HB 2018-Who's who in beech bark disease

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This document summarizes a study on the spatial and temporal patterns of two fungi (Neonectria ditissima and Neonectria faginata) and the beech scale insect (Cryptococcus fagisuga) that cause Beech Bark Disease in Bartlett, New Hampshire. The study aims to determine the current density and distribution of the disease agents across the study area and investigate if nutrient treatments have affected their distribution. So far, preliminary results show the NP treatment plots have lower scale densities than N or P treatment plots. Identification of fungal samples is 80% complete, with most being N. faginata and a small number of N. ditissima found in young stands. Future work will

Science

Spatial and temporal patterns of
Neonectria spp. and beech scale occurrence in Bartlett, NH
Gretchen Dillon, M.S. Student, SUNY-ESF
Committee/Mentors: Mariann Johnston, RuthYanai, andTom Horton
Who's who in beech
bark disease:
N. ditissima vs. N.
faginata

Beech Bark Disease Complex
• Introduced beech scale (Cryptococcus fagisuga)
insect infests American beech (Fagus
grandifolia)
• Fungus (Neonectria sp.) infects tree
• Lesions impact cambium, gradual girdling
• Tree dies slowly from top down
• Roots survive longer, support sprouts
• White Mountains in aftermath phase

Scale Insect
Cryptococcus fagisuga (causal)
Xylococculus betulae (observed,
but causality not demonstrated)
PhotobyJ.Cale
PhotobyM.Johnston

Photo Credit: Gretchen Dillon, SUNY-ESF
Fungal Infection by Neonectria sp.
Photo Credit: Jon Cale, SUNY-ESF
Neonectria ditissima and Neonectria faginata fruiting bodies are
indistinguishable from each other by the naked eye.

Identification of fungus to species
Photos by: Gretchen Dillon, SUNY-ESF
Mean ascospore length is used
to differentiate the two
Neonectria species

BBD at Bartlett - Objectives
• What is the density and distribution of BBD &
causal agents across the Bartlett study area?
• Are there any apparent differences now (2017) in
density or distribution related to N and P
fertilization treatments?
• Establish a baseline record to facilitate
monitoring of future changes in scale insect
densities and Neonectria infections

Experimental Design
• Six stands were selected for monitoring
•Young (C2, C3), Mid (C4, C6), Old (C7,C8)
• Four treatment plots were evaluated per
stand (N, P, NP, Cont)
• Five trees were selected per plot
•Spaced as far apart as possible, 20-35 cm dbh
range

Photographic Documentation
• 8 photos per tree * 120 trees
= 960 photos
• Each is a 5 cm x 10 cm frame
• 1.5 M and 0.5 M height, four
cardinal directions
• Provides a baseline to
monitor future changes
Photos by: Gretchen Dillon, SUNY-ESF
Digital image analysis
ProgressTo Date
• Scale quantification is 25%
complete
• Preliminary Results:
• Treatment is a significant
predictor of scale density
• NP plots have lower scale
densities than N plots (p=0.04) or
P plots (p=0.05), but not different
from Control plots

Fungal Identification
Photos by: Gretchen
Dillon, SUNY-ESF
Photo by: Mariann Johnston, SUNY-ESF
• 306 samples were collected from 75 study trees between
9/22-11/5, 2017
Neonectria
identification
Progress to date
• Identification 80% complete
• 1.6% (4 of 245) are N. ditissima
• Co-occurred, young stands
• Remainder are N. faginata
• Genetic analysis confirms
microscopic ID

Future Work
• Complete Neonectria identification
• Document/map spatial distribution of Neonectria
across stands and treatment plots
• Complete photographic quantification of scale insect
• Is there a nutrient treatment effect now?
• Develop method and conduct photographic
quantification of lesion density
• Establish photo point monitoring and maintenance
schedule
• Establish protocol for detecting changes in future &
investigating relationship to nutrient treatments

Thank you 2017 Field Crew!
Vizma Leimanis, Michael Mahoney, Jason Stoodley, and Grace Lockwood

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Baby boomers are there any surviving germinants from the recent mast year -...

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And you thought there were no earthworms in our forests

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Asm2018 5 yanai

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Sampling intensity affects the ability to detect changes over time in soil nutrient concentrations. Higher sampling intensity through more samples or sampling sites allows for detection of smaller minimum detectable differences. A study analyzed how sampling intensity over time and space impacted the minimum detectable annual percentage change for concentrations of total carbon, nitrogen, and exchangeable magnesium, calcium, and potassium at different soil depths. The results provide guidance on sampling designs to balance detecting desired changes against costs of sample collection and analysis for soil monitoring.

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HB 2018 - Quantifying red maple seed production

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This study examined the effects of nutrient abundance on red maple seed mass and viability by collecting seeds from stands treated with different levels of nitrogen and phosphorus. The results showed that while seed count and viability were dependent on basal area and not nutrient treatments, average seed mass and average viable seed mass were significantly affected by the interaction between nitrogen and phosphorus treatments when accounting for basal area. This suggests that red maple seed mass and viability may be dependent on nutrient abundance, particularly the interaction between nitrogen and phosphorus availability.

HB 2018- Sugar maple leaf and twig traits

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This document summarizes research on the response of sugar maple leaf traits to nitrogen, phosphorus, and vertical gradients within the tree canopy. The study found that specific leaf area, nitrogen concentration, and chlorophyll concentration were higher in leaves from the upper canopy and in trees treated with additional nitrogen and nitrogen+phosphorus. Principal component analysis identified chlorophyll A and soluble ions as important drivers of the nutrient treatment responses. The results indicate that not accounting for vertical variation in leaf traits like nitrogen can lead to overestimates of carbon assimilation.

HB 2018- Back on a shoestring

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This document summarizes research from the Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE) project. The project studies nutrient limitation at 3 forest sites that receive nitrogen, phosphorus, or calcium additions. Key results show that mature forest stands responded significantly to phosphorus additions with increased growth rates, while younger stands responded more to nitrogen. Additionally, adding phosphorus decreased soil nitrogen availability, likely through increased plant or microbial uptake, while nitrogen additions reduced foliar and microbial phosphorus. The interactions between nitrogen and phosphorus limitation are an ongoing area of study.

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HB 2018-Who's who in beech bark disease

1. Spatial and temporal patterns of Neonectria spp. and beech scale occurrence in Bartlett, NH Gretchen Dillon, M.S. Student, SUNY-ESF Committee/Mentors: Mariann Johnston, RuthYanai, andTom Horton Who's who in beech bark disease: N. ditissima vs. N. faginata

2. Beech Bark Disease Complex • Introduced beech scale (Cryptococcus fagisuga) insect infests American beech (Fagus grandifolia) • Fungus (Neonectria sp.) infects tree • Lesions impact cambium, gradual girdling • Tree dies slowly from top down • Roots survive longer, support sprouts • White Mountains in aftermath phase

3. Scale Insect Cryptococcus fagisuga (causal) Xylococculus betulae (observed, but causality not demonstrated) PhotobyJ.Cale PhotobyM.Johnston

4. Photo Credit: Gretchen Dillon, SUNY-ESF Fungal Infection by Neonectria sp. Photo Credit: Jon Cale, SUNY-ESF Neonectria ditissima and Neonectria faginata fruiting bodies are indistinguishable from each other by the naked eye.

5. Identification of fungus to species Photos by: Gretchen Dillon, SUNY-ESF Mean ascospore length is used to differentiate the two Neonectria species

6. BBD at Bartlett - Objectives • What is the density and distribution of BBD & causal agents across the Bartlett study area? • Are there any apparent differences now (2017) in density or distribution related to N and P fertilization treatments? • Establish a baseline record to facilitate monitoring of future changes in scale insect densities and Neonectria infections

7. Experimental Design • Six stands were selected for monitoring •Young (C2, C3), Mid (C4, C6), Old (C7,C8) • Four treatment plots were evaluated per stand (N, P, NP, Cont) • Five trees were selected per plot •Spaced as far apart as possible, 20-35 cm dbh range

8. Photographic Documentation • 8 photos per tree * 120 trees = 960 photos • Each is a 5 cm x 10 cm frame • 1.5 M and 0.5 M height, four cardinal directions • Provides a baseline to monitor future changes Photos by: Gretchen Dillon, SUNY-ESF Digital image analysis ProgressTo Date • Scale quantification is 25% complete • Preliminary Results: • Treatment is a significant predictor of scale density • NP plots have lower scale densities than N plots (p=0.04) or P plots (p=0.05), but not different from Control plots

9. Fungal Identification Photos by: Gretchen Dillon, SUNY-ESF Photo by: Mariann Johnston, SUNY-ESF • 306 samples were collected from 75 study trees between 9/22-11/5, 2017 Neonectria identification Progress to date • Identification 80% complete • 1.6% (4 of 245) are N. ditissima • Co-occurred, young stands • Remainder are N. faginata • Genetic analysis confirms microscopic ID

10. Future Work • Complete Neonectria identification • Document/map spatial distribution of Neonectria across stands and treatment plots • Complete photographic quantification of scale insect • Is there a nutrient treatment effect now? • Develop method and conduct photographic quantification of lesion density • Establish photo point monitoring and maintenance schedule • Establish protocol for detecting changes in future & investigating relationship to nutrient treatments

11. Thank you 2017 Field Crew! Vizma Leimanis, Michael Mahoney, Jason Stoodley, and Grace Lockwood

HB 2018-Who's who in beech bark disease

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HB 2018-Who's who in beech bark disease