Climate change is predicted to cause higher temperatures and increased precipitation, resulting in increased inflow of nutrients to coastal waters in northern Europe. This has been assumed to increase the overall heterotrophy, including enhanced bacterial growth. However, the relative importance of temperature, resource availability and bacterial community composition for the bacterial growth response is poorly understood. In the present study, we investigated effects of increased temperature on bacterial growth in waters supplemented with different nutrient concentrations and inoculated with microbial communities from distinct seasonal periods. Seven experiments were performed in the northern Baltic Sea spanning an entire annual cycle. In each experiment, bacterioplankton were exposed to 2 temperature regimes (in situ and in situ + 4°C) and 5 nutrient concentrations. Generally, elevated temperature and higher nutrient levels caused an increase in the bacterial growth rate and a shortening of the response time (lag phase). However, at the lowest nutrient concentration, bacterial growth was low at all tested temperatures, implying a stronger dependence on resource availability than on temperature for bacterial growth. Furthermore, data indicated that different bacterial assemblages had varying temperature responses and that community composition was strongly affected by the combination of high nutrient addition and high temperature. These results support the concern that climate change will promote heterotrophy in aquatic systems, where nutrient levels will increase considerably. In such environments, the bacterial community composition will change, their growth rates will increase, and their response time will be shortened compared to the present situation.

We introduce the notion of weakly approaching sequences of distributions, which is a generalization of the well-known concept of weak convergence of distributions. The main difference is that the suggested notion does not demand the existence of a limit distribution. A similar definition for conditional (random) distributions is presented. Several properties of weakly approaching sequences are given. The tightness of some of them is essential. The Cramér-Lévy continuity theorem for weak convergence is generalized to weakly approaching sequences of (random) distributions. It has several applications in statistics and probability. A few examples of applications to resampling are given.

Motivated by the analysis of the dependence of knee movement patterns during functional tasks on subject-specific covariates, we introduce a distribution-free procedure for testing a functional-on-scalar linear model with fixed effects. The procedure does not only test the global hypothesis on the entire domain but also selects the intervals where statistically significant effects are detected. We prove that the proposed tests are provided with an asymptotic control of the intervalwise error rate, that is, the probability of falsely rejecting any interval of true null hypotheses. The procedure is applied to one-leg hop data from a study on anterior cruciate ligament injury. We compare knee kinematics of three groups of individuals (two injured groups with different treatments and one group of healthy controls), taking individual-specific covariates into account.

Background and aims: Little is known about health impacts of climate in pre-industrial societies. We used historical data to investigate the association of temperature and precipitation with total and age-specific mortality in Skellefteå, northern Sweden, between 1749 and 1859. Methods: We retrieved digitized aggregated population data of the Skellefteå parish, and monthly temperature and precipitation measures. A generalized linear model was established for year to year variability in deaths by annual and seasonal average temperature and cumulative precipitation using a negative binomial function, accounting for long-term trends in population size. The final full model included temperature and precipitation of all four seasons simultaneously. Relative risks (RR) with 95% confidence intervals (CI) were calculated for total, sex- and age-specific mortality. Results: In the full model, only autumn precipitation proved statistically significant (RR 1.02; CI 1.00–1.03, per 1cm increase of autumn precipitation), while winter temperature (RR 0.98; CI 0.95–1.00, per 1 °C increase in temperature) and spring precipitation (RR 0.98; CI 0.97–1.00 per 1 cm increase in precipitation) approached significance. Similar effects were observed for men and women. The impact of climate variability on mortality was strongest in children aged 3–9, and partly also in older children. Infants, on the other hand, appeared to be less affected by unfavourable climate conditions. Conclusions: In this pre-industrial rural region in northern Sweden, higher levels of rain during the autumn increased the annual number of deaths. Harvest quality might be one critical factor in the causal pathway, affecting nutritional status and susceptibility to infectious diseases. Autumn rain probably also contributed to the spread of air-borne diseases in crowded living conditions. Children beyond infancy appeared most vulnerable to climate impacts.

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