BIODIVERSITY FILTERS
March 14, 2001
This document describes the biodiversity filters used for the UMass Biodiversity Assessment Project.
Section I provides an overview of biodiversity filters. Section II summarizes input data used for the
biodiversity filters. Sections III and IV describe each community and undeveloped block biodiversity
filter. Appendix A [Algorithms.doc] is a technical description of the filter algorithms. Appendix B [GIS
dictionary.doc] is a technical description of all data coverages used in the project.
I. OVERVIEW
Biodiversity conservation is the long-term maintenance of the diversity of life at all levels of
organization from the gene to the landscape, and all the ecological and evolutionary processes and
interconnections that support life. Here, we adopt a more pragmatic focus on the maintenance of viable
populations of all native species (from carnivores to soil bacteria) and communities found in their natural
places, distributed and functioning within their natural range of variability. The Housatonic Biodiversity
Assessment Project is a community-based, coarse-filter approach—we assume that by conserving intact,
biologically-defined natural communities, we can conserve most species and ecological processes. Our
coarse filter is a first step in the process of targeting land for conservation. Field work will be required to
verify predictions made by our broad-scale model, and a further, fine-filter approach will be necessary to
include habitat for species of concern that slip through the cracks—this includes many threatened and
endangered species.
Our approach to biodiversity valuation involves applying one or more “biodiversity filters” to each
point and patch in the landscape. Our landscape is a map of predicted natural communities modeled from
satellite imagery and terrain data (which captures abiotic factors such as elevation, soil moisture, and
solar radiation). We use “filters” as an analogy to camera filters—each biodiversity filter acts as a lens
that allows you to see different aspects of the underlying natural community map. Each filter consists of
a model that applies community-specific criteria to the content, context, spatial character, or condition of
a point or patch in the landscape to arrive at an index of biodiversity value. A filter may, for example,
take into account the size of a natural community patch, its proximity to streams and rivers, the diversity
of soil types in the patch, or the intensity of roads in the vicinity. Each filter takes input parameters that
are supplied separately for each community, and returns a value ranging from 0 (low value for
biodiversity conservation) to 1 (high value). Typically, several filters are applied to the landscape and
then integrated in a weighted linear combination. Weights are supplied by the user to reflect the relative
importance of each filter for each community. This process results in a final “biodiversity value” for each
point in the landscape. Intermediate results are saved to facilitate analysis—thus one can examine not
only a map of the final biodiversity values, but maps of road intensity, natural community patch area, soil
series diversity within forested areas, and so on.
Hierarchical Community Levels
Biodiversity value may be assessed at three hierarchical community levels (Table 1). At the lowest
level are primary communities, which consist of some 60 natural communities as defined by the
Massachusetts Natural Heritage and Endangered Species Program. These natural communities are
aggregated into about twenty secondary communities based on wildlife habitat use. Finally, secondary
communities are aggregated into three tertiary communities: Forests, Nonforested Uplands, and Wetlands
and Aquatic Communities. Thus, each point on the landscape is a member of a primary community, a
secondary community, and a tertiary community. Analysis may be done at any of these three levels.
Filters do not apply to developed land—all cells corresponding to developed land cover types are given a
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Biodiversity Filters
biodiversity value of zero, even though we recognize that even developed land may contribute to the
conservation of biodiversity.
Undeveloped Blocks
Finally, at each of the three community levels, biodiversity value is assessed for undeveloped blocks.
Undeveloped blocks are conservation units of undeveloped land consisting of mixed communities
surrounded by development and major roads. A separate set of block filters operates on these
undeveloped blocks, primarily acting as summaries of the biodiversity values of their component primary,
secondary, and tertiary communities.
Filter Groups
Filters are organized into four groups (Table 2):

Composition filters evaluate the rarity, richness, or evenness of natural communities or abiotic
values in the focal patch, without regard to the context of the patch.

Spatial character filters evaluate the shape or configuration of a patch, without regard to its
composition or context.

Context filters evaluate the composition and configuration of the neighborhood surrounding each
point in a community.

Condition filters evaluate negative effects (usually anthropogenic disturbance) on the ecological
integrity of each point in a community, based on the composition and configuration of the
neighborhood. Several condition filters evaluate points in aquatic communities based on upstream
effects, such as developed land cover within a point’s watershed.
Point and Patch Filters
Some filters are applied to a single point on the landscape; others apply to a community patch. Pointbased filters are applied one 15 m pixel at a time, based on the context and condition of each cell. Patchbased filters evaluate composition and spatial character of an entire patch (all contiguous pixels) of a
community or block; thus all pixels in a patch are assigned the same biodiversity value. A patch is
defined as a group of contiguous cells of the same community type (or in the same undeveloped block).
Combining Biodiversity Values
Results from biodiversity filters are integrated in weighted linear combinations. The user supplies
weights to reflect the relative importance of each filter for each community. Biodiversity values within
each group are multiplied by their weights and added together. These linear combinations are nested:
first, all filters are combined within each group (composition, spatial character, context, and condition).
Then, these four groups are combined to represent biodiversity value at the current community level.
This biodiversity value is then combined with the context from higher community levels. Biodiversity
values are combined in the nesting outlined in Table 2, resulting in six final values at each point: the
community biodiversity value at each of the three community levels, and the undeveloped block value at
each level.
Community context
The value of a higher-level patch influences the value of the lower-level patches it contains. Thus, a
patch of hickory – hop hornbeam forest (at the primary level) will be of higher value for biodiversity if it
is part of a high-valued patch of transitional hardwood forest (at the secondary level). This is reflected by
including a community context value for primary communities (consisting of secondary and tertiary
Biodiversity Filters
3
contexts) and for secondary communities (tertiary context). These context values consist of the
biodiversity value from non-redundant filters at these higher levels.
Linear Fragmenting Features
For most natural communities, a small dirt road or a first-order stream probably doesn’t represent a
patch boundary—individuals or populations of most species can easily cross such features. Thus a
network of forest streams doesn’t fragment the forest in any meaningful sense. On the other hand,
interstate highways and large rivers represent fragmenting features for most communities—a patch of
forest on the north side of I-90 is not meaningfully contiguous with a patch on the south side. Patchbased filters are sensitive to the definition of a patch (e.g. patch area, which assigns a higher value to one
large patch than to two small ones). The classes of roads and streams that are treated as fragmenting
features may be set individually for each community and for undeveloped blocks at each of the three
community levels. Stream communities are considered to be fragmented by all road classes.
Scaling with Logistic Functions
Several of the filters scale measures such as distance, area, or intensity with logistic functions. We
chose logistic functions because (1) they return values between 0 and 1; (2) they are fairly intuitive and
straightforward to parameterize; and (3) they provide a reasonable approximation of ecological processes.
For instance, the effect of a two-lane road on surrounding land may be at a maximum for the first 30
meters or so, then fall off to zero around 100 meters (Fig. 1).
Logistic functions are scaled by two parameters, the inflection point, which determines the horizontal
position of the curve, and the scaling factor, which determines how steeply or shallowly the curve rises or
falls (Fig. 2). By choosing appropriate parameters, logistic curves can approximate linear, exponential,
and threshold relationships.
high 1.0
Effect of road
0.8
0.6
0.4
0.2
none 0.0
0
20 40 60 80 100 120
Distance from road
(m)
Fig. 1. Logistic model of ecological effect
of a road on surrounding land.
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Biodiversity Filters
a.
b.
Inflection point (d50) = 25
Scaling factor (ds) = 5
1.0
1.0
0.8
0.8
0.6
Change in
inflection point
y
0.4
d50
0.6
Inflection point (d50) = 15
d50
y
0.4
ds
0.2
0.2
0.0
0.0
0
10
20
30
40
50
0
10
x
20
30
40
x
Change
in
scaling
factor
c.
d.
Scaling factor (ds) = 2.5
1.0
Positive
logistic
function
y
0.8
0.6
y
x
d50
0.4
ds
0.2
Negative
logistic
function
y
0.0
0
10
20
x
30
40
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x
Fig. 2. Scaling logistic functions. (a) Logistic functions are scaled with two parameters: the
inflection point and the scaling factor. (b) The inflection point (d50) is always the value of x at
which y = 0.5; it determines the horizontal location of the curve. (c) The scaling factor (ds) is
the distance along the x-axis from d50 to the point at which y = 0.75; it determines the
steepness of the curve. (d) Positive logistic functions increase from 0 to 1 as x increases;
negative logistic functions decrease from 1 to 0 as x increases.
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Biodiversity Filters
5
Table 1. Targeted tertiary (underlined), secondary (boldface), and primary communities (bulleted).
Mapped communities will be a subset of these; some primary communities may be combined.
Terrestrial communities follow NHESP’s Natural Community Classification.
Forested Communities
Boreal forest
 High Elevation Spruce-Fir Forest
 Spruce-Fir-Northern Hardwoods Forest
Northern hardwood Forest
 Northern Hardwoods-Hemlock-White Pine Forest
[<75% coniferous]
 Rich Mesic Forest
 Forested Seep
 Calcareous Forested Seep
 Successional Northern Hardwoods
Temperate conifer forest
 Northern Hardwoods - Hemlock - White Pine Forest
[> 75% coniferous]
 Successional White Pine
 Oak - Hemlock - White Pine Forest [>75%
coniferous]
 Hemlock Ravine Community
Mixed transitional forest
 Red Oak - Sugar Maple Transition Forest
 Mixed Oak Forest
 Oak - Hickory Forest
 Dry, Rich Acidic Oak Forest
 Hickory - Hop Hornbeam Forest
 Yellow Oak Dry Calcareous Forest
 Oak - Hemlock - White Pine Forest [< 75%
coniferous]
 White Pine - Oak Forest
 Pitch Pine - Oak Forest
Mixed transitional woodland
 Ridgetop Chestnut Oak Forest / Woodland
 Black / Scarlet Oak Forest / Woodland
 Acidic Talus Forest / Woodland
 Circumneutral Talus Forest / Woodland
 Calcareous Talus Forest / Woodland
Floodplain forest
 Transitional Floodplain Forest
 Small-River Floodplain Forest
 Major-River Floodplain Forest
 High-Terrace Floodplain Forest
 Cobble Bar Forest
Forested wetland
 Red Maple Swamp
 Black Ash Swamp
 Hemlock - Hardwood Swamp
 Spruce - Fir Boreal Swamp
 Spruce - Tamarack Bog
 Black Ash - Red Maple - Tamarack Calcareous
Seepage Swamp
Nonforested Uplands
Shrubland
 Scrub Oak Shrubland
 Pitch Pine / Scrub Oak
 Ridgetop Pitch Pine / Scrub Oak
 Powerline Shrubland
Grassland
 Sandplain Grassland
 Cultural Grassland
Cliffs
 Acidic Rock Cliff
 Circumneutral Rock Cliff
 Calcareous Rock Cliff
Rocky Summits
 Acidic Rocky Summit / Rock Outcrop
 Circumneutral Rocky Summit / Rock Outcrop
 Calcareous Rocky Summit / Rock Outcrop
Wetlands & Aquatic Communities
Palustrine
 Shrub Swamp
 Circumneutral Shrub Swamp
 Level Bog
 Kettlehole Level Bog
 Acidic Shrub Fen
 Highbush Blueberry Thicket (Level Bog)
 Acidic Graminoid Fen
 Calcareous Seepage Marsh
 Calcareous Basin Fen
 Calcareous Sloping Fen
 Wet Meadow
 Kettlehole Wet Meadow
 Shallow Emergent Marsh
 Deep Emergent Marsh
 Pond
 Oxbow Backwater
 Vernal Pool (data not available in this version)
Riverine
 High-gradient Headwater
 High-gradient 1st / 2nd order
 High-gradient 3rd order
 Medium-gradient Headwater
 Medium-gradient 1st / 2nd order
 Medium-gradient 3rd order
 Medium-gradient 4th order
 Low-gradient Headwater
 Low-gradient 1st / 2nd order
 Low-gradient 3rd order
 Low-gradient 4th order
 Low-gradient 5th order
Lacustrine
 Lake
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Biodiversity Filters
Table 2. Biodiversity filters at three community levels.
TERTIARY COMMUNITY
Community biodiversity value
 Community model
Composition
Community rarity
Community richness
Community evenness
Abiotic richness
Abiotic evenness
Land history
Spatial Character
Patch area
Relative patch area
Core area
Relative core area
Context
Similarity
Proximity
Distance to water
Streamflow distance to water
Connectedness
Condition
Edge effects
Road intensity
Development intensity
Watershed road intensity
Watershed development intensity
Point-source pollution
Upstream road crossings
Watershed impoundment
Point impounded
Undeveloped block
Composition
Community richness
Abiotic richness
Abiotic evenness
Abiotic rarity
Spatial Character
Block area
Block extent
Condition
Mean biodiversity value
Median biodiversity value
Maximum biodiversity value
Biodiversity threshold
SECONDARY COMMUNITY
Community biodiversity value
 Community model
Composition
...
Spatial Character
...
Context
...
Condition
...
 Tertiary community context
Undeveloped block
Composition
...
Condition
...
Context
...
PRIMARY COMMUNITY
Community biodiversity value
 Community model
Composition
...
Spatial Character
...
Context
...
Condition
...
 Secondary community context
 Tertiary community context
Undeveloped block
Composition
...
Condition
...
Context
...
7
Input Coverages
II. INPUT COVERAGES
Input coverages for biodiversity filters consist of several parallel grids that represent the entire watershed
as a collection of 15 meter cells.
Land cover grids at the primary, secondary, and tertiary levels are the principal input data used by most
filters.
Undeveloped areas are classified into natural communities, based on classification of satellite and
other remotely-sensed data, terrain analysis, and extensive ground-truthing. Natural communities are
classified at three hierarchical levels: primary (ca. 60 natural communities), secondary (ca. 20
communities), and tertiary communities (forested, nonforested uplands, and wetlands/aquatic).
Terrestrial and wetland communities follow NHESP’s Natural Community Classification. Lentic
waterbodies are classified as ponds or lakes based on size and trophic status. Lotic waterbodies are
delimited by stream link (run between stream junctions, lentic waterbodies, or dams), and classified
by the order and gradient of each stream link
Developed land is from UMass Resource Mapping Project’s 1985/1999 Land Use layer, classified
into five land use intensities (high-intensity urban, low-intensity urban, high-density residential, lowdensity residential, and agricultural). Railroads and five classes of roads are also included.
Abiotic layers represent aspects of each point in the landscape not explicitly represented in the Land
Cover grids. They include: SLU (soil landform unit), elevation, slope, aspect, lithology, soil depth, soil
drainage, soil texture, and soil pH. These abiotic grids are available in both classified derived forms (e.g.,
soil depth, drainage, texture, and pH) and in original raw forms (e.g., soil series). These abiotic grids may
be used in the rarity, richness, and evenness filters.
Flow grids representing the flow of streams and surface flow throughout the watershed are used by
several filters for aquatic communities.
Other layers represent dams, impoundments, point-sources of water pollution, forest land use history and
old-growth stands. These coverages are described with the filters that use them.
III. COMMUNITY FILTERS
Composition
Community rarity – Rarity measures how rare (and presumably imperiled or important to
biodiversity) a community type is. At the primary community level, rarity is measured at two scales:
State rarity (based on Natural Heritage state rarity ranks, S1-S5) and Local rarity, which is calculated
empirically, based on the maximum of area- and frequency-based rarity. Area-based rarity is the
proportion of the cells of the primary community grid in each community type. Frequency-based
rarity is the proportion of patches in each community type. State and local rarity are combined based
on user-supplied weights to give a final weighted rarity metric. At the primary level, the rarity filter
is calculated similarly to other filters; however, instead of being combined with other filters in a
weighted linear combination, primary rarity is used to weight each community in generating the map
of biodiversity value. At the secondary and tertiary community levels, the rarity metric returns the
maximum primary rarity value for the patch.
8
Community Filters
Community richness – Community richness measures the number of different lower-level
communities present in a higher-level patch (e.g., primary communities in a secondary patch). A
weight may be assigned to each community within a patch to represent that community’s contribution
to biodiversity value, thus allowing particularly important communities to be given more weight. By
default all communities are weighted equally. Weighed richness is computed as the sum of weights
of the communities present within the patch. At the secondary community level, community richness
is applied to primary communities within each secondary patch. At the tertiary level, community
richness is applied to secondary communities within each tertiary patch. Community richness is not
available at the primary community level. Community richness is a relative metric—it is always
rescaled from 0 to 1 within each community.
Community evenness – Evenness measures the equitability in area among the component
communities, without consideration of their configuration. Simpson’s evenness index is applied to
component communities in each patch of each community at the current level. At the secondary
community level, community evenness is applied to primary communities within each secondary
patch. At the tertiary level, community evenness is applied to secondary communities. Community
evenness is not available at the primary community level. Community evenness is a relative metric—
it is always rescaled from 0 to 1 within each community.
Abiotic richness – A weight may be assigned to each class of each abiotic layer within a patch to
represent the contribution of that class to biodiversity value. By default all classes are weighted
equally. Weighed richness is computed as the sum of weights of the components present within the
patch. Each class of each abiotic layer may be weighted individually for each community. Each
component of abiotic richness is a relative metric—each abiotic richness metric is rescaled from 0 to
1 within each community. After rescaling, all abiotic richness metrics are integrated in a weighted
linear combination.
Abiotic evenness – For each abiotic layer, Simpson’s evenness index is applied to abiotic classes in
each patch of each community at the current level. Each component of abiotic evenness is a relative
metric—each abiotic evenness metric is rescaled from 0 to 1 within each community. After rescaling,
all abiotic evenness metrics are integrated in a weighted linear combination.
Land history – Land History measures the value given to a patch by primary or old growth forest. A
weight is assigned to each land history class (second growth, primary, or old growth); this metric
returns the weighted proportion of the patch in each class. Land history is a relative metric—it is
always rescaled from 0 to 1 within each community.
Spatial Character
Patch area – Patch area measures the absolute size of a functional patch. The area of each patch (in
hectares) is scaled by a community-specific logistic function.
Relative patch area – Relative patch area is the size of a patch relative to the largest and smallest
patches of that community in the watershed. This metric ranges from 0 (for the smallest patch in each
community in the watershed) to 1 (for the largest).
Core area – Core area refers to the interior area of a patch that is free of edge-effects. Core area is
simply the area of a patch minus the area within the specified distance of each unique patch edge.
Community Filters
9
Edge distances are based on the 75th percentile of the logistic functions supplied for each community
in the Edge Effects filter. Core area is scaled by a community-specific logistic function.
Relative core area – Relative core area is the relative version of core area; it is calculated the same as
core area, but rather than being scaled with a logistic function, it is scaled relatively within each
community. This metric ranges from 0 (for the patch with the smallest core area in each community
in the watershed) to 1 (for the largest).
Context
Similarity – Similarity is a measure of the amount of contrast between the community type at the
focal cell and those of its neighborhood. Contrast is defined for each pair of community types based
on differences in floristics, vegetation structure, naturalness, etc. Presumably, points of a particular
community that are surrounded by similar communities act as larger patches for many component
species, whereas points surrounded by high-contrast communities act as islands. Similarity is
computed as the complement of the mean contrast between the community type at the focal cell and
the communities at neighboring cells, weighted by a logistic function of distance. At each level, the
contrast between each pair of communities is computed empirically based on the Mahalanobis
distance among abiotic and spectral layers. Similarity is a relative metric—it is rescaled from 0 to 1
within each community.
Proximity – Proximity is a measure of the proximity of selected nearby communities that increase
the value of the focal community. For each focal community, cells of other nearby communities are
weighted individually and scaled by a logistic function of distance from the focal cell.
Distance to water – Many terrestrial organisms depend on streams, ponds, and wetlands; thus cells
near water may have a higher value for biodiversity than those far from water. Distance to water is
treated separately from proximity to other land cover types because hydrologic features are especially
important, and because streams, as linear features, do not occupy (much) area in the land cover map,
and thus are underrepresented in the area-based Proximity metric. The distance to a waterbody of
each class (lotic, lentic, and wetlands, either seasonal or permanent) is calculated for the focal cell.
This distance is scaled separately for each hydrological class using a negative logistic function. The
result is the maximum logistic-scaled value.
Streamflow distance to water – This metric is used for temporary or seasonal streams as well as for
wetlands that are likely to be connected to permanent streams via intermittent streams. It is similar to
the distance to water filter, but it measures distance downstream along the flow grid rather than using
Euclidean distance. The hydrological distance to a permanent waterbody of each class (lotic, lentic,
and wetlands) is calculated for the focal cell. This distance is scaled separately for each hydrological
class using a negative logistic function. The result is the maximum logistic-scaled value.
Connectedness – Connectedness measures the connectivity between each focal cell and surrounding
cells. A hypothetical organism in a highly connected cell can reach a large area with minimal
crossing of “hostile” cells. This filter uses a least-cost path algorithm to determine the area that can
be reached from each focal cell. The focal cell gets a community-specific “bank account,” which
represents the distance a hypothetical organism could move through the focal community type. Each
land cover class (including the focal community) is assigned a travel cost, based on the contrast
matrix. The algorithm then creates a least-cost hull around the focal cell, representing the maximum
distance that can be moved from the cell until the “bank account” is depleted. The total area in this
least-cost hull is then scaled relatively within the watershed for each community, such that the cell
10
Community Filters
that can reach the least area has a connectedness of zero, and the cell that can reach the most area has
a connectedness of one.
Condition
Edge effects – Edge effects represent the adverse effects of certain edges on the integrity of patch
interiors; that is, factors that negatively intrude on the patch from its surroundings, such as changes in
microclimate, access to nest predators, or sources of invasive exotics. Edge effects on an individual
cell are indexed by the distance from the focal cell to the nearest edge weighted by the type of edge.
For each community, all other community types, developed land classes, and road classes may be
assigned to one of five edge effect classes. The distance to the nearest edge of each class is calculated
for the focal cell. This distance is scaled separately for each edge class using a negative logistic
function. The edge effects metric is then calculated as the minimum of the logistic-scaled distances.
Note that the edge effect classes and distances defined for this metric are used to derive edge
distances for the Core Area and Relative Core Area filters as well.
Road intensity – Roads can have a number of negative effects on the integrity of a patch, including
road kills, pollution from vehicles, salt runoff, and increased human access. The road intensity index
is based on the proportion of the neighborhood (based on a logistic function of distance) in each road
class. The index is weighted by road class, combined in a linear function, and scaled by a logistic
function.
Development intensity – Agriculture, residential development, and urban development have many
detrimental effects, including pesticides, human-commensal predators, sources of invasive exotics,
increased human presence, and high vehicle density. The development intensity index is based on the
proportion of the neighborhood (based on a logistic function of distance) in each development class.
The index is weighted by development class, combined in a linear function, and scaled by a logistic
function.
Watershed road intensity – The intensity of roads in the watershed of an aquatic community is an
index of water quality, hydrological disruption, and disturbance. Watershed road intensity is similar
to the road intensity filter, but it is calculated for the watershed above an aquatic point, rather than in
a circular window. The “window” for this metric is all cells that are upflow in the flow grid. Note
that this metric is not explicitly scaled by distance, but is implicitly scaled by the dilution factor
introduced as the number of cells in the watershed increases. Watershed road intensity is based on
the proportion of the window in each road class, combined in a linear function weighted by road
class, and scaled by a logistic function.
Watershed development intensity – As with watershed road intensity, watershed development
intensity is similar to development intensity, but is based on cells in the watershed of the focal cell.
Watershed development intensity is based on the proportion of the watershed in each development
class, combined in a linear function weighted by development class, and scaled by a logistic function.
Point-source pollution – This metric measures the intensity of actual or potential point sources of
pollution in the watershed above an aquatic point, as well as past toxic spills. For actual current
discharge points, the point-source pollution metric is based on NPDES-permitted million gallons per
day (MGD) discharge into streams per 100 km2 watershed area. Discharge rates are parameterized
separately for municipal sewage and industrial sources. For potential sources, the metric is based on
the number of underground storage tanks per 100 km2 in the watershed above the focal point.
Historical toxic spills are based directly on pollution concentration mapped at the focal cell (in the
Housatonic watershed, this data represents General Electric’s PCB discharges into the Housatonic
Community Filters
11
River, based on a smoothed map of PCB concentration measured in sediments in the river and
floodplain). Intensity of pollution sources is scaled for each of the four classes (municipal discharge,
industrial discharge, underground tanks, and toxic spills) with negative logistic functions, and the
complement of the maximum value is taken.
Upstream road crossings – Upstream road crossings can have a significant effect on the hydrology
of a stream. This metric counts the number of upstream road crossings per kilometer below any
upstream dams. Crossings are counted only in stream beds (using the stream flow grid), not in the
watershed. Each road class is weighted separately, and the number of crossings per kilometer is
scaled by a logistic function.
Watershed impoundment – This metric is based on the proportion of the watershed above the focal
point that is impounded by dams.
Point impounded – This metric simply measures whether a point is part of an impoundment
maintained by a downstream dam.
IV. UNDEVELOPED BLOCK FILTERS
Undeveloped block filters are applied to each road and development-bounded undeveloped block. These
filters are all patch-based. The act primarily as summaries of biodiversity value at each community level.
The biodiversity value for each undeveloped block is a weighted combination of composition, spatial
character, and condition.
Composition
Community richness – This metric gives the richness of communities in the undeveloped block,
weighted by the maximum biodiversity value for each community in the block. Block community
richness is scaled by the observed richness among all blocks across the watershed.
Abiotic richness – This metric gives richness of classes of any of the abiotic layers within the
undeveloped block. Each class of each abiotic layer may be assigned a weight to indicate its relative
importance to biodiversity. Block abiotic richness is scaled by the observed richness within each
abiotic layer among all blocks across the watershed. After rescaling, relative richness for each abiotic
layer are combined in a weighted linear function to produce an overall abiotic richness metric.
Abiotic evenness – Simpson’s evenness index is computed for classes of any of the abiotic layers in
each undeveloped block, giving a measure of the evenness of class representation. Block abiotic
evenness is scaled by the observed evenness within each abiotic layer among all blocks across the
watershed. After rescaling, relative evenness for each abiotic layer are combined in a weighted linear
function to produce an overall abiotic evenness metric.
Abiotic rarity – Each class of any abiotic layer may be assigned a value based on the empirical rarity
of that class throughout the watershed. The rarity indices for all selected abiotic layers are integrated
in a weighted linear combination.
12
Undeveloped Block Filters
Spatial Character
Block area – The size of an undeveloped block is a partial determinant of the integrity and function
of the communities within it. Block area is calculated as the size of the block in hectares and is scaled
with a logistic function.
Block extent – Block extent refers to the spatial extent of a block unrelated to how convoluted it is.
Block extent is based on the radius of gyration, which is the mean distance from each cell to the patch
centroid. A compact patch has a small radius of gyration, and an elongated patch of the same area has
a larger radius of gyration. For patches of the same shape, a larger area will result in a larger radius
of gyration. Block extent thus measures how far across the landscape a block extends. Block extent
can be useful as a measure of connectivity because a large radius of gyration implies that organisms
can travel a greater distance across the landscape without crossing roads or developed land.
Condition
Mean biodiversity value – The mean biodiversity value for all cells in the undeveloped block.
Median biodiversity value – The median biodiversity value for all cells in the undeveloped block.
Maximum biodiversity value – The maximum biodiversity value of cells in the undeveloped block.
This metric can be used to pick out blocks that contain land of high value, regardless of the overall
value of the block.
Biodiversity threshold – Proportion of cells in the block with biodiversity value greater than a userspecified threshold.