As the climate continues to change, vulnerable wildlife species will need specific management strategies to help them adapt to these changes. One specific management strategy is based on the idea that some locations that species inhabit today will remain suitable over time and should be protected. The climate conditions at those locations will continue to be good enough for species to survive and breed successfully and are referred to as climate refugia. Another management strategy is based on the idea that species will need to shift across the landscape to track suitable conditions and reach climate refugia locations as climate and land uses change over time. The more opportunities we can give species to safely move across the [...]
Summary
As the climate continues to change, vulnerable wildlife species will need specific management strategies to help them adapt to these changes. One specific management strategy is based on the idea that some locations that species inhabit today will remain suitable over time and should be protected. The climate conditions at those locations will continue to be good enough for species to survive and breed successfully and are referred to as climate refugia. Another management strategy is based on the idea that species will need to shift across the landscape to track suitable conditions and reach climate refugia locations as climate and land uses change over time.
The more opportunities we can give species to safely move across the landscape, by linking between their preferred habitat patches, the more likely they will be to adapt to climate change. For example, animals that live in emergent wetlands are more likely to move across the landscape in habitats similar to emergent wetlands. Similarly, animals that breed in coniferous forests are likely to prefer to move through similar coniferous forests. Many conservation tools that have been developed do not take animals’ habitat preferences into account. This project aims to fill this gap by mapping the connections across the northeastern U.S. for each of the major ecosystems present in the region. This will inform conservation managers in decision-making about how to best manage locations to allow for animal movement across the landscape, even if they don’t know specifically where the animals may end up in future.
For the modeling work we will develop and update existing APL and R code to: Objective 1 (ecosystem refugia): delineate cores, build conductance and connectors among the cores, construct graph (network) representations of the connections among cores, score cores based on the graph; Objective 2 (ecosystem-specific regional connectivity): to map connectivity; and Objective 3: to combine connectivity with development probability into and index of vulnerability.
The Web Tool will be built using geoserver, Leaflet, and Shiny.
source
https://bitbucket.org/eplunkett/lcad contains existing R code for the DSL project. Model code created for this project will be added there.
The Web Tool source code will be posted in https://github.com/UMassDSL/WebTools and be mirrored in the USGS GitHub repository.
webToolMaintenanceAndSupport
Core web functionality will be built using Leaflet and Shiny. These are well-supported open-source libraries, which we expect to have a long shelf-life. While commercial hosting of the proposed website represents a viable fallback, we have begun exploring options for long-term hosting at UMass, where server maintenance and security will be handled by UMass Information Technology. The primary data sources used for this project have been developed in our lab, so we can update them as needed. When we apply for future funding for work that updates or creates new data to be used in the portal, we expect to include the cost of migrating data in those specific proposals.
languages
Models: R and APL.
Web Tool: JavaScript, R, leaflet (JavaScript), Shiny (R extension), and geoserver.
restrictions
Model code will be made available under a public license but it is highly dependent on a large associated GIS database and will not be easy to run independent of our cluster.
Web Tool code will be made available under a public license and should be more portable.
environment
Models: Windows.
Web Tool: Linux.
name
Custom code for project
dataProduct
metadata
FGDC
exclusiveUse
No exclusive use.
description
Conductance represents the connections among 2080 refugia cores and the lost 2020 conservation cores. Conductance will be a continuous raster indicating how important each cell in the landscape is to connecting a pair of cores. There will be one of each for each of the target ecosystems (minimum 2).
repository
Data will be available for download from https://umassdsl.org and on ScienceBase.
dataLifespan
5-10 years
qualityChecks
No formal validation. We will visually inspect output for completeness and consistency.
citation
http://umassdsl.org
format
Conductance: GeoTiff
restrictions
None
backupAndStorage
We will store the data on a 38 TB RAID backed up weekly.
dataManagementResources
10% of project.
volumeEstimate
7 GB per system - at least 14 GB total.
dataProcessing
We will use random low cost paths implemented in APL to map conductance.
name
Conductance among 2020 cores
metadata
FGDC
exclusiveUse
No exclusive use.
description
A raster dataset where each cell’s value is its conductance with higher values indicating greater contribution to connectivity for that group of ecological systems across multiple scales and lower values indicating lower contributions and or contribution only to local conductivity.
repository
Data will be available for download from https://umassdsl.org and on ScienceBase.
dataLifespan
5-10 years
qualityChecks
No formal validation. We will visually inspect output for completeness and consistency.
citation
http://umassdsl.org
format
GeoTiff
restrictions
None
backupAndStorage
We will store the data on a 38 TB RAID backed up weekly.
dataManagementResources
60 % of project.
volumeEstimate
7 GB per group - at least 28 GB total.
dataProcessing
We will create a regular grid of points, shift the points slightly to local maxima in IEI, use random low cost paths to assess pairwise connectivity with nearby points, and then assemble the result into a graph representing all the points in the region. We will use graph based centrality metrics to assign regional connectivity scores to each edge in the graph. Finally, we will generate random low cost paths between each pair of points (a second time) and weight them by the edge connectivity score before writing to a raster file.
name
Regional Conductance
metadata
FGDC
exclusiveUse
No exclusive use.
description
The vulnerability grids will highlight areas in which connectivity (for a group of ecosystems) will be highest for areas that have high regional conductivity and high probability of development.
repository
Data will be available for download from https://umassdsl.org and on ScienceBase.
dataLifespan
5-10 years
qualityChecks
No formal validation. We will visually inspect output for completeness and consistency.
citation
http://umassdsl.org
format
GeoTiff
restrictions
None
backupAndStorage
We will store the data on a 38 TB RAID backed up weekly.
dataManagementResources
10 % of project.
volumeEstimate
7 GB per group - at least 28 GB total.
dataProcessing
Vulnerability will be generated by multiplying regional conductance by the probability of development, and (perhaps) rescaling. Higher values will represent areas where both input are high - areas of high regional conductivity that are vulnerable to development.
name
Vulnerability
metadata
FGDC
exclusiveUse
No exclusive use.
description
Conductance represent the connections among 2080 refugia cores. Conductance will be a continuous raster indicating how important each cell in the landscape is to connecting a pair of cores. There will be one of each of the target ecosystems (minimum 2).
repository
Data will be available for download from https://umassdsl.org and on ScienceBase.
dataLifespan
5 - 10 years.
qualityChecks
No formal validation. We will visually inspect output for completeness and consistency.
citation
http://umassdsl.org
format
Conductance: GeoTiff
restrictions
None
backupAndStorage
We will store the data on a 38 TB RAID backed up weekly.
dataManagementResources
10% of project.
volumeEstimate
7 GB per system - at least 14 GB total.
dataProcessing
We will use random low cost paths implemented in APL to map conductance.
name
Conductance among 2080 refugia cores
metadata
FGDC
exclusiveUse
No exclusive use.
description
The cores represent delineated areas of high 2020 Index of Ecological Integrity (IEI) and or Ecosystem Refugia and surrounding buffers for the target system. They will be be classified as "refugia", "lost" or "new" depending on whether the core is present in 2020 and 2080, 2020 only, or 2080 only. These will generated as a 30 m raster covering the 13 state Northeast Region but then will be converted and distributed as a vector polygon shapefile. There will be one for each of the target ecosystems (minimum 2).
repository
Data will be available for download from https://umassdsl.org and on ScienceBase.
dataLifespan
5-10 years
qualityChecks
No formal validation. We will visually inspect output for completeness and consistency.
citation
http://umassdsl.org
format
Polygon ESRI shapefile
restrictions
None
backupAndStorage
We will store the data on a 38 TB RAID backed up weekly.
dataManagementResources
5% of project.
volumeEstimate
Minimal (10 MB ? )
dataProcessing
These will be created with a resistant kernel spreading out from areas of high value such that there is greater spread (and larger cores) where the value remains higher.
name
Ecosystem Cores
existingInput
fees
None
description
Landcover derived from multiple sources including: The Nature Conservancy's Northeast Habitat Classification map, Open Street Map roads, Microsoft Building footprints, National Hydrologic Dataset, National Land Cover Dataset, and National Wetlands Inventory. See full documentation https://umassdsl.org/DSLdocs/DSL_documentation_DSLland.pdf
This dataset has been verified using a hindcasting approach see:McGarigal K; Plunkett EB; Willey LL; Compton BW; DeLuca WV; Grand J. 2018. Modeling non-stationary urban growth: the SPRAWL model and the ecological impacts of development. Landscape and Urban Planning 177:178-190. https://doi.org/10.1016/j.landurbplan.2018.04.018
citation
McGarigal K; Plunkett EB; Willey LL; Compton BW; DeLuca WV; Grand J. 2018. Modeling non-stationary urban growth: the SPRAWL model and the ecological impacts of development. Landscape and Urban Planning 177:178-190.
format
Geotiff (raster GIS file)
restrictions
None
backupAndStorage
We will store the data on a 38 TB RAID backed up weekly.
volumeEstimate
7 GB
dataProcessing
We will use this in combination with the modelled connectivity to map areas of high connectivity that are also threatned by development.