Fire Sync Data Analysis

Christel’s Baby Steps to Temporal and Spatial Analyses

Overview

Conceptual Map Study Design Data Charactistics Data Analysis Roadmap to Success Future Work

Conceptual Map

CLIMATEENSO, PDO,

AMO

SummerDROUGHT

PDSIWinter ppt

Summer soil moisture

FIRE EVENTSSpatial/Temporal

Dynamics-Year events-X, Y coord

FuelOxygen, Ignition

weather

Forest Type, Landscape

position, other

Study Design Observational Post Ex Facto Non-random

Spatial Temporal

Data Characteristics Fire Site

Categorical X,Y information

Fire Event Time Series X,Y information Binary Data Clumping by

climatic region could be count data

Phase Events

ENSO Event Time Series Binary Data?

Other PDO, AMO Events as well? Phases Categories?

PDSI Continuous index Grid Data Time series X,Y information

Data Characteristics Climate - normal Fire data ??Non-linear Correlated observations

Inference? Ecological/Climatological Statistical

Prediction? (Interpolation)

Conceptual Map

CLIMATEENSO, PDO,

AMO

SummerDROUGHT

PDSIWinter ppt

Summer soil moisture

FIRE EVENTSSpatial/Temporal

Dynamics-Year events-X, Y coord

FuelOxygen, Ignition

weather

Forest Type, Landscape

position, other

The Big Science Question

El Nino Influence

Yr Climate

1 A, B, C

2 A-, B, C

3 A-, B, C-

4 …

Asynchronous spatial fire pattern over time??

Conceptual Map

CLIMATEENSO, PDO,

AMO

SummerDROUGHT

PDSIWinter ppt

Summer soil moisture

FIRE EVENTSSpatial/Temporal

Dynamics-Year events-X, Y coord

FuelOxygen, Ignition

weather

Forest Type, Landscape

position, other

Research Questions

El Nino Influence

Yr Climate

1 A, B, C

2 A-, B, C

3 A-, B, C-

4 …

Does drought reflect climatic conditions – spatially and temporally?

Research Approach Superposed Epoch Analysis

Nonparametric methods for correlated time series data

Focuses to find signals around extreme events

Research Approach Superposed Epoch

Analysis 77 sites related to

drought in the year of the fire

Temporal results Descriptive

Mapping

Research Approach Spatial Relationships?

Regionalize Analysis to deflat spatial influence

Test for autocorrelation in distance (x,y)

Research Approach Regionalize

Climatic – PDSI data PCA ordination

Research Approach Response Groups

Fire Event data Clustering

dendrograms Nonmetric

multidimensional Scaling ordination

DCA, species in 4 or less exp un deletedRed Pine Herbs 2002

X Data

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

Y D

ata

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

CC1

CR1

DC1

DR1 LC1LR1

SC1

SR1

CC2

CR2DC2

DR2

LC2

LR2

SC2

SR2

CC3

CR3

DC3

DR3

LC3

LR3

SC3

SR3

CC5

CR5

DC5DR5

LC5

LR5SC5

SR5

Sample Unit = Site

Sample Unit = Site

Sample Unit = Year

Sample Unit = Year

sync

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Dis

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Obj

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Fun

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Info

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0 100

4.1E

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75

8.2E

-02

50

1.2E

-01

25

1.6E

-01

0

_160

0_1

608

_161

2_1

655

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0_1

602

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4_1

609

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3_1

926

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5_1

949

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8_1

975

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1_1

992

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4_1

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8_1

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0_1

605

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9_1

930

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2_1

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7_1

974

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3_1

936

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9_1

982

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2_1

996

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4_1

688

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0_1

669

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7_1

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6_1

977

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965

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1_1

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5_1

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4_1

969

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1_1

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3_1

914

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9_1

621

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3_1

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7_1

610

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5_1

646

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3_1

618

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2_1

687

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3_1

918

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8_1

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2_1

981

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7_1

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7_1

966

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1_1

916

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8_1

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5_1

971

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7_1

929

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3_1

815

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0_1

601

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0_1

627

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0_1

943

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6_1

643

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7_1

6_16

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8_1

623

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5_1

613

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9_1

940

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7_1

611

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9_1

633

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3_1

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0_1

689

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1_1

970

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5_1

912

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8_1

931

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4_1

937

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3_1

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9_1

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4_1

626

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6_1

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749

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2_1

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4_1

641

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5_1

921

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9_1

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9_1

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7_1

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2_1

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1_1

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0_1

650

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1_1

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2_1

909

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3_1

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7_1

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0_1

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9_1

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5_1

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9_1

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2_1

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1_1

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9_1

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6_1

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5_1

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1

Research Approach Analyzing Spatial &

Temporal at the same time??

“Synchrony”

Definition: A process of

adjustment of rhythms due to an interaction

Spatial covariance in population density fluctuations

Synchrony Analysis

Spatial covariance – Point Pattern Analysis Demonstrate scale Identify mechanisms

Endogenous Exogenous

Moran’s I Effect: density independent factor (e.g., climate) overrides local population regulators by large environmental shocks that synchonize the population

Synchrony Analysis

Spatial Autocorrelation Pattern of nearby locations are more

likely to have similar magnitude than by chance alone

Signature of past spatial-temporal patterns

Synchrony Analysis

Spatial Autocorrelation Coefficient Provide an average isotrophic estimation

of autocorrelation at each distance class Formal testing with Confidence Intervals

Bonferroni Adjustment Distances result in + or – relationships Displayed with correlograms

Synchrony Analysis

Variogram Identify and model spatial pattern Predict (kriging) unmeasured areas value

Require parameter fitting & model selection

Synchrony Analysis

Variogram

Roadmap to success

Hypothesis refinement Data statements & tests

Roadmap to success

Exploratory data analysis All datasets

Data format (binary, count, continuous…)

Transformations? Outliers? Possible interaction terms (elevation,

forest type)?

Roadmap to success

Summary Analyses Multivariate/NMS (time or space) Clustering (time or space) Repeated measures SEA Variograms (scale)

Roadmap to success

Statistical Inference & Prediction Model based methods

Future Work Spatio-temporal dynamics

Fire, Drought, Climate oscillations Kurt – drought info Christel – fire info Grant - ENSO

Comparison of dynamics Drought vs fire, etc.

Prediction to unmeasured areas Hierarchal modelling