Research Article |
Corresponding author: Connor D. Crouch ( connor.crouch@usda.gov ) Academic editor: Deepa Pureswaran
© 2024 Connor D. Crouch, Amanda M. Grady, Nicholas P. Wilhelmi, Richard W. Hofstetter, Margaret M. Moore, Kristen M. Waring.
This is an open access article distributed under the terms of the CC0 Public Domain Dedication.
Citation:
Crouch CD, Grady AM, Wilhelmi NP, Hofstetter RW, Moore MM, Waring KM (2024) Extent, impacts and drivers of oystershell scale invasions in aspen ecosystems. NeoBiota 95: 1-33. https://doi.org/10.3897/neobiota.95.121748
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Invasive herbivores that kill foundation tree species pose a major threat to forest ecosystem sustainability. One important foundation tree species in the interior western United States is quaking aspen (Populus tremuloides Michx.), which is threatened by recent outbreaks of an invasive insect, oystershell scale (Lepidosaphes ulmi Linn.; OSS). OSS outbreaks were first reported in 2016, when OSS began causing dieback and mortality of aspen in wildland forest settings in northern Arizona. Since then, OSS has been observed in other locations across Arizona and in other western states, and recent studies in Arizona have highlighted the threat that OSS poses to aspen sustainability, warranting a comprehensive survey of OSS invasions and their impacts on aspen ecosystems. We sampled aspen populations across Arizona and addressed three questions: (1) What is the geographic extent of OSS in Arizona? (2) What impacts does OSS have on aspen? (3) Which biotic and abiotic factors influence the proportion of aspen stems infested by OSS? OSS was present in 29% of our 220 study plots and had a negative impact on aspen forest health. OSS was associated with crown damage and tree mortality, especially of intermediate-sized, recruiting stems. Climate was the most important driver of OSS infestation, with warmer, drier conditions resulting in significantly more OSS. OSS was also associated with less recent fire, presence of ungulate management strategies (e.g. fenced exclosures) and stands with a greater density of aspen saplings. We conclude by providing OSS monitoring and management recommendations, based on our findings, and emphasise that active management – such as prescribed fire, reduced reliance on ungulate exclosures or thinning – is required to suppress OSS populations and mitigate damage to aspen ecosystems.
Arizona, armoured scale insect, climate change, invasion ecology, Lepidosaphes ulmi, mortality, Populus tremuloides, sleeper species
Invasive species pose a major threat to sustainability of forest ecosystems (
Photo of young aspen stand inside an exclosure (2 m tall fence built to exclude ungulates from browsing aspen) experiencing high levels of dieback and mortality from oystershell scale (OSS). Photos on the right show close-ups of OSS on aspen.
OSS is an armoured scale (Hemiptera, Diaspididae) that inserts its stylet through the bark of woody host plants to feed on the fluid of non-vascular cells (
Although the native range and introduction history of OSS are uncertain, the species was likely transported to North America by European settlers on infested plant material (
Critical to managing any invasive species is baseline information on its occurrence and impacts. The first peer-reviewed report of OSS outbreaks in aspen ecosystems indicated that OSS is already widespread in northern Arizona (
Our study area encompassed aspen ecosystems across Arizona. Although OSS affects numerous hosts in Arizona (
We sampled 220 aspen plots that represent the range of conditions under which aspen exists in Arizona (Fig.
Maps showing OSS presence and absence in a 220 study plots across seven major areas (in italics) where aspen occurs in Arizona, USA b study plots in the South Kaibab (left of green National Forest boundary line) and Flagstaff major areas (right of green line) and c study plots along the Mogollon Rim. These three areas are the only ones where OSS presence varied. OSS was present in all plots around Prescott and was absent in all plots in the North Kaibab, White Mountains and Coronado. Size of OSS presence circles is scaled, based on the proportion of aspen stems infested, with larger circles representing higher infestation rates.
To ensure that we obtained a representative sample of aspen sites and conditions, we stratified sites across four variables – elevation (≤ 2,400 m, > 2,400 m); aspect (north/east, south/west); ungulate management (none, fenced exclosure or jackstraw treatment [large piles of woody debris protecting aspen regeneration from ungulate browse]); and fire history (0–2 years post-fire, 2–20 years post-fire, > 20 years post-fire) – resulting in 24 strata. We first sought to obtain one plot for each stratum, which we accomplished for 21 of the 24 strata, before building out a sample that was proportional to how much aspen occurs in each stratum. We assessed aspen’s actual occurrence in each stratum using an observed GIS layer of aspen’s range on three national forest ranger districts surrounding Flagstaff (Flagstaff and Mogollon Rim Ranger Districts of the Coconino National Forest; Williams Ranger District of the Kaibab National Forest) (
When possible, we prioritised re-measurement of existing aspen monitoring plots to reduce the number of redundant plots on the landscape and to facilitate research permission on national forest land. Specifically, we revisited plots previously established by the Coconino National Forest (n = 44), the Apache-Sitgreaves National Forest (n = 5),
Each study plot consisted of two fixed-area, circular plots: an overstorey plot (8 m radius) and a nested regeneration plot (4 m radius) sharing the same plot centre (
For all live aspen in our study plots, we documented the top three damaging agents present on each tree (
We assessed OSS presence and absence in each of the 220 study plots to determine the geographic extent of OSS in Arizona. We used descriptive statistics summarising the proportion of plots and trees infested by OSS to further explore geographic patterns of OSS invasions. We also assessed tree-level OSS severity across the four aspen size classes (i.e. short regeneration, tall regeneration, saplings and overstorey trees) to determine if there were differences in susceptibility to OSS. We analysed all data in R version 4.2.1 (
We assessed OSS impacts on aspen at both the tree and stand levels. At the tree level, we built univariate regressions to quantify the influence of OSS presence and severity on aspen dieback and crown ratio, which are metrics that capture individual stem health (
To assess drivers of OSS invasions in aspen ecosystems, we collected data representing an array of biotic (Table
List of 33 biotic variables considered as potential influencing factors of plot-level oystershell scale (OSS) infestation rate. Plot-level (n = 220) mean, standard error and range are shown.
Influencing factor | Mean | Std error | Range |
---|---|---|---|
Stand structure | |||
Aspen basal areaa | 10.3 | 12.4 | 0–55.9 |
All hosts basal area | 10.3 | 12.4 | 0–55.9 |
Non-host basal area | 10.2 | 14.7 | 0–78.1 |
Aspen overstorey densityb | 172 | 254 | 0–1,194 |
Aspen sapling density | 354 | 866 | 0–6,565 |
Aspen tall regeneration density | 2,399 | 8,169 | 0–89,127 |
Aspen short regeneration density | 8,694 | 17,345 | 0–136,873 |
All hosts overstorey density | 172 | 254 | 0–1,194 |
All hosts sapling density | 356 | 869 | 0–6,565 |
All hosts tall regeneration density | 2,460 | 8,181 | 0–89,127 |
All hosts short regeneration density | 8,745 | 17,332 | 0–136,873 |
Non-host overstorey density | 115 | 168 | 0–945 |
Non-host sapling density | 65 | 180 | 0–1,592 |
Non-host tall regeneration density | 192 | 764 | 0–9,350 |
Non-host short regeneration density | 1,394 | 3,992 | 0–33,224 |
Ungulates | |||
Browsec | 0.30 | 0.31 | 0–1 |
Ungulate barkingc | 0.03 | 0.10 | 0–0.85 |
Total ungulate scatd | 2.6 | 5.0 | 0–35 |
Elk (Cervus canadensis) scat | 1.3 | 3.1 | 0–23 |
Deer (Odocoileus hemionus & O. virginianus couesi) scat | 1.1 | 3.5 | 0–29 |
Cattle (Bos taurus) scat | 0.3 | 1.7 | 0–20 |
Damaging agents c | |||
Sucking & gall-forming insects (excluding OSS) | 0.09 | 0.14 | 0–0.80 |
Bark beetles | 0.01 | 0.03 | 0–0.20 |
Wood-boring insects | 0.22 | 0.21 | 0–0.83 |
Defoliating insects | 0.60 | 0.27 | 0–1 |
Cytospora canker (caused by Cytospora spp.) | 0.02 | 0.05 | 0–0.34 |
Hypoxylon canker (caused by Entoleuca mammatum) | 0.002 | 0.011 | 0–0.10 |
Ceratocystis canker (caused by Ceratocystis spp.) | 0.02 | 0.05 | 0–0.42 |
Sooty bark canker (caused by Encoelia pruinosa) | 0.001 | 0.005 | 0–0.05 |
All cankers | 0.33 | 0.26 | 0–1 |
Foliar & shoot diseases | 0.19 | 0.24 | 0–0.94 |
Decay diseases | 0.04 | 0.08 | 0–0.67 |
Other animal damage (excluding browse & barking) | 0.01 | 0.02 | 0–0.15 |
Using GPS coordinates collected at each plot’s centre, we calculated elevation, aspect and slope using a 30 m2 digital elevation model (Table
List of 66 abiotic variables considered as potential influencing factors of plot-level oystershell scale (OSS) infestation rate. Plot-level (n = 220) mean, standard error and range are shown for continuous variables, whereas percentage of plots in each category is shown for categorical variables.
Influencing factor | Mean | Std error | Range |
---|---|---|---|
Damaging agents | |||
Abiotic damagea | 0.01 | 0.05 | 0–0.61 |
Fire | |||
Fire stratab | 1 (14.1%), 2 (22.7%), 3 (63.2%) | ||
Fire severityc | 1 (65.5%), 2 (9.1%), 3 (11.4%), 4 (8.2%), 5 (5.9%) | ||
Burned twiced | 0 (95.0%), 1 (5.0%) | ||
Management | |||
Ungulate managemente | 0 (67.7%), 1 (32.3%) | ||
Conifer removalf | 0 (87.7%), 1 (12.3%) | ||
Site factors | |||
Elevation (m above sea level) | 2,543 | 237 | 1,976–3,038 |
Aspectg | 0.98 | 0.73 | 0–2 |
Slope (°) | 7.9 | 7.1 | 0.1–29.7 |
Heat load (MJ/cm2/yr) | 0.98 | 0.07 | 0.71–1.08 |
Radiation (MJ/cm2/yr) | 0.96 | 0.08 | 0.64–1.09 |
Major areah | 1 (3.6%), 2 (51.8%), 3 (6.4%), 4 (9.1%), 5 (8.2%), 6 (11.8%), 7 (11.4%) | ||
UTM easting | 453804 | 77968 | 358542–674303 |
UTM northing | 3880092 | 89053 | 3589116–4052723 |
Soils | |||
Soil orderi | 1 (2.7%), 2 (14.1%), 3 (13.2%), 4 (70.0%) | ||
Soil pH in H2O (pH×10) | 63.4 | 2.6 | 55.4–71.4 |
Cation exchange capacity (CEC) (mmol(c)/kg at pH 7) | 232.9 | 22.6 | 176.3–272.15 |
Nitrogen (cg/kg) | 110.0 | 22.5 | 80.0–188.3 |
Soil organic carbon content (dg/kg) | 135.2 | 22.8 | 93.8–193.9 |
Bulk density (cg/cm3) | 147.5 | 5.8 | 130.1–157.8 |
Sand content (g/kg) | 321.7 | 85.6 | 187.5–592 |
Clay content (g/kg) | 269.1 | 51.2 | 129.7–397.7 |
Volumetric fraction of coarse fragments (cm3/dm3) | 179.2 | 61.2 | 75.2–293.0 |
Climate | |||
Degree-days below 0 °C | 323.9 | 103.1 | 109.0–596.0 |
Degree-days above 5 °C | 1,883 | 377.7 | 1,215–2,819 |
Degree-days below 18 °C | 3,823 | 489.9 | 2,656–4,842 |
Degree-days above 18 °C | 137.1 | 94.4 | 24.5–389.0 |
Degree-days above 10 °C and below 40 °C | 909.0 | 254.3 | 464.5–1,521.0 |
Number of frost-free days | 181.2 | 33.8 | 140.5–265.5 |
Frost-free period | 113.4 | 29.5 | 78.0–185.0 |
Winter temperature (maximum)j | 6.0 | 1.6 | 2.5–10.6 |
Spring temperature (maximum) | 13.8 | 1.4 | 10.7–17.8 |
Summer temperature (maximum) | 25.3 | 1.8 | 21.2–29.1 |
Autumn temperature (maximum) | 16.6 | 1.4 | 13.3–20.3 |
Winter temperature (minimum) | -7.6 | 2.2 | -10.8– -2.2 |
Spring temperature (minimum) | -1.2 | 2.0 | -4.4–3.7 |
Summer temperature (minimum) | 9.1 | 2.1 | 6.5–14.0 |
Autumn temperature (minimum) | 0.8 | 2.3 | -1.7–6.2 |
Winter temperature (mean) | -0.8 | 1.8 | -4.2-3.9 |
Spring temperature (mean) | 6.3 | 1.6 | 3.2–10.4 |
Summer temperature (mean) | 17.2 | 1.7 | 13.9–21.1 |
Autumn temperature (mean) | 8.7 | 1.6 | 5.8–12.4 |
Precipitation as snow (annual)k | 135.8 | 56.2 | 29.5–332.0 |
Winter precipitationk | 211.3 | 75.0 | 83.5–516.5 |
Spring precipitation | 148.8 | 34.8 | 66.5–240.0 |
Summer precipitation | 147.8 | 69.9 | 64.5–292.5 |
Autumn precipitation | 130.5 | 71.8 | 62.5–366.0 |
Winter relative humidityl | 51.0 | 5.1 | 44.5–70.5 |
Spring relative humidity | 51.7 | 3.8 | 47.0–66.0 |
Summer relative humidity | 53.0 | 4.8 | 47.0–63.0 |
Autumn relative humidity | 50.8 | 5.9 | 43.0–66.0 |
Winter Hargreaves reference evaporationk | 30.0 | 39.6 | 0–125.5 |
Spring Hargreaves reference evaporation | 272.1 | 25.0 | 192.5–323.0 |
Summer Hargreaves reference evaporation | 463.1 | 31.2 | 396.5–535.0 |
Autumn Hargreaves reference evaporation | 224.1 | 14.1 | 193.5–263.0 |
Winter climatic moisture deficit (CMD)k | 6.2 | 8.2 | 0–26.0 |
Spring climatic moisture deficit (CMD) | 163.6 | 25.0 | 117.0–216.0 |
Summer climatic moisture deficit (CMD) | 325.6 | 76.3 | 177.0–461.5 |
Autumn climatic moisture deficit (CMD) | 139.2 | 38.6 | 57.5–199.0 |
Winter climate moisture index (CMI)k | 18.5 | 7.0 | 5.6–42.7 |
Spring climate moisture index (CMI) | -3.2 | 5.2 | -14.7–9.6 |
Summer climate moisture index (CMI) | -29.9 | 10.3 | -48.3– -7.3 |
Autumn climate moisture index (CMI) | -11.0 | 8.0 | -21.8–15.2 |
Annual dryness indexm | 0.07 | 0.02 | 0.04–0.12 |
Annual heat moisture indexn | 31.8 | 8.2 | 18.1–49.8 |
Summer heat moisture indexo | 118.8 | 52.1 | 45.1–242.4 |
We obtained soils data from SoilGrids (https://www.isric.org/explore/soilgrids), which provides global soil mapping data at 250 m resolution (
We used random forests, structural equation modelling (SEM) and stand- and tree-level regressions to determine which biotic and abiotic factors drive OSS invasions. First, we used random forests to determine which of the 99 predictor variables had the strongest influence on plot-level OSS infestation rate. Random forests are a useful tool for assessing variable importance in regression and classification settings amongst an array of potential predictors (
Once we obtained a list of variable importance from VSURF, we used SEM to assess how the most important predictors and their interactions affect OSS infestation rate. SEM is an insightful tool for ecological research because it allows the user to build models based on theoretical understanding of an ecological system, resulting in a network of causal, multivariate relationships with a complete accounting of direct and indirect relationships and the relative strengths of those relationships (
A priori structural equation model (SEM) illustrating hypothesised directional relationships amongst influencing factors and plot-level OSS infestation rate. Arrows indicate causal relationships, and colours correspond to each of the eight categories of influencing factors. See Tables
We also fitted stand- and tree-level regressions to further assess how various factors influence OSS presence and severity. At the stand level, we took the top 25 factors influencing OSS infestation rate based on random forests and built univariate regressions to quantify relationship direction, strength and significance. We used the nlme package (Pinheiro et al. 2022) to fit linear mixed-effects models with plot-level OSS infestation rate as the response, the 25 individual influencing factors as fixed effects and the hierarchical, nested structure of plots as random effects. At the tree level, we built univariate regressions to determine the influence of aspen tree size on OSS presence and severity. We used the nlme package (Pinheiro et al. 2022) to fit eight linear mixed-effects models with OSS presence and severity as responses, with dbh, height, height-to-diameter ratio and size class as fixed effects and with the hierarchical, nested structure of plots as random effects. As size class is a categorical variable, we used the “anova” function in R (
The mean total aspen basal area, including living and standing dead trees, in our 220 study plots was 14.6 m2 ha-1 (standard error [SE] = 1.0), of which dead trees made up 29.5% (Table
Summary data for aspen stand structure, crown condition and OSS presence. Means and standard errors of variables representing live and dead aspen basal area, live and dead aspen density in different stem size classes, live aspen crown ratio and dieback and OSS presence at the plot and tree levels. For categorical variables, percentage of plots or trees in each level are shown.
Variable | Mean | Std error |
---|---|---|
Live aspen basal area (m2 ha-1) | 10.3 | 0.8 |
Dead aspen basal area (m2 ha-1) | 4.3 | 0.4 |
Total live aspen density (trees ha-1) | 11,618.5 | 1,304.2 |
Total dead aspen density (trees ha-1) | 4,450.5 | 704.2 |
Live aspen short regeneration density (trees ha-1) | 8,693.8 | 1,169.4 |
Dead aspen short regeneration density (trees ha-1) | 3,420.9 | 674.6 |
Live aspen tall regeneration density (trees ha-1) | 2,399.1 | 550.8 |
Dead aspen tall regeneration density (trees ha-1) | 796.7 | 126.6 |
Live aspen sapling density (trees ha-1) | 353.6 | 58.4 |
Dead aspen sapling density (trees ha-1) | 165.5 | 42.6 |
Live aspen overstorey density (trees ha-1) | 172.0 | 17.1 |
Dead aspen overstorey density (trees ha-1) | 67.4 | 8.6 |
Aspen crown ratio (%) | 52.0 | 0.5 |
Aspen crown dieback (categorical) | ||
0% dieback | 34.2 | 0.5 |
1–33% dieback | 44.5 | 0.5 |
34–67% dieback | 13.9 | 0.3 |
68–99% dieback | 7.5 | 0.3 |
Plot-level OSS presence (categorical) | ||
OSS absent | 70.9 | 3.1 |
OSS present | 29.1 | 3.1 |
Tree-level OSS presence (categorical) | ||
OSS absent | 89.3 | 0.3 |
OSS present | 10.7 | 0.3 |
OSS was present in 29% of study plots and occurred in four of seven major areas where aspen occurs in Arizona: South Kaibab, Flagstaff, Prescott and Mogollon Rim (Fig.
OSS infested aspen stems of all sizes, although there was a higher likelihood of infestation on trees taller than 1.37 m (i.e. tall regeneration stems and larger) (Fig.
OSS severity across four aspen stem size classes. Data shown were taken only from the 64 study plots in which OSS was observed and include only live trees. OSS severity was assessed using the rating system devised by
OSS presence at the tree level was significantly (p < 0.001) associated with reduced aspen crown ratio and increased crown dieback, based on univariate regression (Table
Univariate relationships between two measures of aspen stem health (crown ratio and dieback) and OSS presence and severity at the tree level.
Response | Predictor | Coefficient | Std error | p value | Marginal R2 |
---|---|---|---|---|---|
Aspen crown ratio (%) | OSS presencea | -9.83 | 1.25 | < 0.001 | 0.012 |
Aspen crown ratio (%) | OSS severity (%) | -38.87 | 4.33 | < 0.001 | 0.009 |
Aspen crown diebackb | OSS presencea | 0.52 | 0.04 | < 0.001 | 0.031 |
Aspen crown diebackb | OSS severity (%) | 2.06 | 0.14 | < 0.001 | 0.024 |
For the 64 study plots in which OSS was present, plot-level OSS infestation rate was significantly (p = 0.019) associated with increased dead aspen basal area (Table
Univariate relationships between six measures of dead aspen density and plot-level OSS infestation rate (i.e. proportion of stems infested by OSS). These models were fitted using data only from the 64 study plots in which OSS was present.
Response | Predictor | Coefficient | Std error | p value | Marginal R2 |
---|---|---|---|---|---|
Dead aspen basal areaa | OSS (%) | 5.74 | 2.35 | 0.019 | 0.108 |
Total dead aspenb | OSS (%) | 1,029.67 | 1,644.03 | 0.535 | 0.005 |
Dead aspen short regenerationb | OSS (%) | -646.59 | 961.49 | 0.505 | 0.005 |
Dead aspen tall regenerationb | OSS (%) | 1,654.33 | 834.21 | 0.054 | 0.076 |
Dead aspen saplingsb | OSS (%) | 532.32 | 275.75 | 0.061 | 0.070 |
Dead overstorey aspenb | OSS (%) | 33.15 | 35.04 | 0.350 | 0.015 |
We considered 99 potential factors influencing plot-level OSS infestation rate (i.e. proportion of aspen stems infested by OSS), and random forests indicated the five most important influences were autumn evaporation, elevation, degree-days between 10 °C and 40 °C, winter climate moisture index (CMI) and autumn precipitation (Table
Relationships between OSS infestation rate and its most important influencing factors based on random forests. Top 25 most important variables influencing plot-level OSS infestation rate, based on 50 random forest runs, each of which was built using 2000 trees. Univariate relationships between influencing factors and OSS infestation rate were based on linear mixed models. See Tables
Random forests | Univariate regressions | |||
---|---|---|---|---|
Rank | Influencing factor | Coefficient | Marginal R2 | p value |
1 | autumn evaporation | -0.003 | 0.014 | 0.195 |
2 | elevation | < -0.001 | 0.263 | < 0.001 |
3 | degree-days 10–40 °C | < 0.001 | 0.197 | 0.003 |
4 | winter CMI | 0.005 | 0.015 | 0.302 |
5 | autumn precipitation | 0.002 | 0.168 | 0.002 |
6 | winter evaporation | 0.006 | 0.413 | < 0.001 |
7 | winter temp (max) | 0.120 | 0.428 | < 0.001 |
8 | wood boring insects | 0.121 | 0.007 | 0.034 |
9 | winter precipitation | < -0.001 | 0.002 | 0.755 |
10 | clay | < 0.001 | 0.003 | 0.638 |
11 | degree-days < 0 °C | -0.001 | 0.190 | 0.003 |
12 | snow | -0.002 | 0.146 | 0.001 |
13 | spring temp (max) | 0.105 | 0.301 | < 0.001 |
14 | spring CMD | 0.004 | 0.109 | 0.002 |
15 | spring evaporation | 0.002 | 0.037 | 0.085 |
16 | aspen saplings ha-1 | < 0.001 | 0.018 | 0.001 |
17 | host saplings ha-1 | < 0.001 | 0.018 | 0.001 |
18 | degree-days > 5 °C | < 0.001 | 0.198 | 0.003 |
19 | spring temp (min) | 0.071 | 0.210 | 0.004 |
20 | host regeneration ha-1 | < -0.001 | < 0.001 | 0.455 |
21 | UTM easting | < -0.001 | 0.088 | 0.170 |
22 | other animal damage | 0.070 | < 0.001 | 0.891 |
23 | summer temp (mean) | 0.068 | 0.174 | 0.005 |
24 | aspen regeneration ha-1 | < -0.001 | < 0.001 | 0.458 |
25 | fire severity | -0.014 | 0.003 | 0.463 |
The optimal SEM for plot-level OSS infestation rate (AIC = 861.6; Fisher’s C = 1.018 with p = 0.907 [high p value indicates better fit]; response marginal R2 = 0.53, conditional R2 = 0.88 [marginal includes only fixed effects, conditional includes both fixed and random effects]) included seven influencing factors: autumn evaporation, winter CMI, maximum winter temperature, elevation, fire strata, live aspen sapling density and presence of ungulate management (Fig.
Optimal SEM for OSS infestation rate that minimised AIC and maximised response R2. Significant (p < 0.05) path coefficients are shown in bold, and their corresponding paths are depicted as solid lines. In contrast, insignificant coefficients are not in bold, and their corresponding paths are shown as dashed lines. Path thickness indicates strength of its coefficient, with wider paths indicating stronger relationships.
To further assess the relationship between climate and OSS, we searched for thresholds using the top 10 climate variables that random forests indicated were the most important influences of plot-level OSS infestation rate. We identified clear elevational and climatic thresholds beyond which OSS does not occur in Arizona (Fig.
Elevation and climate thresholds beyond which OSS does not occur in aspen ecosystems in Arizona. Relationships between plot-level OSS infestation rate and a elevation b snowfall c autumn evaporation d degree-days above 10 °C and below 40 °C e maximum winter temperature and f maximum spring temperature. Red lines indicate thresholds above or below which OSS does not occur in aspen ecosystems in Arizona.
We also assessed univariate relationships between tree-level OSS presence and severity and four measures of aspen stem size: size class, height, dbh and height-to-diameter ratio. One-way ANOVA indicated that there were significant differences (p < 0.001) in OSS presence and severity amongst the four stem size classes (Table
Univariate relationships between tree-level OSS presence and severity and four measures of aspen stem size (size class, height, dbh and height-to-diameter ratio).
Response | Predictor | Coefficient | Std error | p value | Marginal R2 |
---|---|---|---|---|---|
OSS presence a | size class SR | -12.246 c | 1.936 | < 0.001 | 0.014 |
TR | 1.752 b | 0.189 | < 0.001 | ||
S | 2.905 a | 0.279 | < 0.001 | ||
O | 2.904 a | 0.280 | < 0.001 | ||
OSS severity (%) | size class SR | 0.010 c | 0.008 | 0.239 | 0.021 |
TR | 0.027 a | 0.002 | < 0.001 | ||
S | 0.035 a | 0.004 | < 0.001 | ||
O | 0.011 b | 0.003 | < 0.001 | ||
OSS presencea | height (m) | 0.006 | 0.001 | < 0.001 | 0.004 |
OSS severity (%) | height (m) | 0.001 | < 0.001 | < 0.001 | 0.002 |
OSS presencea | dbh (cm) | -0.003 | 0.001 | < 0.001 | 0.005 |
OSS severity (%) | dbh (cm) | -0.001 | < 0.001 | < 0.001 | 0.012 |
OSS presencea | height:diameter (m) | 0.458 | 0.166 | 0.006 | 0.001 |
OSS severity (%) | height:diameter (m) | 0.114 | 0.056 | 0.042 | 0.001 |
OSS is widely distributed throughout aspen ecosystems in central Arizona (Fig.
Prescott had the highest rates of OSS infestation of the seven major areas we studied, with 100% of plots (n = 17) and 60.3% of live aspen in the region being infested. This is concerning because Prescott also had the highest levels of sustainable aspen recruitment, defined as the number of recruits needed for successful self-replacement of the existing overstorey (
OSS negatively affected aspen health at both the tree and stand levels. Aspen trees infested with OSS had significantly lower crown ratios and higher dieback, indicating reduced stem health. As OSS infestations became more severe, crown ratio significantly decreased, and dieback significantly increased (Table
Density of aspen saplings and saplings of all host species were significantly associated with increased plot-level OSS infestation rate (Table
Climate was the most important factor driving OSS invasions of aspen ecosystems in Arizona. According to random forests, seven of the top 10 and 15 of the top 25 factors influencing OSS infestation rate were climate variables. Moreover, SEM indicated that climate variables, namely autumn evaporation and maximum winter temperature, had the strongest direct effect on infestation rate. Generally, warmer and drier conditions were associated with increased OSS. For example, greater OSS infestation rate was associated with fewer degree-days below 0 °C and more degree-days between 10 °C and 40 °C, with warmer temperatures in winter, spring and summer, with less winter precipitation and annual snowfall and with greater spring climate moisture deficit (CMD) and winter evaporation (Table
Alternatively, the relationship between climate and OSS might be mediated through host stress (
Although previous research has indicated that elevation is an important limiting factor for OSS (
Given OSS’s hypothesised role as a sleeper species and the strong influence of climate on OSS infestation rate, our study suggests that climate change caused OSS population sizes to rapidly increase and to transition from an innocuous pest to a high-impact invasive species. We have shown that OSS is associated with more arid conditions. Therefore, we hypothesise that prolonged, record drought and warmer temperatures over the past 10–20 years (
In addition to climate, fire had a strong influence on OSS. Fire strata was the third most important direct influence on OSS infestation rate based on SEM. Less recent fire resulted in significantly more OSS, suggesting that fire can be an important strategy for managing OSS. Of the 31 study plots that experienced fire in the two years prior to sampling, only two plots were infested with OSS, and the infestation rate in these two plots was low, with only 1.7% and 7.3% of aspen stems infested. In contrast, 40 of the 139 plots that had not experienced fire in the preceding 20 years were infested with OSS. Fire may be an important limiting factor for OSS because it kills OSS both directly and indirectly, by killing hosts upon which OSS is dependent (
We also found that ungulate management strategies, which primarily consisted of fenced exclosures, resulted in significantly more OSS (Fig.
OSS is already widespread across several States in the Interior West, including Arizona, so management tactics intended to eradicate this pest are unlikely to succeed. Eradication is further complicated by OSS’s ability to infest an array of different host species and by the fact that small populations are exceedingly difficult to detect due to OSS’s small size and cryptic colouring (
Our findings can also be used to guide management that seeks to suppress OSS populations and mitigate damage to aspen ecosystems. Our study indicates that three strategies might help to suppress OSS populations: (1) increasing application of fire at the landscape scale, (2) reducing reliance on ungulate exclosures and (3) decreasing aspen stand density. Fire has a negative influence on OSS, and although frequency and size of wildfires will likely continue to increase as climate warming continues (
Another strategy that managers may consider is reducing use of fenced ungulate exclosures. Reducing reliance on exclosures should help reduce OSS population sizes, although this will require finding other ways to overcome chronic ungulate browse that threatens aspen ecosystem resilience, adaptive capacity and sustainability (
A third strategy managers may consider for suppressing OSS populations is reducing aspen stand densities via thinning. Thinning might also promote aspen resistance to drought, as reduced growth rates which occur in dense stands are associated with increased mortality during drought (
We are immensely grateful to Jules Barab, Adam Hackbarth, Al Hendricks, Candle Pfefferle, Kyle Price, Julia Totty and Gabe Traver for helping with data collection and to Darlene Cook, Ben DeBlois, Garry Domis, Dena Forrer, Miles Fule, Dr. Randy Fuller, Marissa Kuntz, Dr. Jim Malusa, Ellen Mering, Mark Nabel, Jessi Ouzts, Mary Price, Gayle Richardson, Dr. Kyle Rodman, Ben Roe, Elwood Rokala, Michael Sedgeman and Mike Stoddard for help identifying aspen study sites, obtaining research permission and/or sharing their study sites. We also thank Dr. Derek Sonderegger for providing statistical advice on our sampling approach, Dr. Matt Bowker for help with obtaining and interpreting soil data and Dr. Anita Antoninka for help with structural equation modelling. Finally, we acknowledge that Northern Arizona University sits at the base of the San Francisco Peaks, on homelands sacred to Native Americans throughout the region. We honour their past, present and future generations, who have lived here for millennia and will forever call this place home. Our research takes place on these homelands and others sacred to Native Americans.
The authors have declared that no competing interests exist.
No ethical statement was reported.
Funding for this research was provided by McIntire-Stennis appropriations from the USDA National Institute of Food and Agriculture to the Northern Arizona University School of Forestry and the State of Arizona, Northern Arizona University’s Presidential Fellowship Program, ARCS Foundation Phoenix, the Arizona Mushroom Society, Brigham Young University’s Charles Redd Center for Western Studies and USDA Forest Service, Forest Health Protection, Emerging Pests Program.
Conceptualization: RWH, KMW, CDC, AMG, MMM, NPW. Data curation: CDC, KMW. Formal analysis: CDC. Funding acquisition: MMM, CDC, AMG, KMW, NPW. Investigation: CDC, AMG, KMW, NPW. Methodology: MMM, CDC, AMG, NPW, RWH, KMW. Project administration: AMG, CDC, NPW, KMW. Resources: NPW, AMG, KMW. Supervision: KMW. Validation: CDC. Visualization: CDC, KMW. Writing – original draft: CDC. Writing – review and editing: CDC, MMM, KMW, NPW, RWH, AMG.
Connor D. Crouch https://orcid.org/0000-0003-0353-5820
Kristen M. Waring https://orcid.org/0000-0001-9935-9432
All of the data that support the findings of this study, including R code, are available via the Environmental Data Initiative (https://doi.org/10.6073/pasta/bd7be772e435ed0ba5585aae5a96f3e7).
Means and standard errors for the 92 continuous variables considered as potential influencing factors of plot-level OSS abundance, summarized across each of the seven major areas in our study
Data type: pdf