Examination of stand, site and climate relationships with r-value - - PowerPoint PPT Presentation

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Examination of stand, site and climate relationships with r-value - - PowerPoint PPT Presentation

Jun 15, 2023 189 likes •398 views

Examination of stand, site and climate relationships with r-value Outline What is an r -value? What are the key stand characteristics, site and climate factors influencing r-values? Can we predict r-values? Management

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Forest Insect Disturbance Ecology Lab

Examination of stand, site and climate relationships with r-value

  • What is an r-value?
  • What are the key stand characteristics, site and climate

factors influencing r-values?

  • Can we predict r-values?
  • Management implications

Outline

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Examination of stand, site and climate relationships with r-value

What is an r-value?

From: (Lux, 2008) larvae + pupae + adults entrance holes

r =

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Forest Insect Disturbance Ecology Lab

Examination of stand, site and climate relationships with r-value

What are the key stand characteristics, site, and climate factors influencing r-values?

  • Survey data from multiple years (2007-2015)
  • Stand characteristics: DBH, # of infested trees, height, age, %

pine etc., SSI (data from field and inventory )

  • Site features: Elevation, latitude, aspect
  • Climate data: daily climate data from multiple climate stations

(min temp, # of cold days, seasonal effects, ppt patterns)

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r-value data collected for multiple years

  • r-surveys conducted 2007-2015
  • Year represents beetle-year (year of

adult beetle attack)

  • Offspring emerge the following year
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Forest Insect Disturbance Ecology Lab

Influence of stand characteristics on r-value

Sources of variability in r-value?

y = 0.1907x - 2.0175 R² = 0.0307 10 20 30 40 50 60 70 80 10 30 50 70 r-value DBH (cm)

r-value vs DBH all plots & all years

  • Climate variation
  • Site factors
  • Beetle populations dynamics
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Forest Insect Disturbance Ecology Lab

Influence of stand characteristics on r-value

Influence of attacking size of beetle population on r-values

  • # of infested trees (tree count) is

an indicator of size of attacking population

2 4 6 8 10 5 10 15 20 25 mean r-value tree count class

y =a*bx *xc

  • Excluded plots where tree count

was < 3 from further analyses

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SLIDE 7

Forest Insect Disturbance Ecology Lab

Influence of stand characteristics on r-value

Mean DBH was the best predictor of r-value

  • Binning data helps to clarify

the relationship

  • Must have >4 plots in DBH

class to be included

y = 0.2011x - 2.0024 R² = 0.8209 4 8 12 10 20 30 40 50 Mean r-value 2cm DBH Class

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SLIDE 8

Forest Insect Disturbance Ecology Lab

Influence of stand characteristics on r-value

Inventory derived SSI was not a good predictor of r-value

  • SSI = Stand Susceptibility

Index developed by Shore and Safranyik (1992) for BC conditions

y = 0.0261x + 2.8726 R² = 0.0942 2 4 6 8 10 20 40 60 80 Mean r-value SSI Class

  • None of the other stand

characteristics were good predictors of r-value

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SLIDE 9

Forest Insect Disturbance Ecology Lab

Influence of stand characteristics on r-value

Effect of DBH on r-value is moderated by climate

  • Cold: min winter temp < -35 °C
  • Mild: min winter temp > -35 °C

0.0 0.1 0.2 0.3 0.4 0.5 0.6 10 20 30 40 50 60 Probability of r = 0 5cm DBH Class (cm)

Effect of winter temperature on r-values

Mild Cold

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Forest Insect Disturbance Ecology Lab 2 4 6 8 500 1000 1500 2000 Mean r-value Elevation class (m)

Effect of Elevation on r-value

Influence of site characteristics on r-value

Both elevation and, to a lesser degree, latitude have an impact on r-values

48 50 52 54 56 58 60 500 1000 1500 2000 Lattitude (N) Elevation class (m)

Latitude vs. Elevation

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Forest Insect Disturbance Ecology Lab

Influence of site characteristics on r-value

Development of Location Temp Effect (LTE)

2 4 6 8

  • 10
  • 8
  • 6
  • 4
  • 2

Mean r-value Location Temp Effect Class (°C)

  • Designed to capture the effect of

elevation and latitude on r-value

  • 1 °C per 100m above 1000m elevation
  • 0.7 °C per degree latitude above 49.6° N
  • Fit with logistic regression equation

y = a/(1 + b*e-cx)

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Forest Insect Disturbance Ecology Lab

Influence of annual climate on r-value

Analysis of annual climate variation on r-values

  • Others have developed detailed

models of MPB development in relation to climate (e.g. Régnière and Bentz, 2007)

  • -37 °C represents a threshold for

MPB winter mortality

  • Only min winter temperature

showed a good relationship with mean r-values

y = 0.2692x + 13.962 R² = 0.4584 1 2 3 4 5 6 7 8 9

  • 50
  • 45
  • 40
  • 35
  • 30
  • 25
  • 20

Mean r Min Temp (C)

Effect of coldest day on r-value

y = a/(1 + b*e-cx)

R= 0.839

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Development of r-value model

Multiple linear regression analysis

y = 1.0074x R² = 0.8772

2 4 6 8 2 4 6 8 Measured r-value Modeled r-value Predicted vs Measured r-value Binned into 2cm DBH Classes

  • r model can be used to predict r-values
  • ver space and time
  • DBH can be estimated as a function of

inventory top height and stand age

  • Min winter temp can be actual or

projected

  • Used in our spatial model: MPB Spread
  • Excluded r-values >20

Term Estimate Std Error t Ratio Prob>|t| Intercept

  • 6.785

0.655

  • 10.36

<.0001 Annual_ T_Min 0.511 0.053 9.66 <.0001 DBH 0.130 0.015 8.98 <.0001 Tree_count 0.535 0.076 7.05 <.0001 LTE 0.223 0.078 2.88 0.0041

MLR Results

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Forest Insect Disturbance Ecology Lab

Management Implications:

Examination of stand, site and climate relationships with r-value

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MPB productivity (r) model: relevance and integration

1. Complement DSS (assume mild winter; combine with SSI, stand size, connectivity, etc.)

1

DSS/Risk assessment

  • Site prioritization
  • Workplan development
  • Zonation

Ground surveys

  • Green-attack detection

r-value surveys

  • Overwinter survival

Dispersal bait deployment

  • Leading edge detection

Aerial surveys

  • Red-attack detection

Green:red surveys Dispersal bait collection Oct.

  • Nov. – Dec.

May – Jun. Jun.– Jul. Aug.– Sep. Sep.

Adapt Do Learn

Control

  • Level 1 (level 2)
  • Jan. – Mar.
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SLIDE 16

MPB productivity (r) model: relevance and integration

1. Complement DSS (assume mild winter; combine with SSI, stand size, connectivity, etc.) 2. Reduce/redirect r-value surveys

2

DSS/Risk assessment

  • Site prioritization
  • Workplan development
  • Zonation

Ground surveys

  • Green-attack detection

r-value surveys

  • Overwinter survival

Dispersal bait deployment

  • Leading edge detection

Aerial surveys

  • Red-attack detection

Green:red surveys Dispersal bait collection Oct.

  • Nov. – Dec.

May – Jun. Jun.– Jul. Aug.– Sep. Sep.

Adapt Do Learn

Control

  • Level 1 (level 2)
  • Jan. – Mar.
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SLIDE 17

MPB productivity (r) model: relevance and integration

1. Complement DSS (assume mild winter; combine with SSI, stand size, connectivity, etc.) 2. Reduce/redirect r-value surveys 3. Inform dispersal bait deployment

3

DSS/Risk assessment

  • Site prioritization
  • Workplan development
  • Zonation

Ground surveys

  • Green-attack detection

r-value surveys

  • Overwinter survival

Dispersal bait deployment

  • Leading edge detection

Aerial surveys

  • Red-attack detection

Green:red surveys Dispersal bait collection Oct.

  • Nov. – Dec.

May – Jun. Jun.– Jul. Aug.– Sep. Sep.

Adapt Do Learn

Control

  • Level 1 (level 2)
  • Jan. – Mar.
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SLIDE 18

MPB productivity (r) model: relevance and integration

1. Complement DSS (assume mild winter; combine with SSI, stand size, connectivity, etc.) 2. Reduce/redirect r-value surveys 3. Inform dispersal bait deployment 4. Inform aerial survey priorities

DSS/Risk assessment

  • Site prioritization
  • Workplan development
  • Zonation

Ground surveys

  • Green-attack detection

r-value surveys

  • Overwinter survival

Dispersal bait deployment

  • Leading edge detection

Aerial surveys

  • Red-attack detection

Green:red surveys Dispersal bait collection Oct.

  • Nov. – Dec.

May – Jun. Jun.– Jul. Aug.– Sep. Sep.

Adapt Do Learn

Control

  • Level 1 (level 2)
  • Jan. – Mar.

4

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SLIDE 19

MPB productivity (r) model: relevance and integration

1. Complement DSS (assume mild winter; combine with SSI, stand size, connectivity, etc.) 2. Reduce/redirect r-value surveys 3. Inform dispersal bait deployment 4. Inform aerial survey priorities 5. Inform Level 3 priorities

DSS/Risk assessment

  • Site prioritization
  • Workplan development
  • Zonation

Ground surveys

  • Green-attack detection

r-value surveys

  • Overwinter survival

Dispersal bait deployment

  • Leading edge detection

Aerial surveys

  • Red-attack detection

Green:red surveys Dispersal bait collection Oct.

  • Nov. – Dec.

May – Jun. Jun.– Jul. Aug.– Sep. Sep.

Adapt Do Learn

Control

  • Level 1 (level 2)
  • Jan. – Mar.
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SLIDE 20

Discussion