Forest Susceptibility to Southern Pine Beetle in the New Jersey Pinelands Carissa F. Aoki Dartmouth College Ecology and Evolutionary Biology
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1 Forest Susceptibility to Southern Pine Beetle in the New Jersey Pinelands Carissa F. Aoki Dartmouth College Ecology and Evolutionary Biology Pinelands Commission Research Series December 17, 2014
2 Outline I. SPB Background II. Forest structure and susceptibility III. Data Methods IV. Pinelands, North and South V. Infested vs. uninfested stands VI. SPB in wetland stands
3 Southern Pine Beetle Dendroctonus frontalis Zimmerman (Coleoptera: Curculionidae)
4 Southern Pine Beetle Dendroctonus frontalis Zimmerman (Coleoptera: Curculionidae) A native insect pest historically occurring across the southern states Last recorded occurrence in New Jersey in the 1930s Current New Jersey outbreak began ~2002 Few outbreaks in the south since the late 90s
5 A Story About A Beetle An excerpt from a video by Milo Johnson
6 Weakened tree enables beetles to get established SPB pheromones + pine tree volatiles = more beetles Mass attack strategy enables beetles to eventually overcome healthy trees Infestations occur in discrete spots Multiple generations during a summer enable spots to grow An outbreaking pest Southern Pine Beetle
7 Southern Pine Beetle Community
8 Southern Pine Beetle in New Southern Jersey pine beetle New Jersey
9 Southern Pine Beetle in New Jersey Adapted by Matt Ayres from Weed et al. 2013; data from NOAA National Climate Data Center
10 Southern Pine Beetle in New Jersey Adapted by Matt Ayres from Weed et al. 2013; data from NOAA National Climate Data Center
11 The Polar Vortex in New Jersey Overnight minimum temperatures January 4-5, 2014 Image courtesy of David Robinson New Jersey State Climatologist
12 The Polar Vortex in New Jersey
13 Southern Pine Beetle on Long Island!
14 Outline I. SPB Background II. Forest structure and susceptibility III. Data Methods IV. Pinelands, North and South V. Infested vs. uninfested stands VI. SPB in wetland stands
15 Why do stand characteristics matter? I. Beetle ability to overcome defenses (resin flow data) II. Host searching behavior: Stand density (basal area, nearest neighbor distance), %Pine within stand III. Host quality (for reproductive success) Age/DBH, height, radial growth IV. Pheromone plume maintenance Canopy closure, canopy base height, understory structure, stand density/basal area
16 Knowledge from southern forests Different pines species have different susceptibility due to resin flow differences Stands with higher density have higher rates of spot initiation and growth Thinning is an effective strategy for SPB prevention (recommendation is to thin stands >27.5 m2/ha to <18.3 m2/ha) Susceptibility has a parabolic relationship with stand age
17 Outline I. SPB Background II. Forest structure and susceptibility III. Data Methods IV. Pinelands, North and South V. Infested vs. uninfested stands VI. SPB in wetland stands
18 Control Plots
19 Control Plots
20 Spot plots
21 Spot Suppression Thatcher et al., eds The Southern Pine Beetle.
22 Spot plots measured adjacent to suppressed area
23 Plot Samples 50 meters Focal tree sampling 5 samples/50 m transect; 3 transects/plot: Hemispherical photo (transect 1 only) Age of focal tree Focal tree DBH Focal tree BA pine Focal tree BA hardwood Focal tree height Focal tree crown base Focal tree crown class Distance FT to nearest neighbor NN DBH and NN BA pine and BA hw Distance FT to 2 nd nearest neighbor SNN DBH and SNN BA pine and BA hw Selected nearest tree to each of 5 equidistant sampling points
24 Why do stand characteristics matter? I. Beetle ability to overcome defenses (resin flow data) II. Host searching behavior: Stand density (basal area, nearest neighbor distance), %Pine within stand III. Host quality (for reproductive success) Age/DBH, height, radial growth IV. Pheromone plume maintenance Canopy closure, canopy base height, understory structure, stand density/basal area
25 Outline I. SPB Background II. Forest structure and susceptibility III. Data Methods IV. Pinelands, North and South V. Infested vs. uninfested stands VI. SPB in wetland stands
26 North vs. South: Basal Area
27 North vs. South: Height, DBH, tree distance
28 North vs. South: Height, DBH, tree distance Shouldn t stands south of the Mullica be LESS susceptible? Lower % pine within stands Lower pine basal area Trees farther apart
29 North vs. South: Tree age Site quality?
30 Outline I. SPB Background II. Forest structure and susceptibility III. Data Methods IV. Pinelands, North and South V. Infested vs. uninfested stands VI. SPB in wetland stands
31 Basal area (pine and total)
32 Distance between trees
33 Tree age and diameter
34 Outline I. SPB Background II. Forest structure and susceptibility III. Data Methods IV. Pinelands, North and South V. Infested vs. uninfested stands VI. SPB in wetland stands
35 A Southern Pine Beetle Mystery in NJ SPB Spots from Flight Data, August 2013
36 Tree defenses? The Growth Differentiation Balance Hypothesis (Loomis 1932, Lorio and Sommers 1986) Herms and Mattson 1992
37 Tree defenses?
38 Next steps Risk model Landscape-scale simulation models (management, fire, climate scenarios) Post-beetle fuels modeling: Ken Clark
39 Related project: southwide data Annual monitoring of SPB and its predator (T. dubius, checkered beetle) Began in 1987 By 2005, at least 7 years of data for 133 forests over 13 states from Texas to Virginia Up to 24 years of continuous data per forest Basis for prediction system (spring trapping for summer outbreak prediction)
40 Related project: southwide data Can predictions be improved? Can predictive models include economic and other nonmarket values to help managers make good choices? Bayesian framework
41 Related project: southwide data
42 QUESTIONS?
43 Acknowledgments New Jersey Forest Service: Lynn Fleming Ron Corcory James Dunn Bill Zipse Dave Finley Marie Cook US Forest Service: Ken Clark Mike Gallagher Bill Oldland Rutgers Pinelands Field Station: John Dighton Field Crew 2013 & 2014: Jacob Ebersole, Jen Freise, Sarah Heyborne, Jeff Ledolter, Becca Novello, Alex Procton Matt Ayres and lab members: Lauren Culler Flora Krivak-Tetley Nina Lany Jeff Lombardo Aaron Weed Funders: USFS EM Base and Fire Programs Garden Club of America NSF GRFP
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