GEOG 566






         Advanced spatial statistics and GIScience

June 11, 2018

Exploring historical constraints on fire across the Central Oregon Pumice Plateau

Filed under: 2018,Final Project,Final Project @ 5:45 pm

Question

My original question was how historical fire occurrence varies spatially on the central Oregon pumice plateau. My question morphed into how did constraints on fire including climate, fuel abundance and continuity, and lodgepole pine forest influence the occurrence of fire.

Dataset

My dataset consists of records of fire occurrence for 52 sample points distributed over an 85,000 ha area.

Study Area & Sample Points

At each sample point the annual occurrence of fire was reconstructed from tree rings during ~1700-1918.  Cross sections were removed from dead trees. Sections were sanded and precisely dated and injuries created by non-lethal low-severity surface fires were dated to their exact year of occurrence.

 

Fires scars collected at individual sample sites were composited into 1 record of fire occurrence at each sample point.  A range of 8 – 28 fires occurred at each sample point (mean 16).  In the Composite graph horizontal lines represent sample points and vertical tick marks represent fire occurrence.

Composite

Hypothesis

I hypothesized that climate, time since fire, and lodgepole pine acts as constraints to fire occurrence across the central Oregon pumice plateau.

Rationale

Earlier investigations demonstrate that fires historically occurred in dry forests in Oregon during drought years.  However fire size has not been related to climate.  It could be that fires of different sizes have additional relationships to previous year climate.  In exercise 2, I checked to see if small fires, large fires, and extensive fires were similarly related to climate.

Recent investigations north and northwest of this study area demonstrated lower fire frequency in lodgepole pine forests and that areas of lodgepole pine forest acted as intermittent barriers to fire spread.

Fuel is an obvious limiting factor to fire spread.  After fire fuel will need to recover sufficiently for fire to spread thus time since fire may be predictive of fire occurrence.

Methods/Approaches

Ex 1 – Mapping fires from Binary Point Data

Prior to exploring constraints on fire occurrence I need to produce maps of fire occurrence.  To do this I used Arc GIS to evaluate using Thiessen Polygons, Kriging, and Inverse Distance Weighting (IDW) to map historical fires. Once I had made maps of fires I visually examined spatial and temporal variation in fire occurrence by creating an animation of fire events over time, mapping fire history statistics, and by identifying fire occurrence groups with cluster analysis and then mapping the groups.

Ex 2 – Determining how fire size and climate were related

I used superposed epoch analysis (SEA) to determine how annual climate and climate in antecedent years was related to fire occurrence.  By breaking fire events into size classes I was able to see if relationships with climate varied with fire extent.

Ex 3 – Using a GLMM to understand the influence of climate, time since fire, and lodgepole pine on annual fire occurrence

I used a generalized linear mixed model (GLMM) to determine the influence of fixed effects (climate, time since fire, and lodgepole) on annual fire occurrence across my study area.  Sample point was included as a random effect to see if the model varied across the study area. I used R2 for GLMM models to determine the relative importance of each fixed effect and the random intercept of sample point in the model.

The Results and the Significance

Ex 1

In exercise 1 I learned the tradeoffs between the 3 difference approaches to mapping fires from binary point data. The Thiessen polygon method provided most efficient, objective, and parsimonious method to map fire perimeters based on the distribution of my sample points.

Fire Extent mapped from Thiessen Polygons

Ex 2

Superposed Epoch analysis demonstrated that extensive fires occurred during years of extreme drought, large fires occurred during average droughts, and small fires were not related to dry or cool wet climate years. No relationships with climate in antecedent years for fires of any size or years without fire were found.

SEA_FireSize

These results demonstrate that climate in antecedent years before a fire is not related to fuel abundance and connectivity. Cool wet years that may have been necessary to produce fine fuels that carry surface fires did not occur before fire years. Only a dry hot year during the fire year was associated with large and extensive fire spread. This suggests that fine fuel production with respect to fire spread is not related to climate. For my investigation this result means that fuel recovery following fire is not moderated by climate after fire. Thus time since fire may be an independent predictor of fire occurrence.

Ex 3

Using the GLMM approach I determined that climate, time since fire (interval), and lodgepole influenced annual fire occurred.  However, lodgepole was weakly significant, and only created a small change in the annual probability of fire. The conditional R2 for the GLMM model demonstrated that the random effect of site accounted for very little of the variance explained in the model. Climate and not interval as I expected explained most of the variance accounted for by the model.

For every 1 unit increase in PDSI the probability of fire occurring decreased by ~25%, for each year without fire (Interval) the probability of fire increased by 7%, and for each percent increase in lodgepole forest around a sample point the probability of fire decreased by 1%.

Significance of Ex 3 – Because lodgepole was significant, but weakly significant I want to explore other metrics that capture how lodgepole varies with respect to each plot.  It could be that lodgepole nearer the plot is more important or it could be its spatial pattern that causes variation in fire occurrence.  I was surprised that interval was less influential than climate. I interpret this as a strong indication that fuel is only limiting for a few short years after fire occurrence in the central Oregon pumice plateau. The small influence of site in the model suggests that bottom-up controls (microclimate, slope, local composition) that are site specific have little influence on fire frequency and fire region.

My next step is to focus on a single explanatory variable and switch to using site as a fixed effect with an interaction with that explanatory variable.  In addition I plan to fit 52 models, one for each site, using a single explanatory variable.  This would switch from a GLMM approach to a GLM approach, and allow me to map coefficients at each site on a map

Software Learning

In R I learned to use the LME4 package, learned how to perform SEA analysis, learned several new scripts for data wrangling, and learned how to make graphical summaries of fire history in ggplot.

In ArcGIS I learned to create animations of fire events and how to map fires with three different interpolations. I also used kriging to summarize variation in fire metrics in exercise 1.

Statistical Learning

I learned the pros and cons of different techniques to map fires, the ins and out of SEA analysis, and got an initial understanding of GLMMs. I plan to spend a lot more time working with GLMMs now that I’ve had this introduction and have found a flexible tiered approach to linear models.

References

Bolker, B.M.,Brooks,M.E.,Clark,C.J.,Geange,S.W.,Poulsen,J.R.,Stevens,M.H.H. & White, J.S.S. (2009) Generalized linear mixed models: a practicalguide for ecology and evolution. Trends in Ecology & Evolution, 24,127–135.

Hessl A, Miller J, Kernan J, Keenum D, McKenzie D. 2007. Mapping Paleo-Fire Boundaries form Binary Point Data: Comparing Interpolation Methods. The Professional Geographer 59:1, 87-104.

Florian Hartig (2018). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.2.0. http://florianhartig.github.io/DHARMa/

Michael H. Prager & John M. Hoenig (2011) Superposed Epoch Analysis: A Randomization Test of Environmental Effects on Recruitment with Application to Chub Mackerel, Transactions of the American Fisheries Society, 118:6, 608-618, DOI: 10.1577/1548-8659(1989)118<0608:SEAART>2.3.CO;2

Nakagawa S. and Schielzeth H (2012) A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution Vol 4(2):133-142.

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3 Comments »

  1.   leatherl — June 15, 2018 @ 2:03 pm    

    Cool project, Andrew! It’s curious to me that your second and third exercises seem to indicate distinct relationships between climate and fire occurrence! My comments are similar to Sam’s and Julia’s, especially since the climate metric will probably have a huge influence on fire occurrence– was PDSI the climate variable in the SEA, as well as the GLMM? To what do you attribute the contrasting implications of these two analyses?

  2.   jonesju — June 15, 2018 @ 7:05 am    

    Excellent work. Need to provide more explanation of the graphs, showing the odds ratios. Explore how this would change with a different drought index, such as a standardized precipitation evaporation index, or a snowpack reconstruction. Consider using geographically weighted regression. You could use GWR next to check for the role of site – use a single predictor variable and see if fire occurrence (or return interval) responds differently to PDSI if different locations.

  3.   swanssam — June 15, 2018 @ 6:32 am    

    Hi Andrew, nice work. I’m curious what metric of climate you used for your analyses. Is there a drought index you settled on like the Palmer Drought Index, an atmospheric oscillation like the Pacific Decadal Oscillation, or by climate do you just mean temperature and precipitation? I’m sorry to have missed your presentation, but good luck in your future work!

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