Download Surveying Vegetation Structure

Lower Canopy Information
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Useful in Assessing Site Quality
Examining Structural Patterns
Wildlife-Habitat relationships
Biological Diversity quantification
Biomass of secondary forest “products”
Multiresource Inventory Component
ESRM 304
Site Quality
 Productive capacity of forest land
 Useful for …
o
o
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Determining what species are suitable
Predicting growth potential
Evaluating ecosystem resiliency
Determining management priorities
Land valuation
Site Quality
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are needed to see this picture.
Site Index for Ponderosa Pine, 100-yr basis
Site Quality
Potential for forest
growth can be
identified by using
assemblages of lower
canopy vegetation
QuickTime™ and a
decompressor
are needed to see this picture.
Scots pine growing in
Finland …
Site Quality
Closer to home …
QuickTime™ and a
decompressor
are needed to see this picture.
Examining Structural Patterns
•
Northwest ecosystems contain many different
vegetation patterns
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Types, amounts, and distribution of vegetation
patterns define water quantity and quality,
wildlife habitat, timber resources
•
Vegetation patterns impact forest processes
such as streamflow, erosion, and succession
•
forest landscapes are created and maintained
through a balance of disturbance and recovery
processes.
Four major
stages of
stand
development
WildlifeHabitat
Vagrant shrew
Townsend’s mole
Meadow voles
Jumping mice
Deer mouse
Gophers
Ground squirrels
Chipmunks
Relationships
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Marsh and Trowbridge’s shrews
Southern red-backed vole
Tree and flying squirrels
Keen’s mouse
Shrew-mole
Coast mole
Biological Diversity Quantification
 Indexes attempt to combine abundance,
composition, dominance into single no.
 Diversity at different scales
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Landscape level
Community-Ecosystem level
Population-species level
Genetic level
Diversity at Different Scales
Community-ecosystem Level (e.g., Lower
Canopy)
o How have management activities or other natural
disturbances affected species diversity?
o What is the function of a species in the
community?
o Where are the areas of high species richness,
endemism, or rarity and how well are they
protected?
Community Metrics
o Richness, composition, Shannon, Simpson
Lower Canopy
Structure & Diversity
Horizontal structure / diversity
o Species Richness
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Number of species present, ni
o Species Composition
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pi = amt. of species i / amt. all spp.
o Shannon Index (H’)
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H’ = -∑pi . ln(pi)
o Simpson’s Index (D)
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D = ∑[ni(ni-1)] / [N(N-1)]
usually expressed as 1/D
Lower Canopy
Structure &
Diversity
Vertical structure /
diversity
BSD is directly
related to FSD
Biomass of secondary forest products
 Secondary Forest Products
o Floral arrangements (salal, ferns)
o Mushrooms
o Fiddle heads (Ferns)
o Others …
Biomass of secondary forest products
Some Biomass Equation examples:
Shrubs
RUUR (trailing blackberry):
VACCI (Vaccinium species):
Ferns
ATFI (lady fern):
PTAQ (bracken fern):
TAB = –1.214 + 0.8392 (COV)
TAB = 0.0 + 1.644 (COV)
TAB = 0.0 + 1.235 (COV)
TAB = 0.0 + 3.1057 (COV)
Multiresource Inventory Component
The type of information needed for
managing any land parcel includes a
multitude of resource values.
Integrated multipurpose resource
inventories, or multiresource inventories
have been developed for this purpose
In general, we need to know the
quantity, quality, and extent of the
resources.
Multiresource inventories
Relative priority for assessing each resource (Low, Med, High)
depends on the inventory objectives:
Area
Est.
Owner
Patterns
Accessibility
Vol.
Est.
Growth
& Drain
Critical
Habitat
Scenic
Views
Other
Uses
Timber
Value
H
L
H
H
L
M
L
L
Recreation
M
H
H
L
L
H
H
M
Mgt. Plan
H
M
M
H
H
H
M
M
Survey
Objective
Inventory Planning Checklist
A comprehensive plan ensures all
facets of the inventory are considered
data to be collected
financial support needed
logistical support required
compilation procedures
Inventory Planning Checklist
Be sure to consider the following
1. Purpose of the inventory
2. Background information
3. Description of Area
4. Information required in final report
Inventory Planning Checklist
5. Sample survey design
Define target population
Define sample unit
Define required accuracy and precision
Will need to construct confidence interval
estimate ± “t-multiplier” x standard error of
estimate
y  t  sy
Decide how samples will be collected
Decide how many sample units will be
measured
Know budgeting limitations for field work
Inventory Planning Checklist
6. Photo, satellite, other remotely
sensed info. interpretation
procedures
7. Fieldwork procedures
8. Compilation and calculation
procedures
9. Final report
10. Maintenance
5.
Sample survey design
Define target population
All Douglas-fir trees in a certain area with a DBH of
at least 5.6”
Specify units of measure: “…metric tons of carbon of
all Douglas-fir trees …”
Define sample unit
Fixed-area plots: 1/5, 1/10, 1/20, 1/40-acre sizes
common for overstory trees; 1/100-acre, or less for
seedling regeneration
Transects: common for understory and groundstory
vegetation, LOD
Individuals: a deer, a hiker on a trail, a log
Groups: truckload of logs, herd of deer, group of
hikers
5.
Sample survey design
Define required accuracy and precision
Depends on survey objectives (and a bit on
convention)
Multiresource surveys / Stewardship plans
Want est. of mean within 10 –20% of pop. mean w/ 70–
90% C.I.
Land acquisition surveys / Timber sale survey
Want est. of mean within 5 –10% of pop. mean w/ 95%
C.I.
Special surveys (timber trespass, regeneration,
insect/disease)
Varies with application
5.
Sample survey design
Decide how sample units will be selected
Simple Random Sampling (SRS)
Systematic sampling
Stratified random sampling
Two-phase sampling
Multistage sampling
Cluster sampling
Purposive sampling
Convenience sampling
Decide how many sample units will be
measured
Know what equations will be used to compute
estimates
Use of statistical formulas preferred
5.
Sample survey design
For SRS infinite populations (or sampling with
replacement)
2
2
z  CV 
n
k
2
E
n=
number of sample units required for desired
precision E, with confidence level implied by z
z=
standard normal deviate (Z-table or table following)
CV = coefficient of variation: std. dev. divided by mean as
percent, for forest to be sampled:
E=
k =
CV  s y (100)
allowable error or desired accuracy (in percent) for
the quantity of interest (e.g. biomass, volume,
carbon, etc.)
correction term to simplify computations
5.
Sample survey design
Confidence
level
80%
90%
95%
99%
z-value
1.282
1.645
1.960
2.576
k
1.31
1.87
2.44
3.79
5.
Sample survey design
For SRS in finite populations (or sampling
without replacement)
n
Nz  CV 
2
2
NE 2  z 2  CV 
2
k
N = Total number of sampling units in
population, all other symbols are as
before
5.
Sample survey design
Rules of thumb
For ~ 1/10 acre plots in highly variable populations (having a
CV of at least 50%):
Area (acres)
Up to 10
11 – 40
41 – 80
81 – 200
200 +
number of samples (n)
~ 10
~ 1 per acre
20 + 0.5 (area in acres)
40 + 0.25(area in acres)
Use sample size formulas
5.
Sample survey design
Know budgeting limitations for field work
Simple cost model
Ct =
Co + n C1
where
Ct = Total cost of survey
Co = Overhead cost, including planning,
organization, analysis, compilation, etc.
C1 = Cost per sampling unit
n = number of sampling units to be measured
Number of sample units is then limited by:
n = (Ct - Co) / C1
Summary Remarks
Multiresource surveys require careful planning to
achieve desired goals with minimum amount of
work
Difficult to achieve same accuracy / precision for
every resource – priorities must be set according to
survey goals
Consider all ten (10) planning steps in design
Know and carefully define target population,
sampling frame, sampling units, decide how many
samples to measure, know budgeting limitations
Summary Remarks
Diversity at different scales
o Landscape
o Community
Community – Lower Canopy Structure &
Diversity
Horizontal / Vertical Structure
o Population - Species
o Genetic
Summary Remarks
Need info on structure, variability, processes
for:
o Grouping of stands into productivity classes
o Building inventory on critical habitat conditions
o I.D.-ing wildlife-habitat relationships
o Enhancement of grouping stands into risk classes
o Development of management targets for
Silvicultural manipulations
Managing potential fire hazard
Biological diversity maintenance