Long-term population dynamics: Theory and reality in a peatland ecosystem

Abstract

Population dynamics is a field rich in theory and poor in longâ term observational data. Finding sources of longâ term data is critical as ecosystems around the globe continue to change in ways that current theories and models have failed to predict. Here we show how longâ term ecological data can improve our understanding about palaeoâ population change in response to external environmental factors, antecedent conditions and community diversity. We examined a radiometrically dated sediment core from the Didachara Mire in the mountains of southâ western Georgia (Caucasus) and analysed multiple biological proxies (pollen, fern spores, nonâ pollen palynomorphs, charcoal, diatoms, chrysophyte cysts, midges, mites and testate amoebae). Numerical techniques, including multivariate ordination, rarefaction, independent splitting and trait analysis, were used to assess the major drivers of changes in community diversity and population stability. Integrated multiâ proxy analyses are very rare in the Caucasus, making this a unique record of longâ term ecological change in a global biodiversity hotspot. Synthesis. Population changes in the terrestrial community coincided primarily with external environmental changes, while populations within the peatland community were affected by both internal and external drivers at different times. In general, our observations accord with theoretical predictions that population increases lead to greater stability and declines lead to instability. Random variation and interspecific competition explain population dynamics that diverged from predictions. Population change and diversity trends were positively correlated in all taxonomic groups, suggesting that populationâ level instability is greater in more diverse communities, even though diverse communities are themselves more stable. There is a continuing need to confront population theory with longâ term data to test the predictive success of theoretical frameworks, thereby improving their ability to predict future change.