Complex Effects of Landscape Modification on Plant Functional Connectivity

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Sustainable management of Canada's natural resources is jeopardized by human landscape alteration and climate change, which affect species in multiple and complex ways. In plants, small isolated populations in remnant habitat patches may suffer from increased risk of local extinction, reduced chance of recolonization, loss of genetic diversity and thus potential for adaptation to climate change, and reduced fitness due to inbreeding. These consequences of human landscape alteration depend largely on how well seeds and pollen move across the landscape. Many plants rely on different vectors (e.g., wind, birds, mammals, or insects) for pollination and seed dispersal, so that the exchange of individuals and their genes between habitat patches (functional connectivity) depends on the interaction of the dispersal vectors with the intervening landscape (matrix resistance), which may differ between pollen and seeds. To address these issues, my lab will capitalize on a 20-year landscape modification experiment aimed at restoring functional connectivity of calcareous grasslands by reconnecting abandoned patches with intact grasslands through rotational shepherding. We will combine ecological and genetic data to tease apart landscape effects on animal-mediated seed and pollen dispersal in a species of conservation concern. We will use computer simulation experiments to assess landscape determinants of pollen and seed dispersal processes and to simulate the cascading effects of landscape modification on population persistence and population genetic structure. Finally, we will integrate ecological and population genetic data analysis methods in a novel 'spatial component regression' framework that allows the joint statistical modeling and testing of hypotheses about multiple processes that contribute to spatial patterns in the data. Overall, the novel statistical approach will provide much needed integration and development of methods suitable for the complexity of landscape genetic data. These research projects will train six graduate students and five undergraduate students in skills including international fieldwork, molecular genetic analysis, statistical modeling, spatial analysis, and computer simulation modeling.