Model complexity and population predictions. The alpine marmot as a case study

Abstract

Summary1. During the past 15 years, models have been used increasingly in predictive population ecology. Matrix models used for predicting the fates of populations are often extremely basic, ignoring density dependence, spatial scale and behaviour, and often based on one sex only. We tested the importance of some of these omissions for model realism, by comparing the performance of a variety of population models of varying levels of complexity. 2. Detailed data from more than 13 years of behavioural and demographic research on a population of alpine marmots Marmota marmota in Berchtesgaden National Park, southern Germany, were used to parameterize four different population models. The models ranged from a simple population-based matrix model, to a spatially explicit behaviour-based model. 3. The performance of the models was judged by their ability to predict basic population dynamics under equilibrium conditions. Only a spatially explicit individual-based model ignoring optimal behaviour predicted dynamics significantly different to those observed in the field, highlighting the importance of considering realistic patterns of behaviour in spatially explicit models. 4. Using realistic levels of environmental and demographic stochasticity, variance in population growth rates predicted by all models was high, even within the range of population densities experienced in the field. This emphasizes the difficulty of using population-level field data to determine overall patterns of density dependence for use in population models. 5. All models were also used to predict potential density-dependent effects on alpine marmot population growth. In this regard, the models differed greatly. It was concluded that the simplest matrix model was adequate for making predictions regarding population sizes or densities under equilibrium conditions, but that for predictions requiring an understanding of transient dynamics only the behavioural model would be adequate.6. An emergent feature of this study of alpine marmot population dynamics was the prediction of a demographic Allee effect with a profound influence on population dynamics across a very broad range of population sizes. Three mechanisms were identified as underlying this Allee effect: stochastic skews in sex ratio and demographic composition at low population sizes; less efficient social thermoregulation during hibernation in small groups; and difficulties with mate finding during dispersal, even at relatively high population sizes.

Restricted dispersal reduces the strength of spatial density dependence in a tropical bird population

“…This is because the demographic consequences of environmental change will be dictated by the way dispersal reshapes distribution patterns. This argues for the development of mechanistic models of population dynamics, incorporating spatial processes with dispersal, which can then be used for forecasting population responses to environmental change (Stephens et al 2002). It also argues against a phenomenological approach to density dependence which is typical of current population viability models (Henle et al 2004).…”

Modelling the mating system of polar bears: a mechanistic approach to the Allee effect

“…However, our study not only extends their framework to the mating biology of polar bears, but is also the first to compare the predicted pairing dynamics with the observed pairing data, and thus to seek empirical validation for the proposed model structure. This step is crucial for predictive models, which should not only maximize realism, but also accurately fit historical data (Stephens et al 2002;Haefner 2005).…”

Social Information Links Individual Behavior to Population and Community Dynamics

“…Differences between panels are highlighted in red. The straight horizontal arrow represents non-information-mediated mechanisms by which local density affects individual fitness [ 2 7 1 _ T D $ D I F F ] [27,[42][43][44]90,91]. The diagonal arrow represents the effect of social information on local recruitment (e.g., conspecific attraction and/or repulsion) [26,41].…”

“…Mechanisms for negative density dependence include disease spread, competition for finite resources, and interference. Conversely, positive density dependence [27,33,35,36] can be driven not only by the information-mediated mechanisms mentioned above, but also through mechanisms not mediated by social information, such as reductions in abiotic stressors [42][43][44].…”