Canadian Journal of Forest Research
Incidence and Ecology of the Chaga fungus (Inonotus obliquus) in Hardwood New England/Acadian Forests
Journal: Canadian Journal of Forest Research
Manuscript ID cjfr-2020-0144.R2
Manuscript Type: Article
Date Submitted by the 20-Jul-2020 Author:
Complete List of Authors: Brydon-Williams, Rhys; University of New Hampshire, Natural Resources and the Environment Munck, Isabel; USDA Forest Service, Northeastern Area State and Private Forestry Asbjornsen,Draft Heidi; University of New Hampshire, Natural Resources Keyword: Chaga, Non-Timber Forest Products, Birch, Plant Pathology, Fungus
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Incidence and Ecology of the Chaga fungus (Inonotus obliquus) in Hardwood New
England/Acadian Forests
Rhys Brydon-Williams: Corresponding Author
University of New Hampshire
82c Main Street, Rochester, NH, 03868
978-904-1787 Draft
I. A. Munck
USDA Forest Service: State & Private Forestry
271 Mast Road, Durham, NH, 03824
H. Asbjornsen
University of New Hampshire
105 Main Street, Durham, NH, 03824
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1 ABSTRACT
2 Inonotus obliquus is a fungal pathogen of birch trees (Betula spp.) and other hardwoods that
3 produces a sterile conk known colloquially as Chaga. Chaga has medicinal value as an anti-
4 mutagen and for gastro-peptic relief. Chaga harvesting has recently increased throughout its
5 natural range in North America, including the White Mountain National Forest (WMNF). There
6 is currently a lack of knowledge on Chaga resource incidence and ecology in North America,
7 which this project sought to rectify. Two surveys were conducted in 2017 and 2018 in the
8 WMNF, totaling 2,611 sampled trees. Positive correlations were found between Chaga presence
9 and average stand tree age, diameter at breast height (DBH), and elevation. Overall Chaga 10 frequency was low (3.75%). However, Draftsclerotia were widely distributed throughout the study 11 area, with infected trees clustering. Chaga presence did not correlate with stand-level species
12 composition or annual basal area increment, though it did appear with significantly greater
13 frequency in yellow birch trees compared to other birch species. Additional damages related to
14 biotic and abiotic stressors did not correlate with Chaga presence, except for those resulting
15 directly from Chaga presence. These results have important silvicultural and forest management
16 implications for Chaga harvest practices across its North American range.
17 KEYWORDS
18 Chaga, Non-timber Forest Products, Birch, Plant pathology, Fungus
19 1. INTRODUCTION
20 Inonotus obliquus is a basidiomycetous fungal pathogen of the family Hymenochaetaceae
21 and causal agent of the sterile conk known as Chaga (from Russian “чага,” or “mushroom”). The
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22 Chaga fungus is found almost exclusively on birch trees (Betula spp.), to which it is parasitic
23 (Sinclair et al, 2005). Identifiable by the sterile conk extruded through the bark of infected trees,
24 Chaga appears similar to charred wood, hence the colloquial name “Tinder Conk;” examples of
25 these conks may be observed in Figure 1. This conk is not a fruiting body but instead a
26 sclerotium, or mycelial mass containing nutrient reserves (Szczepkowski et al, 2013).
27 Chaga is almost exclusively found on birch trees, particularly white birch species (B.
28 papyrifera, B. pendula, B. pubescens) and yellow birch (B. alleghaniensis), although Chaga
29 infection has been reported for black birch (B. lenta), water birch (B. occidentalis), and grey
30 birch (B. populifolia) (Lee et al. 2008). The distribution of Chaga is “circumboreal” as it spans 31 throughout the Northern Hemisphere, typicallyDraft within dense birch growth interspersed with 32 coniferous boreal forest. In North America, Chaga has been reported throughout Canada and in
33 the United States in the Northeast, the Great Lakes region, Alaska, and high-altitude areas of the
34 Appalachian Mountains with significant yellow birch growth (Sinclair et al., 2005). However, no
35 intensive studies have been conducted into habitat and distribution of the Chaga resource in
36 North America. Furthermore, accurate estimates of Chaga habitat worldwide are difficult to
37 determine as the fungus is not usually included in forest inventory data in Europe and Russia
38 (Pilz, 2004).
39 One study of birch stands (B. pendula and B. pubescens) in the Ulyanovsk region of
40 Russia found that Chaga seemed to prefer trees of sprout origin, mature stands, and oligotrophic
41 soils. Anthropogenic disturbance in the birch stands also had positive correlation with Chaga
42 incidence (Balandaykin et al., 2015). Furthermore, in a study conducted in seven regional forests
43 in Poland from 1995-2011, Chaga had higher incidence in stands aged 60 years or older and
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44 associated strongly with mixed birch-coniferous forests, bog forests, and wet broadleaved forests
45 (Szczepkowski et al, 2013). However, these studies were restricted to European South Boreal
46 forests, and thus the results may not be directly transferrable to Chaga distribution in North
47 America.
48 Improving knowledge about the Chaga resource is particularly critical given its medicinal
49 benefits. Chaga has an extensive history as a folk remedy for stomach ailments and cancer,
50 particularly in Russia and Northern Europe, with use documented among the Khanty people of
51 Siberia (Lee et. al, 2008) and among the Sami people of Northern Scandinavia (Magnani, 2016).
52 The Chaga sclerotium contains triterpenoids, which have anti-mutagenic properties such as 53 growth inhibition of carcinoma cells (ZhongDraft et al, 2015), and one study documented that Chaga 54 extract can bolster immune response and reduce inflammation (Kim 2005).
55 Chaga is distributed throughout circumboreal regions of North America, including the
56 Acadian forest ecoregion that extends from northern New England in the U.S. to eastern Canada
57 (Sinclair et al., 2005). Much of this region has experienced increased harvesting activity in
58 recent years due to growing awareness of the product’s health benefits and subsequent rising
59 consumer demand. For example, harvesting of Chaga has increased in the Canadian province of
60 Ontario, with some providers claiming to harvest as much as 40,000 kilograms of Chaga per
61 month (Mihell, 2017). Similarly, the White Mountain National Forest (WMNF), located
62 towards the southern limits of the Acadian forest region, has also experienced increased harvest
63 activity over the past decade according to WMNF forest managers (Carpenter et al., 2017).
64 Currently, Chaga harvesting within the WMNF is not subject to special use permitting; however,
65 given the growing pressures on the Chaga resource, managers may need to consider establishing
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66 such regulations in the future. Unfortunately, the baseline data on Chaga ecology, abundance,
67 and distribution needed to inform the development of best management practices (BMPs) to
68 guide permitting efforts are currently lacking for this region.
69 Consequently, this study was developed to address the following three primary
70 objectives: (i) quantify the Chaga resource abundance, presence, and volume in the WMNF, (ii)
71 determine possible effects of Chaga presence on birch tree health, and (iii) determine possible
72 effects of Chaga infection on tree growth. The results from this research will be valuable in
73 developing BMPs for sustainable harvesting of the Chaga resource in the WMNF and across
74 other circumboreal forests in North America. 75 2. METHODS Draft 76 2.1 White Mountain National Forest
77 The White Mountain National Forest is a federally managed National Forest occupying
78 303,859 square hectares in the Northeastern United States. The WMNF is primarily used for
79 recreation and research, but is also open to small-scale logging and other commercial purposes.
80 Extensive clear-cut logging occurred in the region from the beginning of European settlement
81 until the exhaustion of the local timber resource in the mid-19th century, and continued with
82 varying degrees of intensity until the 1970s (Niering, 1992).
83 This logging activity, combined with frequent natural disturbances such as pathogens and
84 ice storms, have resulted in much of the WMNF consisting of either early-successional or
85 relatively young mature mixed hardwood forests. Two regionally common birch species, Betula
86 papyrifera and B. alleghaniensis, have come to thrive in these frequently-disturbed areas,
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87 especially on moist sites with poor soils (Davis et al., 1999). According to 2009-2012 Forest
88 Service Inventory (FIA) data, birch stands (i.e. greater than 40% basal area composed of birch
89 species) account for 25,787 hectares or roughly 8% of the total area of the WMNF.
90 The composition and structure of WMNF forests vary greatly with elevation: at mid-
91 elevations, northern hardwood forests predominate, while lower elevations typically consist of
92 either mixed oak-hickory hardwood forest or early succession white-pine forest. Spruce-fir
93 forests dominate at higher elevations. There are also less common forest types, such as mixed
94 maple-basswood-ash forest, as well as non-forested high-elevation alpine communities (Davis et
95 al., 1999). The history of exploitation and management in the WMNF and resultant plant 96 communities, including the prevalence ofDraft birch species, are features common to the larger 97 hardwood New England/Acadian forest region, from the Green Mountains of Vermont to the
98 Chaleur uplands of Quebec/New Brunswick (Davis et al., 1999). Consequently, it is reasonable
99 to assume that the results of this study are broadly applicable to birch-Chaga relationships
100 throughout the region, though individual site context undoubtedly varies widely.
101 2.2 Survey Design
102 The WMNF Chaga assessment was undertaken in two phases: an initial survey conducted
103 in 2017 to establish baseline data on Chaga, which was then used to inform a more targeted and
104 detailed survey in 2018. Sampled stands were selected from a list of U.S. Forest Service WMNF
105 stands with greater than 40% birch basal area inventoried during 2009-2012 (Fig. 2). The 2017
106 stands were selected by whether their waypoint was within 500 meters of a road, to improve
107 accessibility.
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108 A total of 41 birch stands were surveyed between August and December 2017. Stand
109 inventory data such as time of establishment or stand age, mean DBH, percent slope, and site
110 elevation, were obtained from the WMNF. Stands were initially stratified by total species DBH
111 (diameter at 1.7 m) as reported in the 2009-2012 survey data, based on U.S. Forest Service
112 terminology: regeneration (mean DBH < 11 cm), poletimber (mean DBH >11 cm but < 28 cm),
113 and sawtimber (mean DBH > 28 cm). Field work continued into winter as external symptoms of
114 Chaga are not season specific.
115 Logistic regression analyses of the 2017 dataset showed positive correlations between
116 Chaga presence and arithmetic mean plot DBH (x² = 18.35 at p<0.0001), stand age (x² = 10.30 at 117 p<0.001), and site elevation (x² = 3.91 atDraft p<0.0479). Thus, the 2018 survey plots were selected 118 to target specific stand conditions related to tree age (over 75 years), tree DBH (mean plot DBH
119 > 28 cm) and elevation (along a gradient of 342 to 1029 meters). Stands surveyed in 2018 were
120 not limited to 500 meters from a road. A total of 25 stands were sampled from June to September
121 2018.
122 2.3 Survey Methodology
123 A systematic survey utilizing belt transects was employed for both years. Each stand
124 from the 2009-2012 inventory has a central waypoint with ascribed geographic coordinates.
125 From the waypoint, four 10-meter wide belt transects were laid out along the cardinal directions.
126 Within each transect, the first ten birch trees encountered were sampled. Eight variables were
127 collected for each transect tree: date of collection; transect direction; birch species; diameter at
128 breast height (DBH); Chaga sclerotia presence; live crown ratio; indication of previous harvest;
129 and geographic coordinates. 6
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130 Live crown ratio, the proportion of the stem occupied by the crown, was included as a
131 method of estimating overall tree health as declining tree health is associated with a decreasing
132 percentage of live crown (DeYoung, 2019). Live crown is defined in the USFS FIA Field Guide
133 as “the point on the tree where most live branches/twigs above that point are continuous and
134 typical for a [given] tree species (and/or tree size).” In the 2018 survey, percentages of broken
135 and dead branches within the live crown ratio were estimated visually for each tree. Latitude and
136 longitude were originally recorded exclusively for Chaga-positive trees in 2017 to ascertain
137 preferred Chaga habitat. Other fungi fruit/sclerotia, pathogens, and damage were recorded to
138 explore relationship between Chaga incidence and other factors associated with deteriorating
139 host health (Table 3). In 2018, coordinates were expanded to include all sampled transect trees to
140 determine possible clustering patterns. Draft
141 For all Chaga-positive trees within each transect, the following variables were recorded:
142 sclerotium size, sclerotium height, and number of sclerotia. In addition, variable-radius (prism)
143 plots using a BAF 4 angle gauge were taken at each site to provide a “neighborhood sample” of
144 species composition and basal area by stand. Prism plots were organized differently between
145 2017 and 2018. In 2017, the first three trees (or fewer, in cases where total Chaga incidence was
146 less) within each transect recorded as having Chaga present became the center of a prism plot.
147 To enhance the consistency and accuracy of basal area estimates, the 2018 survey was modified
148 for a consistent three prism plots per stand, one at the end of each transect, rather than prism
149 plots based on Chaga presence/absence. In both years, the same variables were recorded to
150 characterize the stand conditions at each prism plot tree: species, DBH (cm), Chaga
151 presence/absence, and live crown ratio.
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152 A Garmin 60CSx GPS unit (Garmin International, Olathe, KS) was used to locate
153 waypoints. Diameter at Breast Height (DBH, usually specified as ~1.3 m) was measured in
154 centimeters using either calipers or a DBH tape. A Nikon Forestry Pro laser clinometer (Nikon
155 USA, Melville, NY) was used to approximate sclerotia heights above two meters, using binoculars
156 to detect possible sclerotia in the canopy. Sclerotium size was recorded in cubic centimeters based
157 on width, length, and extension from tree. Any sclerotium found above 2 m on the sampled tree
158 had size roughly estimated using the clinometer. All data were entered into the Survey123
159 application (ESRI, Middleton, MA), which allowed recording of data, coordinates, and pictures
160 for each tree sampled. Coordinates from the sampled trees were entered into ARCMap (v. 10.5,
161 ESRI, Middleton, MA) to be translated to a WMNF basemap. Statistical analyses were performed
162 using JMP (v.13-.14, SAS, Cary, NC)Draft on a Windows 10 PC laptop, with the exception of
163 multivariate analyses performed with PC-ORD (v7, Wild Blueberry Media, Corvallis, OR).
164 2.4 Tree Cores
165 As a primary project objective was to determine possible association between Chaga
166 infection and tree growth, cores were obtained from a subsample of trees for ring width analysis
167 in the 2018 field season. In each Chaga-present stand, two cores were taken from a Chaga-infected
168 tree, and two from an uninfected (control) tree. Uninfected trees of a similar size (i.e., within 10
169 cm DBH of the adjacent cored tree) and of the same birch species were selected as controls. Cores
170 were taken from the base of each tree, above any buttressing or root flare and typically at a height
171 of 30-50 cm, to avoid damaging the commercial value of the tree. A 5.2 mm increment borer was
172 used to bore to a depth of at least 10 cm. This depth was chosen in order to capture tree rings dating
173 back at least 10 years. This time period was considered the most relevant for assessing the Chaga
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174 fungus’ effect on wood production, given that the standard infestation period is 10-60 years (Shigo,
175 1969). The cores were then air-dried before mounting on wooden blocks for tree ring analysis.
176 A total of 51 cores from 29 trees were collected from the field for ring width analysis, of
177 which 16 (55%) were infected with Chaga, while 13 (45%) were not. After mounting and sanding
178 the sample cores, the MeasureJ2X microscope-YUX-Excel interface program in the UNH
179 Ecohydrology laboratory was used to measure and input tree core ring widths into an Excel
180 spreadsheet, from which basal area increment (BAI) measurements for each year recorded could
181 be determined. BAI was calculated as follows: (i) determining cumulative radii (measurement of
182 total growth occurring up to a given year, in mm), (ii) calculating total area (cm²) of growth, 183 expressed in the equation Y = (π*C²)*0.01Draft where C = cumulative radius, and (iii) subtracting each 184 year’s total from previous in column, expressed in the equation BAI = Y₂ - Y₁. These indices
185 provided an estimation of the annual basal area increment for each tree (Jennings et al., 2015).
186 2.5 Univariate Analyses
187 Categorical logistic regression was used when presence was compared with continuous X
188 variables such as DBH. As our primary interest was to understand patterns in Chaga presence
189 and distribution, the Target Level was set as 1. Analyses of Variance (ANOVAs) were employed
190 in instances where the variables were either both continuous (bivariate), or where the Y variable
191 was continuous but the X variables were categorical (one-way). If both variables were
192 categorical, contingency analyses were employed.
193 2.6 Multivariate Analyses
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194 Multivariate analyses were performed on the species basal area and damages datasets to
195 determine whether multiple covarying variables were associated with Chaga presence. The
196 datasets were loaded into PC-ORD with community variables listed in the main matrix and
197 environmental variables placed in the second matrix. Independent variables were then ordinated
198 by Chaga presence. In both instances, the following sequence of transformation, ordination, and
199 analysis was performed:
200 1. Row-and-Column analyses assessed skew (bias) and kurtosis (peakiness), which was
201 used to inform the need for data transformations and/or relativizations. Species or damages were
202 deleted if they were present in less than 5% of sample units, and all entirely zero value rows 203 were deleted. Beals smoothing (replacementDraft of community data values with probabilities of 204 species appearing, preserving strongest patterns) was then utilized due to the large number of
205 zero values in both sets (McCune et al., 2002).
206 2. Non-Metric Multidimensional Scaling (NMDS) ordination was performed to explore
207 multivariate relationships in Chaga data matrices. This ordination was chosen as it does not
208 assume linearity between variables. Bray-Curtis (Sørensen) distance was used for these
209 ordinations as this measure explores shared abundances and does so most effectively with binary
210 environmental data such as presence/absence. “Runs” were applied to both real and randomized
211 data, a “run” being a series of solutions moving down in dimensionality from the highest number
212 of axes possible given the X variables to just one axis (McCune et al., 2002).
213 Results from these “runs” were then assessed for interpretability based on five results: (i)
214 dimensionality of the data, (ii) final stress value of the “best solution,” (iii) Monte Carlo test
215 results (the probability that similar final stress could have occurred by chance), (iv) “Stability” of 10
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216 the best solution, where “instability” is the standard deviation in stress over the preceding ten
217 iterations, and (v) proportion of variance in the dataset represented by each final axis (McCune et
218 al., 2002).
219 3. A Multi-Response Permutation Procedure (MRPP) was performed. MRPP is a non-
220 parametric procedure for determining quantitatively whether sample unit groups are different
221 from each other (McCune et al., 2002). MRPP produces a “test statistic” T which describes the
222 separation between groups, the value A, which describes the effect size of within-group
223 homogeneity as compared with random expectations, and a p-value indicating the statistical
224 significance of T. 225 4. Lastly, an Indicator Species AnalysisDraft (ISA) was performed to derive the “indicator 226 value,” of how well each species separate across multiple groups of sample units (Dufrene et al.,
227 1997). Pertinent results include the number of randomizations used in the built-in Monte Carlo
228 test, the statistically significant indicator values for each species in each group, and related P-
229 values.
230 2.7 Spatial Analysis
231 From Survey123, the transect tree coordinate data were transferred first to an Excel
232 spreadsheet, where it was combined with the Chaga presence/absence data for the trees surveyed,
233 and then moved to ARCMap for conversion to shapefile. A Cluster and Outlier Analysis using
234 the Anselin Local Moran’s I statistic was then performed. This analysis was used to identify
235 whether any apparent similarity (cluster) or dissimilarity (outlier) in Chaga presence was more
236 pronounced than could be expected to occur randomly. The Cluster and Outlier analysis
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237 generates statistics used to determine whether adjacent features have similar values: (i) Z-score:
238 indicating whether surrounding features have similar values; (ii) P-value of Z-score; and (iii)
239 Cluster/Outlier Type (COType), which indicates statistical significance of clusters and outliers at
240 95% confidence.
241 3. RESULTS
242 3.1 Distribution in Relation to Tree Species and Size
243 A total of 2,611 trees were sampled over both field seasons, 1,611 in 2017 and 1,000 in
244 2018. Of these transect trees, 1,488 (57%) were white birch, 1,056 (40%) were yellow birch, and
245 67 (3%) were black birch. Sampled black birch trees did not have Chaga, and grey birch trees 246 were absent from transects. Chaga incidenceDraft was rare: only 3.75% (71 total) of birch trees 247 sampled hosted one or more sclerotia. However, 37 (56%) of stands sampled contained at least
248 one tree with Chaga presence.
249 Among Chaga-infected birches, infection positively correlated with yellow birch
250 presence: despite making up only 40% of the total birch trees surveyed, 63.4% of all Chaga trees
251 were yellow birch while the remaining 36.6% were white birch. Contingency analysis of Chaga-
252 positive data in JMP resulted in x² = 17 at p<0.0002 for Chaga presence between yellow and
253 white birch trees, denoting significant difference in frequency of Chaga presence between
254 species. In a separate logistic regression, Chaga presence also correlated positively with DBH
255 across all plots (x² = 24 at p<0.0001). However, Chaga presence did not correlate with live
256 crown ratio (x² = 0.000028, p<0.9958). Chaga-infected trees had a median DBH of 28 cm for
257 both birch species, out of a range of 10-182 cm.
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258 3.2 Distribution in Relation to Site Characteristics
259 Chaga presence, number of trees with Chaga, and the proportion of trees with Chaga in
260 transects were analyzed against plot-level variables in JMP: mean plot DBH (cm), stand age
261 (years), elevation (m), and percent slope. All three variables correlated positively with stand age
262 and mean plot DBH, while only Chaga presence correlated with elevation (Table 1). There were
263 no correlations between any Chaga variable and percent slope of the site. In addition, stand age
264 and average transect tree DBH also correlated positively (p<0.0049), indicating that the sampled
265 trees were acquiring greater diameters as they aged.
266 3.3 Sclerotia Characteristics
267 Chaga-infected trees had a medianDraft sclerotia number of two per tree, a median sclerotia 268 height of three meters, and a median sclerotium size of 15.24 cm³. Median stand age for Chaga
269 presence was 109 years. Sclerotium size showed a weak negative association with number of
270 sclerotia per tree (r² = -0.22, p<0.0102). The number of sclerotia also correlated with Chaga
271 incidence by birch species (f = 18, p<0.0001). Infected white birch trees had a mean of 3.5
272 sclerotia per tree, with a yellow birch mean of 2.5 per tree. The correlation between number of
273 sclerotia per tree and stand age was negative, with number per tree decreasing with increasing
274 stand age (f = 18.2, p<0.0233). Sclerotium height did not correlate with any other variable.
275 3.4. Chaga presence in Relation to Species Composition and Basal Area
276 Basal areas of each species for each stand were regressed with Chaga presence per plot to
277 determine potential correlations between individual species presence and Chaga presence (Table
278 2). Yellow birch was the only species whose basal area was significantly greater in stands with
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279 Chaga (x² = 20.72 at p<0.019). Species composition and basal area of non-birch species by stand
280 did not appear to associate with Chaga presence. There was no significant association between
281 the basal area of Chaga positive trees and the total basal area of prism plots sampled (r² = 0.001,
282 p<0.9842).
283 A multivariate analysis was performed on the basal area dataset to explore in greater
284 detail the relationship between Chaga presence and species composition across the sampled
285 stands. First, rare species were deleted, reducing the number of species from 19 to 10. The real
286 and randomized data were subjected to 250 runs, concluding in a final minimum stress of 10.48
287 for a three-dimensional solution. This final stress is considered an acceptable ordination with low 288 risk of false inferences (McCune et al., 2002).Draft The Monte Carlo test of 250 runs resulted in p = 289 0.004 for all axes, indicating that the NMDS extracted stronger axes than possible through
290 chance. The two-dimensional scatterplot of the resulting ordination can be seen in Figure 3, with
291 Axes 1 and 2 displayed as these represented the greatest amounts of variance.
292 The ordinated dataset was then subjected to MRPP with Chaga presence as the grouping
293 variable. This process resulted in T = 0.548, A = -0.0015, and p = 0.65. With a non-negative T
294 and a negative A value, this test appeared to show little separation between groups with a small
295 “effect size” of within-group homogeneity, and the high p-value denoted that the sample units of
296 each group were not more similar to each other than would be expected if they each belonged to
297 a different group. Plots with and without Chaga presence were thus not more heterogenous in
298 terms of species composition than would be expected through chance, and did not explicitly
299 differ in species composition between each other.
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300 The final step was the ISA: of the ten species remaining in the dataset, the basal areas
301 most closely associated with Chaga presence were yellow birch, balsam fir, sugar maple, and
302 American beech, all of which had an Indicator Value (IV) around 50. However, these results
303 were not statistically significant (p>0.05). Thus, the ISA confirmed that non-birch species
304 composition and basal area did not significantly associate with Chaga presence in sampled
305 stands.
306 3.5 Host Tree Damage Trends
307 The relationship between other pathogens and other factors associated with deteriorating
308 host health and Chaga incidence were explored. In 2017, dieback and broken branches were 309 listed as nominal variables; however, inDraft 2018, these variables were represented in percentage of 310 live crown. All damage variables were recorded as presence/absence. Contingency analyses were
311 performed between each damage variable and Chaga presence in order to discern correlations
312 (Table 3). Of the 16 damages, only four correlated with Chaga presence: mechanical wounds (i.e.
313 human- or weather-related tree damage), Neonectria canker, Trametes versicolor (Turkey Tail, a
314 common saprophytic fungus), and white fungal rot. White fungal rot is here classified as any
315 fungal rot that consumes lignin, leaving lighter cellulose behind: it is often associated with
316 Chaga infection, but can also result from other forms of fungi (Sinclair et al., 2005). These
317 damages were analyzed as a measure of tree health in order to assess whether Chaga associated
318 with other damaging agents and whether these agents had synergistic or detrimental effects on
319 tree health, which was determined not to be the case.
320 Multivariate analysis was also conducted on this data set to evaluate the relationship
321 between the various damages and Chaga presence. Rare species in the host tree damage dataset 15
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322 (present in < 5% of trees) and any species with entirely zero values were deleted. Beals
323 smoothing was then applied, resulting in acceptable skew (s = 0.726) and kurtosis (k = -0.325).
324 NMDS was performed on the data with 250 runs, resulting in a final minimum stress of 9.23 at
325 two dimensions. Taken along with Monte Carlo p-values of p<0.0196, these results indicated a
326 dataset with solid interpretability which was unlikely to be replicable by chance. The two axes
327 present in the two-dimensional solution represented 95% of the total variance of the dataset. This
328 ordination was graphed and joint plots were applied (Figure 4). Mechanical wounds, target
329 canker, and white fungal rot all associated significantly with Chaga presence.
330 The MRPP resulted in T = -5.45, A = 0.0024, and p = .002. The negative T-value and 331 small p-value implied that Chaga-positiveDraft and Chaga-negative trees separated strongly, while 332 sample units of each group were statistically more similar than had they belonged to different
333 groups. This was consistent with NMDS results, which showed these damages moving towards a
334 different association than the other damages. However, the low A-value denoted that sample
335 units within the groups were roughly as heterogenous as could be expected through chance (A =
336 1 is total homogeneity). Chaga incidence was correlated with white rot, mechanical wounds and
337 T. versicolor. Inonotus obliquus exploits wounds to infect trees, causes white rot, and may kill
338 portions of the stem which can then be colonized by T. versicolor. Thus, these correlations are
339 expected. Chaga incidence was not correlated with incidence of other pathogens (Armillaria,
340 Ganoderma, etc.) and thus these pathogens did not exhibit a synergistic relationship contributing
341 to deteriorating tree health.
342 The ISA revealed that mechanical wounds, target canker, and white fungal rot were most
343 closely associated with Chaga presence, each with IVs of approximately 50. Both target canker
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344 and white fungal rot had a p-value lower than p<0.05, which meant that these pathogens were
345 statistically significant indicators of the “Chaga present” group. The p-value (p<0.008) of target
346 cankers meant that it was more indicative of Chaga presence than any other damage type in the
347 dataset. The ISA indicated association between Chaga presence and target canker and white
348 fungal rot, which supported the assertion of difference between groups as evaluated in the
349 MRPP.
350 3.6 Cluster/Outlier Analysis
351 The results of the Cluster-Outlier analysis showed strong clustering among Chaga-present
352 trees: all nine trees listed in the COType results as statistically significant were Chaga-present, 353 accounting for 24% of the 2018 Chaga-positiveDraft trees. There were four discrete groupings: the 354 two closest were 10.5 km apart, while the greatest distance between clusters was 40 km. These
355 clusters did not appear to associate with elevation, occurring over a gradient of 511 meters (342
356 to 853 m). In addition, all significant spatial data outliers were Chaga-absent trees; a lack of
357 Chaga-present outlier trees further indicates clustering effects.
358 3.7 Host Tree Health and Growth Rates
359 Analysis of the cores collected in 2018 showed the oldest core recording 144 years, while
360 the youngest recorded only 11 years. Regression of mean BAIs against Chaga presence by
361 sample resulted in no correlation (x² = 0.344, p<0.5572). Mean BAIs were calculated by tree
362 (different BAIs for cores from the same tree were averaged) and regressed, again yielding no
363 correlation. Logistic regressions of both mean core and mean tree BAI for the decade 2007-2018
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364 also showed no correlation, nor did mean ring width by core when compared with Chaga
365 presence.
366 Mean ring width by tree correlated negatively with Chaga presence, but this association
367 was not strong (p<0.02), and mean ring width by tree for the decade 2007-2018 showed no
368 correlation with Chaga presence. Two ANOVA tests were run with the variables reversed,
369 analyzing mean BAI for both trees and cores by Chaga presence. These results indicated no
370 correlation: mean core BAI by Chaga presence was r² =0.006 at p<0.56, while mean tree BAI by
371 Chaga presence was r² =0.0099 at p<0.6068. It thus appears that birch tree wood production had
372 no significant impact on Chaga presence, nor did Chaga presence appear to impact basal area 373 increment by year. Draft 374 While some noise was expected in the estimate of BAI due to variation in bole
375 dimensions, this was determined to result in only a small reduction in the power of the statistical
376 tests relating growth rate to infection status. Furthermore, B. papyrifera typically exhibits very
377 little buttressing or root flare in any size class. Although B. alleghaniensis' bole morphology is
378 more variable, prominent buttresses are usually only present in the largest size classes, on steep
379 slopes with shallow soil, or in trees that germinated on nurse logs. These represented a minority
380 of trees surveyed, as “sawtimber” class trees accounted for only 29% (761 total) of transect trees.
381 4. DISCUSSION
382 4.1 Trends in Chaga Presence, Distribution, and Ecology
383 Chaga presence correlated positively with tree DBH, stand age (and therefore average
384 tree age), and with yellow birch as a host. Yellow birch could be a preferred host for the Chaga
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385 fungus due to its hardiness to damage and infection, typically larger diameters, and greater
386 heights at maturity compared to other birch species (Burns et al., 1990). Chaga infection may
387 therefore associate more frequently with yellow birches as it stands a greater chance at a longer
388 infestation period and a larger, more consistent nutrient flow. Our findings are consistent with
389 the aforementioned study by Balandaykin et al. (2015), which reported correlations in Russian
390 forests between Chaga presence and both stand maturity and anthropic disturbance. However, the
391 underlying mechanisms controlling host specificity or preference cannot be discerned from our
392 data.
393 At the stand level, correlations between Chaga presence and both site elevation and 394 yellow birch presence support the tree-levelDraft results. Yellow birch prefers to grow in moist 395 transitional zones between low-elevation northern hardwood forests and high-elevation spruce-
396 fir forests, particularly at elevations above 900 meters (Burns et al., 1990). Greater proportions of
397 yellow birch at higher elevations thus increases probability of Chaga presence. Additionally,
398 conditions that are favorable for yellow birch, such as increased humidity in valley ecosystems,
399 may also favor Chaga infestations, though this possibility was not examined in this study.
400 Our finding of a clustering effect in Chaga-present stands is unsurprising given what is
401 known about Chaga dispersal dynamics. The Chaga fungus is transmitted through spores carried
402 by wind and insect vectors (Lee et al, 2008). Older stands with large yellow birch populations
403 would typically support substantial numbers of large diameter, mature yellow birch trees with
404 suitable infection vectors, thereby facilitating multiple infections in close proximity. However, as
405 the clustering analysis was performed only on 2018 data, additional work is still needed to better
406 understand landscape-scale patterns.
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407 4.2 Management Implications
408 A primary objective of this project was to establish the baseline data to inform decisions
409 regarding the need for special-use permitting for Chaga harvest in the WMNF. Out of all trees
410 surveyed during this project, only one displayed signs of previous harvest. Part of this is likely
411 due to the remoteness of many sites sampled; however, even in stands less than 500 meters from
412 a road or in those abutting trails, there was little indication of prior harvest. A further possible
413 deterrent is sclerotia height: a mean height of 4.9 meters places many sclerotia out of effective
414 reach.
415 Despite the relative scarcity of the Chaga resource (which could be considered a strong 416 motivation for regulatory action), the lackDraft of obvious signs of harvest activity in stands with 417 large Chaga quantities, combined with the wide geographic distribution and large variation in
418 sclerotia sizes and heights, suggests a WMNF Chaga resource in little risk of exhaustion,
419 obviating a permit system for the time being. A total value of the Chaga resource in the WMNF
420 was estimated at around $USD870,000, based on an estimated value of $USD30/dry pound, an
421 estimated total of 14,691 cm³ of Chaga present per WMNF stand, and assuming an equivalent
422 weight of 1 cm³ of dried Chaga material to 1 cm³ of water (a high estimate). However, a more
423 comprehensive determination of the total quantity of Chaga present in the WMNF and the degree
424 of harvest activity that could be sustained without exhausting the resource is beyond the scope of
425 this paper.
426 Our findings on the specific characteristics of sclerotia dimensions and distribution also
427 pointed to trends with possible relevance for future management. Sclerotium size correlated
428 negatively with number of sclerotia per tree, indicating that a tree with more sclerotia tends to 20
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429 have smaller individual volumes. In addition, white birch correlated positively with increased
430 numbers of sclerotia, though the greater frequency of Chaga presence in yellow birches would
431 appear to make this correlation less important for management purposes. More interesting for
432 understanding the potential of the Chaga resource for management and cultivation is the negative
433 correlation between number of Chaga sclerotia and stand age: older trees tended to have fewer,
434 larger sclerotia while younger trees had more numerous but smaller sclerotia.
435 This result is noteworthy when coupled with the species sclerotia size data: white birch is
436 generally more prone to damage and stress effects than yellow birch, and thus tends to have a
437 shorter lifespan (Burns et al., 1990). However, whether this is coincidental or causative is 438 unclear: older trees would generally be expectedDraft to have increased wounding and there is no 439 clear explanation for why the abundance of sclerotia would decline over time as trees age. This
440 study was not designed to explore possible causative relationships between individual infected
441 tree age and number of Chaga sclerotia present, and thus future monitoring is necessary. There
442 was also no clear indication of whether overall Chaga volume differs between fewer large
443 sclerotia or several smaller sclerotia, and the lack of correlation between sclerotia size and birch
444 tree species makes singling out a particular birch species for possible management and
445 cultivation tenuous without further research.
446 5. CONCLUSION
447 Chaga incidence across individual trees in the WMNF region surveyed in this study is
448 rare but wide in distribution, and can be found wherever there are birch trees in general and
449 yellow birch in particular. The resource is most closely associated with higher elevation, yellow
450 birch-dominated stands of advanced age, though it does not appear to otherwise associate with 21
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451 any other species presence or to tree damage, apart from the damages it directly promotes or the
452 wounds by which the fungal infection proliferates. Considering the broad range of the resource,
453 the wide variance in size and number of sclerotia, the remoteness of much of its habitat, its
454 occasional presence above the reach of foragers, and the absence of previous harvest activity in
455 sampled stand, there does not appear to be an urgent need currently for a special permitting
456 process for the Chaga resource in the WMNF. Nevertheless, our finding of a positive association
457 between yellow birch presence and increasing stand age (x² = 4.58 at p<0.0323) suggests that
458 targeting older stands with high yellow birch basal area would maximize Chaga harvest
459 potential, hence any future regulatory actions should focus on such stands. 460 Our results did not show any clearDraft indication of whether Chaga is a primary or ancillary 461 pressure on birch tree health. Chaga presence did not correlate with live crown ratio and both the
462 median and modal live crown ratios for all Chaga-infected trees was 25%, with a mean value of
463 23%. Despite significant numbers of sampled birch trees with sub-optimal live crown ratios,
464 there did not appear to be strong association between tree health and Chaga presence. It is
465 doubtful that the associations with damage experienced by Chaga-infected trees are causative of
466 Chaga presence, but rather, vice versa. Given the relatively low number of Chaga-infected trees
467 with < 10% live crown, we can also speculate that Chaga is not a singularly aggressive pressure
468 on birch tree health.
469 Information on how the Chaga resource and its host tree reacts to harvest requires further
470 exploration, and there are many other potentially correlating variables that could provide greater
471 understanding of Chaga ecology and distribution. Major knowledge gaps that need to be
472 addressed with future work include determining how Chaga responds to harvest, as well as
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473 identifying underlying factors affecting host-selectivity by I. obliquus. These data would provide
474 a useful framework for developing informed, sustainable Best Management Practices for Chaga
475 management and/or cultivation, essential to continued utilization of the Chaga resource,
476 especially if current trends of increasing consumer demand and harvesting pressure continue.
477 VI. ACKNOWLEDGEMENTS
478 Funding for this project was made possible through the University of New Hampshire College of
479 Life Science and Agriculture (COLSA) and the United States Forest Service, grant number 17-
480 CA-11420004-063. We would like to thank the WMNF staff and Forest Managers for their
481 continued access and support, and to Dr. Jeffrey Garnas, Rebecca Lilja, Michael Bohne, 482 Matthew Vadeboncouer, Bronwyn Williams,Draft and Hannah Callahan for their assistance in the 483 field, office, and lab.
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TABLES
Response Test Stand Age Mean Plot Elevation Variable Performed (years) DBH (cm) (m) Chaga Logistic x²=16.88, x²=21.94, x²=4.53, Presence Regression p<0.0331 p<0.0001 p<0.0331
Number of Chaga- infected Bivariate r²=0.31, r²=0.28, r²=0.19, Trees ANOVA p<0.0115 p<0.0196 p<0.1094
Percent Chaga- Draft positive in Transect Bivariate r²=0.29, r²=0.31, r²=0.14, Trees ANOVA p<0.00194 p<0.0099 p<0.27
Table 1: Stand summary variables with Chaga sclerotium presence. Statistically significant results have been bolded.
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Mean Basal Chi-square Species Area/prism plot with Chaga Tree Species Code (m²) Presence P-value of x² White Birch WB 3.5 1.15 p<0.2833 Yellow Birch YB 2.5 20.72 p<0.0001 Black Birch BB 0.31 1.011 p<0.3145 Red Spruce RS 2.43 3.56 p<0.0593 Balsam Fir BF 0.6 0.74 p<0.3891 Red Oak RO 0.21 1.011 p<0.3145 Eastern Hemlock EH 0.14 0.28 p<0.5942 Green Ash GA 0.73 0.72 p<0.39 Sugar Maple SM 9.75 2.75 p<0.0971 Red Maple RM 7.35 3.13 p<0.0768 Quaking Aspen QA 0.57 0.72 p<0.40 White Pine WP 0.73 0.86 p<0.3527 White Ash WA 0.82 1.31 p<0.2524 American Draft Beech AB 2.2 0.034 p<0.85 Norway Maple NM 0.23 0.43 p<0.5133 Black Cherry BC 0.39 0.42 p<0.5133 Black Oak BO 0.07 0.14 p<0.71 Black Spruce BS 0.09 0.14 p<0.71 Striped Maple STM 0.39 0.72 p<0.40
Table 2. Prism plot data and logistic regression results regarding stand species composition in stands with >5% birch basal area in the WMNF. Statistically significant regressions have been bolded.
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Contingency Chi- P-value of Chi- Damage Code Type of Damage Square Value Square PI Phellinus ignarius 0.032 p<0.8575 Mechanical Wound (human- or weather- MW related damage) 12.5 p<0.0004 TC Target (Nectria) canker 46.75 p<0.0001 Ganoderma applanatum GA (Artist's Conk) 0.563 p<0.453 Fomes fomentarius FF (Tinder Conk) 0.049 p<0.8243 AD Animal Damage 0.84 p<0.36 WR White Fungal Rot 25.219 p<0.0001 BR Brown Fungal Rot 0.126 p<0.7223 BT Broken Top 0.1865 p<0.1721 BBO Bronze Birch Borer 1.5 p<0.22 BRL Burl 0.192 p<0.66 Hang-Up (caught in HU sampled tree) Draft 0.189 p<0.6641 ER Exposed Roots 2.962 p<0.2274 Fomitopsis betulina BP (Birch Polypore) 0.133 p<0.7155 Armillaria mellea AM (Honey Mushroom) 1.311 p<0.2522 Trametes versicolor TV (Turkey Tail) 8.865 p<0.0029 UF Unidentified Fungus 0.026 p<0.8728
Table 3. Transect tree damages and their individual regressions against Chaga presence/absence. Statistically significant regressions have been bolded.
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FIGURES
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Figure 1. Large, advanced Inonotus obliquus infections in otherwise healthy yellow birch (A. Betula alleghaniensis) and white birch (B. B. papyriferia) trees.
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Fig. 2: Stands in the White Mountain National Forest (WMNF) containing 40% or greater birch species basal area, as of 2009-2012 inventories. Basemap used: Imagery with Labels (WGS84). Software used: ARCMap v. 10.5, ESRI, Middleton, MA. No permissions required to republish figure.
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Fig. 3. Scatterplot of best solution of prism plot data NMDS with Chaga presence overlay. Axes 1 and 2 have been selected due to greatest total variance.
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Fig. 4: Scatterplot of best solution of tree damage NMDS with Chaga presence overlay. Axes 1 and 2 have been selected as best solution is two-dimensional.
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