A detailed sensitivity analysis comparing how alternative modeling methods, varying data inputs, and treatment can influence model predictions

These data accompany task 3 as described in the final report, "Comparability of landscape connectivity products for large-scale landscape planning."

Data and Resources

Field Value
accessLevel public
bureauCode {010:00}
catalog_@context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
catalog_conformsTo https://project-open-data.cio.gov/v1.1/schema
catalog_describedBy https://project-open-data.cio.gov/v1.1/schema/catalog.json
identifier c888d6db-cf34-4e85-9982-5f79e16b2249
metadata_type geospatial
modified 2018-11-05
publisher LCC Network
resource-type Dataset
source_datajson_identifier true
source_hash a87ea837b17b77e79715dea36966cd46573af2db
source_schema_version 1.1
theme {geospatial}
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • amerigeo
  • amerigeoss
  • biosphere
  • biospheric-indicators
  • biota
  • calibration-validation
  • ckan
  • climate-indicators
  • data-analysis-and-visualization
  • earth-science
  • earth-science-services
  • ecological-dynamics
  • environment
  • geo
  • geoss
  • land-surface
  • landscape
  • landscape-ecology
  • landscape-management
  • landscape-patterns
  • landscape-processes
  • migratory-rates-routes
  • models
  • national
  • north-america
  • species-migration
  • species-population-interactions
  • united-states
isopen False
license_id notspecified
license_title License not specified
maintainer (Point of Contact, Principal Investigator); Landscape Conservation Cooperative Network (Point of Contact)
maintainer_email lccdatasteward@fws.gov
metadata_created 2025-11-20T22:50:19.230112
metadata_modified 2025-11-20T22:50:19.230116
notes These data accompany task 3 as described in the final report, "Comparability of landscape connectivity products for large-scale landscape planning."
num_resources 4
num_tags 28
title A detailed sensitivity analysis comparing how alternative modeling methods, varying data inputs, and treatment can influence model predictions