Regeneration and Fuel Loading with Varying Overstory Retention in Redwood Stands 10 Years after Transformation to Multiaged Management

Thesis committee presentation by Judson Fisher

Thank you

• Thank you for being here
• exciting time
• appreciate your willingness to see me through to the finish
• by the end
  • good understanding of project
  • some idea of where you’ll be able to offer support/feedback

Today’s plan

1. Introduction
2. What we did
3. Some results
4. My questions
5. Your questions

A multiaged silviculture experiment

10 years ago, Pascal implemented a large scale, replicated, manipulative silvicultural experiment to demonstrate and study the transformation from even-aged to multiaged management in redwood dominated forests in the Jackson Demonstration State Forest in Mendocino County. My thesis serves as a 10-year report of the remeasurement of several key factors of that experiment —- sprout heights, regeneration density, and fuels.

An important part of ecological forest management

• Diversity of forest structures == Diversity of silvicultural techniques
• Predominance of even-aged management
• This work contributes to our understanding of multiaged stand development
• Greater understanding -> increased adoption

Why study multiaged management?

Suitability of redwood forests

• Timber value
• Shade tolerance
• Reliable regeneration

Why do this study in redwood forests?

Fire-informed management

• Slow adoption of fire-informed management on the North Coast
• Redwood is a fire-adapted species
• Historical record shows frequent fire
• What is the pyrosilvicultural significance of our management actions?

Why study fuels in addition to tree growth?

Today’s plan

1. Introduction
2. What we did
3. Some results
4. My questions
5. Your questions

Now that you have and idea of the what and why, I’ll explain more of the details of what we did.

What we did

1. Implementation
2. Sampling procedure
3. Analysis

I’ve broken this section into 3 parts.

By the end of this section you’ll understand how the treatments and how they were applied across the landscape.

Initial harvest: 2012

• 16 one-hectare blocks
  • GS: Group selection
  • LD: Low density dispersed
  • HA: High density aggregated
  • HD: High density dispersed

These four treatments were replicated across four sites.

This is how the treatments are defined. They are listed in order from least, to greatest amount of competition (light and growing space).

The treatments are defined in terms of retained trees after harvest

And this is that the treatments mean, in a schematic representation.

One site

GS

LD

HA

HD

What we did

1. Implementation
2. Sampling procedure
3. Analysis

At the end of this next section, you’ll understand how my sampling design resulted in response variables across three categories:

• Sprout heights
• Regeneration density
• Fuels

The plot structure

• Macro plot (Outline)
• Regeneration plots (green circles)
• Fuel transects (orange lines)
• Fuel sampling cylinders (orange circles)

Here I define the various aspects of the sampling design.

Next, I’ll describe what we sampled in each of these.

Sprout height

• 25 each of tanoak and redwood sprout clumps were selected for measurement
• Tallest sprout in clump measured at years 1, 5, and 10

Regeneration density and composition

We recorded diameter and species of all sprouts and seedlings taller than 1.4 m

Downed woody fuel

• Particles were tallied by size class
• Counts were converted to load (Mg/ha) using linear intersect theory and parameters from the literature

A transect

Litter and duff

• Litter and duff depth measured from a representative location within the sampling cylinder
• Depth-to-load equation taken from the literature

Vegetation

• We estimated percent cover and average height
• Converted to load using a constant bulk density

Sampling review

That was a lot of response variables. Here they are summarized in three categories.

What we did

1. Implementation
2. Sampling procedure
3. Analysis

By the end of this section, you’ll have an idea of they methods I used to anlyze these data.

Sprout height

• Frequentist framework
• Model selection was performed using AIC
• Treatment and year were factors
• Tested various random effects
• Included fixed effect model for dispersion
• Model checked using simulated residuals

Regeneration density

I report on:

• Species diameter frequency distributions and
• Basal area weighted frequency distributions

for each treatment.

Fuel

• Bayesian framework
• Separate models for six different fuel classes
• Used \(gamma\) hurdle model: zeros modeled separately as a proportion
• Priors determined from data with support from literature
• Model checked with posterior predictive distribution

Today’s plan

1. What we did
2. Why we did it
3. Some results
4. My questions
5. Your questions

Sprout height model

• redwood is growing faster than tanoak
• redwood is taller than tanoak
• the difference in slow down in growth rates is negligible across treatments
• redwood is more variable than tanoak
• Is the slow down in growth rate different between species?
• Is the slow down statistically significant?

Sprout height: year 10

Our data were only sufficient to detect a statistical difference for height in year 10 for the GS treatment

Regeneration density: frequency

• the number of tanoak in the smallest size class increases with greater competition.
• the number of stems in other size classes follow a decreasing trend
• The decreasing trend is more pronounced for redwood

Regeneration density: basal area

• This emphasizes the decreasing trend in redwood abundance
• The trend is less clear for tanoak

Fuel: means

• 1hr fuels are slightly higher in HD and HA and lower in LD and GS. Likely due to inputs from residual trees.
• 10– and 100hr fuels are very similar across treatments, but slightly increasing with increasing light.
• duff & litter are highest in HD and lowest in HA. Why?
• veg is lowest in HA, highest in GS

Fuel: comparisons

• 1hr HA > GS, and almost HA > LD. Also HD almost > GS
• Duff & Litter HD maybe greater than HA
• veg GS > HA; LD maybe > HA

Today’s plan

1. What we did
2. Why we did it
3. Some results
4. My questions
5. Your questions

My questions

• How to add post-pct to the story
• Potential questions generated by regen. distributions
• “Asking questions” of the height model: confidence intervals

Today’s plan

1. What we did
2. Why we did it
3. Some results
4. My questions
5. Your questions

Questions?

learn more at online at:

https://fisher-j.github.io/multi-age

Thanks again

Downed woody fuel classes

Class name	Size (cm)	Transect length (m)
1 hr	< 0.64	1
10 hr	0.64 - 2.5	2
100 hr	2.5 - 7.6	4
1,000 hr	> 7.6	10

Vegetation: Load calc.

We calculated average load for herbaceous and woody vegetation:

\[load = ht. \times pct. cover \times \rho,\]

where \(\rho\) is a bulk density of 8 and 18 Mg/ha/m for herbaceous and shrub components, respectively.

Fuel bed depth

We estimated the combined average fuel bed depth of all litter and downed woody debris within the sampling cylinder.

Regeneration density: frequency

Year 1 height measurement