10.5061/DRYAD.SN02V6X32
Atkins, David
0000-0002-1565-9356
University of Wyoming
Davis, Seth
0000-0002-9901-2516
Colorado State University
Stewart, Jane
Colorado State University
Probability of occurrence and phenology of pine wilt disease transmission
by insect vectors in the Rocky Mountains
Dryad
dataset
2020
FOS: Other natural sciences
McInntyre-Stennis*
COL00508
McInntyre-Stennis
COL00508
2021-01-20T00:00:00Z
2021-01-20T00:00:00Z
en
384026 bytes
5
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
1. Pine wilt disease, caused by pinewood nematode (Bursaphelenchus
xylophilus; PWN), is a damaging and globally distributed insect-vectored
forest pathogen. Native forest tree mortality associated with PWN is newly
reported from the Front Range of Colorado, but there is no regional
information on PWN frequency or biology of local insect vectors, limiting
management options. 2. A sampling array was established to survey PWN in
native pines (Pinus ponderosa) and longhorn beetles (Monochamus clamator
& Monochamus scutellatus) over two years and across natural and
urban forest landscapes. We developed flight phenology models and
evaluated effects of landscape factors on vector abundance and probability
of infection. 3. Flight phenology was similar for vectors; Monochamus
flight initiated in mid-July and continued into October for both species.
We report the first M. clamator–PWN association in the United States. PWN
was distributed in the region at rates lower than reported from its
putative native range: 3.6 and 4.2% of sampled pines and beetles,
respectively, tested positive for PWN. Many host trees were outwardly
asymptomatic; infection frequency in tree populations varied considerably
and four epicenters of vector infectivity were identified. 4. Epicenters
varied in timing of anomalous infective vector frequency—some epicenters
had high abundances of infected beetles early in the growing season
whereas others had high abundances of infected beetles late in the growing
season, though PWN-positive beetles were captured at all sites. Monochamus
populations were found primarily in natural forest stands but migrated to
urban areas late in the growing season. The only landscape factor
positively correlated with abundances of both Monochamus species was
distance to previous wildfire. 5. Synthesis and applications: PWN
epicenters in the southern Rocky Mountains exhibit specific temporal
windows of vector activity that differ from proximal sites. Urban forests,
where the disease was initially observed in the region, do not support
vector populations. Our results suggest that natural forest landscapes in
the region are important reservoirs of PWN and vector populations are
especially abundant near burned stands. Collectively, our findings are
important for timing disease management activities appropriately and help
to distinguish priority areas for mitigation efforts.
Site measurements and sampling of PWN in trees and beetles A sampling
array was established to regionally survey for PWN in host trees and
insect vectors. Sites were established in wildland-urban interface (WUI;
N=32; Stewart et al. 2007) and urban (N=12) greenspaces (Table S1). Sites
were located 50-500 m from roads where ponderosa pine (P. ponderosa) was
the dominant canopy species. Urban greenspaces were located in
municipalities of Fort Collins, Loveland, Boulder, and Golden (Colorado,
USA) where Austrian pine (P. nigra) or Scots pine (P. sylvestris) were
dominant canopy species (3 locations in each municipality). Urban
greenspaces were selected to be as close to WUI forests (west) as possible
while still having >5 trees within the urban greenspace. Sites
elevation ranged from 1725-2567 m (GIS, ARCMAP 10.4, ESRI, Inc.). At WUI
sites several forest measurements were conducted on 0.04 hectare
fixed-area plots including tree species and diameters-at-breast-height
(DBH; 1.3 m), crown-class (suppressed, intermediate, co-dominant, or
dominant), presence/absence of visible fungal infection, and fire damage
for all trees with DBH > 2 cm (Figure S2-S2). Site aspect and
hillslope were recorded. Landscape variables including distance to
recently burned stands (km), distance to edge of ponderosa pine canopy
(km), percent canopy cover (250 m radius), and distance to nearest urban
area (population >2,000; km) were derived for each study site
using a geographic information system (GIS, ARCMAP 10.4, ESRI, Inc.) (USDA
Forest Service). Heat-load index (McCune & Keon 2002), a metric of
radiative forcing (MJ·cm-1·year-1) incorporating slope, aspect, and
latitude, was also calculated for each site. These variables were used to
develop predictive models of beetle abundance. To estimate PWN infection
frequency in host trees at sample sites, branch and sawdust samples were
taken from a subset of 6-10 randomly selected ponderosa pine trees per
site (DBH > 10 cm). A 20-cm section proximal to the bole from each
of 2 branches was taken from each selected tree using a pole pruner.
Sawdust was collected from 2 holes drilled on opposing N and S aspects at
1.3 m height on the bole with an auger-style drill bit (15 mm) to a depth
of 6 cm. Tissues samples from each tree were homogenized into a composite
sample and nematodes were extracted using the Baermann funnel (Viglierchio
and Schmitt 1983); extracted nematodes were stored at -20°C until
molecular testing. To sample insect vectors, black crossvane traps were
centrally placed in each plot and (described in Morewood et al. 2002)
supplied a diffuse pesticide (No. Pest 2 Strips; Dichlorvos; 18.6%
2,2-dichlorovinyl dimethyl phosphate; Hot Shot Corp., St. Louis MO) to
kill captured insects. Traps were baited with lures containing host tree
volatiles, ethanol, monochamol and ipsdienol (Monochamus lite combo lure -
lot #546371; Synergy Semiochemicals, Victoria BC). In 2018 and 2019, traps
were visited weekly after commencement of the beetle flight season (July)
until flight termination (October) (2018: N=13 weeks; 2019: N=15 weeks).
Urban sites were sampled only during 2019. Collected specimens were stored
at -20°C until molecular testing to preserve nematode DNA. All captured
Monochamus beetles and Baermann extracts of wood tissues (WUI N=289,
Urban N = 42 samples) were subsequently analyzed for the presence of PWN
using a molecular assay. Beetles were bisected longitudinally and
homogenized using a sterile micropestle prior to analysis. Baermann
extracts of tree tissues and homogenized beetle tissues were tested for
PWN using a loop-mediated isothermal amplification (LAMP) assay (Bx
Detection Kit, Lot #’s 29000H-L, Nippon Gene Co., Tokyo Japan) according
to methods of Kikuchi et al. (2009). Samples were resolved to the
individual level (i.e., all sampled trees and captured beetles were
tested). This molecular assay is commonly employed in the invasive range
of PWN and is 1,000 times more sensitive than traditional PCR approaches
(Kikuchi et al., 2009). Presence/absence data was recorded via this assay
as opposed to attempting to quantify nematode load/beetle for two reasons;
1) the captured vectors were far too numerous, and 2) the aim of the study
was to identify areas where PWN was present rather than evaluate vector
competency. Data analysis All analyses were performed in the R statistical
programming environment and unless otherwise stated use a Type I error
rate of α=0.05 for assigning statistical significance (R Core Team 2019).
Flight phenology for M. clamator and M. scutellatus was modeled using a
2-parameter logistic regression (function ‘nplr’) with ordinal day as the
independent variable and cumulative proportion of captures as the response
variable. Initiation, peak, and termination of flight were approximated
using 10%, 50%, and 90% cumulative capture for each species and site ×
year combination to evaluate differences in phenology between vectors and
years (Figure S7, S8). Flight synchrony was estimated by solving growth
rate of logistic curves at 50% capture—greater flight synchrony is
consistent with more rapid logistic growth (Dell & Davis 2019).
Only sites with 10 or more captures recorded for each species were
considered reliable for informing species-level flight phenology models
(M. clamator N=30 sites, M. scutellatus N=18 sites). Phenology thresholds
(mean dates of flight initiation, peak, and termination) and flight
synchrony were compared between beetle species using a 2-sample Student’s
t-test. To evaluate effects of landscape factors on vector abundances,
beetle capture abundances were modeled for each species using a
multiple-regression model selection using distance to burned stands
(burned since year 2000), elevation, heat-load index, distance to
ponderosa pine cover boundary, distance to city edge, and canopy cover as
predictors. Trap-capture data were root-transformed where necessary in
order to meet assumptions of normality and heteroscedasticity. Sample year
was included as a random effect, and models were selected via minimization
of AIC (Akaike 1974) with a ΔAIC threshold of 2 (function ‘dredge’).
Vector beetle species and sex ratios were compared using Chi-squared
tests. The probability of vector association with PWN was evaluated using
a log-likelihood mixed-effects modeling approach. Factors considered
included both vector species (N=2 factor levels) and sex (N=2 factor
levels), as well as day-of-year of capture (continuous effect), and
capture year (N=2 factor levels). Site (N=44) was included as a random
effect and evaluated using a likelihood ratio test (P=1). The recency of
first reports of PWN in Colorado indicate that the disease may not be
established uniformly in the region, with infections radiating from
central locations or (i.e., epicenters). This may be observable via
differences in patterns of disease incidence throughout the growing
season. Here, disease epicenters were defined as sites with an occurrence
of spatiotemporal outliers in the frequency of infection in vector
captures during either year (Kitron 1998). Identification of epicenters
was made using a scanning statistic to identify sites or aggregate zones
where the rate of infection is dissimilar to proximal areas. Epicenters
were identified using the function ‘scan_eb_poisson’ with 999 Monte-Carlo
iterations (package ‘scanstatistics’). This function computes an
expectation-based Poisson scan statistic useful for identifying anomalous
spatiotemporal clusters of disease incidence and is commonly employed in
human epidemiological studies for a similar purpose (Kulldorff et al.
2005). The method compares all possible temporal windows for each group in
each zone list to test a null hypothesis of spatiotemporal randomness
using a likelihood ratio statistic. This analysis allows for the
possibility of sampling from within a population of vectors twice by
considering similarities in infection frequencies between nearest-neighbor
groups and across the flight season. The site list used all possible
levels of nearest-neighbor combinations for sites grouped within an area
while excluding combinations that would include a nearest-neighbor from a
geographically discrete (>5 km distance) area based on reported
vector flight capacity (Akbulut & Linit 1999; Togashi &
Shigesada, 2006). To validate findings, a second model using sites with an
identified spatiotemporal anomaly with an interactive site-by-date term
was used to test the hypothesis that the likelihood of capturing infected
vectors is higher early in the vector flight season. This pattern is
consistent with results reported from the southeastern United States where
PWN is long-established (Pimentel et al. 2014). Observing significant
interactive effects with date would serve as further evidence that
previously identified epicenters reflect patterns observed in established
systems.
The datasets uploaded here correspond to the following methods section
from "Probability of occurrence and phenology of pine wilt disease
transmission by insect vectors in the Rocky Mountains (Atkins, Davis,
Stewart, 2021). A brief description of the analyses is appended following
the methods. All analyses were performed in the R statistical programming
environment and unless otherwise stated use a Type I error rate of α=0.05
for assigning statistical significance (R Core Team 2019). Flight
phenology for M. clamator and M. scutellatus was modeled using a
2-parameter logistic regression (function ‘nplr’) with ordinal day as the
independent variable and cumulative proportion of captures as the response
variable. Initiation, peak, and termination of flight were approximated
using 10%, 50%, and 90% cumulative capture for each species and site ×
year combination to evaluate differences in phenology between vectors and
years (Figure S7, S8). Flight synchrony was estimated by solving growth
rate of logistic curves at 50% capture—greater flight synchrony is
consistent with more rapid logistic growth (Dell & Davis 2019).
Only sites with 10 or more captures recorded for each species were
considered reliable for informing species-level flight phenology models
(M. clamator N=30 sites, M. scutellatus N=18 sites). Phenology thresholds
(mean dates of flight initiation, peak, and termination) and flight
synchrony were compared between beetle species using a 2-sample Student’s
t-test. To evaluate effects of landscape factors on vector abundances,
beetle capture abundances were modeled for each species using a
multiple-regression model selection using distance to burned stands
(burned since year 2000), elevation, heat-load index, distance to
ponderosa pine cover boundary, distance to city edge, and canopy cover as
predictors. Trap-capture data were root-transformed where necessary in
order to meet assumptions of normality and heteroscedasticity. Sample year
was included as a random effect, and models were selected via minimization
of AIC (Akaike 1974) with a ΔAIC threshold of 2 (function ‘dredge’).
Vector beetle species and sex ratios were compared using Chi-squared
tests. The probability of vector association with PWN was evaluated using
a log-likelihood mixed-effects modeling approach. Factors considered
included both vector species (N=2 factor levels) and sex (N=2 factor
levels), as well as day-of-year of capture (continuous effect), and
capture year (N=2 factor levels). Site (N=44) was included as a random
effect and evaluated using a likelihood ratio test. Flight phenology data
contained within the file 'All_Flight_10_or_more_per_site.csv'.
This file contains cumulative flight capture data for all sites that
recorded >10 captures during the 2018 and 2019 field season. Column
'site' indicates the site for which the captures occurred (can
be compared to supplemental table for coordinates), the column
'doy' references the day of the year (1-365), and 'cp'
is the cumulative proportion of flight captures recorded (0-1). These data
were analyzed using the 'nplr'package as described above.
Landscape modeling data is contained within the file 'PWN Landscape
data.csv'. the column 'site' again corresponds to the site
as listed in the supplemental table. 'total' is the total number
of beetles captured across both years, while 'mocl' refers to M.
clamator captures and 'mosc' M. scutellatus captures.
'pipo' is the distance to the nearest eastern edge of ponderosa
pine cover as estimated using the above described GIS data.
'f09' and 'f16' were distances to fire (km) that had
occured from 2000-2009 and 2010-2019, respectively, while 'fire'
is distance to burned area of any age. 'city' indicates distance
to neared population center (km) derived as described above.
'canavg' is the average canopy cover (%) within 250m of the site
as estimated using the above described GIS data. 'x' and
'y' are UTM coordinates, elevation is the square-root of
elevation (m), 'year' is the capture year, 'hli' is
the heat-load index derived as described in McCune et al 2002,
'pos' is the total abundace of beetles that tested positive for
PWN. All tree-health data are recorded in 'PWN Site-Lvel
Data.csv'. The full stem inventory (all growth > 1in DBH) is
similarly recorded in the file 'stem inventory.csv'. This
includes the survey data containing 'site', as described above,
'spec', the 4-letter FIA abbreviation for each tree specis,
'DBH' diameter at breast height (inches), 'CRN CL'
crown class (dominant, co-dominant, intermediate, supressed) CRN DB% crown
dieback observed (%), flagging is the presence/absence (1/0) of any dead
branch tip sections, 'CANKER' is the presence/absence (1/0) of
any fungal cankers on the tree bole, DW. MIST is the presence/absence
(1/0) of dwarf mistletoe infection, MECH DMG. is the presence/absence
(1/0) of mechanical damage, including large broken limbs or bole scarring,
fire is the FIA burn severity rating (0-3), Slope is the slope of the site
in degrees, aspect is the aspect of the site in degrees, ASPECT W/C
indicates whether a site is a Warm (91-270) or Cold (271-90)
aspect. 'PWN Site-Lvel Data.csv' contains only the samples that
were screened for PWN, as noted in the PWN (1/0) column. Beetle screening
data are contained in 'beetle.csv' which also contains date and
site of capture, beetle species (4-letter code) and whether the beetle was
positive/negative (1/0) for PWN. 'map_data_pub_3panel.csv'
contains all the input data necessary to create the map from the
publication, besides the above referenced GIS layers which are publicly
available.