The intermediate disturbance hypothesis (IDH) and the dynamic equilibrium model (DEM) are influential theories in ecology. The IDH predicts large species numbers at intermediate levels of disturbance and the DEM predicts that the effect of disturbance depends on the level of productivity. However, various indices of diversity are considered more commonly than the predicted number of species in tests of the hypotheses. This issue reaches beyond the scientific community as the predictions of the IDH and the DEM are used in the management of national parks and reserves. In order to compare responses with disturbance among measures of biodiversity, we used two different approaches of mathematical modelling and conducted an extensive meta-analysis. Two-thirds of the surveyed studies present different results for different diversity measures. Accordingly, the meta-analysis showed a narrow range of negative quadratic regression components for richness, but not evenness. Also, the two models support the IDH and the DEM, respectively, when biodiversity is measured as species richness, but predict evenness to increase with increasing disturbance, for all levels of productivity. Consequently, studies that use compound indices of diversity should present logical arguments, a priori , to why a specific index of diversity should peak in response to disturbance.

Ecological research on algal-derived metabolites with antimicrobial activity has recently received increased attention and is no longer only aimed at identifying novel natural compounds with potential use in applied perspectives. Despite this progress, few studies have so far demonstrated ecologically relevant antimicrobial roles of algal metabolites, and even fewer have utilized molecular tools to investigate the effects of these metabolites on the natural community composition of bacteria. In this study, we investigated whether the red alga Bonnemaisonia asparagoides is chemically defended against bacterial colonization of its surface by extracting surface-associated secondary metabolites and testing their antibacterial effects. Furthermore, we compared the associated bacterial abundance and community composition between B. asparagoides and two coexisting macroalgae. Surface extracts tested at natural concentrations had broad-spectrum effects on the growth of ecologically relevant bacteria, and consistent with this antibacterial activity, natural populations of B. asparagoides had significantly lower densities of epibacteria compared with the coexisting algae. Terminal restriction fragment length polymorphism analysis further showed that B. asparagoides harboured surface-associated bacteria with a community composition that was significantly different from those on coexisting macroalgae. Altogether, these findings demonstrate that B. asparagoides produces surface-bound antibacterial compounds with a significant impact on the abundance and composition of the associated bacterial community.

The value of an ecological indicator is no better than the uncertainty associated with its estimate. Nev-ertheless, indicator uncertainty is seldom estimated, even though legislative frameworks such as theEuropean Water Framework Directive stress that the confidence of an assessment should be quantified.We introduce a general framework for quantifying uncertainties associated with indicators employed toassess ecological status in waterbodies. The framework is illustrated with two examples: eelgrass shootdensity and chlorophyll a in coastal ecosystems. Aquatic monitoring data vary over time and space; vari-ations that can only partially be described using fixed parameters, and remaining variations are deemedrandom. These spatial and temporal variations can be partitioned into uncertainty components operatingat different scales. Furthermore, different methods of sampling and analysis as well as people involved inthe monitoring introduce additional uncertainty. We have outlined 18 different sources of variation thataffect monitoring data to a varying degree and are relevant to consider when quantifying the uncertaintyof an indicator calculated from monitoring data. However, in most cases it is not possible to estimateall relevant sources of uncertainty from monitoring data from a single ecosystem, and those uncertaintycomponents that can be quantified will not be well determined due to the lack of replication at differ-ent levels of the random variations (e.g. number of stations, number of years, and number of people).For example, spatial variations cannot be determined from datasets with just one station. Therefore, werecommend that random variations are estimated from a larger dataset, by pooling observations frommultiple ecosystems with similar characteristics. We also recommend accounting for predictable patternsin time and space using parametric approaches in order to reduce the magnitude of the unpredictablerandom components and reduce potential bias introduced by heterogeneous monitoring across time. Wepropose to use robust parameter estimates for both fixed and random variations, determined from a largepooled dataset and assumed common across the range of ecosystems, and estimate a limited subset ofparameters from ecosystem-specific data. Partitioning the random variation onto multiple uncertaintycomponents is important to obtain correct estimates of the ecological indicator variance, and the magni-tude of the different components provide useful information for improving methods applied and designof monitoring programs. The proposed framework allows comparing different indicators based on theirprecision relative to the cost of monitoring