Interpreting BioFinder Results

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Interpreting Results

BioFinder was created to help developers, scientists, planners, educators, and others better understand the richness and distribution of biological diversity throughout Vermont. It can be used both as a clearinghouse for natural heritage data, and as a powerful tool to help in identifying ecologically important locations. To get the most out of BioFinder, it is important to understand the following:

 

Landscape Scale

Vermont Conservation Design is a carefully designed map that takes a holistic approach to addressing ecological function across the Vermont landscape. Instead of identifying and mapping components individually, Vermont Conservation Design identifies the manner in which landscape components are connected and work together to create the most crucial base for ecological interactions across the state. The design is based around the following datasets, all which can be found in the "Component Layers" section:

  • Interior Forest Blocks
  • Connectivity Blocks
  • Riparian Wildlife Connectivity
  • Surface Water and Riparian Areas
  • Physical Landscape Diversity

Vermont Conservation Design

These datasets were chosen because as a group, maintaining or enhancing these features is likely to conserve the majority of Vermont's species and natural communities, even as the climate changes. Put another way, these maps outline the areas of land that need to remain healthy and intact if we want to provide plants, animals, and natural resources the best chance of survival over time. On the other hand, a decline in the quality of these lands is likely to correspond to a decline in the state's ecological function as a whole.

To create this map, Vermont Fish and Wildlife Department biologists assigned "priority" or "highest priority" status to the five component datasets, taking into account the regional context in which each component was found. In other words, a smaller interior forest block in the Champlain Valley may qualify as "highest priority," because large forest blocks are less common in the Champlain Valley than in the Green Mountains or Northeast Kingdom. To learn more about how these priority and highest priority areas were assigned for each component, see the component abstracts for each dataset, linked through the BioFinder maps.

Because a fully functional landscape includes all of the components mapped, the map displayed amasses all priority areas on any of the layers. Lands mapped on any of the component maps as "highest priority" are given "highest priority" status on the compilation. Land mapped as "priority" is likewise assigned "priority" status, unless covered by another component's "highest priority" rank.

Species and Community Scale

This dataset represents lands and waters highly important for maintaining individual species or groups of species that contribute to Vermont's biodiversity. Species and Community ScaleSimilar to landscape-scale priorities, the dataset was created by assigning a priority status to numerous components, then amassing these components so that a location appearing as "highest priority" on any component appears as "highest priority" on the map. The components include:

Highest Priority:

  • Highest Priority Wildlife Crossings
  • Representative Lakes
  • Exemplary Surface Waters
  • Vernal Pools
  • Wetlands
  • Rare Species
  • Rare Natural Communities

 

Priority:

  • Priority Wildlife Crossings
  • Uncommon Species
  • Uncommon Natural Communities
  • Common (Representative) Natural Communities
  • Grasslands and Shrublands
  • Mast Stands

 

As of 2016, the data that constitute priority rankings are undergoing rigorous scientific study. Currently, entire components are ranked as either "priority" or "highest priority," as listed above. For example, rare natural communities are all given "highest priority" status, while uncommon natural communities are labeled "priority." As state biologists learn more about the relative importance of individual occurrences within each natural community, however, the data displayed on BioFinder maps will change to reflect the most up-to-date understanding. For example, some occurrences of a single rare natural community are more important to local and state ecological function than others, and once it is known which these are, the status of some individual rare natural communities may change to "priority."

As you interact with this map, please remember that all data were collected for use at the state or town level. Though you can zoom in to individual parcels, for example, you need to understand the limitations of each of the datasets you're using. Some of these layers contain omissions, and these omissions may be critical when translating data into implementation measures. Wherever possible, the collection of field inventory information will likely enhance a community's understanding of these resources.

 

Scale and Accuracy

BioFinder was created to show ecological function and biodiversity at a statewide scale. Data for each component was mapped to 10m x 10m cells (pixels), covering the entire state—254,096,429 cells in all. Data for many of the components is highly accurate at this cell level (for example, rare species, natural communities). The accuracy for other components (e.g. the network of connected lands) can diminish as one zooms in. Because of these accuracy issues at the local scale, BioFinder cannot replace site visits or site-specific data and analyses and should only be used to gain a general understanding of components likely to be at play.

In instances where robust field data were not spatially comprehensive or available to adequately describe a component, models were employed. For example, Physical Landscapes, , Surface Water and Riparian Areas, and Riparian Wildlife Connectivity rely on a Land Type Associations model that identifies areas of similar geology, landform, potential vegetation, and other factors. Connectivity and Interior Forest Blocks similarly rely on a suite of models to determine the most likely areas used for wildlife movement. The Wildlife Road Crossings data set relies on a "cost surface" model that predicts ease of movement for far ranging mammals.