Johan P. Dahlgren

Diversity of ageing across the tree of life

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.

Advancing population ecology with integral projection models: a practical guide

Summary 1. Integral projection models (IPMs) use information on how an individual’s state influences its vital rates – survival, growth and reproduction – to make population projections. IPMs are constructed from regression models predicting vital rates from state variables (e.g. size or age) and covariates (e.g. environment). By combining regressions of vital rates, an IPM provides mechanistic insight into emergent ecological patterns such as population dynamics, species geographic distributions or life-history strategies. 2. Here, we review important resources for building IPMs and provide a comprehensive guide, with extensive R code, for their construction. IPMs can be applied to any stage-structured population; here, we illustrate IPMs for a series of plant life histories of increasing complexity and biological realism, highlighting the utility of various regression methods for capturing biological patterns. We also present case studies illustrating how IPMs can be used to predict species’ geographic distributions and life-history strategies. 3. IPMs can represent a wide range of life histories at any desired level of biological detail. Much of the strength of IPMs lies in the strength of regression models. Many subtleties arise when scaling from vital rate regressions to population-level patterns, so we provide a set of diagnostics and guidelines to ensure that models are biologically plausible. Moreover, IPMs can exploit a large existing suite of analytical tools developed for matrix projection models.

Variation in vegetative and flowering phenology in a forest herb caused by environmental heterogeneity

Timing of seasonal plant development can affect biotic interactions and plant fitness. Phenology is governed largely by temperature and may therefore be affected by global climate warming, making this an important area of research. Several factors in addition to temperature may cause differences in phenology. We studied the influence of local environment, plant size, and reproductive effort on shoot emergence and flowering time of 290 individuals of Actaea spicata (Ranunculaceae), distributed among 25 plots in four populations. We used multiple regression and structural equation models (SEM) to study causal relationships. Among plots, soil temperature and canopy cover explained 63% of the variation in shoot emergence. Soil temperature, slope, and canopy cover together explained 83% of the variation in flowering time. Within plots, small plants on steep south-facing slopes with high soil potassium concentrations emerged earlier in the year. Plants emerging earlier flowered earlier, but no environmental factors affected flowering time directly. We found no effects of reproductive effort. Our results support the view that flowering time of temperate forest herbs is constrained by several environmental factors acting indirectly through effects on shoot emergence time.

Specific leaf area as a superior predictor of changes in field layer abundance during forest succession

Question: How accurately can a suite of suggested functional traits predict plant species response to succession from semiopen woodland to closed deciduous canopy forest? Location: Southeastern Sweden. Methods: Abundance of 46 field-layer plant species in a temperate deciduous forest, measured as frequency of occupied plots, was estimated in 1961, 1970 and 2003. Abundance change over time across species was tested for correlations with functional traits and literature information on habitat preference. Results: Increase in abundance was positively correlated with specific leaf area (SLA), weakly negatively correlated with seed mass and not significantly correlated with plant height or start, peak and length of the flowering period. Change in abundance was correlated with the Ellenberg light indicator value, whereas no correlations were found with Ellenberg values for nitrogen, calcium and moisture, or forest preference according to the literature. Conclusions: SLA was a better predictor of how field layer plants responded to succession from semi-open woodland to closed canopy forest than empirically-derived measures of habitat preference. The same holds for SLA in relation to seed size, indicating that interactions in the established life-cycle phase are more important than the recruitment phase for species response to succession.

Alternative regression methods are not considered in Murtaugh (2009) or by ecologists in general

Murtaugh (2009) recently illustrated that all subsets variable selection is very similar to stepwise regression. This, however, does not necessarily mean both methods are useful. On the contrary, the same problems with overfitting should apply. Ecologists should, if model building is indeed necessary, consider more reliable regression methods now available.

Advancing environmentally explicit structured population models of plants

The relationship between the performance of individuals and the surrounding environment is fundamental in ecology and evolutionary biology. Assessing how abiotic and biotic environmental factors in ...

Novel antagonistic interactions associated with plant polyploidization influence trait selection and habitat preference

Polyploidization is an important mechanism for sympatric speciation in plants. Still, we know little about whether plant polyploidization leads to insect host shifts, and if novel interactions influence habitat and trait selection in plants. We investigated herbivory by the flower bud gall-forming midge Dasineura cardaminis on tetraploids and octoploids of the herb Cardamine pratensis. Gall midges attacked only octoploid plant populations, and a transplantation experiment confirmed this preference. Attack rates were higher in populations that were shaded, highly connected or occurred along stream margins. Within populations, late-flowering individuals with many flowers were most attacked. Galling reduced seed production and significantly influenced phenotypic selection on flower number. Our results suggest that an increase in ploidy may lead to insect host shifts and that plant ploidy explains insect host use. In newly formed plant polyploids, novel interactions may alter habitat preferences and trait selection, and influence the further evolution of cytotypes.

Population size affects vital rates but not population growth rate of a perennial plant

Negative effects of habitat fragmentation on individual performance have been widely documented, but relatively little is known about how simultaneous effects on multiple vital rates translate into effects on population viability in long-lived species. In this study, we examined relationships between population size, individual growth, survival and reproduction, and population growth rate in the perennial plant Phyteuma spicatum. Population size positively affected the growth of seedlings, the survival of juveniles, the proportion of adults flowering, and potential seed production. Analyses with integral projection models, however, showed no relationship between population size and population growth rate. This was due to the fact that herbivores and pathogens eliminated the relationship between population size and seed production, and that population growth rate was not sensitive to changes in the vital rates that varied with population size. We conclude that effects of population size on vital rates must not translate into effects on population growth rate, and that populations of long-lived organisms may partly be able to buffer negative effects of small population size on vital rates that have a relatively small influence on population growth rate. Our study illustrates that we need to be cautious when assessing the consequences of habitat fragmentation for population viability based on effects on only one or a few vital rates.

Climate warming alters effects of management on population viability of threatened species: results from a 30-year experimental study on a rare orchid

Climate change is expected to influence the viability of populations both directly and indirectly, via species interactions. The effects of large-scale climate change are also likely to interact with local habitat conditions. Management actions designed to preserve threatened species therefore need to adapt both to the prevailing climate and local conditions. Yet, few studies have separated the direct and indirect effects of climatic variables on the viability of local populations and discussed the implications for optimal management. We used 30 years of demographic data to estimate the simultaneous effects of management practice and among-year variation in four climatic variables on individual survival, growth and fecundity in one coastal and one inland population of the perennial orchid Dactylorhiza lapponica in Norway. Current management, mowing, is expected to reduce competitive interactions. Statistical models of how climate and management practice influenced vital rates were incorporated into matrix population models to quantify effects on population growth rate. Effects of climate differed between mown and control plots in both populations. In particular, population growth rate increased more strongly with summer temperature in mown plots than in control plots. Population growth rate declined with spring temperature in the inland population, and with precipitation in the coastal population, and the decline was stronger in control plots in both populations. These results illustrate that both direct and indirect effects of climate change are important for population viability and that net effects depend both on local abiotic conditions and on biotic conditions in terms of management practice and intensity of competition. The results also show that effects of management practices influencing competitive interactions can strongly depend on climatic factors. We conclude that interactions between climate and management should be considered to reliably predict future population viability and optimize conservation actions.

Nonlinear relationships between vital rates and state variables in demographic models

To accurately estimate population dynamics and viability, structured population models account for among-individual differences in demographic parameters that are related to individual state. In the widely used matrix models, such differences are incorporated in terms of discrete state categories, whereas integral projection models (IPMs) use continuous state variables to avoid artificial classes. In IPMs, and sometimes also in matrix models, parameterization is based on regressions that do not always model nonlinear relationships between demographic parameters and state variables. We stress the importance of testing for nonlinearity and propose using restricted cubic splines in order to allow for a wide variety of relationships in regressions and demographic models. For the plant Borderea pyrenaica, we found that vital rate relationships with size and age were nonlinear and that the parameterization method had large effects on predicted population growth rates, X (linear IPM, 0.95; nonlinear IPMs, 1.00; matrix model, 0.96). Our results suggest that restricted cubic spline models are more reliable than linear or polynomial models. Because even weak nonlinearity in relationships between vital rates and state variables can have large effects on model predictions, we suggest that restricted cubic regression splines should be considered for parameterizing models of population dynamics whenever linearity cannot be assumed.