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 implications • F orest I nsect D isturbance E cology L ab
Examination of stand, site and climate relationships with r-value What is an r-value? larvae + pupae + adults r = entrance holes From: (Lux, 2008)
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) F orest I nsect D isturbance E cology L ab
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
Influence of stand characteristics on r -value Sources of variability in r -value? r -value vs DBH all plots & all years • Climate variation 80 70 • Site factors 60 • Beetle populations dynamics 50 y = 0.1907x - 2.0175 r -value 40 R² = 0.0307 30 20 10 0 10 30 50 70 DBH (cm) F orest I nsect D isturbance E cology L ab
Influence of stand characteristics on r -value Influence of attacking size of beetle population on r -values 10 • # of infested trees (tree count) is 8 an indicator of size of attacking y =a*b x *x c population mean r -value 6 • Excluded plots where tree count 4 was < 3 from further analyses 2 0 0 5 10 15 20 25 tree count class F orest I nsect D isturbance E cology L ab
Influence of stand characteristics on r -value Mean DBH was the best predictor of r -value 12 • Binning data helps to clarify the relationship y = 0.2011x - 2.0024 R² = 0.8209 • Must have >4 plots in DBH 8 Mean r -value class to be included 4 0 10 20 30 40 50 2cm DBH Class F orest I nsect D isturbance E cology L ab
Influence of stand characteristics on r -value Inventory derived SSI was not a good predictor of r -value 10 y = 0.0261x + 2.8726 • SSI = Stand Susceptibility R² = 0.0942 8 Index developed by Shore and Safranyik (1992) for BC Mean r- value conditions 6 • None of the other stand 4 characteristics were good predictors of r-value 2 0 0 20 40 60 80 SSI Class F orest I nsect D isturbance E cology L ab
Influence of stand characteristics on r-value Effect of DBH on r -value is moderated by climate Effect of winter temperature on r -values • Cold: min winter temp < -35 °C 0.6 • Mild: min winter temp > -35 °C 0.5 Mild Probability of r = 0 0.4 Cold 0.3 0.2 0.1 0.0 10 20 30 40 50 60 5cm DBH Class (cm) F orest I nsect D isturbance E cology L ab
Influence of site characteristics on r -value Both elevation and, to a lesser degree, latitude have an impact on r -values 8 Latitude vs. Elevation 60 Effect of Elevation on r-value 58 6 Mean r -value Lattitude (N) 56 4 54 52 2 50 0 48 0 500 1000 1500 2000 0 500 1000 1500 2000 Elevation class (m) Elevation class (m) F orest I nsect D isturbance E cology L ab
Influence of site characteristics on r -value Development of Location Temp Effect (LTE) • Designed to capture the effect of 8 elevation and latitude on r -value 6 -1 °C per 100m above 1000m elevation Mean r -value -0.7 °C per degree latitude above 49.6° N 4 • Fit with logistic regression equation 2 y = a/(1 + b*e -cx ) 0 -10 -8 -6 -4 -2 0 Location Temp Effect Class (°C) F orest I nsect D isturbance E cology L ab
Influence of annual climate on r -value Analysis of annual climate variation on r -values • Others have developed detailed 9 Effect of coldest day on r -value models of MPB development in 8 relation to climate (e.g. Régnière and Bentz, 2007) 7 y = 0.2692x + 13.962 R² = 0.4584 6 • -37 °C represents a threshold for Mean r MPB winter mortality 5 4 • Only min winter temperature showed a good relationship with 3 y = a/(1 + b*e -cx ) mean r -values 2 R= 0.839 1 0 -50 -45 -40 -35 -30 -25 -20 Min Temp (C) F orest I nsect D isturbance E cology L ab
Development of r -value model Multiple linear regression analysis • Excluded r-values >20 MLR Results Predicted vs Measured r -value Binned into 2cm DBH Classes Term Estimate Std Error t Ratio Prob>|t| 8 Intercept -6.785 0.655 -10.36 <.0001 Annual_ T_Min 0.511 0.053 9.66 <.0001 y = 1.0074x DBH 0.130 0.015 8.98 <.0001 R² = 0.8772 6 Tree_count 0.535 0.076 7.05 <.0001 LTE 0.223 0.078 2.88 0.0041 Measured r -value • r model can be used to predict r-values 4 over space and time • DBH can be estimated as a function of 2 inventory top height and stand age • Min winter temp can be actual or projected 0 0 2 4 6 8 • Used in our spatial model: MPB Spread Modeled r -value
Examination of stand, site and climate relationships with r -value Management Implications: F orest I nsect D isturbance E cology L ab
MPB productivity ( r ) model: relevance and integration 1 1. Complement DSS (assume mild winter; combine with SSI, DSS/Risk assessment • Site prioritization stand size, connectivity, etc.) • Workplan development • Zonation Dispersal bait Oct. collection Ground surveys • Green-attack detection Sep. Nov. – Dec. Adapt Green:red surveys Aug.– Sep. Do Learn Jan. – Mar. Aerial surveys Control • Red-attack detection • Level 1 (level 2) Jun.– Jul. May – Jun. Dispersal bait deployment r-value surveys • Leading edge detection • Overwinter survival
MPB productivity ( r ) model: relevance and integration 1. Complement DSS (assume mild winter; combine with SSI, DSS/Risk assessment • Site prioritization stand size, connectivity, etc.) • Workplan development 2. Reduce/redirect r -value • Zonation surveys Dispersal bait Oct. collection Ground surveys • Green-attack detection Sep. Nov. – Dec. Adapt Green:red surveys Aug.– Sep. Do Learn Jan. – Mar. Aerial surveys Control • Red-attack detection • Level 1 (level 2) Jun.– Jul. May – Jun. Dispersal bait deployment r-value surveys 2 • Leading edge detection • Overwinter survival
MPB productivity ( r ) model: relevance and integration 1. Complement DSS (assume mild winter; combine with SSI, DSS/Risk assessment • Site prioritization stand size, connectivity, etc.) • Workplan development 2. Reduce/redirect r -value • Zonation surveys Dispersal bait Oct. collection Ground surveys 3. Inform dispersal bait • Green-attack detection Sep. deployment Nov. – Dec. Adapt Green:red surveys Aug.– Sep. Do Learn Jan. – Mar. Aerial surveys Control • Red-attack detection • Level 1 (level 2) Jun.– Jul. May – Jun. Dispersal bait deployment r-value surveys 3 • Leading edge detection • Overwinter survival
MPB productivity ( r ) model: relevance and integration 1. Complement DSS (assume mild winter; combine with SSI, DSS/Risk assessment • Site prioritization stand size, connectivity, etc.) • Workplan development 2. Reduce/redirect r -value • Zonation surveys Dispersal bait Oct. collection Ground surveys 3. Inform dispersal bait • Green-attack detection Sep. deployment Nov. – Dec. Adapt 4. Inform aerial survey priorities Green:red surveys Aug.– Sep. Do Learn Jan. – Mar. Aerial surveys Control • Red-attack detection • Level 1 (level 2) 4 Jun.– Jul. May – Jun. Dispersal bait deployment r-value surveys • Leading edge detection • Overwinter survival
MPB productivity ( r ) model: relevance and integration 1. Complement DSS (assume mild winter; combine with SSI, DSS/Risk assessment • Site prioritization stand size, connectivity, etc.) • Workplan development 2. Reduce/redirect r -value • Zonation surveys Dispersal bait Oct. collection Ground surveys 3. Inform dispersal bait • Green-attack detection Sep. deployment Nov. – Dec. Adapt 4. Inform aerial survey priorities Green:red surveys 5. Inform Level 3 priorities Aug.– Sep. Do Learn Jan. – Mar. Aerial surveys Control • Red-attack detection • Level 1 (level 2) Jun.– Jul. May – Jun. Dispersal bait deployment r-value surveys • Leading edge detection • Overwinter survival
Discussion
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