BACKGROUND:The Be Active Eat Well (BAEW) community-based child obesity prevention intervention was successful in modestly reducing unhealthy weight gain in primary school children using a multi-strategy and multi-setting approach. OBJECTIVE: To (1) examine the relationship between changes in obesity-related individual, household and school factors and changes in standardised child body mass index (zBMI), and (2) determine if the BAEW intervention moderated these effects. METHODS: The longitudinal relationships between changes in individual, household and school variables and changes in zBMI were explored using multilevel modelling, with measurement time (baseline and follow-up) at level 1, individual (behaviours, n ¼ 1812) at level 2 and households (n ¼ 1318) and schools (n ¼ 18) as higher levels (environments). The effect of the intervention was tested while controlling for child age, gender and maternal education level. RESULTS: This study confirmed that the BAEW intervention lowered child zBMI compared with the comparison group (À0.085 units, P ¼ 0.03). The variation between household environments was found to be a large contributor to the percentage of unexplained change in child zBMI (59%), compared with contributions from the individual (23%) and school levels (1%). Across both groups, screen time (P ¼ 0.03), sweet drink consumption (P ¼ 0.03) and lack of household rules for television (TV) viewing (P ¼ 0.05) were associated with increased zBMI, whereas there was a non-significant association with the frequency the TV was on during evening meals (P ¼ 0.07). The moderating effect of the intervention was only evident for the relationship between the frequency of TV on during meals and zBMI, however, this effect was modest (P ¼ 0.04). CONCLUSIONS: The development of childhood obesity involves multi-factorial and multi-level influences, some of which are amenable to change. Obesity prevention strategies should not only target individual behaviours but also the household environment and family practices. Although zBMI changes were modest, these findings are encouraging as small reductions can have population level impacts on childhood obesity levels.

Chaotic dynamics are thought to be rare in natural populations, but this may be due to methodological and data limitations, rather than the inherent stability of ecosystems. Following extensive simulation testing, we applied multiple chaos detection methods to a global database of 175 population time series and found evidence for chaos in >30%. In contrast, fitting traditional one-dimensional models identified <10% as chaotic. Chaos was most prevalent among plankton and insects and least among birds and mammals. Lyapunov exponents declined with generation time and scaled as the -1/6 power of mass among chaotic populations. These results demonstrate that chaos is not rare in natural populations, indicating that there may be intrinsic limits to ecological forecasting and cautioning against the use of steady-state approaches to conservation and management. MainChaos was introduced to ecology nearly 50 years ago 1,2 to provide an explanation for widespread fluctuations in abundance of natural populations. If common, chaos would offer the promise of short-term predictability while setting hard limits on long-term forecasting 3 . It would also mean that the "stable ecosystem" paradigmthe theoretical justification for linear statistical models of ecological dynamics 4 and steady-state management policies 5would need rethinking. However, despite considerable effort, the evidence for chaos in natural populations remains limited to a handful of examples (e.g. [6][7][8][9] ); the most recent global meta-analysis of chaos concluded that only 1 out of 634 ecological time series was chaotic 10 .

We tested the hypothesis that ocean temperature effects on productivity for northeast Pacific pink (Oncorhynchus gorbuscha), sockeye (Oncorhynchus nerka), and chum salmon (Oncorhynchus keta) changed after 1988–1989, coincident with a decline in Aleutian Low variance. Nonstationary temperature effects were tested with three different analytical methods (correlation, mixed-effects models, and variable coefficient generalized additive models) applied to spawner–recruit time series from 86 wild runs between Puget Sound and the northern Bering Sea. All three methods supported the hypothesis, with evidence for change in temperature effects that was strongest in the Gulf of Alaska, British Columbia, and Washington and weakest in the Bering Sea. Productivity for all three species showed generally positive responses to ocean temperature in Alaska before 1988–1989, but generally neutral responses after 1988–1989. British Columbia and Washington salmon showed either neutral responses to temperature (pink), negative responses that weakened after 1988–1989 (sockeye), or a switch from neutral to negative responses (chum). We conclude that the inverse response of Alaskan and more southern salmon populations to temperature variability is a time-dependent phenomenon.

There has been a recent demand for forecasting in ecology, particularly in the field of ecosystem management. Empirical dynamic modelling (EDM), an equation‐free nonlinear forecasting method, is receiving growing attention, but it requires long time series to produce accurate predictions. Though most ecological time series are short, spatial replicates are often available. Here we explore how utilizing available spatial data can improve our ability to forecast ecological dynamics. There are several ways to incorporate spatial information into EDM and not all have been applied in ecology. We compare spatial EDM approaches used in ecology and physics and introduce a flexible Bayesian model that makes use of prior movement information. We test these methods on simulated data generated with three population dynamics models with varying levels of complexity, time series length, spatial symmetry and heterogeneity. Adding spatial data generally improves accuracy, though the best method depends on the spatial process. We applied the methods to empirical fisheries data, highlighting the complexity of real population dynamics. Leveraging spatial data is an effective way to overcome the problem of short ecological time series. Since the best forecasting method depends on the underlying dynamics, we suggest that users apply several in concert and that this may be useful in identifying spatial heterogeneity in dynamics.