Assessing variation in population abundance over time and across space is a long-standing goal of population ecologists. Up to now, two main approaches have been mostly used to identify the factors driving observed fluctuations in population abundance. First, a pattern-oriented approach, based on the monitoring of population size, involves the analysis of time series of counts. In the most recent applications, these analyses lead to partitioning observed changes in population growth into different contributing factors, like current or past population density, environmental conditions, or demographic stochasticity. Second, a process-oriented approach, based on the monitoring of demographic parameters, involves the construction of age- or stage-structured demographic models. The steady increase of case studies aiming to monitor known-aged recognizable animals over most of their lifespan, the availability of statistical methods allowing reliable estimates of demographic parameters to be obtained from field data, and the development of a powerful framework to build a large range of matrix population models have all led to this process-oriented approach becoming a standard tool of population ecologists. It has become the gold standard in the context of both the management of exploited populations and the conservation of endangered populations. However, analyses of detailed monitoring of individuals have also revealed the existence of marked individual differences in most life history traits studied so far, which have been mostly ignored until now when using population-scale demographic modelling. To account for such sources of within-population variation, a trait-based demographic approach is required. Nowadays, Integral Projection Models (IPMs) provide a way to obtain more realistic demographic models that encompass the association between demographic parameters and, for instance, phenotypic traits. In their most extended version, IPMs include the four biological functions that are necessary and sufficient to obtain the distribution of a given continuous trait in a population at a given time from the distribution of the same trait in the same population one time-step before. These functions are the survival function linking survival probability to the trait value, the recruitment function linking the number of recruits to the trait value, the growth function linking the trait value at time t+1 to the trait value at time t, and the inheritance function linking the trait value of the offspring to the trait value of the parents.
Following the British Ecological Society Symposium “Demography Beyond the Population” that was held in Sheffield about one year ago, four papers derived from this symposium have just been published in Journal of Animal Ecology as part of the British Ecological Society Cross Journal Special Feature: Demography Beyond the Population. From the analysis of the contents of these four papers it appears that a new, integrated demography, comes of age.
The contribution by Brooks et al. (2016) targets the relationship between the focal trait and the environmental conditions. These authors aim to quantify how well the past and current environmental effects on demographic rates are represented by body size, and how much variation remains to be explained. They use soil mites as a model organism. From a new method based on Generalized Additive Mixed Models, the authors show that the propensity of individual size to predict reliably environmental conditions varies among demographic parameters. Thus, developmental rates are mostly dependent of body size, whereas reproductive rates are mostly dependent of environmental conditions. The contribution by Childs et al. (2016) start with the observation that the inheritance function is based on phenotypic associations between parents and offspring at different ages, which makes unclear how to model micro-evolutionary dynamics using standard IPMs. Using data from the long-term study of the Great tit population at Wytham Woods, these authors construct a new model to examine the potential for egg-laying date synchrony (measured as the difference between egg-laying date and the date when the larval biomass of winter moth peaks) to evolve. From a new and useful framework, these authors find that the micro-evolutionary dynamics of labile traits and concomitant ecological change can be predicted by integrating quantitative trait information into data-driven structured models. This paper makes clear the importance of collecting longitudinal data to describe reliably variation in the focal trait, in the life history and in demographic parameters. The importance of the quality of the data available to feed an IPM is also pointed out in the contribution by Metcalf et al. (2016), which focus on epidemiological dynamics. Epidemiological dynamics requires the integration of infection, which varies in a complex way both within and among individuals, and of population demography. The authors analyse two case studies (on rodent malaria and on human measles) and demonstrate that IPMs offer a suitable way to study population ecology of infectious diseases. Their two case studies suggest that “IPMs might provide a tractable, data-driven alternative” to partial differential equations commonly used, provided the required data are available. Moreover, IPMs offer a framework for theoretical investigations. Plard et al. (2016) use the IPM framework to quantify the influence of phenotypic variation on population dynamics, by comparing two contrasting case studies (short-lived vs. long-lived species). They assess how population growth rate is influenced by shifts in the mean and variance of a given phenotypic trait. They find that non-linearities in the response of demographic parameters to changes in the trait are more influential for population dynamics than the strength of the response, and that the influence of phenotypic variation on population dynamics is much greater influence in short-lived than in long-lived species.
Although these four contributions address different questions using different biological models, they share the same overall framework of trait-based demography. This common framework demonstrates that an integrated demography has now emerged as a new discipline. Integrated demography allows us to move away from describing demographic patterns using key synthetic metrics such as population growth rate, generation time, or net reproductive rate towards identifying the ecological, life history, and evolutionary processes that generate these patterns. By connecting trait variation with demography, integrated demography offers a bridge between ecology and evolution and appears as an especially suitable approach to model eco-evolutionary dynamics.
Senior Editor, Journal of Animal Ecology