DOI: 10.5061/dryad.gb5mkkwt0
title: Trap success data for capturing hemlock woolly adelgid

description: Hemlock woolly adelgid (HWA), Adelges tsugae Annand, threatens hemlock
 forests throughout eastern North America. Management efforts focus on
 early detection of HWA to ensure rapid management responses to control and
 stop the spread of this pest. This study’s goal was to identify an
 affordable, efficient trap to aid with airborne environmental DNA (eDNA)
 sampling approaches as an early monitoring tool for HWA. We initially
 compared HWA detection success between a standard sticky trap, commonly
 used for HWA monitoring, and trap designs potentially compatible with eDNA
 protocols (i.e., passive trap, funnel trap, and motorized trap). Passive,
 funnel, and motorized traps’ estimated capture success probabilities
 compared to sticky traps were 0.87, 0.8, and 0.4, respectively. A
 secondary evaluation of a modified version of the motorized trap further
 assessed trap performance and determined the number of traps needed in a
 set area to efficiently detect HWA. By modifying the original motorized
 trap design, its estimated capture success probability increased to 0.67
 compared to a sticky trap. Overall, the cumulative capture success over
 the 16-week sampling period for the motorized trap was 94%  and
 99% for the sticky trap. The number of traps did impact capture success,
 and trap elevation and distance to infested hemlocks influenced the number
 of adelgids captured per trap. As environmental DNA (eDNA)-based
 monitoring approaches continue to become incorporated into invasive
 species surveying, further refinement with these types of traps can be
 useful as an additional tool in the manager’s toolbox.
description: Trap Design
 Efficiency The trap design testing took place at Pioneer Park
 (PIPK), Muskegon, Michigan, USA (Fig. 2; lat. 43.284127°, long.
 -86.368278°) a site with confirmed HWA infestations. Pioneer Park is 58.7
 ha (145 ac) of county park and campground property along Lake Michigan. We
 designated the HWA infestation level as high based on a sistens count
 assessment outlined by Evans and Gregoire (2007). All traps were deployed
 in areas with known infested hemlock trees to test our trap
 designs. All four trap designs (motorized, passive, funnel,
 and sticky traps) were deployed for four weeks in the month of July 2020,
 which is during the sistens crawler stage. We organized our experiment in
 a randomized block design with five blocks. Each block comprised 36 cells
 for a total area of 625 m<sup>2</sup>. Trap contents were
 collected on a weekly basis for a total of four collection
 periods. We assessed
 differences in HWA capture success for each of the four trap designs
 within each block and evaluated HWA distribution between blocks to account
 for potential effects of spatial variation in HWA across the study site.
 To assess adelgid capture success of the motorized and passive traps, we
 examined the petroleum-jelly-coated microscope slides under a Nikon SMZ645
 dissecting microscope and counted the total number of HWA crawlers from
 the four slides of each trap. To assess adelgid capture success for the
 funnel traps, we counted crawlers in funnel traps by placing each trap’s
 contents into an individual petri dish and examining the contents
 underneath a dissecting microscope. To obtain adelgid counts for the
 sticky traps, we counted adelgids on each sticky trap using methods
 previously described by Dreistadt et al. (1998). Adelgids were counted on
 a 2.5-cm-wide vertical column down the center of each sticky insect card
 using a dissecting microscope. We used this technique on each of the five
 cards that made up every sticky trap.
 To determine if spatial variation
 in HWA prevalence across our sampling site might impact our capture
 results, we evaluated HWA presence within each designated block at Pioneer
 Park by counting the number of ovisacs on hemlock branches using a method
 from the Pennsylvania Department of Conservation and Natural Resources
 (Johnson, 2020). This was quantified at the block level since differing
 amounts of HWA between blocks could impact trap success in catching HWA.
 We randomly selected 10 trees within every block and numbered the lower
 crown branches within 7.5 m of the ground starting on the north side and
 moving clockwise around the tree. We used a random number generator to
 select five branches around each tree and counted the number of ovisacs
 within a 25 cm length of the distal part of each
 branch. All analyses were conducted using the program R v
 4.0.3 (R Core Team, 2020). HWA estimates within each block and adelgid
 capture assessment data were non-normal despite transformations, thus we
 chose non-parametric analyses. To determine whether there were differences
 in HWA prevalence between blocks, we assessed differences between the
 average number of ovisacs counted from each block with a Kruskal-Wallis
 test using the package stats v 3.6.2. We estimated the probability that a
 non-sticky trap would capture HWA when a corresponding sticky trap (same
 block and same collection date) also captures HWA with a Wilson score
 interval (Wilson, 1927) using the package binom v 1.1-1. We also assessed
 differences in capture success between the different trap types using a
 generalized linear mixed model (GLMM), with trap type as the fixed effect
 and block as a random effect; the sticky trap was used as the reference.
 This was performed in the R package lme4 v 1.1-27.1 (Bates et al., 2015).
 Tukey’s post-hoc test was performed with the package multcomp v 1.4-20
 (Hothorn et al., 2008) to evaluate differences in capture success across
 trap types. All statistical analyses used an alpha value of 0.05 to
 determine statistical differences.
 Evaluation of Capture Success
 Related to Number of Traps and Landscape Features
 The second part of our study took
 place at North Ottawa Dunes
 (Fig. 2; lat. 43.090484°, long. -86.247998°), a 240.2-ha (593-ac) Ottawa
 County Parks property of wooded sand dunes bordering Lake Michigan. This
 is a site with a known HWA infestation, and we designated the infestation
 level as low based on a sistens count assessment outlined by Evans and
 Gregoire (2007) (Sanders, 2021). We obtained Ottawa County Parks survey
 data (January–October 2020) with GPS locations of all hemlock trees within
 the park, as well as the locations of hemlock trees where visual surveys
 previously detected the presence of HWA ovisacs. 
 Within North Ottawa Dunes, we
 established a 36.5-ha (90-ac) circle over our study area and sectioned it
 into 30 equal parts. The 30 equal sections (3 acres each) were divided
 into five replicate groups (A-E), with six sections per group. Each of
 these six sections hosted a different number of paired motorized and
 sticky traps. Section one contained one pair of motorized and sticky
 traps, section two contained two pairs of traps, so on and so forth up to
 the sixth section containing six trap pairs. This resulted in a total of
 105 motorized and 105 sticky traps for the entire 36.5-ha (90-ac) area,
 and the density of the traps within each section ranged from 1 trap per
 0.2 ha (0.5 ac) to 1 trap per 1.2 ha (3 ac). In every replicate group, the
 number of trap pairs and trap placement within each section was randomly
 assigned. Traps were attached to a 1.5 m pole, and the motorized and
 sticky traps were placed 2 m apart at each trap location. Traps were
 deployed for 16 weeks from April 7 through July 28, 2021, during both
 annual HWA egg-hatching events. Petroleum-jelly-coated slides from the
 motorized traps were collected biweekly and placed in 50 mL vials, and
 sticky traps were collected biweekly in clear, plastic storage
 bags.  After each biweekly collection, we counted the
 number of adelgids observed on each trap. For the motorized traps, the
 number adelgids present on the four petroleum-jelly-coated slides were
 observed using a Nikon SMZ645 dissecting microscope, counted, and
 recorded. We assessed the number of adelgids collected on each sticky trap
 using the same method previously described for our trap design assessment
 (Dreistadt et al., 1998). For both the motorized and sticky traps, 20% of
 traps per collection period were recounted for quality assurance
 (R<sup>2</sup> = 0.99). 
 We created maps predicting
 distribution of HWA with the count data for each motorized trap by means
 of the inverse distance weighted (IDW) spatial interpolation method using
 ArcMap v 10.4.1 (ESRI, 2016) to visualize how adelgid counts varied in our study
 area throughout the summer. The IDW method predicts likely HWA numbers
 based on a linear-weighted combination of count data for sample locations.
 This method is appropriate for clustered data. IDW predicts values for
 unsampled locations by assuming those values are related more to closer
 data points than to those that are farther away. We used a power of 2 and
 a nearest-neighborhood search of 8 points in the analysis, so more
 localized trap counts influenced predictions of the nearby unsampled
 locations and accounted for all cardinal directions surrounding a
 location. All statistical analyses performed in R used v
 4.0.3 (R Core Team, 2020). We estimated the probability that a motorized
 trap would detect HWA when the corresponding sticky trap detected HWA with
 a Wilson score interval (Wilson, 1927) using the package binom v 1.1-1 to
 evaluate how our modifications to the motorized trap improved capture
 success compared to our initial trap design. We also used a GLMM to
 evaluate if the number of capture successes and failures differed between
 the sticky and motorized traps where trap type was considered a fixed
 effect, and the collection week and group ID (A-E) were included as random
 effects. This was performed in the R package lme4 v 1.1-27.1 (Bates et
 al., 2015). To assess the level of spatial autocorrelation in
 the number of adelgids captured across our traps, we calculated Moran’s I
 using the program GeoDa (https://geodacenter.github.io/faq.html).  Euclidean distances were calculated between each trap point. The bandwidth was set to 0.001 so that the median number of neighbors for each point (i.e., trap) was five (min neighbors = 1; max neighbors = 8). We performed the same analysis for each two-week collection period when crawlers were present to test for significant spatial autocorrelation with 999 permutations. To determine if the number of traps deployed within each 1.2-ha (3-ac) section influenced whether an adelgid was captured. We used a GLMM to evaluate if a capture success within a section was correlated with the number of traps within each section. This analysis focused on data collected from April 21 – July 28, when adelgid crawlers were present. In the full model, the fixed effect included the number of traps per section. The collection week and replicate group ID (groups A-E) were included as random effects; sections with one trap were used as the reference. The null model included the random effects collection date and group ID (A-E). We then used an ANOVA to determine if the addition of the fixed effect significantly improved the model. This analysis was run using the lme4 package v 1.1-27.1 (Bates et al., 2015). We used the R package multcomp v 1.4-20 (Hothorn et al., 2008) for post-hoc analyses to evaluate significant differences in capture success between each number of traps per section using a Tukey’s post-hoc test. We also used a generalized linear model (GLM) to predict the number of traps that would need to be deployed within a 1.2-ha (3-ac) section to have a catch probability of 0.9 or greater. This analysis was performed for the active crawler period (April 21–July 28) and again with a subset of that data that represented the peak crawler period (May 19–June 16).   We assessed if trap elevation, slope, aspect, and Euclidean distance to the nearest HWA-infested hemlock impacted the number of adelgids caught in a motorized trap. The adelgid count data were non-normal and over-dispersed. Because of this, we used a GLM with a negative binomial distribution using the package MASS v 7.3-53.1. The full model consisted of adelgid counts as the dependent variable and Euclidean distance, elevation, slope, and aspect as the independent variables. A reduced GLM model was also run after removing the non-significant terms, and the optimal model was selected using the lowest Akaike’s Information Criterion (AIC). All analyses used an alpha value of 0.05 to determine statistical differences.
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