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 CLIMATE ENSO, PDO, AMO Summer DROUGHT PDSI Winter ppt Summer soil moisture FIRE EVENTS Spatial/Temporal Dynamics -Year events -X, Y coord Fuel Oxygen, 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 CLIMATE ENSO, PDO, AMO Summer DROUGHT PDSI Winter ppt Summer soil moisture FIRE EVENTS Spatial/Temporal Dynamics -Year events -X, Y coord Fuel Oxygen, Ignition weather Forest Type, Landscape position, other

The Big Science Question El Nino Influence Yr Climate 1A, B, C 2A-, B, C 3A-, B, C- 4… Asynchronous spatial fire pattern over time??

Conceptual Map CLIMATE ENSO, PDO, AMO Summer DROUGHT PDSI Winter ppt Summer soil moisture FIRE EVENTS Spatial/Temporal Dynamics -Year events -X, Y coord Fuel Oxygen, Ignition weather Forest Type, Landscape position, other

Research Questions El Nino Influence Yr Climate 1A, B, C 2A-, B, C 3A-, 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

Sample Unit = Site

Sample Unit = Year

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