DOI: 10.5061/dryad.3tx95x6mx
title: Arthropods in urban agroecosystems Seattle 2019

description: Like other urban green spaces, urban community gardens can act as
 biodiversity refugees, especially for small organisms like arthropods. In
 turn, arthropods can provide important ecosystem pest control services to
 these agroecosystems. Thus, an often-asked question among urban gardeners
 is how to improve gardens and surrounding areas for natural enemies and
 associated pest control services. We examine how local vegetation and
 garden characteristics, as well as the surrounding landscape composition
 affect ground-dwelling beetles (Coleoptera: Carabidae and Staphylinidae),
 spiders (Aranea), opilionids (Opilionida), and ladybird beetles
 (Coleoptera: Coccinellidae), all of which are important predators. In the
 summer 2019, we collected predators, vegetation, ground cover, and garden
 and landscape characteristic data of ten community gardens in the city of
 Seattle, Washington. We found that different groups of natural enemies are
 associated with different environmental variables and at different scales;
 probably related to differences in their dispersal capabilities, habits,
 and diets. Floral variables (# of flowers, # of species in flower) had a
 negative effect on non-flying natural enemies (spiders, opilionids, and
 ground-dwelling beetles), but not on flying ones (ladybird beetles). The
 only taxa that was significantly affected by a landscape-scale variable
 was Opilionida, the only group examined that exclusively disperses by
 ground. Our results show contrasting results to similar studies in
 different regions and highlight the need to expand the taxa and regions of
 study.
description: Study site We conducted the study in the
 city of Seattle, Washington, located in the U.S. Pacific Northwest
 (47.6062° N, 122.3321° W). Seattle's population in 2020 was estimated
 to be 737,015 in an area of 83 square miles (Office of Planning and
 Community Development 2023). While Seattle is among the fastest growing
 cities in the US, the city is committed to protecting urban biodiversity
 in its various green-spaces (City of Seattle 2018) and has an increasing
 demand for urban agriculture. The Community Garden program alone oversees
 89 community gardens throughout the city. These gardens occupy about 10
 hectares where food is grown for gardeners and for the general public City
 of Seattle 2023).   Our study took place in 10 of these
 urban community gardens. The gardens are managed in an allotment style
 where households rent and cultivate individual plots within the garden.
 The chosen gardens range in size from 240 to 16,187
 m<sup>2</sup>, housing 21 to 259 individual plots, have been
 in operation from 5 to 46 years, and are &gt;2km from each other. All
 selected gardens are administered by Seattle Department of
 Neighborhoods' P-Patch Program which requires use of organic
 gardening inputs and methods (Seattle Department of Neighborhoods, 2020).
 Thus, no synthetic chemicals including pesticides, insecticides,
 herbicides, weed killers, and fertilizers are allowed anywhere in the
 gardens. To standardize the sampling area of our study
 sites, we established a 20 x 20 m plot in the center of each garden. Our
 samplings and observations were limited to these areas for the duration of
 our study. Landscape-scale variables
 We used land-cover data from the 2011 National Land Cover
 Database (NLCD, 30-m resolution (Homer et al. 2015) and calculated the
 percentage of land-cover types in 500-m buffers from the center of each
 garden. The 500m buffer has been used to study landscape effects of many
 taxa (Schmidt et al. 2008, Concepción et al. 2008, Batáry et al. 2012,
 Otoshi et al. 2015). We used five land-cover categories established by the
 National Land Cover Database (NLCD): developed open, developed low,
 developed medium/high (we combined the NLCD categories of “developed,
 medium intensity” and “developed, high intensity into one
 category)<strong>,</strong> and natural/semi-natural (which
 included deciduous forest, evergreen forest, mixed forest, shrub/scrub,
 herbaceous, hay/pasture), and agricultural (listed in the NLCD as
 “cultivated crops”) (Multi-Resolution Land Characteristics 2023). In
 addition, we calculated the proportion of urban parks in the 500m buffers
 using the City of Seattle parks map available through the King County GIS
 website (https://kingcounty.gov/services/gis.aspx). These parks are
 managed by the city and have a variety of uses and
 characteristics. We included urban parks as one of our
 landscape variables because from studies in rural agricultural systems, we
 know that farms embedded in landscapes with a higher proportion of natural
 habitats (i.e. forests, wetlands, grasslands) support higher local density
 and diversity of beneficial arthropods, even in fields with low local
 vegetation diversity (Tscharntke et al. 2005, Bianchi et al. 2006,
 Chaplin‐Kramer et al. 2011). In cities, especially rapidly expanding ones
 like Seattle, nearby ‘natural’ or ‘semi-natural’ areas consist largely of
 urban parks and reserves— habitats which may be vital to connect
 apparently isolated urban green-spaces (Langellotto et al. 2018). Much
 like fragments of forests, grasslands, and wetlands in rural agricultural
 landscapes (Landis et al. 2000, Schellhorn et al. 2014), urban parks may
 provide alternative resources, prey and shelter, thus enhancing natural
 enemy abundance and diversity in nearby urban
 agroecosystems.<em> </em> Garden-scale
 variables Vegetation was sampled three times between
 June and August 2019, approximately a month in between sampling periods.
 Vegetation was sampled within the same standardized 20 x 20 m plot in each
 garden. Canopy cover was measured using a concave spherical densitometer
 at the center of each plot in addition to 10 m to the North, South, East
 and West of the center.  Inside each of the 20 x 20 m plots, we counted
 and identified all trees and shrubs (woody vegetation). We also recorded
 the number of trees and shrubs in flower. Within the 20 x 20 m plot, we
 then selected eight locations to place 1 x 1 m plots. To randomly select
 each of the eight locations, we first marked four 5 x 20 m strips within
 the 20 x 20 m. For each strip, using a random number table from 0-20, we
 chose two random numbers (which represented, in meters, the distance from
 0 to 20 m from the beginning to the end of the <em>length</em>
 of the strip). We then walked along the edge the strip until reaching the
 randomly chosen distances and then used a second random number table from
 0-5 (which represented, in meters, the distance from 0 to 5 m from one
 edge to other of the <em>width</em> of the strip) to choose
 the location of the plot. We repeated this procedure for the four 5 x 20 m
 strips for a total of eight randomly chosen plots. 
 Within each of these plots, we measured the height of the tallest
 herbaceous vegetation, and counted the total number of flowers and total
 number of crops and ornamentals in flower. We identified each plant
 species and estimated the percentage of cover of each plant type (crop,
 grass, ornamental, weed, herbaceous). Within each of these 1 x 1m plots,
 we also estimated the percentage of ground-cover make-up of bare soil,
 mulch/wood chips, straw and leaf litter. In addition,
 we obtained information on garden size (garden area in
 m<sup>2</sup>, and number of individual plots), and garden age
 (years since establishment) from the city of Seattle community garden
 information website (City of Seattle 2023). Natural
 enemies At each garden site we conducted three rounds
 of natural enemies sampling. This included sampling ground-dwelling
 beetles (Carabidae and Staphylinidae), spiders (Aranea) and opilionids
 (Opilionida), and ladybird beetles (Coleoptera: Coccinellidae). We sampled
 natural enemies three times between June and August, 2019. The first round
 of sampling occurred between June 24th - 26th, the second round between
 July 17th - 19th, and the final round between August 12th - 13th. Natural
 enemies were sampled using a combination of visual and trapping sampling
 methods (see below). We estimated total abundance across all sampling
 methods and sampling periods for the focus natural enemies
 (ground-dwelling beetles, spiders, opilionids, and ladybird beetles) (see
 data analysis).  We lumped Carabidae and Staphylinidae into one
 category—ground-dwelling beetles—and estimated abundance for all. Per time
 limitations, we only were able to further identify spiders (to family) and
 ladybird beetles (to species). Thus, in addition to abundance, for spiders
 we also estimated family richness and for ladybird beetles, species
 richness across all sampling methods and periods.
 Visual Sampling Using the same randomized
 methodology described for the vegetative sampling, eight 0.5 x 0.5 m
 quadrants within each garden’s 20 x 20 m plot were selected. In each of
 these 0.5 x 0.5 m plots, one person visually searched in the vegetation
 for ten minutes for ladybird beetles, spiders, opilionids and ground
 beetles. All specimens were collected and preserved in vials with alcohol
 (with the exception of minimal escaped specimens we were unable to
 collect; we ID’d these specimens visually in the field to family for
 spiders and morphospecies for ground and ladybird beetles).  We recorded
 the number of individuals (for all), family (spiders), and species
 (ladybird beetles). Traps Four
 random trap locations were selected in each 20 x 20 m plot using the
 aforementioned randomization methodology. At each location, four 7.62 cm x
 12.7 cm yellow sticky cards (BioQuip Products Inc.,<em>
 </em>Compton, CA, USA) on 20cm wire stakes were placed in each
 corner of a 0.5 x 0.5 m quadrant. A pitfall trap was placed in the middle
 of the quadrant flush with the ground, filled up one third with water and
 dish soap. After 24 hours the traps were retrieved and the specimens were
 identified. Data analysis For
 abundances of spiders, opilionids, and ground beetles, we summed the total
 number of individuals from both the pitfalls and visuals (none were found
 in sticky cards) and across the three sampling periods. Similarly, for
 ladybird beetles we summed the total abundance data from both sticky cards
 and visuals (none were found in pitfalls) and across the three sampling
 periods. For species/family richness (ladybeetles and spiders,
 respectively), we counted the total number of species/families sampled
 across all sampling methods and periods. These total abundances and
 species/family richness across sampling methods and periods were then used
 as response variables for the analyses.
description: # Arthropods in urban agroecosystems Seattle 2019
 This data set contains our
 response variables: abundance and species/family richness data for
 ground-dwelling beetles (Carabidae and Staphylinidae), spiders (Aranea)
 and opilionids (Opilionida), and ladybird beetles (Coleoptera:
 Coccinellidae) in 10 urban community gardens in Seattle, WA (USA).
 Arthropods were counted/collected via visual counts, pitfalls, and sticky
 traps in a 20m x 20m plot in the center of each garden. The dataset also
 contains our effect variables related to garden management (ground cover
 and vegetation, size and age of garden) and to landscape composition
 extracted from the 2011 National Land Cover Database (NLCD, 30-m
 resolution) in 500-m buffers from the center of each garden. Data were
 collected 3 times (rounds) during the summer 2019. ## Description of the
 data and file structure Data file:
 'SeattleBUGS2019.summaries.wide.csv' Column A: Round 1-3
 represents sampling round. 1= June 2019; 2= July 2019; 3= August 2019
 Column B: name of urban community garden. Gardens are part of the city of
 Seattle Community Garden program
 ( Columns
 C to I: response variables | Column Title | Definition | Description  | |
 :--------------------- |
 :----------------------------------------------------------- |
 :----------------------------------------------------------------------------- | | abundance.gb | abundance ground beetles including carabids and staphilinids | abundance estimated as the sum of all methods and all pots per sampling round  | | abundance.rove | abundance of staphilinids | abundance estimated as the sum of all methods and all pots per sampling round  | | abundance.carabids | abundance of carabids | abundance estimated as the sum of all methods and all pots per sampling round  | | abundance.lb | abundance of ladybeetles  | abundance estimated as the sum of all methods and all pots per sampling round  | | richness.lb | # of species of ladybeetles | richness estimated as the sum of species with all methods per sampling round  | | spider.abundance | abundance of spiders and oplionids | abundance estimated as the sum of all methods and all pots per sampling round  | | spider.family.richness | number of families  | richness estimated as the sum of familes with all methods per sampling round  | Columns J to N: effect variables 1, garden general information | Column Title | Definition | Description  | | :--------------------- | :----------------------------------------------------------- | :----------------------------------------------------------------------------- | | numplots | number of plots in garden  | how many registered plots are there in the garden as per Seattle p-patch program webpage | | date | year garden was established | year garden was established as per   per Seattle p-patch program webpage | | age | age of the garden | estimated based on the year of establishment to 2019 when sampling took place | | size\_sqft | size of the garden in square feet  | size of the garden in square feet as per Seattle p-patch program webpage | | size\_sqm | size of the garden in square meters | size of the garden in square meters calculated based on square feet area in previous column  | columns O to AA: effect variables 2, landscape-scale variables, extracted from the 2011 National Land Cover Database (NLCD, 30-m resolution) in 500-m buffers from the center of each garden. We used five land-cover categories established by the National Land Cover Database (NLCD): developed open, developed low, developed medium/high (we combined the NLCD categories of “developed, medium intensity” and “developed, high intensity into one category)**,** and natural/semi-natural (which included deciduous forest, evergreen forest, mixed forest, shrub/scrub, herbaceous, hay/pasture), and agricultural (listed in the NLCD as “cultivated crops”) (Multi-Resolution Land Characteristics 2023). In addition, we calculated the proportion of urban parks in the 500m buffers using the City of Seattle parks map available through the King County GIS website ( | Column Title | Definition | Description  | | :--------------------- | :----------------------------------------------------------- | :----------------------------------------------------------------------------- | | prop.parks.500m | proportion of area cover with urban parks  in a 500m buffer  | the proportion of urban parks in the 500m buffers using the City of Seattle parks map available through the King County GIS website ( | | Open.500m | proportion of area cover with open spaces in a 500m buffer | A landuse category  established by the National Land Cover Database (NLCD) | | developed.low\.500m | proportion of area covered with low intensity urban developed areas  in a 500m buffer | A landuse category  established by the National Land Cover Database (NLCD) | | developed.medium.500m | proportion of area covered with medium intensity urban developed areas  in a 500m buffer | A landuse category  established by the National Land Cover Database (NLCD) | | developed.high.500m | proportion of area covered with high intensity urban developed areas  in a 500m buffer | A landuse category  established by the National Land Cover Database (NLCD) | | deciduous.forest.500m. | proportion of area covered with deciduos forests in a 500m buffer | A landuse category  established by the National Land Cover Database (NLCD) | | evergreen.forest.500m | proportion of area covered with evergreen forests in a 500m buffer | A landuse category  established by the National Land Cover Database (NLCD) | | mixed.forest.500m | proportion of area covered with mixed forests in a 500m buffer | A landuse category  established by the National Land Cover Database (NLCD) | | shrub.500 | proportion of area covered with shrubby vegetation in a 500m buffer | A landuse category  established by the National Land Cover Database (NLCD) | | herbaceuous.500m | proportion of area covered with  herbaceous vegetation in a 500m buffer | A landuse category  established by the National Land Cover Database (NLCD) | | hay.pasture.500m | proportion of area covered with pasture in a 500m buffer | A landuse category  established by the National Land Cover Database (NLCD) | | nat.seminat.500m | proportion of area covered with natural and seminatural areas in a 500m buffer | sum of the proportions of deciduous forest, evergreen forest, mixed forest, shrub/scrub, herbaceous, hay/pasture | | prop.ag.500m | proportion of area covered with agricultural areas in a 500m buffer | listed in the NLCD as “cultivated crops” | Columns AB to AU: effect variables 03, garden-scale variables, ground cover and vegetation Vegetation was sampled three times between June and August 2019, approximately a month in between sampling periods. Vegetation was sampled within the same standardized 20 x 20 m plot in each garden. Canopy cover was measured using a concave spherical densitometer at the center of each plot in addition to 10 m to the North, South, East and West of the center.  Inside each of the 20 x 20 m plots, we counted and identified all trees and shrubs (woody vegetation). We also recorded the number of trees and shrubs in flower. Within the 20 x 20 m plot, we then selected eight locations to place 1 x 1 m plots. To randomly select each of the eight locations, we first marked four 5 x 20 m strips within the 20 x 20 m. For each strip, using a random number table from 0-20, we chose two random numbers (which represented, in meters, the distance from 0 to 20 m from the beginning to the end of the *length* of the strip). We then walked along the edge the strip until reaching the randomly chosen distances and then used a second random number table from 0-5 (which represented, in meters, the distance from 0 to 5 m from one edge to other of the *width* of the strip) to choose the location of the plot. We repeated this procedure for the four 5 x 20 m strips for a total of eight randomly chosen plots.  Within each of these plots, we measured the height of the tallest herbaceous vegetation, and counted the total number of flowers and total number of crops and ornamentals in flower. We identified each plant species and estimated the percentage of cover of each plant type (crop, grass, ornamental, weed, herbaceous). Within each of these 1 x 1m plots, we also estimated the percentage of ground-cover make-up of bare soil, mulch/wood chips, straw and leaf litter. In addition, we obtained information on garden size (garden area in m2, and number of individual plots), and garden age (years since establishment) from the city of Seattle community garden information website (City of Seattle 2023). | Column Title | Definition | Description  | | :--------------------- | :----------------------------------------------------------- | :----------------------------------------------------------------------------- | | mean.canopy.cover | Mean canopy cover | measured as the proportion of a fixed area covered by high vegetation (tree/shrub crowns); measured with concave spherical densitometer at the center of each plot in addition to 10 m to the North, South, East and West of the center | | Trees.Shrub.richness | Tree and shrub species richness | Number of species of trees and shrubs within a 20m x 20m plot in the center of each garden | | tree.shrub.abund | Tree and Shrub abundance | number of individual  trees and shrupbs within a 20m x 20m plot in the center of each garden | | tree.shrub. Inflower | Number of trees and shrubs in flower | Number of trees and shrubs within a 20m x 20m plot that were in flower at the time of sampling | | bare.soil | Average of % bare soil | averaged across plots,  | | grass | Average of % grass | averaged across plots,  | | herb.cover | Average of % herbaceous plants | averaged across plots,  | | rocks | Average of % rocks | averaged across plots,  | | leaflitter | Average of % leaf litter | averaged across plots,  | | mulch | Average of % mulch/ straw/ wood chips | averaged across plots,  | | veg.height | Average of height of tallest non-woody vegetation (cm) | averaged across plots,  | | no.white.flowers | Average of no. white flowers | averaged across plots,  | | no.red.flowers | Average of no. red flowers | averaged across plots,  | | no.pink.flowers | Average of no. pink flowers | averaged across plots,  | | no.orage.flowers | Average of no. orange flowers | averaged across plots,  | | no.yellow.flowers | Average of no. yellow flowers | averaged across plots,  | | no.blue.flowers | Average of no. blue flowers | averaged across plots,  | | no.purple.flowers | Average of no. purple flowers | averaged across plots,  | | no.flowers | Average of no. flowers | averaged across plots,  | | no.spp.inflower | Average of # species in flower | averaged across plots,  |