The Habitat Suitability Analysis was built using ArcGIS Pro's ModelBuilder tool. This program does not have an option to save the model's inputs as a relative file path. As a result, the model may not run because it's searching for each layer's original file path. If this happens, we have included a file titled Habitat_Suitability_Analysis_Script that outlines the processes we used to build the model. This submission contains three folders and three supplemental files. The folder titled "Data" includes all of the raw data and data input in the Habitat Suitability Analysis. The folder titled "Scripts" describes the steps to build the Habitat Suitability Analysis model in ArcGIS Pro. The Results folder contains the Habitat Suitability Analysis model and the data that was input into the model. The supplemental files are a file titled "Dryad_Folder_Contents" which describes the contents of every folder in this submission, and a file titled "Habitat_Suitability_Analysis_README" which contain...

Pacific lamprey (Entosphenus tridentata) are native fish to the Columbia River Basin. Over the past 60 years, anthropogenic disturbances have contributed to a 95% decline of historical population numbers. Member-tribes of the Columbia River Inter-Tribal Fish Commission have acknowledged the importance of Pacific lamprey to the Columbia River ecosystem and expressed concern about the loss of an essential tribal cultural resource. As a result, the Columbia River Inter-Tribal Fish Commission created the Tribal Pacific Lamprey Restoration Plan to halt their decline, re-establish the species, and restore the population to sustainable, harvestable levels throughout their historical range. Limited knowledge about the movement and preferred habitat of larval Pacific lamprey, such as optimal habitat conditions, demographic information, and species resilience, results in challenges to monitor and protect the species. Pacific lamprey is known to use the mainstem Columbia River to migrate between their spawning grounds and the Pacific Ocean. However, dams, levees, and culverts within the Columbia River Estuary and adjacent tributaries have restricted the lamprey's access to spawning grounds and other upstream habitats. These restrictions have prompted conservation and restoration efforts to better understand how Pacific lamprey utilizes the Columbia River Estuary. Here, we address these knowledge gaps in an effort to aid restoration initiatives by completing a Habitat Suitability Analysis to determine where optimal larval Pacific lamprey habitat may exist in the Columbia River Estuary. The project identified the spatial and temporal distribution of suitable habitat for larval Pacific lamprey and generated recommendations to address habitat-related knowledge gaps and further evaluate anthropogenic threats to their recovery. The results of the Habitat Suitability Analysis suggest that habitat conditions in the Columbia River itself are unable to support larval lamprey year-round, but may provide suitable habitat on a seasonal basis due to spatial and temporal limitations. However, we stress that our analyses were necessarily limited to aquatic conditions and that the temperature of the water column used in our analyses may differ from the temperature within fine sediments, where larval lamprey burrow. Our results imply that suitable lamprey habitat is present at times throughout the year in the Columbia River Estuary, and these locations can be used to support habitat restoration and conservation strategies for improving the species' recovery. Anthropogenic threats to the Columbia River continue to alter habitat conditions, including average water temperature, salinity, and sedimentation. Laboratory experiments have provided insight into the potential impacts of changing temperature and salinity on larval Pacific lamprey, where elevated water temperatures can affect their development and elevated salinity levels can result in larval mortality. In addition, anthropogenic disturbances such as dams, levees, and culverts have cut off the Columbia River Estuary's floodplain habitats from the mainstem Columbia River, decreased sedimentation rates, and separated adult lamprey from the floodplains and tributaries that they use to spawn. The presence of these barriers in the region can inhibit the distribution of fine sediments in the river, limiting where larval lamprey burrow and develop. The burrowing behavior of larval lamprey has yet to fully be investigated in the Columbia River Estuary. Limited research may be due to the lack of resources for studying Pacific lamprey's life cycle, habitat, and population dynamics since they are not federally designated as an endangered species, like resident salmonid species. This has further added to the challenge of understanding the species and restoring its population to sustainable numbers. To the best of our knowledge, this project is the first to explore spatial and temporal trends of suitable larval Pacific lamprey habitat conditions in the Columbia River Estuary. The Habitat Suitability Analysis provides technical information about the presence and distribution of suitable conditions to address habitat-related uncertainties. The member-tribes of the Columbia River Inter-Tribal Fish Commission and their collaborators can incorporate the information into current and future Pacific lamprey restoration, conservation, and education programs to enhance general understanding of lamprey populations throughout the Columbia River Basin. Key recommendations are provided to address additional knowledge gaps and prioritize future restoration projects in the Columbia River Basin including the refinement of the Habitat Suitability Analysis, evaluation of barrier effects on Pacific lamprey passage, and assessment of climate change scenarios on larval lamprey habitat. The Habitat Suitability Analysis uses salinity, temperature, and geomorphology data to identify suitable larval Pacific lamprey ...

Site Wind Right (SWR) is The Nature Conservancy’s approach to promoting smart, renewable wind energy in the right places, that is, where wind development is unlikely to encounter significant wildlife-related conflict, project delays and cost overruns. It represents a collaborative effort involving more than 50 scientists and GIS professionals from across the organization who built upon published studies of wind and wildlife interactions.This map shows the “wind belt” of the United States, a utility-scale, land-based 80-meter resolution wind speed map. Used to illustrate high wind resource areas within the central U.S., this map contains modified NREL and AWS Truepower wind maps (http://energy.gov/eere/wind/windexchange).

The interactive online map uses GIS technology and pulls from more than 100 data sets on wind resources, wildlife habitat, current land use and infrastructure to help inform siting decisions across 17 states in the Central US. The region encompasses nearly 80 percent of the country’s current and planned onshore wind capacity. The map was created to identify appropriate areas for wind develop, and is an important source of information for screening early in the project siting process. It can also be used to support application of the US Fish and Wildlife Service Land-Based Wind Energy Guidelines, specifically Tier 1 and Tier 2 evaluations.

Combining the SWR map with other land suitability factors suggests that over 1,000 gigawatts of wind energy may be developed exclusively in areas of low conservation impact. The results of this analysis indicate that it is possible to accelerate a clean, low-impact energy future, one that supports energy, climate and conservation goals.

Region: Great Plains/Midwest U.S.Partners: Local, state, and federal wildlife agencies, partners, and conservation professionals

Website: nature.org/sitewindrightContacts: Chris Hise [chise@tnc.org]; Nathan Cummins [ncummins@tnc.org]

Software: ArcMap (10.1-10.6), ArcGIS Pro (2.2), ModelBuilder, ArcGIS Online

Data sources: U.S. Fish and Wildlife Service, U.S. Geological Survey, The Nature Conservancy, State DNR and Heritage programs, and other data. For details, see methods paper, Appendix A (nature.org/sitewindright)

This layer contains American Community Survey (ACS) 2016-2020 5-year estimates in order to determine if a Census tract is considered an opportunity zone/low income community. According to Tax Code Section 45D(e), low income Census Tracts are based on the following criteria:The poverty rate is at least 20 percent, ORThe median family income does not exceed 80 percent of statewide median family income or, if in a metropolitan area, the greater of 80 percent statewide median family income or 80 percent of metropolitan area median family incomeThe layer is visualized to show if a tract meets these criteria, and the pop-up provides poverty figures as well as tract, metropolitan area, and state level figures for median family income. When a tract meets the above criteria, it may also qualify for grants or findings such Opportunity Zones. These zones are designed to encourage economic development and job creation in communities throughout the country by providing tax benefits to investors who invest eligible capital into these communities. Another way this layer can be used is to gain funding through the Inflation Reduction Act of 2022. The data was downloaded on October 5, 2022 from the US Census Bureau via data.census.gov:Table B17020: Poverty Status in the Past 12 Months - TractsTable B19113: Median Family Income in the Past 12 Months (in 2020 inflation-adjusted dollars) - Tracts, Metropolitan area, StateVintage of the data: 2016-2020 American Community SurveyBoundaries used for analysis: TIGER 2020 Tract, Metro, and State Boundaries with large hydrography removed from tractsData was processed within ArcGIS Pro 3.0.2 using ModelBuilder to spatially join the metropolitan and state geographies to tracts.To see the same qualification on 2010-based Census tracts, there is also an older 2012-2016 version of the layer.

Descarga aquí el metadato:https://aplicaciones.siatac.co/geonetwork/srv/spa/catalog.search#/metadata/1742d666-50c8-4573-823e-5c5189ac0bbdDescarga aquí el shapefile:https://opendata.arcgis.com/datasets/31b4f21bfb6047659d5bc2b335d99eff_0.zipCorresponde a la capa de cicatrices por quemas en la Amazonía colombiana desde marzo del 2017 a escala 1:100.000. Para generar esta capa se seleccionan las imágenes satelitales, del programa LandSat; deben tener menos del 30% de nubes. Se hace una verificación de la cantidad puntos de calor detectados durante el mes de monitoreo, para corroborar cuales Path Row que cubren la región amazónica (4-57, 4-58, 4-59, 4-60, 4-61, 4-62, 4-63, 9-59, 9-60, 7-58, 7-59, 7-60, 7-61, 5-57, 5-58, 5-59, 5-60, 5-61, 5-62, 3-57, 3-58, 3-59, 8-58, 8-59, 8-60, 6-57, 6-58, 6-59, 6-60, 6-61, 6-62) deben priorizarse para la descarga.Para el procesamiento y clasificación de las imágenes, y los diferentes geoprocesos se usan herramientas del software ArcGis (Esri, 2022a). Con este programa se aplican los “Model Builder” que se han generado para este procesamiento, los cuales hacen parte de los flujos de trabajo (Workflow) construidos en la plataforma SIATAC. Con las imágenes se generan dos composiciones de color RGB , (1) una que integra el Índice de Vegetación de Diferencia Normalizada - NDVI (B5-B4/B5+B4), el Radio Normalizado de Quema-NBR (B5-B7/ (B5+B7) y la banda del infrarrojo cercano -IR (B5); (2) la otra composición se hace con las bandas B7-B5-B2; estas composiciones resaltan las áreas que han sufrido procesos de quema de la vegetación (Murcia & Otavo, 2018).Con la composición RGB (1) se hace una clasificación no supervisada tipo clúster (Clúster Iso) (Esri, 2022b) y se generan 11 clases. Sobre esta capa ráster se hace una verificación visual para determinar cuál de las 11 clases corresponde a las cicatrices, este proceso se hace con respaldo en el protocolo metodológico (Murcia et al., 2018) y las dos composiciones ya generadas. Una vez seleccionada la clase que se ha verificado como cicatrices, se hace una reclasificación binaria de las unidades, en la que uno (1) son cicatrices y cero (0) las otras clases. En el mismo proceso (Model Builder) se hace la vectorización y se genera la capa de polígonos de cicatrices.Luego se hace una verificación visual de los polígonos generados, para descartar aquellos que no son cicatrices, para esto se aplican los criterios previstos en el protocolo metodológico (Murcia et al., 2018) teniendo como referente las dos composiciones previamente generadas. Con la capa resultado se hace un proceso de análisis espacial de intersección (Esri, 2022c) para descartar las cicatrices que ya fueron clasificadas en el mes anterior.A la capa resultante se le hace control de calidad para verificar la exactitud temática, validando aspectos como delimitación, errores por omisión y errores por comisión. De igual modo, se verifica que la capa cumpla con todos los criterios de topología como la correcta adyacencia entre polígonos, y se aprueba la capa.En el siguiente paso, la capa aprobada se integra en un WorkFlow (Esri, 2022d) de la base de datos en la plataforma SIG de Esri, del SIATAC. Luego se aplica un proceso SIG de intersección mediante el cual se clasifican las cicatrices que se ubican en áreas que eran bosques, según la capa de bosques más reciente generada por el IDEAM (Ideam, 2022). Sobre los polígonos restantes, se aplica el mismo proceso SIG (intersección) con la capa de coberturas de la tierra, del periodo más reciente (Sinchi, 2022) y se clasifican las cicatrices que se ubican en donde había vegetación secundaria u otras coberturas, principalmente pastos.La capa resultante se somete a un proceso de análisis espacial de intersección para generar la información de las cicatrices con el tipo de cobertura vegetal afectada, por cada Unidad Espacial de Referencia (UER): Grandes paisajes, Jurisdicción de Corporaciones Autónomas Regionales o de Desarrollo sostenible, Estado legal del territorio, Departamentos y Municipios. Para finalizar, las estadísticas se publican en el portal del Sistema de Información Ambiental Territorial de la Amazonia colombiana -SIATAC (https://siatac.co/cicatrices-de-quema/).BIBLIOGRAFÍAMurcia, U. & Otavo, S. (2018). La amazonia se quema: Detección de áreas con mayor ocurrencia de incendios de vegetación como estrategia para la prevención y control. Revista Colombiana Amazónica No 11 de 2018, 59-72. https://sinchi.org.co/11-revista-colombia-amazonica.Cañon I., Gordillo G., León A., Murcia U., Romero H., Velásquez M. (2018). Protocolo para el monitoreo de cicatrices por quemas en la Amazonia colombiana. 46pp.Esri. (2022a). ArcGIS Desktop.https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview.Esri. (2022b). Clasificación no supervisada de clúster ISO.https://pro.arcgis.com/es/pro-app/2.8/tool-reference/spatial-analyst/iso-cluster-unsupervised-classification.htmEsri. (2022c). Intersección (Análisis).https://pro.arcgis.com/es/pro-app/latest/tool-reference/analysis/intersect.htmEsri. (2022d). ArcGIS Workflow Manager (Análisis).https://www.esri.com/en-us/arcgis/products/arcgis-workflow-manager/overviewIdeam. (2022). Sistema de Monitoreo de bosques y carbono SMBYC.https://smbyc.ideam.gov.co/MonitoreoBC-WEB/reg/indexLogOn.jspSinchi. (2022). Sistema de Monitoreo de las Coberturas de la tierra de la Amazonia colombiana SIMCOBA. Datos abiertos.https://datos.siatac.co/pages/coberturasDiccionario de datos:objectid: Corresponde al identificador propio de cada registro dentro de la capa de informaciónarea_ha: Corresponde al área en hectáreas de la unidad seleccionadaarea_km2: Corresponde al área en kilómetros cuadrados de la unidad seleccionadaano: Corresponde al año de publicación de la cicatriz de quemaorigen: Corresponde a la cobertura que fue afectada por la cicatriz de quemames: Corresponde al mes de publicación de la cicatriz de quemafecha_registro: Corresponde a la fecha de publicación de la cicatriz de quemashape: Corresponde a geometría del elementost_area(shape): Corresponde al área del elementost_length(shape): Corresponde al perímetro del elementoFuente:Modelos de Funcionamiento y Sostenibilidad del Laboratorio SIG y SRBogotá D.C., Colombia siatac.coCalle 20 # 5 - 44Código Postal: 110311 Teléfono: +57 (1) 4442060Horario de atención: 8:00 am - 5:00 pm de Lunes a Viernes Información de contacto:Establecer previo contacto telefónico o a través de correo electrónico, para realizar la solicitud o fijar una cita en el horario de atención.