DOI: 10.5061/dryad.0rxwdbs21
title: Urban environments have species-specific associations with invasive insect herbivores

description: Urban areas are expanding rapidly, with the majority of the global and US
 population inhabiting them. Urban forests are critically important for
 providing ecosystem services to the growing urban populace, but their
 health is threatened by invasive insects. Insect density and damage are
 highly variable in different sites across urban landscapes, such that
 trees in some sites experience outbreaks and are severely damaged while
 others are relatively unaffected. To protect urban forests against damage
 from invasive insects and support future delivery of ecosystem services,
 we must first understand the factors that affect insect density and damage
 to their hosts across urban landscapes. This study explores how a variety
 of environmental factors that vary across urban habitats influence density
 of invasive insects. Specifically, we evaluate how vegetational
 complexity, distance to buildings, impervious surface, canopy temperature,
 host availability, and density of co-occurring herbivores impact three
 invasive pests of elm trees: the elm leaf beetle (Xanthogaleruca luteola),
 the elm flea weevil (Orchestes steppensis), and the elm leafminer (Fenusa
 ulmi). Except for building distance, all environmental factors were
 associated with density of at least one pest species. Furthermore, insect
 responses to these factors were species-specific, with direction and
 strength of associations influenced by insect life history. These findings
 can be used to inform future urban pest management and tree care efforts,
 making urban forests more resilient in an era where globalization and
 climate change make them particularly vulnerable to attack. Keywords:
 urban forest, invasive species, impervious surface, temperature, species
 interactions.
description: <strong>Insect Density</strong> At
 each sampling period, we measured insect density on four branches of each
 tree, one branch in each cardinal direction (N, S, E, and W). The sampling
 unit was a 30 cm terminal branch (Dahlsten et al., 1993; Rodrigo et al.,
 2019), and we assumed equal leaf area per branch.<strong>
 </strong>All sampled branches were in the lower canopy up to 3
 meters from the ground, and branches that could not be reached from the
 ground were accessed using a ladder. Sampled branches were haphazardly
 chosen from a distance where insects were not distinguishable to avoid
 sampling bias. On each tree branch, we
 counted<strong>
 </strong>individuals of each observable insect stage:
 beetle eggs, larvae, and adults (the beetle<em> </em>pupates
 in cryptic locations such as under bark or in the soil, and thus
 pupae<strong>
 </strong>were not counted); weevil<em>
 </em>leaf mines and adults; and the number of leaves with<em>
 </em>leafminer mines. Individual leafminer mines were not counted
 because adult females lay multiple eggs per leaf, and it is common for
 mines to merge and become indistinguishable from one another as larvae
 develop. Thus, it was not possible to count the number of individual mines
 for this species. Leafminer adults were not counted because this stage had
 disappeared for the season by the start of the first sampling period. The
 total number of leaves on each branch was also recorded. In addition to
 serving as the response variable for our environmental hypotheses, insect
 density of each species was also used as predictor variables for the
 co-occurring herbivore hypothesis. Tree 0 indicates the
 end of the dataset. <strong>Urban Site
 Factors</strong> <strong><em>Host
 Availability (AllElm_Density)</em></strong>
 We measured host availability digitally by counting the number of
 elm trees within a 100 meter buffer around each tree using QGIS version
 3.10.12 (QGIS Development Team, 2022) and a dataset of publicly managed
 trees provided by municipal forestry departments. We chose a 100 meter
 radius because significant changes in insect density are detectable for
 multiple insect species at this spatial scale (Sperry et al., 2001).
 Although Siberian elm is a preferred host of the insects in this system,
 other species of elm may also serve as hosts and were thus included in
 this data set. Following digital assessment, we verified all
 counts <em>in situ</em> to capture any visible privately owned
 trees and verify that trees in the dataset were still alive and present in
 the field. Despite efforts to avoid spatial autocorrelation, four trees
 had 100 meter buffers that overlapped with the buffer of another tree
 (that is, two locations where two trees had overlapping buffers). Because
 the maximum overlap was &lt;14% of the buffer area, we retained these
 trees in our analyses. <strong style="text-indent:0px;"><em>Vegetational Complexity
 (SCI_0_500)</em></strong> We measured the
 structural complexity of the vegetation in a 10 x 10 meter area around
 each tree following Shrewsbury & Raupp (2000, 2006). Specifically,
 we sectioned off a 10 x 10 meter area around each study tree and divided
 this area into one hundred  1<sup>2</sup> meter plots. In each
 of these plots, we recorded five vegetation categories: ground cover
 (e.g., mulch or turf grass), herbaceous plants (e.g., garden
 annuals/perennials, tall native grasses), shrubs (e.g., hydrangea,
 boxwood, barberry), understory trees (e.g., juniper, plum, crabapple,
 small Siberian Elm), or overstory trees (those with mature canopy
 including ash, pine, and other elm). One point was awarded for each
 vegetation type present, resulting in 0-5 points awarded in each plot. To
 quantify complexity of the vegetation in a continuous way, points were
 summed for all one hundred plots. Thus, each tree received a vegetational
 complexity score between 0 and 500. 
 <strong><em>Building Distance (Building
 Distance_m)</em></strong> To assess the
 local availability of structures for insect overwintering, we measured the
 distance of each sampled tree to the nearest building in meters as in
 Speight et al (1998). This was performed digitally using QGIS version
 3.10.12 (QGIS Development Team, 2022) and the ESRI Standard Basemap, which
 displays built structures.
 <strong><em>Impervious Surface
 (ImperviousSurface_20m)</em></strong>
 Impervious surface data were obtained through the USGS
 Multi-Resolution Land Characteristics Consortium (Dewitz & US
 Geological Survey, 2021) on a 30 x 30 meter scale and processed using QGIS
 version 3.10.12 (QGIS Development Team, 2022). We used the zonal
 statistics tool to calculate the percentage of impervious surface within a
 20 meter buffer surrounding each sampled tree, which is more predictive of
 herbivorous insect density than impervious surface at larger spatial
 scales (Just et al., 2019). Although impervious surface data were not
 available at a smaller spatial scale, the zonal statistics tool allowed us
 to obtain an estimate of impervious surface within 20 meters of each tree
 using 30 x 30 meter data by computing<strong> </strong>an
 average impervious surface value based on weighted averages of the extent
 to which each 30 x 30 meter pixel overlapped with the 20 meter buffer
 around a tree. <strong><em>Canopy
 Temperature (MeanTemp_Night)</em></strong>
 Canopy temperature at each tree was measured every 1.5 hours via
 the iButton Thermochron (model DS1921G-F5). Temperature logging began at
 7:30AM MST on June 12 and ended at 7:30AM MST on August 25 for a total of
 1,185 data points per logger. We placed each logger in a compostable
 container to prevent contact with direct sunlight and attached them with a
 zip tie to branches approximately 2-3 meters from the ground. We placed
 temperature loggers on the east side of the tree wherever possible or on
 the west side of the tree if a stable eastern location was not available.
 Despite efforts to minimize contact with direct sunlight, several loggers
 recorded artificially inflated temperatures. This made mean and maximum
 temperatures impractical for analysis. We used mean nighttime temperature
 in the following analyses (7:30PM-7:30AM MST, n=666 measurements per
 logger) because the urban heat island effect is less variable, occurs more
 frequently, and is more intense in urban canopies at night compared to the
 day (Du et al., 2021; Sun et al., 2019).