Abstract
AbstractBiocontrol by wild insects and other organisms is an important service provided to agriculture, but few studies have linked the role of this service to urban garden crop production. In 15 urban food gardens in Sydney, Australia, we assessed predation and parasitism of two sentinel prey species, recorded pest control activities undertaken by gardeners and the produce yielded by garden crops. We observed substantial removal of sentinel prey (mean removal 22% for Tenebrio molitor larvae and 59% for Helicoverpa armigera) but no parasitism. Vertebrate predators primarily consisted of urban adapted birds and mammals common throughout Australian cities. We measured a range of local and landscape scale environmental variables including plant richness and abundance, light, canopy cover, building density and distance to remnant vegetation. We found that gardeners undertook only basic pest control activities with little chemical use, yet high amounts of produce were harvested. Pest control services were poorly explained by environmental variables. Low active pest control activities, and high predation rates suggest pests are either well controlled or in low numbers in the surveyed urban food gardens. Given the vertebrate predators were generalist birds and mammals common to many parts of urban Australia, the provision of predation services to urban gardens by these taxa could be widespread across the continent.
Funder
Commonwealth Department of Education and Training
University of New England
Publisher
Springer Science and Business Media LLC
Reference52 articles.
1. Arnold JE, Egerer M, Daane KM (2019) Local and landscape effects to biological controls in urban agriculture—a review. Insects 10:215
2. Atwood D, Paisley-Jones C (2017) Pesticides industry sales and usage 2008–2012 market estimates. United States Environmental Protection Authority, Washington
3. Australian Bureau of Statistics (2018) Census data packs 2016. http://www.abs.gov.au/websitedbs/D3310114.nsf/Home/2016%20DataPacks. Accessed 16 Apr 2018
4. Bates D, Mächler M, Bolker B, Walker S (2015). “Fitting Linear Mixed-Effects Models Using lme4.” J Stat Softw 67(1):1–48
5. Barton, K. (2009). MuMIn: multi-model inference. http://r-forge.r-project.org/projects/mumin/