lower canopy information esrm 304 useful in assessing site quality examining structural patterns ...

Lower Canopy Information

ESRM 304

Useful in Assessing Site Quality

Examining Structural Patterns

Wildlife-Habitat relationships

Biological Diversity quantification

Biomass of secondary forest “products”

Multiresource Inventory Component

Site Quality

Productive capacity of forest land

Useful for …o Determining what species are

suitableo Predicting growth potentialo Evaluating ecosystem

resiliencyo Determining management

prioritieso Land valuation

Site Quality

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Site Index for Ponderosa Pine, 100-yr basis

Site Quality

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Potential for forest growth can be identified by using assemblages of lower canopy vegetation

Scots pine growing in Finland …

Site QualityCloser to home …

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Examining Structural Patterns

• Northwest ecosystems contain many different vegetation patterns

• 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

Wildlife-Habitat

Relationships

Vagrant shrewTownsend’s moleMeadow volesJumping miceDeer mouseGophersGround squirrelsChipmunks

Marsh and Trowbridge’s shrewsSouthern red-backed vole Tree and flying squirrelsKeen’s mouse Shrew-moleCoast mole

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Biological Diversity Quantification Indexes attempt to combine abundance, composition, dominance into single no.

Diversity at different scaleso Landscape levelo Community-Ecosystem levelo Population-species levelo Genetic level

Diversity at Different ScalesCommunity-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 Metricso Richness, composition, Shannon, Simpson

Lower Canopy Structure & DiversityHorizontal structure / diversityoSpecies Richness

Number of species present, ni

oSpecies Compositionpi = amt. of species i / amt. all spp.

oShannon Index (H’)H’ = -∑pi . ln(pi)

oSimpson’s Index (D)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 Productso Floral arrangements (salal, ferns)

o Mushrooms o Fiddle heads (Ferns)o Others …

Biomass of secondary forest products

Some Biomass Equation examples:

ShrubsRUUR (trailing blackberry): TAB = –1.214 + 0.8392 (COV)VACCI (Vaccinium species): TAB = 0.0 + 1.644 (COV)

FernsATFI (lady fern): TAB = 0.0 + 1.235 (COV)PTAQ (bracken fern): TAB = 0.0 + 3.1057 (COV)

Multiresource Inventory ComponentThe 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 purposeIn 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:

Survey Objective

AreaEst.

OwnerPatterns

Access-ibility

Vol. Est.

Growth & Drain

CriticalHabitat

ScenicViews

OtherUses

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

Inventory Planning ChecklistA 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 inventory2. Background information3. Description of Area4. Information required in final report

Inventory Planning Checklist5.Sample survey designDefine target populationDefine sample unit Define required accuracy and precision

Will need to construct confidence intervalestimate ± “t-multiplier” x standard error of estimate

Decide how samples will be collectedDecide how many sample units will be measuredKnow budgeting limitations for field work

y ±t⋅sy

Inventory Planning Checklist6. Photo, satellite, other

remotely sensed info. interpretation procedures

7. Fieldwork procedures 8. Compilation and

calculation procedures9. Final report10. 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 unitFixed-area plots: 1/5, 1/10, 1/20, 1/40-acre sizes common for overstory trees; 1/100-acre, or less for seedling regenerationTransects: common for understory and groundstory vegetation, LODIndividuals: a deer, a hiker on a trail, a logGroups: 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 samplingStratified random samplingTwo-phase samplingMultistage samplingCluster samplingPurposive samplingConvenience sampling

Decide how many sample units will be measured

Know what equations will be used to compute estimatesUse of statistical formulas preferred

5. Sample survey design For SRS infinite populations (or sampling with replacement)

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 = allowable error or desired accuracy (in

percent) for the quantity of interest (e.g. biomass, volume, carbon, etc.)

k = correction term to simplify computations

( )22

2

z CVn k

E+=

(100)CV s y=

5. Sample survey design

Confidence level z-value k80% 1.282 1.3190% 1.645 1.8795% 1.960 2.4499% 2.576 3.79

5. Sample survey design For SRS in finite populations (or sampling without replacement)

N = Total number of sampling units in population, all other symbols are as before

( )( )

22

22 2

Nz CVn k

NE z CV+=

+

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) number of samples (n)Up to 10 ~ 1011 – 40 ~ 1 per acre41 – 80 20 + 0.5 (area in

acres)81 – 200 40 + 0.25(area in

acres)200 + Use sample size

formulas

5. Sample survey design Know budgeting limitations for field work

Simple cost modelCt = Co + n C1

whereCt = 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 measuredNumber 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 workDifficult to achieve same accuracy / precision for every resource – priorities must be set according to survey goalsConsider all ten (10) planning steps in designKnow and carefully define target population, sampling frame, sampling units, decide how many samples to measure, know budgeting limitations

Summary RemarksDiversity at different scalesoLandscapeoCommunity

Community – Lower Canopy Structure & Diversity

Horizontal / Vertical Structure

oPopulation - SpeciesoGenetic

Summary RemarksNeed 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 relationshipso Enhancement of grouping stands into risk classes

o Development of management targets forSilvicultural manipulationsManaging potential fire hazardBiological diversity maintenance