AOBPreview published online on March 26, 2009
Annals of Botany, doi:10.1093/aob/mcp069
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Modelling pollination services across agricultural landscapes
1 Conservation and Science Dept, Lincoln Park Zoo, 2001 N. Clark St, Chicago, IL 60614, USA
2 Dept of Enironmental Sciences, Policy and Management, 137 Mulford Hall, University of California, Berkeley, CA 94720-3114, USA
3 Conservation Science Program, World Wildlife Fund – US, 1250 24th Street NW Washington, DC 20037, USA
4 Dept of Entomology, 119 Blake Hall, 93 Lipman Drive, Rutgers, The State University, New Brunswick, NJ 08901, USA
5 Dept of Biology, Bryn Mawr College, Bryn Mawr, PA 19010, USA
6 Dept of Biological Sciences, California State University-Sacramento, 6000 J Street, Sacramento, CA 95819, USA
* For correspondence. E-mail ericlonsdorf{at}lpzoo.org
Received: 11 November 2008 Returned for revision: 15 December 2008 Accepted: 12 February 2009
Background and Aims: Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested.
Methods: Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA).
Key Results: Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model.
Conclusions: The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery.
Key words: Agriculture, bees, ecosystem services, landscape ecology, model, land use, pollinators
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