Catchment vegetation and temperature mediating trophic interactions and production in plankton communities.

Climatic factors influence the interactions among trophic levels in an ecosystem in multiple ways. However, whereas most studies focus on single factors in isolation, mainly due to interrelation and correlation among drivers complicating interpretation and analyses, there are still only few studies on how multiple ecosystems respond to climate related factors at the same time. Here, we use a hierarchical Bayesian model with a bioenergetic predator-prey framework to study how different climatic factors affect trophic interactions and production in small Arctic lakes. Natural variation in temperature and catchment land-cover was used as a natural experiment to exemplify how interactions between and production of primary producers (phytoplankton) and grazers (zooplankton) are driven by direct (temperature) and indirect (catchment vegetation) factors, as well as the presence or absence of apex predators (fish). The results show that increased vegetation cover increased phytoplankton growth rate by mediating lake nutrient concentration. At the same time, increased temperature also increased grazing rates by zooplankton. Presence of fish increased zooplankton mortality rates, thus reducing grazing. The Arctic is currently experiencing an increase in both temperature and shrub vegetation cover due to climate change, a trend, which is likely to continue. Our results point towards a possible future general weakening of zooplankton grazing on phytoplankton and greening of arctic lakes with increasing temperatures. At the same time, the impact of the presence of an apex predator indicate considerable local variation in the response. This makes direction and strength of global change impacts difficult to forecast.

Predation and fragmentation portrayed in the statistical structure of prey time series.

BACKGROUND: Statistical autoregressive analyses of direct and delayed density dependence are widespread in ecological research. The models suggest that changes in ecological factors affecting density dependence, like predation and landscape heterogeneity are directly portrayed in the first and second order autoregressive parameters, and the models are therefore used to decipher complex biological patterns. However, independent tests of model predictions are complicated by the inherent variability of natural populations, where differences in landscape structure, climate or species composition prevent controlled repeated analyses. To circumvent this problem, we applied second-order autoregressive time series analyses to data generated by a realistic agent-based computer model. The model simulated life history decisions of individual field voles under controlled variations in predator pressure and landscape fragmentation. Analyses were made on three levels: comparisons between predated and non-predated populations, between populations exposed to different types of predators and between populations experiencing different degrees of habitat fragmentation. RESULTS: The results are unambiguous: Changes in landscape fragmentation and the numerical response of predators are clearly portrayed in the statistical time series structure as predicted by the autoregressive model. Populations without predators displayed significantly stronger negative direct density dependence than did those exposed to predators, where direct density dependence was only moderately negative. The effects of predation versus no predation had an even stronger effect on the delayed density dependence of the simulated prey populations. In non-predated prey populations, the coefficients of delayed density dependence were distinctly positive, whereas they were negative in predated populations. Similarly, increasing the degree of fragmentation of optimal habitat available to the prey was accompanied with a shift in the delayed density dependence, from strongly negative to gradually becoming less negative. CONCLUSION: We conclude that statistical second-order autoregressive time series analyses are capable of deciphering interactions within and across trophic levels and their effect on direct and delayed density dependence.