Abstract
Growing concern about wild and managed pollinator populations has led to efforts to create floral habitat to promote bees and other pollinators, especially in agricultural lands where they make important contributions to crop pollination. These actions all require practitioners to determine what mixture of plant species to select to best support diverse and functionally important pollinators. Like in the selection of areas for nature reserves, plant choices must balance function against differences in cost. To date, plant mixes have been compiled using expert opinion based on the performance of individual species, but researchers and practitioners have called for a systematic approach to optimize mixtures. We applied a decision analytic approach to select best flowering mixes to meet two specified objectives at the least cost: maximizing total bee richness or maximizing crop-pollinating bees. The model identified best plant mixes for each objective across a range of budgets. Accounting for the variation in costs among plant species allowed for substantially more cost effective mixes with little loss in achieving the target objective. Including multiple objectives simultaneously revealed the power of the approach to meet complex goals. The resulting mix supported over 96% of the bee species for both goals at no difference in cost. This gain in efficiency could dramatically increase the extent of habitat implemented and remove financial barriers to adoption by stakeholders and conservation practitioners.
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