237 research outputs found
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Designing Sustainable Landscapes: Climate Stress Metric
Climate is a major factor in determining ecosystem distribution, composition, structure and function. Therefore, with climate change it is reasonable to anticipate heterogeneous climate stress across the landscape in response to heterogeneous shifts in climate normals (Iverson et al. 2014). The climate stress metric assesses the estimated climate stress that may be exerted on a focal cell in 2080 based on departure from the current climate niche breadth of the corresponding ecosystem. Essentially, this metric measures the magnitude of climate change stress at the focal cell based on the current climate niche of the corresponding ecosystem and the predicted change in climate (i.e., how much is the climate of the focal cell moving away from the current climate niche of the corresponding ecosystem) between 2010-2080 based on the average of two climate change scenarios (see below) (Fig. 1). Cells where the predicted climate suitability in the future decreases (i.e., climate is becoming less suitable for that ecosystem) are considered stressed, and the stress increases as the predicted climate becomes less suitable based on the ecosystem\u27s current climate niche model. Conversely, cells where the predicted climate suitability in the future increases (i.e., climate is improving for that ecosystem) are considered unstressed and assigned a value of zero.
The climate stress metric is an element of the ecological integrity analysis of the Designing Sustainable Landscapes (DSL) project (see technical document on integrity, McGarigal et al 2017). Consisting of a composite of 21 stressor and resiliency metrics, the index of ecological integrity (IEI) assesses the relative intactness and resiliency to environmental change of ecological systems throughout the northeast. As a stressor metric, climate stress values range from 0 (no effect from climate stress) to a theoretical maximum of 1 (severe effect; although in real landscapes, the metric never reaches 1). Note that the climate stress metric is computed separately for each ecosystem because each ecosystem has its own estimated climate niche (see below). This contrasts with all other stressor metrics, which are computed ihttps://scholarworks.umass.edu/data/1037/thumbnail.jp
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Designing Sustainable Landscapes: Climate Data
Climate [updated 5/9/2018] -- This document describes how we model climate change. More specifically, this document describes the source of the climate change data that we are using and the methods we are applying to downscale the data to meet our needs
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Designing Sustainable Landscapes: Traffic settings variable
Traffic is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Traffic measures the estimated probability of an animal crossing the road being hit by a vehicle given the mean traffic rate, an important determinant of landscape connectivity for mobile terrestrial organisms. It is based on an empirical model of mean vehicles per day, using point counts of traffic, and a transformation to estimate the mortality rate for road crossings. Traffic is a dynamic settings variable, increasing in future timesteps with urban growth.https://scholarworks.umass.edu/data/1012/thumbnail.jp
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Designing Sustainable Landscapes: Watershed habitat loss, watershed imperviousness, road salt, sediment, nutrients, and dam intensity metrics
This document describes a suite of stressor metrics that assess the various effects of development in the watershed of the focal cell, as opposed to a (usually) circular window around the focal cell, as with the other metrics. These metrics are used for lotic, lentic, and wetland systems. All effects are weighted by a the time of flow from each stressor source to the focal cell, thus, stressor sources that fall within a stream have a greater effect than those in distant uplands within the watershed. These share a common algorithm, but each has unique parameters. These metrics are elements of the ecological integrity analysis of the Designing Sustainable Landscapes (DSL) project (see technical document on integrity, McGarigal et al 2014). Consisting of a composite of 21 stressor and resiliency metrics, the index of ecological integrity (IEI) assesses the relative intactness and resiliency to environmental change of ecological systems throughout the northeast. These stressor metrics range from 0 (no effect) to maximum values that differ for each metric (severe effect). See Table 1 for parameters for each metric.https://scholarworks.umass.edu/data/1025/thumbnail.jp
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Designing Sustainable Landscapes: Modeling Urban Growth
1. Urban growth [update under development] -- This document describes how we model urban growth
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Designing Sustainable Landscapes: Stream gradient settings variable
Stream gradient is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Stream gradient is a measure of the percent slope of a stream, which is a primary determinate of water velocity and thus sediment and nutrient transport, and habitat for aquatic plants, invertebrate, fish, and other organisms. Stream gradient is often approximated by categories such as pool, riffle, run, and cascade. Stream gradient is 0% for lentic waterbodies, palustrine, and uplands. It ranges from 0% to infinity (theoretically) for streams.https://scholarworks.umass.edu/data/1011/thumbnail.jp
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Designing Sustainable Landscapes: Local and Regional Conductance
Local and HUC6 regional conductance are two of the principal Designing Sustainable Landscapes (DSL) landscape conservation design (LCD) products, which are best understood in the context of the full LCD process described in detail in the technical document on landscape design (McGarigal et al 2017). These particular products were initially developed for the Connecticut River watershed as part of the Connect the Connecticut project (www.connecttheconnecticut.org) — a collaborative partnership under the auspices of the North Atlantic Landscape Conservation Cooperative (NALCC), and subsequently developed for the entire Northeast region as part of the Nature\u27s Network project (www.naturesnetwork.org).https://scholarworks.umass.edu/data/1047/thumbnail.jp
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Designing Sustainable Landscapes: Modeling Ecological Integrity
Integrity [updated 3/7/2017] -- This document describes our coarse filter assessment based on the concept of landscape ecological integrity. Here, we define ecological integrity and the four major components of integrity that we quantify: intactness, resiliency, ecosystem diversity and adaptive capacity, and describe the various indices used to quantify each component
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Designing Sustainable Landscapes: Resiliency metrics: similarity, connectedness, and aquatic connectedness
This document describes three resiliency metrics that measure a system’s ability to recover from disturbance or stress, as opposed to the other metrics, which assess sources of anthropogenic stress. Resiliency is both a function of the local ecological setting, since some settings are naturally more resilient to disturbance and stress (e.g., an isolated wetland is less resilient to species loss than a well-connected wetland because the latter has better opportunities for recolonization of constituent species), and the level of anthropogenic stress, since the greater the stressor the less likely the system will be able to fully recover or maintain ecological functions. All three of these metrics are based on assessing the distance from the focal cell to cells in its neighborhood in ecological settings space, as defined by a suite of 24 ecological settings variables. The settings variables are an attempt to capture the geophysical attributes that are primary determinants of ecological systems, e.g., temperature, sunlight, moisture, hydrology, and soils (McGarigal et al 2017). Settings also include several anthropogenic variables, such as development, traffic rates, and impervious surfaces. Ecological distance is low for points that fall nearby in settings space (e.g., two points that are on dry ridgetops with similar soils and climate), and higher for points that are further apart in settings space (e.g., a ridgetop and a valley wetland). Ecological distance is highest between natural and anthropogenic points (e.g., the ecological distance between a forest and a point in the middle of an expressway is extremely high, despite any similarities in landform or climate). Note that ecological distance is unrelated to physical distance (although two points that are nearby are more likely to share similar ecological settings).https://scholarworks.umass.edu/data/1031/thumbnail.jp
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Designing Sustainable Landscapes: Wind exposure settings variable
Wind exposure is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Wind exposure gives the mean sustained wind speed (m/s) at 50 m height. High wind speeds can shape natural communities, especially on exposed high peaks.https://scholarworks.umass.edu/data/1026/thumbnail.jp
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