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Wagenius, S. and R, Shaw. Seedling recruitment in Echinacea angustifolia: a three year experiment revealing the interacting effects of prescribed burns and vegetation. In Review, Restoration Ecology.
Abstract: Prescribed burns and extant vegetation are likely to profoundly affect seedling recruitment, a crucial phase in population establishment. We conducted a three-year experiment to quantify management and environmental effects on seedling recruitment during reintroduction of Echinacea angustifolia to stands of recently planted native grasses and to fields abandoned from agriculture 40 years ago. The experimental design was factorial with four grassland vegetation types and four different regimes of prescribed burning. We collected seeds from remnant prairie populations in western Minnesota USA and overseeded them in study plots each October 2000-2002. We monitored seedling recruitment the following spring in the field and determined germinability of the seeds in the laboratory. Germinability ranged from 20-37% and differed significantly among collection years. In the field, recruitment occurred in every treatment combination but tended to be very low (1-15% of seeds sown). Vegetation type, burn treatment, and year interacted significantly in their effects on seedling recruitment. Prescribed burns during the spring prior to seed sowing tended to enhance recruitment, but to differing degrees depending on the year and vegetation. Burning in the spring after sowing reduced recruitment. Old fields with native warm-season grasses tended to yield the fewest seedlings, of the four vegetation types studied. Strategies to reintroduce this species should include burning in the spring before sowing, sowing large quantities of seed, and avoiding burning in the spring following sowing.
Key words: fire, seedling recruitment, Minnesota, reintroduction, restoration, tallgrass prairie, fragmentation, Echinacea angustifolia

Geyer, C. J., Wagenius, S. and R. G. Shaw. Aster models for life history analysis. In review, Biometrika.
Abstract: We present a new class of statistical models designed for life history analysis of plants and animals. They allow joint analysis of data on survival and reproduction over multiple years, allow for variables having different statistical distributions, and correctly account for the dependence of variables on earlier variables (for example, that a dead individual stays dead and cannot reproduce). We illustrate their utility with an analysis of data taken from an experimental study of Echinacea angustifolia sampled from remnant prarie populations in western Minnesota. Statistically, they are graphical models with some resemblance to generalized linear models and survival analysis. They have directed acyclic graphs with nodes having no more than one parent. The conditional distribution of each node given the parent is a one-parameter exponential family with the parent variable the sample size. The model may be heterogeneous, each node having a different exponential family. We show that the joint distribution is a flat exponential family and derive its canonical parameters, Fisher information, and other properties. These models are implemented in an R package 'aster' available from CRAN.
Key words: Conditional Exponential Family; Curved Exponential Family; Flat Exponential Family; Generalized Linear Model; Graphical Model; Maximum Likelihood; Nuisance Variable
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