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.

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 m², housing 21 to 259 individual plots, have been in operation from 5 to 46 years, and are >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), 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. 

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 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).

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., 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.