This Story Map describes the importance of the ocean and how it makes life on earth possible. This Story Map was compiled using the Cascade Story Map Template - Beta (Released on July 16, 2016).The content in the Story Map comes from Esri's Living Atlas of the World.

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and other as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new two-mapping model approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one crop type model to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the other crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively. With overall mapping accuracies for annual maps reaching in 60s to 70s percent, this approach shows strong potential for generating crop type maps of current year in September.

The regional ecological coherence scheme identifies ecological continuity of regional importance, i.e. the spaces necessary for species to ensure their life cycle (food, reproduction, shelter) and in particular their displacement. This diagram provides an overview of existing continuity. It also proposes actions to maintain or improve these continuity.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes:

Percent of neighborhood over 8% of slope

Slope Classes

0 - 20%

400

21% -50%

300

51% - 80%

200

81%

100

Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:

Change in elevation

Relief Class ID

0 – 30 meters

10

31 meter – 90 meters

20

91 meter – 150 meters

30

151 meter – 300 meters

40

301 meter – 900 meters

50

900 meters

60

The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:

Percent of neighborhood over 8% slope in upland or lowland areas

Profile Class

Less than 50% gentle slope is in upland or lowland

0

More than 75% of gentle slope is in lowland

1

50%-75% of gentle slope is in lowland

2

50-75% of gentle slope is in upland

3

More than 75% of gentle slope is in upland

4

Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

What happens in the vast stretches of the world's oceans - both wondrous and worrisome - has too often been out of sight, out of mind. The sea represents the last major scientific frontier on planet earth - a place where expeditions continue to discover not only new species, but even new phyla. The role of these species in the ecosystem, where they sit in the tree of life, and how they respond to environmental changes really do constitute mysteries of the deep. Despite technological advances that now allow people to access, exploit or affect nearly all parts of the ocean, we still understand very little of the ocean's biodiversity and how it is changing under our influence. The goal of the research presented here is to estimate and visualize, for the first time, the global impact humans are having on the ocean's ecosystems. Our analysis, published in Science, February 15, 2008 (http://doi.org/10.1126/science.1149345), shows that over 40% of the world's oceans are heavily affected by human activities and few if any areas remain untouched. This dataset contains raw stressor data from 17 different human activities that directly or indirectly have an impact on the ecological communities in the ocean's ecosystems. For more information on specific dataset, see the methods section. All data are projected in WGS 1984 Mollweide.

Human activities alter ecosystems everywhere, causing rapid biodiversity loss and biotic homogenization. These losses necessitate coordinated conservation actions guided by biodiversity and species distribution spatial data that cover large areas yet have fine-enough resolution to be management-relevant (i.e., ≤ 5 km). However, most biodiversity products are too coarse for management or are only available for small areas. Furthermore, many maps generated for biodiversity assessment and conservation do not explicitly quantify the inherent tradeoff between resolution and accuracy when predicting biodiversity patterns. Our goals were to 1) generate predictive models of overall breeding bird species richness and species richness of different guilds based on nine functional or life history-based traits across the conterminous US at three resolutions (0.5, 2.5, and 5 km), and 2) quantify the tradeoff between resolution and accuracy, and hence relevance for management, of the resulting biodiversity maps. We summarized eighteen years of North American Breeding Bird Survey data (1992-2019) and modeled species richness using random forests, including 66 predictor variables (describing climate, vegetation, geomorphology, and anthropogenic conditions), 20 of which we newly derived. Among the three spatial resolutions, the percent variance explained ranged from 27% to 60% (median = 54%; mean = 57%) for overall species richness and 12% to 87% (median = 61%; mean = 58%) for our different guilds. Overall species richness and guild-specific species richness were best explained at 5-km resolution using approximately 24 predictor variables based on percent variance explained, symmetric mean absolute percentage error, and root mean squared error values. However, our 2.5-km resolution maps were almost as accurate and provided more spatially detailed information, which is why we recommend them for most management applications. Our results represent the first consistent, occurrence-based, and nationwide maps of breeding bird richness with a thorough accuracy assessment that are also spatially detailed enough to inform local management decisions. More broadly, our findings highlight the importance of explicitly considering tradeoffs between resolution and accuracy to create management-relevant biodiversity products for large areas.

This aggregate-level dataset links poor relief data recorded on 1 January 1891 with several variables from corresponding 1891 census data, all at the level of the registration district (RD). Specifically, the numbers of men and women receiving indoor and outdoor relief in the ‘non-able-bodied’ category (taken as a proxy of the numbers of older-age men and women on relief) are accompanied with a series of socio-economic variables calculated from census data on the population aged 60 years and over (our definition of ‘old age’).

Thus, the dataset fulfils two objectives:

1. To start reconciling poor relief data from the House of Commons Parliamentary Papers archive with transcribed Integrated Census Microdata (I-CeM) available at the UK Data Service (UKDS).

2. To capture geographical variations in the proportion of older-age men and women on poor relief as well as in several household, occupational and migratory compositions recorded in the census, consulting data from 1891 as a pilot study in anticipation of an extended project covering all censuses from 1851-1911.

   The study of old age in history has generally had a narrow focus on welfare needs. Specific studies of the extreme poverty, or pauperism, of older people in late nineteenth-century London by Victorian contemporary Charles Booth (1840-1916) have remained remarkably influential for historical research on old age (Booth, 1894; Boyer and Schmidle, 2009). Old age is also examined through institutional care, particularly workhouse accommodation (Lievers, 2009; Ritch, 2014), while the subgroup of the elderly population that were not poor has been underexplored. However, my PhD thesis shows that pauperism was not a universal experience of old age between 1851 and 1911. Using transcribed census data for five selected counties in England and Wales, I find that pauperism was contingent upon many socio-economic factors recorded in census datasets, such as the occupational structure of older people, their living arrangements and their capacity to voluntarily retire from work based on their savings, land and capital. I find that, in some districts of the northern counties of Cheshire and the Yorkshire West Riding, the proportion of men described in the census as 'retired' and the proportion of women 'living on their own means' was greater than the respective proportions of men and women on welfare. For elderly men in particular, there were regional differences in agrarian work, where those in northern England are more likely to run smallholding 'family farms' whereas, in southern England, elderly men generally participate as agricultural labourers. I find that these differences play an important part in the likelihood of becoming pauperised, and adds to the idea of a north-south divide in old age pauperism (King, 2000). Furthermore, pauperism was predicated on the events and circumstances of people throughout their life histories and approaching their old age.

My fellowship will enable me to expand upon these findings through limited additional research that stresses an examination of the experiences of all older people in England and Wales. Old age has to be assessed more widely in relation to regional and geographical characteristics. In this way, we refine Booth's London-centric focus on the relationship between poverty and old age. My fellowship will achieve these objectives by systematically tracing the diversity of old age experiences. A pilot study will link welfare data recorded on 1 January 1891 from the House of Commons Parliamentary Papers archive with the socio-economic indicators contained in the 1891 census conducted on 5 April, all incorporated at the level of c. 650 registration districts in England and Wales. I will also visit record offices to extract data on the names of older people recorded as receiving welfare in materials related to the New Poor Law, thereby expanding on the PhD's examination of the life histories of older people.

With the key findings from my PhD presented above, I will spend my time addressing a wider audience on my research. As I will argue in blogs and webinars addressed to Age UK, the International Longevity Centre UK and History and Policy, a monolithic narrative of old age as associated with welfare dependency and gradual decline has been constructed since Booth's research in the late nineteenth century. This narrative has remained fixed through the growth of our ageing population, and the development of both old age pensions and the modern welfare state. My research alternatively uses historical censuses that reveal the economic productivity of older people in a manner that is not satisfactorily captured in present day discourse. I will also receive training on how to address my PhD to local schools, through the presentation of maps that present variations in the proportions of older people receiving welfare, and in the application of transcribed census data.

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. These data map hub and corridor elements within the green infrastructure. The Green Infrastructure Assessment was developed to provide decision support for Maryland's Department of Natural Resources land conservation programs. Methods used to identify and rank green infrastructure lands are intended soley for this use. Other applications are at the discretion of the user. The Maryland Department of Natural Resources is not responsible for any inaccuracies in the data and does not necessarily endorse any uses or products derived from the data other than those for which the data were originally intended. Maryland's green infrastructure is a network of undeveloped lands that provide the bulk of the state's natural support system. Ecosystem services - such as cleaning the air - filtering water - storing and cycling nutrients - conserving soils - regulating climate - and maintaining hydrologic function - are all provided by the existing expanses of forests - wetlands - and other natural lands. These ecologically valuable lands also provide marketable goods and services - like forest products - fish and wildlife - and recreation. The Green Infrastructure serves as vital habitat for wild species and contributes in many ways to the health and quality of life for Maryland residents. To identify and prioritize Maryland's green infrastructure - we developed a tool called the Green Infrastructure Assessment (GIA). The GIA was based on principles of landscape ecology and conservation biology - and provides a consistent approach to evaluating land conservation and restoration efforts in Maryland. It specifically attempts to recognize: a variety of natural resource values (as opposed to a single species of wildlife - for example) - how a given place fits into a larger system - the ecological importance of natural open space in rural and developed areas - the importance of coordinating local - state and even interstate planning - and the need for a regional or landscape-level view for wildlife conservation. The GIA identified two types of important resource lands - hubs"" and ""corridors."" Hubs typically large contiguous areas - separated by major roads and/or human land uses - that contain one or more of the following: Large blocks of contiguous interior forest (containing at least 250 acres - plus a transition zone of 300 feet) Large wetland complexes - with at least 250 acres of unmodified wetlands; Important animal and plant habitats of at least 100 acres - including rare - threatened - and endangered species locations - unique ecological communities - and migratory bird habitats; relatively pristine stream and river segments (which - when considered with adjacent forests and wetlands - are at least 100 acres) that support trout - mussels - and other sensitive aquatic organisms; and existing protected natural resource lands which contain one or more of the above (for example - state parks and forests - National Wildlife Refuges - locally owned reservoir properties - major stream valley parks - and Nature Conservancy preserves). In the GIA model - the above features were identified from Geographic Information Systems (GIS) spatial data that covered the entire state. Developed areas and major roads were excluded - areas less than 100 contiguous acres were dropped - adjacent forest and wetland were added to the remaining hubs - and the edges were smoothed. The average size of all hubs in the state is approximately 2200 acres. Corridors are linear features connecting hubs together to help animals and plant propagules to move between hubs. Corridors were identified using many sets of data - including land cover - roads - streams - slope - flood plains - aquatic resource data - and fish blockages. Generally speaking - corridors connect hubs of similar type (hubs containing forests are connected to one ano

Persons with severe and persistent mental illness (SPMI) make up a vulnerable group within mental healthcare and society. Not only do they suffer from long-term, serious psychiatric disorders; they often also experience considerable problems in their psychosocial functioning. Research has disclosed that the care needs of this target group are complex, and that the life expectancy of these persons is significantly lower than in the general population. Given (1) the lower life expectancy of persons with SPMI, (2) the higher suicide risk related to mental disorders, and (3) the legalization and practice of medical assistance in dying in an increasing number of countries, it is of utmost importance to map the ethical aspects and challenges of end-of-life care needs in persons with SPMI. Therefore, we charted the way end-of-life care is provided for them by means of a scoping review of the scientific literature, with an emphasis on the ethical aspects surrounding it. We explore existing ethical dilemmas; the underlying ethical values, principles and attitudes; and the locus and stakeholders of ethical dialog regarding end-of-life care in persons with SPMI. The results indicate that the four guiding principles of biomedical ethics can well be identified in the literature, and are each addressed in their own specific way: Autonomy in relation to questions regarding the decision-making capacity of persons with SPMI; Justice in relation to access to quality care and the presence of stigma; and Non-maleficence and Beneficence in relation to the ongoing debate regarding the benefits and obstacles in applying palliative care approaches in the context of psychiatry, and the status of the futility-concept therein. Personal virtues and attitudes in care professionals, like compassion, non-abandonment and upholding dignity are key, as care professionals are the main advocates of persons with SPMI, which often lack an extensive social network. Further, we find that the ethical dialog is mainly focused on care professionals and relatives, rather than the persons with SPMI themselves. This is reflected in the existing research that often had the voices of the latter missing. Future research may benefit from the inclusion of persons with SMPI’s first-hand accounts. End-of-life care for persons with SPMI may benefit from identifying and integrating (locally developed) good practices like cross-sectoral education, specific care models, and ethics support.

This layer is subset of World Ecological Facets Landform Classes Image Layer. Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Aim: Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend’s big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions. Methods: We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species’ distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts. Results: The estimated distribution differed between the combined (full dataset) and phenologically-explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scnearios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Main conclusion: Comparison of phenologically-explicit models with combined models indicate the combined models better predict the extent of the known range of C. townsendii in California. However, life history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species’ responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions. Methods Study area and survey data The study area covers the U.S. state of California, which has steep environmental gradients that support an array of species (Dobrowski et al. 2011). Because California is ecologically diverse, with regions ranging from forested mountain ranges to deserts, we examined local environmental needs by modeling at both the state-wide and ecoregion scale, using U.S. Environmental Protection Agency (EPA) Level III ecoregion designations and there are thirteen Level III ecoregions in California (Table S1.1) (Griffith et al. 2016). Species occurrence data used in this study were from a statewide survey of C. townsendii in California conducted by Harris et al. (2019). Briefly, methods included field surveys from 2014-2017 following a modified bat survey protocol to create a stratified random sampling scheme. Corynorhinus townsendii presence at roost sites was based on visual bat sightings. From these survey efforts, we have visual occurrence data for 65 maternity roosts, 82 hibernation roosts (hibernacula), and 91 active-season non-maternity roosts (transition roosts) for a total of 238 occurrence records (Figure 1, Table S1.1). Ecogeographical factors We downloaded climatic variables from WorldClim 2.0 bioclimatic variables (Fick & Hijmans, 2017) at a resolution of 5 arcmin for broad-scale analysis and 30 arcsec for our ecoregion-specific analyses. To calculate elevation and slope, we used a digital elevation model (USGS 2022) in ArcGIS 10.8.1 (ESRI, 2006). The chosen set of environmental variables reflects knowledge on climatic conditions and habitat relevant to bat physiology, phenology, and life history (Rebelo et al. 2010, Razgour et al. 2011, Loeb and Winters 2013, Razgour 2015, Ancillotto et al. 2016). To trim the global environmental variables to the same extent (the state of California), we used the R package “raster” (Hijmans et al. 2022). We performed a correlation analysis on the raster layers using the “layerStats” function and removed variables with a Pearson’s coefficient > 0.7 (see Table 1 for final model variables). For future climate conditions, we selected three general circulation models (GCMs) based on previous species distribution models of temperate bat species (Razgour et al. 2019) [Hadley Centre Global Environment Model version 2 Earth Systems model (HadGEM3-GC31_LL; Webb, 2019), Institut Pierre-Simon Laplace Coupled Model 6th Assessment Low Resolution (IPSL-CM6A-LR; Boucher et al., 2018), and Max Planck Institute for Meteorology Earth System Model Low Resolution (MPI-ESM1-2-LR; Brovkin et al., 2019)] and two contrasting greenhouse concentration trajectories (Shared Socio-economic Pathways (SSPs): a steady decline pathway with CO2 concentrations of 360 ppmv (SSP1-2.6) and an increasing pathway with CO2 reaching around 2,000 ppmv (SSP5-8.5) (IPCC6). We modeled distribution for present conditions future (2061-2080) time periods. Because one aim of our study was to determine the consequences of changing climate, we changed only the climatic data when projecting future distributions, while keeping the other variables constant over time (elevation, slope). Species distribution modeling We generated distribution maps for total occurrences (maternity + hibernacula + transition, hereafter defined as “combined models”), maternity colonies , hibernacula, and transition roosts. To estimate the present and future habitat suitability for C. townsendii in California, we used the maximum entropy (MaxEnt) algorithm in the “dismo” R package (Hijmans et al. 2021) through the advanced computing resources provided by Texas A&M High Performance Research Computing. We chose MaxEnt to aid in the comparisons of state-wide and ecoregion-specific models as MaxEnt outperforms other approaches when using small datasets (as is the case in our ecoregion-specific models). We created 1,000 background points from random points in the environmental layers and performed a 5-fold cross validation approach, which divided the occurrence records into training (80%) and testing (20%) datasets. We assessed the performance of our models by measuring the area under the receiver operating characteristic curve (AUC; Hanley & McNeil, 1982), where values >0.5 indicate that the model is performing better than random, values 0.5-0.7 indicating poor performance, 0.7-0.9 moderate performance and values of 0.9-1 excellent performance (BCCVL, Hallgren et al., 2016). We also measured the maximum true skill statistic (TSS; Allouche, Tsoar, & Kadmon, 2006) to assess model performance. The maxTSS ranges from -1 to +1:values <0.4 indicate a model that performs no better than random, 0.4-0.55 indicates poor performance, (0.55-0.7) moderate performance, (0.7-0.85) good performance, and values >0.80 indicate excellent performance (Samadi et al. 2022). Final distribution maps were generated using all occurrence records for each region (rather than the training/testing subset), and the models were projected onto present and future climate conditions. Additionally, because the climatic conditions of the different ecoregions of California vary widely, we generated separate models for each ecoregion in an attempt to capture potential local effects of climate change. A general rule in species distribution modeling is that the occurrence points should be 10 times the number of predictors included in the model, meaning that we would need 50 occurrences in each ecoregion. One common way to overcome this limitation is through the ensemble of small models (ESMs) (Breiner et al. 2015., 2018; Virtanen et al. 2018; Scherrer et al. 2019; Song et al. 2019) included in ecospat R package (references). For our ESMs we implemented MaxEnt modeling, and the final ensemble model was created by averaging individual bivariate models by weighted performance (AUC > 0.5). We also used null model significance testing with to evaluate the performance of our ESMs (Raes and Ter Steege 2007). To perform null model testing we compared AUC scores from 100 null models using randomly generated presence locations equal to the number used in the developed distribution model. All ecoregion models outperformed the null expectation (p<0.002). Estimating range shifts For each of the three GCMs and each RCP scenario, we converted the probability distribution map into a binary map (0=unsuitable, 1=suitable) using the threshold that maximizes sensitivity and specificity (Liu et al. 2016). To create the final maps for each SSP scenario, we summed the three binary GCM layers and took a consensus approach, meaning climatically suitable areas were pixels where at least two of the three models predicted species presence (Araújo and New 2007, Piccioli Cappelli et al. 2021). We combined the future binary maps (fmap) and the present binary maps (pmap) following the formula fmap x 2 + pmap (from Huang et al., 2017) to produce maps with values of 0 (areas not suitable), 1 (areas that are suitable in the present but not the future), 2 (areas that are not suitable in the present but suitable in the future), and 3 (areas currently suitable that will remain suitable) using the raster calculator function in QGIS. We then calculated the total area of suitability, area of maintenance, area of expansion, and area of contraction for each binary model using the “BIOMOD_RangeSize” function in R package “biomod2” (Thuiller et al. 2021).

The requirement to display sensitive areas relating to the life history of commercially important fish species in British waters is well recognized. Sensitive areas have previously been described as spawning and nursery grounds. Here we consider only areas where there is evidence of aggregations of 0 group fish and/or larvae of key commercial species. 0 group fish are defined as fish in the first year of their lives. These fish sensitivity maps were originally generated to provide a spatial and temporal description of where physical damage could potentially occur to fish species at sensitive stages in essential habitats of their life cycle. Sources of damage in this context referred to seismic surveying conducted by the offshore Oil and Gas industry during their site investigations. In addition to the acoustic energy that the seismic survey activities generate, we should now add other percussive impact noises from pile driving seabed foundation pins into the seabed, such as those required for offshore renewable energy sites. The spatial location of these fish life history events and their potential interaction with offshore industries can heavily influence the planning, costs and delivery of these offshore developments. It is imperative that these maps reflect the current extent of these areas.

Earth is the only terrestrial planet in the solar system with continents, and hence understanding their evolution is vital to unravelling what makes Earth special – our liquid oceans, oxygenated atmosphere, and ultimately, life. The continental crust is also host to all our mineable mineral deposits, and hence it has played a key role in the establishment of human civilisation. This link between the crust and human development will be even more prominent through the need for critical metals, as our society transitions toward green technologies. In this talk, we will discuss the link between the time-space evolution of the continental crust and the location of major mineral systems. By using isotopic data from micron-scale zircon crystals, we can map the crustal architectures that control the large-scale localisation of numerous mineral provinces. This work demonstrates the intimate link between the evolution of the continents, the understanding of mineral systems, and ultimately our continued evolution as an industrialised society.

These data depict areas of the U.S. where NAWCA projects should be developed and favored based on benefits to wetland-associated shorebirds. The idea is that protecting, enhancing, or restoring wetlands within these landscapes has greater value than conducting these same conservation activities outside the landscape boundary. Shorebirds include families of birds that dependent on wetland, grassland, and beach habitats to complete portions of their life cycles, such as, avocets and stilts, plovers, oystercatchers, sandpipers, and phalaropes. Technical Question #3 for the North American Wetlands Conservation Act (NAWCA) asks applicants to describe how the proposed grant and match activities will address the national and/or continental geographic priorities for wetland habitat conservation as outlined in the four major migratory bird conservation plans and makes reference to geographic priority maps for these bird groups. These data were compiled by the U.S. Shorebird Conservation Partnership in consultation with regional U.S. Fish and Wildlife Service Migratory Bird biologists, state shorebird biologists, NGO shorebird experts, and staff of the bird habitat Joint Ventures to develop a national-scale map that depicts areas where habitat acquisition, enhancement, and restoration would yield the greatest benefits for the highest priority wetland-associated shorebird species. The original shorebird map illustrated coarse areas (i.e. blobs) of national importance to shorebirds (2004). These areas focused on wetland landscapes that were used by large numbers of migrant shorebirds (>20,000 individuals) or that supported high densities of breeding or wintering shorebirds. The landscape approach was chosen because of the geographic variability in shorebird use of discrete versus dispersed sites throughout the nation and the attempt to balance use during the breeding season with migration and wintering. The same landscape approach was used for the 2012 revision, with the goal of improving the usefulness of the coarse map. The 2004 “blob” map did not define very precisely the boundary of important areas. To remedy that problem, we used (generally) the EPA’s Level IV Ecoregion boundaries, which are available for every state except Alaska and Hawaii. We produced state-scale maps designating refinements to the previous blob map for review. Maps originate from one national, scalable shapefile that will be available to applicants and other users. Although the idea of providing levels of importance to sites (e.g., WHSRN categories) has been previously considered, we believe the complexity would be difficult to portray on a single map, particularly considering the differences between breeding and migrations sites discussed above. Regional importance of sites to shorebirds is assessed separately as a second part of Technical Question #3.

What happens in the vast stretches of the world's oceans - both wondrous and worrisome - has too often been out of sight, out of mind. The sea represents the last major scientific frontier on planet earth - a place where expeditions continue to discover not only new species, but even new phyla. The role of these species in the ecosystem, where they sit in the tree of life, and how they respond to environmental changes really do constitute mysteries of the deep. Despite technological advances that now allow people to access, exploit or affect nearly all parts of the ocean, we still understand very little of the ocean's biodiversity and how it is changing under our influence. The goal of the research presented here is to estimate and visualize, for the first time, the global impact humans are having on the ocean's ecosystems. Our analysis, published in Science, February 15, 2008 (http://doi.org/10.1126/science.1149345), shows that over 40% of the world's oceans are heavily affected by human activities and few if any areas remain untouched. Global data for marine ecosystems are largely non-existent; here we used available data for several ecosystems, modeled the distribution of many other ecosystems, and assumed a uniform distribution for several intertidal ecosystems for which no data exist. We recognize that differences exist in how people classify ecosystems; for example, estuaries are often considered an ecosystem, but here we focus on the ecosystems (also often labeled ‘habitats’) that occur within estuaries (salt marsh, intertidal mud, beach, soft sediment, mangroves, etc.). All ecosystem data were represented at 1 km2 resolution. This dataset contains maps for 20 distinct marine ecosystems used in the impacts model. More information on data sources can be found in the methods section.

Carbon Dioxide (Difference from Global Mean, Best Available, OCO-2) from NASA GIBSTemporal coverage: 2002 SEP - 2012 FEBThe Carbon Dioxide (L3, Free Troposphere, Monthly) layer displays monthly Carbon Dioxide in the free troposphere. It is created from the AIRX3C2M data product which is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Monthly Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is monthly gridded data at 2.5x2 degreee (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This quantity is not a total column quantity because the sensitivity function of the AIRS mid-tropospheric CO2 retrieval system peaks over the altitude range 6-10 km. The quantity is what results when the true atmospheric CO2 profile is weighted, level-by-level, by the AIRS sensitivity function.The Atmospheric Infrared Sounder (AIRS), in conjunction with the Advanced Microwave Sounding Unit (AMSU), senses emitted infrared and microwave radiation from Earth to provide a three-dimensional look at Earth's weather and climate. Working in tandem, the two instruments make simultaneous observations down to Earth's surface. With more than 2,000 channels sensing different regions of the atmosphere, the system creates a global, three-dimensional map of atmospheric temperature and humidity, cloud amounts and heights, greenhouse gas concentrations and many other atmospheric phenomena. Launched into Earth orbit in 2002, the AIRS and AMSU instruments fly onboard NASA's Aqua spacecraft and are managed by NASA's Jet Propulsion Laboratory in Pasadena, California. More information about AIRS can be found at https://airs.jpl.nasa.gov.References: AIRX3C2M doi:10.5067/Aqua/AIRS/DATA339ABOUT NASA GIBSThe Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.The Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (originally known as EOS AM-1) and Aqua (originally known as EOS PM-1) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.GIBS Available Imagery ProductsThe GIBS imagery archive includes approximately 1000 imagery products representing visualized science data from the NASA Earth Observing System Data and Information System (EOSDIS). Each imagery product is generated at the native resolution of the source data to provide "full resolution" visualizations of a science parameter. GIBS works closely with the science teams to identify the appropriate data range and color mappings, where appropriate, to provide the best quality imagery to the Earth science community. Many GIBS imagery products are generated by the EOSDIS LANCE near real-time processing system resulting in imagery available in GIBS within 3.5 hours of observation. These products and others may also extend from present to the beginning of the satellite mission. In addition, GIBS makes available supporting imagery layers such as data/no-data, water masks, orbit tracks, and graticules to improve imagery usage.The GIBS team is actively engaging the NASA EOSDIS Distributed Active Archive Centers (DAACs) to add more imagery products and to extend their coverage throughout the life of the mission. The remainder of this page provides a structured view of the layers currently available within GIBS grouped by science discipline and science observation. For information regarding how to access these products, see the GIBS API section of this wiki. For information regarding how to access these products through an existing client, refer to the Map Library and GIS Client sections of this wiki. If you are aware of a science parameter that you would like to see visualized, please contact us at support@earthdata.nasa.gov. https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+Available+Imagery+Products#expand-AerosolOpticalDepth29ProductsNASA GIS API for Developers https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+API+for+Developers

Intermittent streams that cease to flow during dry periods represent more than half of the global river network and are particularly common in arid and semi-arid regions. They are characterized by high spatial and temporal variability in aquatic habitat, forming a shifting habitat mosaic that supports diverse assemblages of native and endemic species. Climate projections for much of the world predict greater variation in precipitation and increasing drought severity, suggesting a need to better understand species’ responses to habitat variability within intermittent stream ecosystems. Here we explored changes in the distribution and abundance of aquatic vertebrates in response to a wide range of annual hydrologic conditions within a Mediterranean-climate intermittent stream in California. We conducted wetted habitat and vertebrate abundance surveys during the dry season for seven years, spanning both extreme wet and extreme dry annual precipitation conditions, and characterized the variation in the size, persistence, and spatial configuration of wetted habitats in relation to the observed abundance and composition of fishes, amphibians, and aquatic reptiles. We characterized the habitat mosaic using a k-means clustering approach and identified three habitat types with distinct size, persistence, and connectivity distributions. We found that some wetted habitat units persisted across all years, regardless of antecedent precipitation, whereas others dried in all but the wettest years. We determined that persistent pools, a subset of wetted habitats present in the stream, support diverse assemblages of native species even during extreme dry conditions, while transient pools act as important habitat for particular species and life stages, including a young-of-year minnow species. Linear mixed models indicated that species’ abundances were influenced by habitat quantity, quality, and connectivity. Our results suggest that intermittent streams can be characterized as shifting habitat mosaics that support unique species and life stages across space and time and play an important role in maintaining regional aquatic biodiversity. Methods Dataset Collection Wetted Channel Surveys We conducted surveys of wetted habitat conditions from 2014 to 2021 (except 2020, due to Covid-19 restrictions) along the 5-kilometer study reach in Coyote Creek (Figure 1b). In 2014, we surveyed at the end of the dry season (i.e., September; Bogan et al. 2019). Beginning in 2015, we expanded the study to include monthly surveys from the onset of disconnection (typically July or August) through the end of the dry season (i.e., until the first autumn rainfall, typically October or November). The timespan within which surveys were conducted encompassed extreme low (2021: 263 mm) and high (2017: 1011 mm) precipitation years, including the final years of a statewide multi-year drought between 2012-2016, capturing the wide range of interannual hydrologic variability observed in the system. Between 2014-2016, monthly wet-dry mapping surveys were conducted by field crews walking the channel with a handheld Garmin eTrex GPS unit (+/- 15 m accuracy) and recording the coordinates at every point of surface water disconnection and reconnection along the channel. Disconnected reaches were defined as portions of the stream channel where there was a break in continuous surface water connectivity, with surface water patches being at least 1 m in length and > 0.10 meters deep. These coordinates were then used to digitize a streamline in ArcGIS 10.7.1 representing wet and dry reaches at each survey. Between 2017-2021, surveys were conducted by field crews using a Bad Elf GPS (+/- 1 m accuracy) connected to ArcCollector 21.0.4 on a tablet, using the same methods to map the presence of surface water. This latter method automatically digitizes and georeferences the wet and dry reaches along the stream. We used these data to create monthly maps of wetted habitat conditions across all years of the study. Habitat and Biological Surveys Along our 5-kilometer study stretch where we conducted wet-dry mapping, we identified five representative 250-meter reaches for more intensive habitat and biological sampling (Figure 1b). We conducted habitat and biological surveys for each wetted habitat unit within each 250-meter reach. A wetted habitat unit was defined as any disconnected surface water patch, which included both flowing reaches and isolated pools, with an area greater than 30 cm2. This size threshold was chosen as habitat patches smaller than 30 cm2 were unlikely to support aquatic vertebrate species. For each wetted habitat unit, presence and abundances of vertebrates were estimated using a rapid assessment approach, in which species abundance was estimated by visually scanning the wetted habitat unit for amphibians, reptiles, and fishes in the water column (see the complete taxa list on Table 1). Water clarity in Coyote Creek is generally high during the dry season, and species can be identified at depths of 2 m (Bogan et al., 2019). We also documented abundances of California floater mussels (Anodonta californiensis) because they are large (up to 12cm), have a life stage dependent upon freshwater fishes, and are of conservation concern. In addition to the visual assessment, we conducted spot sampling in undercut areas and along the benthos using a dip-net to identify benthic-oriented vertebrates. Each species observed was grouped into one of five abundance classes for analyses based on visual estimation: 1-10; 11-100; 101-500; 501-1000; >1000 individuals. Visual estimation of abundance was completed by two observers (typically RAL and SMC). We distinguished between juvenile and adult life stages for some amphibians (Rana draytonii, red-legged frog; Rana boylii, yellow-legged frog) and fish (Hesperoleucus venustus, coastal roach; Catostomus occidentalis, Sacramento sucker; Ptychocheilus grandis, Sacramento pikeminnow). To standardize sampling effort among the units, the time spent sampling was proportional to the surface area of the habitat unit. For each wetted habitat unit, we collected and compiled a suite of metrics that relate to habitat suitability, including measures of quantity, quality, and connectivity. We measured the maximum length, maximum width, and maximum depth of each wetted habitat unit as habitat quantity metrics. We also calculated a series of connectivity measures for each habitat unit. These include distance to nearest patch, which is the Euclidean distance from the focal habitat unit to the nearest habitat unit, and distance to permanent pool, which is the Euclidean distance from the focal unit to the nearest permanent pool, which are a subset of pools that persisted during the driest conditions surveyed. Distance-weighted area was calculated as the distance to the nearest wetted habitat unit, divided by the area of the focal habitat unit. To characterize the long-term wetted persistence of the focal unit, we calculated the proportion of years in which the focal habitat unit had surface water in the month of September. Beginning in 2017, we additionally measured several physicochemical parameters, including water temperature, dissolved oxygen, pH, and conductivity at each wetted habitat unit.

Background: Although Daphnia is increasingly recognized as a model for ecological genomics and biomedical research, there is, as of yet, no high-resolution genetic map for the genus. Such a map would provide an important tool for mapping phenotypes and assembling the genome. Here we estimate the genome size of Daphnia magna and describe the construction of an SNP array based linkage map. We then test the suitability of the map for life history and behavioural trait mapping. The two parent genotypes used to produce the map derived from D. magna populations with and without fish predation, respectively and are therefore expected to show divergent behaviour and life-histories. Results: Using flow cytometry we estimated the genome size of D. magna to be about 238 mb. We developed an SNP array tailored to type SNPs in a D. magna F2 panel and used it to construct a D. magna linkage map, which included 1,324 informative markers. The map produced ten linkage groups ranging from 108.9 to 203.6 cM, with an average distance between markers of 1.13 cM and a total map length of 1,483.6 cM (Kosambi corrected). The physical length per cM is estimated to be 160 kb. Mapping infertility genes, life history traits and behavioural traits on this map revealed several significant QTL peaks and showed a complex pattern of underlying genetics, with different traits showing strongly different genetic architectures. Conclusions: The new linkage map of D. magna constructed here allowed us to characterize genetic differences among parent genotypes from populations with ecological differences. The QTL effect plots are partially consistent with our expectation of local adaptation under contrasting predation regimes. Furthermore, the new genetic map will be an important tool for the Daphnia research community and will contribute to the physical map of the D. magna genome project and the further mapping of phenotypic traits. The clones used to produce the linkage map are maintained in a stock collection and can be used for mapping QTLs of traits that show variance among the F2 clones.

Future Land Use designation based on zoning dataset. It shows the categories of land uses desired over time, and their intensities. The map reflects the land uses that correspond to the long term vision, goals and policies expressed in the master plan, and it constitutes the most direct link between the Master Plan and the Comprehensive Zoning Ordinance. It is important to note, however, that the Future Land Use Map is not a zoning map and it does not govern design or function.Zoning regulates land use to promote smart growth and preserve the quality of life in communities. Permitted Use are allowed by right, subject to compliance with appropriate standards. Conditional Use require City Planning Commission review with a recommendation forwarded to the City Council for final action.

These data depict areas of the U.S. where NAWCA projects should be developed and favored based on benefits to wetland-associated landbirds. Landbirds is a term used to encompass families of birds dependent on terrestrial habitats to complete portions of their life cycles, including warblers, thrushes, sparrows, finches, hummingbirds, flycatchers, raptors and other groups.Technical Question #3 for the North American Wetlands Conservation Act (NAWCA) asks applicants to describe how the proposed grant and match activities will address the national and/or continental geographic priorities for wetland habitat conservation as outlined in the four major migratory bird conservation plans and makes reference to geographic priority maps for these bird groups. These data were compiled by the Partners in Flight Science Committee in collaboration with the Bird Habitat Joint Ventures to develop a national-scale map that depicts areas where habitat acquisition, restoration and management would yield the greatest benefits for the highest priority wetland-associated landbird species, including White-crowned Pidgeon, Mangrove Cuckoo, Willow Flycatcher (southwestern subspecies), Yellow-billed Cuckoo (western subspecies), Nelson’s Sparrow, Seaside Sparrow, Saltmarsh Sparrow, Tri-colored Blackbird, Rusty Blackbird, & Prothonotary Warbler. This map was created through consultation with Bird Habitat Joint Ventures (JVs). JV Science Coordinators were asked to identify and justify priority areas, reconfiguring, adding, or eliminating areas identified on the previous version of this map (2005). Justifications were requested to be based on best-available, referenced population data, although no specific criteria were provided for inclusion. The development of consistent inclusion criteria is stymied by the absence of national and regional scale information on abundance and densities for most birds. This lack of data, coupled with the wide diversity and habitat usage of wetlands by landbirds, makes it very difficult to derive a consistent data-driven method for describing important landscapes for wetland-associated landbirds and setting standard thresholds for national importance. Fortunately, the data we need to generate a data-driven map of important areas during the annual cycle is being collected in disparate geographies and more areas are coming online every year; these efforts will lead to a greater understanding of the distribution / relative abundance / trends of landbirds which will improve subsequent map revisions. JVs were also asked to provide digital shape files based on actual spatial data (wetland, soils, or topographic data), wherever possible, for compilation into a modern GIS. Once submissions from JVs were compiled, they were reviewed by the Partners in Flight Science Committee, whose combined knowledge and experience encompassed the taxonomic diversity of landbirds across the U.S. The committee considered the map and justifications holistically as well as specifically according to their expertise.The committee endorsed the submissions, recognizing that the short timeline given to Joint Ventures precluded better coordination of priority areas across JV boundaries. The map is an improvement to the previous version, but additional iteration would be improved by additional deliberation and decisions about stringency and comparability of justifications; use of objective criteria to the extent possible given data limitations or at least attempts towards consistent philosophies underpinning subjective decisions; appropriate levels of resolution in depiction of identified areas, and most importantly, inclusion of additional field information or mapping refinements as they become available in the future.For more information, contact Ken Rosenberg at kvr2@cornell.edu and/or relevant JV staff for particular areas.