This dataset is a component of a complete package of products from the Connect the Connecticut project. Connect the Connecticut is a collaborative effort to identify shared priorities for conserving the Connecticut River Watershed for future generations, considering the value of fish and wildlife species and the natural ecosystems they inhabit. Click here to download the full data package, including all documentation.
This dataset depicts the potential capability of the landscape throughout the Connecticut River Watershed to provide habitat for Moose (Alces alces) based on environmental conditions existing in approximately 2010. Landscape capability integrates factors influencing climate suitability, habitat capability, and other biogeographic factors affecting the species’ prevalence in the area. All locations are scored on a scale from 0 to 1, with a value of 0 indicating no capacity to support the species and 1 indicating optimal conditions for the species.
This species dataset is one of a larger set of results developed by the Designing Sustainable Landscapes project led by Professor Kevin McGarigal of UMass Amherst. The species datasets developed under the project include the following:
1. Landscape capability datasets for a set of species intended to represent a broader set of wildlife species, and associated ecosystems, that collectively encompass a majority of the terrestrial, wetland, and coastal ecosystems of the Northeast. For each species, the datasets include projections of future landscape capability, taking into account several scenarios of possible future development, climate, and forest change, for the years 2030 and 2080.
2. Datasets for each species that compare 2010 results to future scenarios for 2030 and 2080. These include areas where the species could most likely be expected to persist, areas where it might be able colonize with future climate change, and areas where the species might experience a loss of suitable habitat.
More information and detailed documentation for the Designing Sustainable Landscapes project, which includes many additional datasets besides the species datasets, is available at: http://www.umass.edu/landeco/research/dsl/dsl.html.
The 2010 Northeast Landscape Capability Dataset for this species represents the integration of three models:
1. A habitat capability model developed using a spatially explicit, GIS-based wildlife habitat modeling framework called “HABIT@” developed by the Landscape Ecology Lab of the University of Massachusetts Amherst.
2. A climate niche model based on an analysis of the climate conditions (circa 2010) that are most suitable for the species in eastern North America.
3. A prevalence model intended to capture biogeographic factors influencing the distribution of a species that are not reflected in the habitat capability or climate niche models.
Both the climate niche and prevalence models are based on field surveys for the species (e.g., the Breeding Bird Survey).
The habitat capability models developed using HABIT@ reflect the quantity, quality, and accessibility (collectively referred to as “capability”) of habitat across the landscape for each year assessed. The habitat models are based on ecological settings grids (spatial datasets) developed for the Northeast, such as cover type (largely derived from the Northeast Terrestrial Habitat Map prepared by The Nature Conservancy and Northeastern states), roads and development, and forest biomass (for forest species). The models are spatially-explicit: the value at each cell (location) depends not only on the resources available at that cell, but on resources available in the neighborhood, on the configuration of those resources, and nearby impediments to movement. However, HABIT@ does not model population dynamics or population viability.
Detailed documentation on the development of all of the species datasets, including this Northeast Landscape Capability Dataset, are available at: http://www.umass.edu/landeco/research/dsl/documents/dsl_documents.html. The documentation includes a list of all the species for which models have been or are being developed and discusses limitations and constraints for using the datasets.