A great deal of effort is spent protecting geographically peripheral populations of widespread species. We consider under what conditions it is appropriate to expend resources to protect these populations. The conservation value of peripheral populations depends upon their genetic divergence from other conspecific populations. Peripheral populations are expected to diverge from central populations as a result of the interwoven effects of isolation, genetic drift, and natural selection. Available empirical evidence suggests that peripheral populations are often genetically and morphologically divergent from central populations. The long‐term conservation of species is likely to depend upon the protection of genetically distinct populations. In addition, peripheral populations are potentially important sites of future speciation events. Under some circumstances, conservation of peripheral populations may be beneficial to the protection of the evolutionary process and the environmental systems that are likely to generate future evolutionary diversity.

We present a conceptual framework for choosing native plant material to be used in restoration projects on the basis of ecological genetics. We evaluate both the likelihood of rapid establishment of plants and the probability of long‐term persistence of restored or later successional communities. In addition, we consider the possible harmful effects of restoration projects on nearby ecosystems and their native resident populations. Two attributes of the site to be restored play an important role in determining which genetic source will be most appropriate: (1) degree of disturbance and (2) size of the disturbance. Local plants or plants from environments that “match” the habitat to be restored are best suited to restore sites where degree of disturbance has been low. Hybrids or “mixtures” of genotypes from different sources may provide the best strategy for restoring highly disturbed sites to which local plants are not adapted. Cultivars that have been modified by intentional or inadvertent selection have serious drawbacks. Nevertheless, cultivars may be appropriate when the goal is rapid recovery of small sites that are highly disturbed.

Matrix projection models are among the most widely used tools in plant ecology. However, the way in which plant ecologists use and interpret these models differs from the way in which they are presented in the broader academic literature. In contrast to calls from earlier reviews, most studies of plant populations are based on < 5 matrices and present simple metrics such as deterministic population growth rates. However, plant ecologists also cautioned against literal interpretation of model predictions. Although academic studies have emphasized testing quantitative model predictions, such forecasts are not the way in which plant ecologists find matrix models to be most useful. Improving forecasting ability would necessitate increased model complexity and longer studies. Therefore, in addition to longer term studies with better links to environmental drivers, priorities for research include critically evaluating relative ⁄ comparative uses of matrix models and asking how we can use many short-term studies to understand long-term population dynamics.

Lichen and bryophyte communities differed between managed second-growth and unmanaged old-growth grand fir forests in northwestern Montana in all three strata examined: lower canopy, trunk, and ground. Old-growth forests had larger trees, greater structural diversity, greater volumes of coarse woody debris, fewer species of vascular plants, more species of trunk epiphytes, higher β diversity, and higher γ diversity than second-growth forests. Although pendent fruticose lichens were common in both stand age classes, species of Alectoria were more abundant in old growth, while second growth was dominated by Bryoria spp. Nitrogen-fixing foliose lichens were more common in all strata of old growth, and Lobaria pulmonaria, a common N-fixing species in old growth, was absent in second growth. Cladonia spp. were more numerous in second-growth forests. Nearly all species of leafy liverworts were more common in old growth and typically occurred on rotting wood. Many of these liverworts were absent from second growth. Our results suggest that many species of lichens and bryophytes find optimum habitat in old-growth forests and that these species will become less common as silvicultural practices continue to convert old growth to younger aged forests. Key words: bryophytes, diversity, forests, lichens, Montana, old growth.

Uncertainty associated with ecological forecasts has long been recognized, but forecast accuracy is rarely quantified. We evaluated how well data on 82 populations of 20 species of plants spanning 3 continents explained and predicted plant population dynamics. We parameterized stage-based matrix models with demographic data from individually marked plants and determined how well these models forecast population sizes observed at least 5 years into the future. Simple demographic models forecasted population dynamics poorly; only 40% of observed population sizes fell within our forecasts' 95% confidence limits. However, these models explained population dynamics during the years in which data were collected; observed changes in population size during the data-collection period were strongly positively correlated with population growth rate. Thus, these models are at least a sound way to quantify population status. Poor forecasts were not associated with the number of individual plants or years of data. We tested whether vital rates were density dependent and found both positive and negative density dependence. However, density dependence was not associated with forecast error. Forecast error was significantly associated with environmental differences between the data collection and forecast periods. To forecast population fates, more detailed models, such as those that project how environments are likely to change and how these changes will affect population dynamics, may be needed. Such detailed models are not always feasible. Thus, it may be wiser to make risk-averse decisions than to expect precise forecasts from models.