Pollinator Losses and Plant Function: An Assessment of Pollination Network Resilience in the face of Climate ChangeEPA Grant Number: F13B20393
Title: Pollinator Losses and Plant Function: An Assessment of Pollination Network Resilience in the face of Climate Change
Investigators: Briggs, Heather Mae
Institution: University of California - Santa Cruz
EPA Project Officer: Lee, Sonja
Project Period: September 1, 2014 through September 1, 2016
Project Amount: $84,000
RFA: STAR Graduate Fellowships (2013) RFA Text | Recipients Lists
Research Category: Academic Fellowships , Fellowship - Ecology
Pollinator species are increasingly at risk of local and global extinction from the effects of human activities, including habitat loss, introduction of alien species and global climate change (GCC). Specifically, GCC is projected to disrupt the overlap in plant flowering time and pollinator emergence/foraging activity, leading to potentially mismatched interactions between both plants and animals. While it seems intuitive that GCC could reduce plant reproduction through a reduction in visitation from pollinators, recent work based on pollination networks actually suggests that plant communities will be highly resilient, meaning that plants will be resistant to linked extinctions (plant extinctions that result from pollinator extinctions) when faced with GCC. The simulation models that generate these predictions of resilience are based on a number of implicit assumptions, namely that any insect that happens to visit a flower is an effective pollinator of that plant species. A wide range of studies, however, have shown that many flower visitors are ineffective at pollinating plant species they visit, and some actually reduce plant reproduction. This work will take a two-pronged approach of modeling combined with empirical field studies to evaluate the importance of this assumption to the conclusion that pollinator networks are resilient. Furthermore, this work will evaluate pollinator network resilience under future climate change scenarios.
This project includes the following tasks: (1) Build a pollination network model for an empirical pollination network using existing long-term data sets; (2) set parameters for estimates of the directionality of plantpollinator interactions (positive and negative); (3) assess network resilience to linked extinctions by comparing networks with a more realistic assignment of positive to negative interactions to those with only positive interactions; and (4) assess resilience to linked extinctions under climate change scenarios by comparing network models that include an assignment of interaction directionality to those that do not.
Pollination networks with more realistic characterization of the effectiveness of plant-pollinator interactions will exhibit substantially less resilience relative to previous network studies. Incorporating a more realistic distribution of interaction direction (negative or positive) should predict more realistic “extinction cascades,” in which pollinators, as well plants, go extinct, leading to the extinction of more plants. Such cascades cannot occur when all interactions are considered positive, as a single link will be enough to maintain a plant-pollinator interaction; therefore current models may be overestimating predictions of resilience. Furthermore, global climate change will impact such realistically characterized networks more dramatically than those predicted by current models.
Potential to Further Environmental/Human Health Protection
The idea that plant-pollinator networks are highly resilient to pollinator species losses has been so broadly adopted by the ecological research community that it can almost be considered a paradigm. If relaxing one key assumption of those simple models changes the results, it could have a major effect on the way that ecologists, environmental policymakers and land managers think about plant-pollinator networks and their resilience to environmental changes. This work will provide a strong basis for improved predictive models that will be useful in anticipating likely changes in pollination services and designing strategies to maximize ecosystem resilience.