A SPATIAL MODELING AND DECISION SUPPORT SYSTEM FOR CONSERVATION OF BIOLOGICAL DIVERSITY
COMPUTATIONAL ADVANCES
Regional Vegetation Classification And Mapping
Coastal sage scrub classification and
conservation assessment
Coastal sage scrub classification and conservation assessment
The national Gap Analysis Program is mapping vegetation alliances, which are
defined by vegetation structure and dominant canopy species, using a 1
km² minimum mapping unit. Natural vegetation is rarely uniform over a
square kilometer. It is more typical at this sampling scale to encounter a
mosaic of two or more vegetation types in recurring stands associated with
slope aspect, hillslope position, soil type and/or land use and disturbance
history. For the California Gap Analysis Project, rather than generalizing
this heterogeneity to a single, dominant type, we devised a strategy for
encoding such areas as a vegetation mosaic in a single landscape unit.
For each landscape unit, we recorded a primary vegetation cover type, which
was the most widespread vegetation type or land use/land cover type in the
polygon, a secondary cover type when present, a tertiary type (for some
regions) and the fraction of the landscape covered by each type (10% classes).
Up to 3 species were recorded for each type.
In contrast to a traditional, small-scale vegetation map, a landscape unit
thus may contain information on up to three species assemblages and up to 9
plant species that are dominant or co-dominant when the vegetation is viewed
over many hectares. One can query the database for distribution data on
individual species, unique combinations of species, or vegetation types
defined by physiognomy and/or composition. Maps of vegetation types are
subsequently derived from the database by applying tables that relate each
species assemblage to a particular vegetation classification system.
The vegetation database for California contains information for 21,000 map
units or "landscapes." While it has been difficult to compile data
on individual dominant species, and the data are uneven in their quality and
timeliness, the resulting database provides an unprecedented opportunity to
explore biogeographic patterns and to conduct conservation analyses for
dominant plant species and species combinations. As part of our IBM-ERP
research, we have tested different approaches to analyzing these species data.
We conducted an analysis of coastal sage shrublands in southwestern California
to compare mapped distributional patterns of the dominant overstory species to
geographic patterns of community composition that have been documented in
previous, plot-based phytogeographic analyses. We also quantified the current
ownership and management status of the coastal sage scrub type, its dominant
species, and common species combinations. Based on a divisive information
analysis of species occurrence data (Figure 19), assemblages of dominant shrub
species exhibited three main distribution types, that is: south coastal, north
coastal, and interior (Figure 20). However, species distribution boundaries
showed very little coincidence and the pattern of species combinations was
more suggestive of broad compositional gradients than of well defined types or
geographic assemblages. All coastal scrub species and species complexes were
mapped predominantly on private lands, many of which are under intense
pressure from urban expansion. Most conservation efforts to date have focused
on areas in Orange, Riverside, and San Diego Counties that are habitat for the
threatened California gnatcatcher (Polioptila californica). Our
analysis (Davis et al. 1994) highlighted the need to consider more northerly
and interior elements as well. For example, practically all landscapes
dominated by Salvia leucophylla are private lands of the western
Transverse ranges, north of the current range of the gnatcatcher.
Figure 19. Divisive information classification of 598 landscapes in which
coastal sage scrub is a primary or secondary vegetation type, based on species
composition data. Boxes record the number of samples in each node of the
tree. The height of the horizontal lines indicates the information captured
by that split.
Figure 20. Composite distribution of 12 classes of coastal sage scrub
identified by divisive information analysis displayed over a shaded relief
image. Dashed lines are the geographic boundaries of associations as proposed
by Westman (1983).
Bayesian classifier of communities from species assemblages
An inevitable problem in compiling regional vegetation databases is
discrepancies among vegetation classification schemes. As described above, for
the California Gap Analysis we stored preclassified data on vegetation cover
and species composition in order to maintain as much flexibility as possible
in relating our data to other vegetation maps. Thus we derive a vegetation
class from a list of dominant species occurring in a landscape mosaic.
Although simple in principle, in practice this is challenging because of the
large number of unique combinations of species that we encounter at our
mapping scale. We tried several algorithms to assign a particular species
triplet to a class within a particular vegetation classification system.
A simple Bayesian pattern recognition approach was chosen. This technique was
developed in early research on expert systems. Basically, we have
characterized each vegetation class by its likelihood of containing particular
species. Then when given a list of species, we can work backwards (using
Bayes' rule) to determine which vegetation class is most likely to contain
that particular set of species, and thus assign the class on that basis. That
is, rather than trying to construct a rulebase that associates a particular
species with a set of possible vegetation classes, we construct a rulebase
that lists the species associated with each vegetation class.
We first used this method to translate species information in the vegetation
polygons for the Sierra Nevada region into a standard community classification
of vegetation in California. Based on the descriptions in this system, for
each class we have rated the species most commonly found as dominants as to
their relative likelihood to be present in the class. We have also given
every species in the database a default rating of its likelihood to be present
in classes where it is not specifically identified as a dominant. These
ratings are stored in a simple ASCII table that is read as input into a Perl
script which we have written to classify the species triplets.
This method has performed reasonably well for the GAP database in several
regions in California. With over 1600 unique combinations of species triplets
in the Sierra Nevada region, the method provided an economical and efficient
way to classify each of these triplets. The method is not perfect and
occasionally misclassifies triplets relative to human interpretation. But
often this is because more information, such as edaphic characteristics, is
needed to identify a class, or that the class has been inadequately described
in the original system. After review of the probabilities of class
assignments, a final set of classification assignments were made into a
look-up table in a second Perl script.
Bayesian classifier of communities from species
assemblages
Next Section