Evolution drives, and is driven by, demography. A genotype moulds its phenotype’s age patterns of mortality and fertility in an environment; these two patterns in turn determine the genotype’s fitness in that environment. Hence, to understand the evolution of ageing, age patterns of mortality and reproduction need to be compared for species across the tree of life. However, few studies have done so and only for a limited range of taxa. Here we contrast standardized patterns over age for 11 mammals, 12 other vertebrates, 10 invertebrates, 12 vascular plants and a green alga. Although it has been predicted that evolution should inevitably lead to increasing mortality and declining fertility with age after maturity, there is great variation among these species, including increasing, constant, decreasing, humped and bowed trajectories for both long- and short-lived species. This diversity challenges theoreticians to develop broader perspectives on the evolution of ageing and empiricists to study the demography of more species.

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.

Regressions and meta-regressions are widely used to estimate patterns and effect sizes in various disciplines. However, many biological and medical analyses use relatively low sample size (N), contributing to concerns on reproducibility. What is the minimum N to identify the most plausible data pattern using regressions? Statistical power analysis is often used to answer that question, but it has its own problems and logically should follow model selection to first identify the most plausible model. Here we make null, simple linear and quadratic data with different variances and effect sizes. We then sample and use information theoretic model selection to evaluate minimum N for regression models. We also evaluate the use of coefficient of determination (R 2) for this purpose; it is widely used but not recommended. With very low variance, both false positives and false negatives occurred at N < 8, but data shape was always clearly identified at N � 8. With high variance, accurate inference was stable at N � 25. Those outcomes were consistent at different effect sizes. Akaike Information Criterion weights (AICc w i) were essential to clearly identify patterns (e.g., simple linear vs. null); R 2 or adjusted R 2 values were not useful. We conclude that a minimum N = 8 is informative given very little variance, but minimum N � 25 is required for more variance. Alternative models are better compared using information theory indices such as AIC but not R 2 or adjusted R 2. Insufficient N and R 2-based model selection apparently contribute to confusion and low reproducibility in various disciplines. To avoid those problems, we recommend that research based on regressions or meta-regressions use N � 25.

We analyzed and modeled the demography of Eryngium cuneifolium, an herbaceous species endemic to the fire‐prone Florida scrub, using 10 annual censuses (1990– 1999) of 11 populations at Archbold Biological Station. Nearly every aspect of the demography of this plant is affected by time since fire. Year, time since fire, life history stage, and plant age affected survival, growth, and fecundity of E. cuneifolium, but time since fire and life history stage had the most consistent effects. Survival, flowering stem production, and early seedling survival were highest in recently burned sites. Long‐term survival, growth, and fecundity were highest for yearling cohorts recruiting recently after fire, with the largest contrast between plants recruiting two years postfire and those recruiting more than a decade postfire. Prior (historical) stage also affected individual plant fates. For example, plants with prior stasis or regression in stage subsequently died in greater numbers than plants with prior advancement in stage. Historical analyses did not suggest any cost associated with the initiation of flowering. We used a matrix selection approach to explicitly model Eryngium cuneifolium population viability in relation to fire. This simulation strategy included preserving observed data and variances within projection matrices formed for individual combinations of population and year. We built 54 of these matrices, each with six stages (seed bank, yearlings, vegetative plants, and three reproductive stages). Each of these matrices also represented a specific time since fire. In building matrices, we minimized the use of pooled data while retaining specific matrices whenever possible. In this way, we preserved both the correlation structure within individual matrices (populations, years) and protected patterns among matrices across the time‐since‐fire gradient. To deal with less‐detailed data on recruitment processes, we evaluated 13 fertility and seed bank scenarios that bracketed a range of outcomes. All scenarios were similar in showing the positive effects of fire on the demography of E. cuneifolium. The scenario with high seed bank survival (0.5) and low germination rates (0–0.005) was the best predictor of observed postfire years of peak aboveground population size (∼8 yr) and aboveground population disappearance (30–34 yr), and also did a good job of reproducing observed population trajectories. Finite rates of increase (λ) were >1 only during the first decade postfire but then declined beyond a decade postfire. Although prior (historical) stage affected most individual demographic parameters, it did not significantly influence finite rates of increase. Elasticities were highest for stasis and germination from the seed bank. Elasticities for survival increased with time since fire, while growth and fertility elasticities decreased. In historical models (those with information on stage from the second‐to‐last year), the elasticities for stasis were higher and the elasticities for growth lower, compared to models witho...