“Predicting the impacts of genetic improvements and climate change on GA-FL-AL productivity and...

Southeastern Forest Productivity and Sustainability in a Changing World

Tim Martin and Gary Peter

Carlos Gonzalez-Benecke

School of Forest Resources and Conservation

University of Florida

The Pine Integrated Network: Education, Mitigation, and Adaptation Project (PINEMAP) is a Coordinated Agricultural Project funded by the USDA National Institute of Food and Agriculture, Award #2011-68002-30185. For more information, visit http://www.pinemap.org.

Randy Wynne & Evan Brooks

Virginia Tech

Elevated Atmospheric CO2 and Climate

What We Know

• Atmospheric CO2 is rising due to human activity• Average temperatures are rising, mostly due to the

effects of atmospheric CO2

– Warmer and more frequent hot days and nights are virtually certain

– Warm spells and heat waves are very likely to increase

• Altered precipitation regimes are likely, but effects will vary across the globe (and across regions) and are more difficult to predict and may include– Likely increased intensity of rainfall events– Increased/decreased rainfall depending on location

• Increased tropical cyclone activity likely

IPCC 4th Assessment

Forest Free-Air CO2 Enrichment (FACE)

Pinus taedaDuke Forest, NC

Populus tremuloidesRhinelander, WI

Long-Term Growth Response to Elevated CO2

Depends on Availability of Other Resources

Elevated CO2

Ambient CO2

McCarthy et al. 2010. New Phytologist 185:514-528.

Disturbance effects can be large, but the science of predicting disturbance

lags predictions of temperature and

precipitation

PINEMAP:

Pine Integrated Network: Education,

Mitigation and Adaptation Project

Google Earth

A NIFA-Funded

Climate Change CAP

The Pine Integrated Network: Education, Mitigation, and Adaptation Project (PINEMAP) is a

Coordinated Agricultural Project funded by the USDA National Institute of Food and Agriculture,

Award #2011-68002-30185. For more information, visit http://www.pinemap.org.

NIFA Coordinated Agricultural Project

(“CAP”) Program – Climate Change

• Large (initially 5 yr, $20 million) commodity-focused projects

• Multi/inter/transdisciplinary• Include research, education, outreach• Focus on climate mitigation and adaptation

PINEMAP Goals

To create, synthesize, and disseminate the necessary knowledge to enable southern forest landowners

• to harness pine forest productivity to mitigate atmospheric CO2,

• to more efficiently utilize nitrogen and other fertilizer inputs,

• and to adapt their forest management approaches to increase resilience in the face of changing climate.

PINEMAP’s Climate Projection Dataset

• Multivariate Adaptive Constructed Analogs (MACA) Downscaled Climate Data

– Typical GCM grid scale is dozens to hundreds of miles

– Statistical downscaling method

– Projections for “baseline”, 1950-2005, and for future time period, 2006-2100

– 20 different GCMs used by IPCC 5th Assessment

– Two different emission scenarios:

• RCP 4.5 (moderate emissions)

• RCP 8.5 (high emissions)

• 2-3C temperature rise (3.6 – 5.4 F) in SE US

• Moderate precip increases in eastern SE US, moderate decreases in western SE US

• Less certainty than temperature prediction

PINEMAP Regional Modeling

• Sub-regional Timber Supply Model (SRTS)

• Water Supply Stress Index (WaSSI)

• Growth and yield (climate-responsive)

• Physiological Principles Predicting Growth (3-PG)

Suite of complementary models focused on understanding opportunities and risks under future

predicted climate

Inputs: Soil

• Soils data

– USGS Soil Survey data (SSURGO), 2013 edition• Fine spatial resolution (some polygons as narrow as tens of meters)

• Available water in the soil was derived from the “added value” table and joined directly to mapunit polygon features

• Soil texture component polygon features were aggregated by weighted area to mapunit polygon features

Inputs: Soil

• 12-digit hydrological unit (HUC) features from the USGS

– ~18000 HUCs across the PINEMAP region of interest• Median size: ~22000 acres

– Aggregate inputs and/or outputs to this resolution for model inter-comparison

Inputs: Soil

• Soil features aggregated by weighted area to HUC resolution

– Climate features were treated similarly for WaSSI

Inputs: Climate

• Climate Data

– MACA downscaled climate data, from 1950-2095• Daily temporal resolution, 1/16 degree spatial resolution

Inputs: Soil and Climate

• For efficient processing of G&Y and 3PGlob, the soil HUC features were intersected with an index grid for the climate data

– Each sub-feature thus had a unique combination of soil and climate data Sub-features associated with an example HUC

Example simulation: slash pine

State variables

Subsidiary variables

Climate & site Inputs

Losses

Material f low s

Influences

Carbon

Water

Trees

Key to colours & shapes

Subsidiary variables

+

H20 Rain

gC

Soil H20

ET

+

+

+

_

_

+

wSx

Deadtrees

Stocking

+

+

_

wS+

wS>wSx

_ _

N

+

+

__

Stress

VPD

T

FR

f

+

_

+

_

+

++

+

+

DBH

F/SR

LAILUE

SLA

+

+

_

NPP

Stem

Foliage

Roots

GPP

CO2

C,N

Litter

+

3-PG Model (Landsberg and Waring, 1997)

BA

Physiological Principles that Predict Growth

Diagram by Peter Sands

JasperTX

Santa RosaFL

DoolyGA

ColletonSC

AlachuaFL

PolkFL

JeffersonFL

BrantleyGA

CovingtonAL

CovingtonMS

WashingtonLA

Historical Mean Annual Temperature (°C)

Slash Pine simulation locations

11 sites in SE US4 sites in Northern Limit

Age (years)

0 5 10 15 20 25

Ab

ov

e G

ro

un

d B

iom

ass

(M

g h

a-1

)

0

50

100

150

200

250

300

Age (years)

0 5 10 15 20 25

Ab

ove G

rou

nd

Bio

mass

(M

g h

a-1

)

0

50

100

150

200

250

300

Age (years)

0 5 10 15 20 25

LA

I (m

2 m

-2)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Climate Change Effect on Slash Pine Productivity

Alachua - FL Colleton- SC

Age (years)

0 5 10 15 20 25

LA

I (m

2 m

-2)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

High

Med.

Low

High

Med.

Low

Historical

RCP 4.5

RCP 8.5

Climate Change Effect on Slash Pine Productivity

Mean Annual Temperature (C)

18 20 22 24

Ch

an

ge i

n A

bove G

rou

nd

Bio

mass

(Mg h

a-1

)

0

10

20

30

40

50

SI = 19

SI = 23

SI = 28

RCP 8.5

Notes:• All effects positive• Growth stimulation

greater on low quality sites

• Growth stimulation greater in cool sites on northern edge of range

• Interacting effects of fertilizer not considered here

• Rangewide loblolly simulations underway

SI=62 ft

SI=75 ft

SI=92 ft

PINEMAP

Decision Support System

• Focus on:

– Identifying opportunities

– Quantifying and mitigating risk

26

Conceptual

draft design

Take Home Messages - Productivity

• Climate is changing

• Where temperature rises and water balance remains favorable, there will likely be opportunities for increased productivity

• Impacts of disturbance have the potential to “swamp” biological effects, but unfortunately are very difficult to predict

• Loblolly has a much larger and more climatically variable range – stay tuned for rangewide simulations

Pine Integrated Network: Education, Mitigation, and Adaptation project (PINEMAP) is a Coordinated Agricultural Project funded by the USDA National Institute of Food and Agriculture, Award #2011-68002-30185

Two Genetics Objectives

Deliver deployment guidelines for genotypes suited for varied climatic conditions to maximize resiliency and reduce adverse impacts of climate change on productivity

Analyze genetics of breeding and natural populations to discover alleles in genes controlling important adaptation and mitigation traits that enable future tree breeding strategies


Genetic Diversity in the Natural

Population of Loblolly

• Western & Eastern populationsAnalysis for population structure based on ~3000 genetic variants in 622 trees from across the range is consistent with a two population model

Eckert A J et al.

Genetics 2010;185:969-

982

Schmidtling, R., 2003, ISHS Acta

Horticulturae, 615: 203-209


Genetic Basis & Evolution of Fitness-

related Traits in Loblolly

• A central goal is to identify alleles in genes in breeding populations that affect growth and adaptation to improve prediction of performance and minimize risks of loss

Eckert A J et al. Genetics 2013;195:1353-1372

Height


Loblolly Pine Breeding Populations

Wild Seed Selection

Phenotypic selectionLocation of origin = Provenance

Cycle 1Genetic Testing

Common garden plantings to evaluate maladaptation, growth, form, disease…

SelectionRank parents for genetic merit

Cycle 2 BreedingInter-mate to maximize genetic gain and manage relatedness

Genetic Testing

Multi-environment,progeny/clonal tests

Cycle 3 Selection Rank

…

Northern ACP

Piedmont

Upper Gulf

Central ACP

Southern ACP

Lower Gulf

ACP - Atlantic Coastal Plain

Mnt.

Lob

>95% of the seedlings planted have been through at least 1 cycle of selection

# Families % Planted

OP seedlings 15 89

FS seedlings 7 8.5

Clones - 2.5

2013 Seedling Survey


Seed Movement

R.C. Schmidtling, 2001, Southern Pine

Seed Sources, USFS General Technical

Report SRS-44


Current USFS Seed Deployment Guidelines

R.C. Schmidtling, 2001, Southern Pine

Seed Sources, USFS General Technical

Report SRS-44


Background

Universal Response Function (URF)

• First developed in 2010 Wang et al. Ecological Applications, 20(1), 2010, pp. 153-163

• Caveats:

– Gene flow, demographic history, and colonization contribute to between and within population variation

– Weather records miss a lot of site to site variation

Yij = f(weather at the test site i

and climate at the provenance j)


What we hoped to add:

• Try different statistical techniques

– Estimate predictive reliability

– Incorporate risk assessments

• Refine predictions from the provenance to the family level to support development of the DSS

• Identify important abiotic stressors that limit growth


Methodology

• PINEMAP used five non-overlapping common garden provenance test series established by CFGRP, NCSUTIP, WGTIP to cover the entire loblolly pine range

Families

Test Sites


Data

• University of Florida– Provenance-Progeny (PP) Tests

• Established: 1982-1983 • 58 families, 7 test sites• Height, DBH & rust resistance – age 5, 10, 15

– Florida Wild-Seed (FWS) Tests• Established: 2001-2002• 211 families, 10 test sites• Height, DBH & rust resistance – age 6

• North Carolina State University– Plantation Selection Seed Source Study (PSSSS)

• Established: 1994• 140 families, 16 test sites• Height, DBH, rust resistance – age 4, 8

• Texas A&M University– Geographic Seed Source Study (GSSS)

• Established 1974-1978• Series I: 25 families, 40 test sites, Series II 17 families, 26 test sites• Height, DBH, rust resistance – age 5, 10, 15, 20


Historical Weather Data from 1970-

2010

– PRISM

– SECC

– Idaho


Regression Methods

• Ordinary Least Squares Regression

• LASSO

• Multiple Linear Regression

• Multinomial Logit Regression

• Ridge Regression


LocationsUniversity of Florida

Provenance-Progeny Tests (top) and Florida Wild-Seed Tests (bottom); approximate locations of the test sites are marked with numbers


Methods & ResultsUniversity of Florida

• Attempted several regression methods, settled on LASSO

• Used several weather data sources (including PRISM and SECC), settled on Idaho weather data

• Response variables: Height, DBH, Volume per acre (age 10 for PP, and age 6 for FWS)

• Variation explained by the models:

– Provenance-Progeny tests:60% for Height, 39% for DBH, and 59% for Volume

– Florida Wild-Seed tests: 62% for Height, 57% for DBH, and 63% for Volume


Pooled LASSO Regression Analysis of

FL Provenance and Wild Seed Tests

Parameters Estimates SE partial R2 R2 F-statistic p-value VIFIntercept 1.118 0.004 0S_Mean Warmest 3 days -0.258 0.006 0.154 0.154 634.5 <.0001 3.64S_Mean Coldest 3 days -0.149 0.002 0.180 0.334 945.69 <.0001 2.13S_Log Annual Heat Moisture Index 1.811 0.042 0.154 0.488 1047.14 <.0001 3.50S_LAHM2 4.961 0.220 0.048 0.536 362.87 <.0001 1.74S_Specific Humidity 0.046 0.002 0.042 0.578 348.26 <.0001 2.41P_LONGITUDE -0.016 0.001 0.022 0.600 191.72 <.0001 1.40PS_MC3 0.016 0.001 0.017 0.617 158.41 <.0001 1.02P_MC32 -0.006 0.001 0.004 0.621 37.52 <.0001 1.36P_Hum 0.009 0.002 0.001 0.622 9.22 0.0024 1.54P_MC3 -0.007 0.002 0.001 0.623 9.04 0.0027 2.27

Height, Age 6


Differences in Predicted Annual

Temperature & Precipitation 2040-2069

vs. 1971-2000

20 of 20 models predict 2-3oC

increase in min & max temps


Building a Seed Deployment Tool for the

PINEMAP Decision Support System

• We are working with Ryan Boyles and Heather Dinon to develop a seed deployment tool that incorporates downscaled future climate predictions

• The web based tool will generate maps for provenances based on future predictions of mean minimum coldest month temperature

• The tool will depict risks associated with planting material from more southern sites of origin that are moved up

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