in the northeast. Purple martins, barn swallows, and tree swallows exhibited synchrony across smaller spatial scales. The extensive within-species synchrony and limited concordance suggest that population trajectories of these aerial insectivores are responding to large-scale but complex and species- and region-specific environmental conditions (e.g. climate, land use). A single driver of trends for aerial insectivores as a guild appears unlikely.

1. Capture-recapture (CR) techniques are commonly used to gain information about population dynamics, demography and life-history traits of populations. However, traditional CR models cannot separate mortality from emigration. Recently developed spatial-capture-recapture (SCR) models explicitly incorporate spatial information into traditional CR models, thus allowing for individuals' movements to be modelled explicitly. 2. In this paper, we extend SCR models using robust-design data to allow for both processes in which individuals can disappear from the population, mortality and dispersal, to be estimated separately. We formulate a general robust-design spatial capture-recapture (RD-SCR) model, explore the properties of the model in a simulation study, and compare the results to a Cormack-Jolly-Seber model and a non-spatial robust-design model with temporary emigration. In the case study, we fit several versions of the general model to data on field voles (Microtus agrestis) and compare the results with those from the non-spatial models fitted to the same data. We also evaluate assumptions of the fitted models with a series of simulation-based posterior predictive goodness-of-fit checks that are applicable to SCR models in general and the RD-SCR model in particular. 3. The simulation results show that the model preforms well under a wide range of dispersal distances. Our model outperforms the traditional CR models in terms of both accuracy and precision for survival. The case study showed that adult females have an approximately 3.5 times higher mortality rate than adult males. Males have larger home-ranges and disperse longer distances than females, but both males and females mostly move their activity centres within their previous home-range between trapping sessions at three week intervals. 4. Our RD-SCR model has several advantages compared to other approaches to estimate "true" survival instead of only "apparent" survival. Additionally, the model extracts information about space use and dispersal distributions that are relevant for behavioural studies as well as studies of life-history variation, population dynamics and management. The model can be widely applied due to the flexible framework, and other variations of the model could easily be implemented.

Experian have access to consumer expenditure estimates for around 80 countries which use a common methodology incorporating sources from national statistics authorities aligned with globally consistent sources (Eurostat/UN/OECD). Expenditure estimates comprise household goods - durable, semi-durable and non-durable - and services in the domestic market. Consumer spending for 12 different product categories, including Food and Non-Alcoholic Beverages, Housing, Clothing and Footwear, Transport, Education and others.

While human inflammatory skin diseases' cellular and molecular features are well-characterized, their tissue context and systemic impact remain poorly understood. We thus profiled human psoriasis (PsO) as a prototypic immune-mediated condition with a high preference for extra-cutaneous involvement. Spatial transcriptomics (ST) analyses of 25 healthy, active, and clinically uninvolved skin biopsies, and integration with public single-cell transcriptomics data revealed striking differences in immune microniches between healthy and inflamed skin. Tissue scale-cartography further identified core disease features across all active lesions, including the emergence of an inflamed suprabasal epidermal state and the presence of B lymphocytes in lesional skin. Notably, both lesional and distal non-lesional samples were stratified by skin disease severity, and not by the presence of systemic disease. This segregation was driven by macrophage-, fibroblast- and lymphatic-enriched spatial regions with gene signatures associated with metabolic dysfunction. Taken together, these findings suggest that mild and severe forms of PsO have distinct molecular features and that severe PsO may profoundly alter the cellular and metabolic make up of distal unaffected skin sites. Additionally, our study provides an unprecedented resource for the research community to study spatial gene organization of healthy and inflamed human skin.

Aim: Explore the spatial variation of the relationships between species richness (SR), phylogenetic diversity (PD) and environmental factors to infer the possible mechanisms underlying patterns of diversity in different regions of the globe. Location: Global. Time period: Present day. Major taxa studied: Terrestrial mammals. Methods: We used a hexagonal grid to map SR and PD of mammals and four environmental factors (temperature, productivity, elevation and climate-change velocity since the Last Glacial Maximum). We related those variables through direct and indirect pathways using a novel combination of Path Analysis and Geographically Weighted Regression to account for spatial non-stationarity of path coefficients. Results: SR, PD and environmental factors relate differently across the geographic space, with most relationships varying in both, magnitude and direction. Species richness is associated with lower phylogenetic diversity in much of the tropics and in the Americas, which reflects the tropical origin and the recent diversification of some mammalian clades in these regions. Environmental effects on PD are predominantly mediated by their effects on SR. But once richness is controlled for, the relationships between environmental factors and PD (i.e. PDSR) highlight environmentally driven changes in species composition. Environmental-PDSR relationships suggest that the relative importance of different mechanisms driving biodiversity shifts spatially. Across most of the globe, temperature and productivity are the strongest predictors of richness, while PDSR is best predicted by temperature. Main conclusions: Richness explains most spatial variation in PD, but both dimensions of biodiversity respond differently to environmental conditions across the globe, as indicated by the spatial mismatches in the relationships between environmental factors and these two types of diversity. We show that accounting for spatial non-stationarity and environmental effects on PD while controlling for richness uncovers a more complex scenario of drivers of biodiversity than previously observed.

Aim: Migratory animals regularly move between often distant breeding and non-breeding ranges. Knowledge about how these ranges are linked by movements of individuals from different populations is crucial for unravelling temporal variability in population spatial structuring and for identifying environmental drivers of population dynamics acting at different spatio-temporal scales. We performed a large-scale individual-based migration tracking study of the lesser kestrel (Falco naumanni), an Afro-Palearctic migratory raptor, to determine the patterns of migratory connectivity of European breeding populations. Location: Europe, Africa. Methods: Migration data were recorded using different devices (geolocators, Argos PTTs, GPS loggers) from 87 individuals breeding in the three core European populations, located in the Iberian, Italian and Balkan peninsulas. We estimated connectivity by the Mantel correlation coefficient (rM), and computed both the degree of separation between the non-breeding areas of individuals from the same population (i.e., the population spread) and the relative size of the non-breeding range (i.e., the non-breeding range spread). Results: European lesser kestrels migrated on a broad-front across the Mediterranean Sea and Sahara Desert, with different populations using different routes. Iberian birds migrated to western Sahel (Senegal, Mauritania, western Mali), Balkan birds migrated chiefly to central-eastern Sahel (Niger, Nigeria, Chad), whereas Italian ones spread from eastern Mali to Nigeria. Spatial differentiation of non-breeding areas led to a strong migratory connectivity (rM = 0.58), associated with a relatively high population (637 km) and non-breeding range (1149 km) spread. Main conclusions: Our comprehensive analysis of the non-breeding distribution of European lesser kestrel populations revealed a strong migratory connectivity, a rare occurrence in long-distance avian migrants. The geographic conformation of the species' breeding and non-breeding ranges, together with broad-front migration across ecological barriers, promoted the differentiation of migratory routes and non-breeding areas. Strong connectivity could then arise because of both high population spread and broad non-breeding range.