New Analysis Projects in the Interior West FIA Program

John D. ShawInterior West Forest Inventory and Analysis

USDA Forest Service

Interior West FIA User Group WebcastApril 13, 2010

State Reports• UT, CO, and AZ making their way through the end of the pipeline

• ID and MT next – will hold special topics workshop next week

• NM will be done using 70% of plots, starting at end of 2012

• 10-year reports will likely have a trend analysis emphasis

Forest Reports• Clearwater, ID Panhandle, and Nez Perce

• Humboldt-Toiyabe and Bridger-Teton

Basic Reporting

Stand Density IndexConsistency of definitions and analysis

From: Reineke, L.H. 1933. J. Agr. Res. 46(7):627-638.

Stand Density Index

• Wrapping up work on SDImax and related issues

• Paper given at National Silviculture Workshop• Work has progressed in consultation with silviculturists and

Forest Management Service Center

• FIA-based SDIs to be included in FVS• Series of papers to be submitted this year

AVGOFSDI

A_FORTYP: 221.0000030

20

10

0

Std. Dev = 89.61

Mean = 187.6

N = 195.00

529 830450

Growth and Growing Stock Analysis: What is the influence of compositional and structural diversity on productivity?

Ponderosa pine example

1474 plots with PP component

Jim LongDepartment of Wildland Resources

Ponderosa pine Other spp

Pure, even-aged

Mixed, irregularPure, irregular

Mixed, even-aged

Dividing Stands (plots) by Composition and Structure

From Long and Shaw. 2010. Forestry (Oxford). In press.

Analysis Approach

• FIA data include periodic increment (remeasurement, cores)

• Divide plots into categories based on composition and structure

• Model current annual increment as a function of stocking (SDI), stand height, and site quality (site index)

• Fit model to pure, even-aged stand data (reference condition)

• PAI (m3/ha/yr) = 0.0302 * SDIsum0.7050 * HT-0.4783 * SI1.5191

(r2 = 0.86)

• Use reference equation to predict PAI of other classes

• Analyze residuals for differences

Pure, even-aged

Mixed, irregularPure, irregular

Mixed, even-aged

ReferenceModel

Residuals vs Reference

Model

From Long and Shaw. 2010. Forestry (Oxford). In press.

• No significant differences among groups

• There are lots of reasons to manage for compositional and structural diversity, but productivity doesn’t appear to be one of them

• Greater variability in irregular, mixed stands may be related to diversity of associates

• Easy to repeat this test for other target species

Take-home Points

Average annual mortality rates for aspen and coniferous species in Colorado by measurement year, 2002-2006

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

2002 2003 2004 2005 2006

Measurement year

Per

cent

Aspen

Conifers

From: Thompson, M.T. et al. 5-year Interim Report for Colorado (in press)

FIA Annual Inventory Data Capture Temporal Trends

Average annual mortality rates for aspen and coniferous species in Colorado by measurement year, 2002-2006.

Analysis Approach• Lots of need to address emerging issues – e.g., MPB

• See presentations by Mike Thompson (next) and Ray Czaplewski (3:15)

• Many studies on insect effects analyze the aftermath

• FIA plots in place during onset, peak, and decline of large-scale events

• Anatomy of an MPB epidemic

• Develop a series of postulates based on literature

• Test each postulate against FIA time series

Jim LongDepartment of Wildland Resources

Relative density

Mor

talit

y (%

)

early

late

Ln TPHA

Ln Q

MD

early

late

Rank of % mortality- +

Sta

nd-L

evel

Mor

talit

y

100%

early

late

Relative DBH- +

Cum

ulat

ive

Mor

talit

y

100%

early

late

Susceptibility vs density(Anhold et al. 1996)

Progression in size-density space

Progression by size class Progression of severity

Relative density

Mor

talit

y (%

)

early

late

late

Possible asymmetry over time…

Simplified from Anhold et al. (1996) WJAF

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2008

2007

2006

2005

2004

2003

2002

Preliminary results for stand-level mortality over timeM

orta

lity

of lo

dgep

ole

pine

com

pone

nt

Percentile rank of stand-level mortality

Rank of % mortality- +S

tand

-Lev

el M

orta

lity

early

late

• Early results look promising

• Results appear to uphold some models

• e.g., Anhold et al.

• Some surprises

• Mixtures don’t appear to have special “immunity”

• Appears to be moving into low-density stands faster than into high-density stands (implications for thinning)

• Statistical methods are somewhat trailing• Czaplewski and Thompson are refining analysis of pseudo-panel data

• Not looking at the spatial component -- yet

Take-home Points

USING FOREST INVENTORY AND ANALYSIS DATA TO QUANTIFY WILDLIFE HABITAT IN FORESTED LANDSCAPES: AN OVERVIEW OF POTENTIAL APPLICATIONS

Chris Witt USFS Inventory, Monitoring and Analysis, Rocky Mountain Research Station,

Ogden, Utah

Mexican Spotted Owl Recovery Plan

• Listed as “Threatened” by USFWS in 1993

• Recovery Plan issued in 1995

• Goals of the Plan include: – no loss of existing habitat – review of plan effectiveness

after ten years

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

Pine-OakForest type

Crown Cover ≥45 percent

≥ 4.86 trees >45.7 cm DBH /

ha

Basal Area ≥10.2 m2

> 20% SDI from>45.7 cm DBH

> 25% SDI from30.5 - 45.7cm

DBH

All thresholdsmet

Habitat Component

Hecta

res

1999 Federal lands 2007 Federal lands1999 Private lands 2007 Private lands

Error bars represent 95% C.I.

Pine-Oak Forest TypeGila Mountains Recovery Unit: Arizona

Lewis’ Woodpecker Breeding and Nesting

Photo: Tom Grey

Used annual inventory data (2000 – 2007) to quantify forests providing important breeding habitat for M. lewis.

Crown cover

Woody understory

Snag densities

Results: Forest Type, Crown Cover, Woody Cover, Snags

0

50,000

100,000

150,000

200,000

250,000

Aspen Lodgepole pine White fir Douglas-fir Ponderosa pine

Forest type

Acr

es

Suitable at >13 % woody cover Suitable at >25 % woody cover

Error bars represent 95% confidence intervals

Additional Research: Utah State University/UDWR

• Established “phantom” plots at known M. lewis nest sites in aspen (n = 16 in 2009)

• Compare data to existing P2 plot data

• Visit FIA plots to conduct bird counts (with emphasis on M. lewis)

• Produce a perpetual monitoring tool for M. lewis and other forest vertebrates on Utah’s Sensitive Species List

Aspen, Heartrot, and Cavity-Nesting Aspen, Heartrot, and Cavity-Nesting BirdsBirds

• Aspen trees infected with Phellinus tremulae facilitates cavity excavation.

• Tree and stand characteristics that promote infection in the western U.S. are largely unknown

• Regional differences in tree and stand characteristics could play a role in bird assemblages.

Infected vs. Non-infected standsInfected vs. Non-infected stands

Not infected: n = 3023, mean = 17.89, SD = 8.31Infected: n = 3023, mean = 21.21, SD = 9.29

F (1, 6044) = 213.66, p < 0.0001

Not infected: n = 392, mean = 31.64, SD = 33.35Infected: n = 392, mean = 48.99, SD = 33.92

F (1, 782) = 52.11, p < 0.0001

Ecoregional AnalysisLegend

ASPEN PLOTS

CEC Ecoregions

Arizona/New Mexico Mountains

Idaho Batholith

Middle Rockies

Northern Basin and Range

Southern Rockies

Wasatch and Uinta Mountains

IW-FIA states

Compared purity, age, diameter and infectionrate between six ecoregions.

These areas contain > 95% of all aspen in the Intermountain region

Ecoregional Comparisons:Ecoregional Comparisons:Stand PurityStand Purity

Infection rate: 31.1 13.6 19.6 12.3 19.8 11.7

Infection rate: 31.1 13.6 19.6 12.3 19.8 11.7

Ecoregional Comparisons:Ecoregional Comparisons:Tree AgeTree Age

SummaryFIA data are being used to:

– Help managers with habitat treatment expectations(large sample size, systematic sampling of resource)

– Track changes (or lack thereof) in habitat quality after management plan implementation(remeasurement of plots over time)

– Quantify existing habitat for target species or guilds(plot and condition-level variables that reflect species needs)

– Identify limiting habitat features on a landscape-scale(strategic-level population estimates of habitat features)

Forest Genetics Sampling

• IW-FIA cooperating with RMRS and other geneticists to explore grid-based genetics sampling• All tree species + select pathogens

• Expect ~15K samples

• Focal species for first round of analysis:• Aspen – Bryce Richardson (RMRS-Provo), Karen Mock (USU)• Douglas-fir (Sam Cushman (RMRS-Moscow), FS genetics lab• Armillaria – Ned Klopfenstein (RMRS-Moscow), Mee-Sook Kim (Kookmin University, South Korea), Marylou Fairweather (R3 FHP)

• Several objectives:• Collection feasibility• Database proof-of-concept (i.e., genetic traits stored like DIA and HT)• Biogeographic analyses

• Future opportunities (winners vs losers after future events)

• E-Genetics: grab a sample and see what is (and was) there

Tree Ring Archiving and Analysis Project

• Complete cores were collected by IW-FIA during 1980s and 1990s periodic inventories

• Primarily used for aging and recent increment• Had done limited full-core reading and no analysis

• Initial estimate of ~8000 cores from most IW states• ~90% considered salvageable

• Established contract with Utah State University dendroecology lab for reading and archiving (2-year project)

• JVA for initial analysis in two areas (overlapping 2-year project)• Basic growth and yield modeling

• e.g., FVS large-tree diameter growth model• Climate studies

• About 8 months in on reading / archiving

R. Justin DeRoseDepartment of Wildland Resources

About 50 of these in FIA storage…

Sorting the good, the bad, and the ugly…

Getting organized by county within state

What we have so far…

What we have so far…

(all species)

What we have so far…

40 pinyons >300 years old

• Tree ring chronologies are typically on the most climate-sensitive sites

• ITRDB sites are relatively sparse, but have been used to develop climate surfaces for space in between

• Chronologies on FIA grid can test relationships across landscapes and elevation gradients

Tree Rings and Climate

CO2 ppm

1451 Pinyon – Raw Ring Width Data

CO2 ppmSmoothed ring widthRing width

CO2 ppm

1451 Pinyon – Growth for Diameter

Expected diameter growthNormalized ring width

Questions?

John D. ShawUSDA Forest Service Rocky Mountain Research StationOgden Forestry Sciences Lab507 25th StreetOgden, UT 84401

Phone: (801) 598-5902Email: jdshaw@fs.fed.usWeb: www.fs.fed.us/rm/ogden