No abstract

Although the direct effects of eutrophication are well known, its indirect effects are poorly understood and the interaction with non-nutrient factors may alter some expected relationships. We analyzed the reliability of community-level metrics derived from three zooplankton groups as predictors of eutrophication in urban man-made lakes. Univariate and multivariate correlation analyses were used to test for relationships between environmental variables and community metrics derived from zooplankton data. Our results indicated that rotifer community metrics were the best eutrophication indicators. The main implication of our results is that arguments against the use of simple community-level metrics as indicators of eutrophication cannot be generalized. Our findings also suggest the need of complete sample analyses (i.e., identification and counting) to estimate reliable ecological indicators of eutrophication.

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Given the increasing use of systematic reviews and meta‐analyses in ecology, their protocols should be closely followed to ensure quality. Several checklists are available to guide researchers towards a high‐quality meta‐analytic study. Freshwater ecology studies have a tradition of using experimental studies, which provide the ideal data to test hypotheses using meta‐analysis. Here, we evaluated the quality of 114 meta‐analyses in freshwater ecology and 86 meta‐analyses in ecology and evolution for comparative purposes. We found that many studies are still using the term meta‐analysis incorrectly and that this error persisted over time. The quality of the studies that did conduct a formal meta‐analysis has improved. Thus, we speculate that available guidelines are being effective in improving the quality of meta‐analytic studies. Quality was not associated with the impact factor of the journal where the meta‐analyses were published or with the average number of citations. In addition to the incorrect use of the term, we found that many studies failed to: report heterogeneity statistics, evaluate temporal changes in effect size, conduct publication bias analyses, address the collinearity among moderators, and provide the data. In general, meta‐analyses in ecology and evolution have only a slightly better average score than meta‐analyses in freshwater ecology. Although the quality of meta‐analyses in freshwater ecology has improved over time, there is much room for improvement. Authors should not label their studies as meta‐analyses if these methods were not used. Compliance with checklists should be widely fostered as meta‐analyses are increasingly being used to summarise findings in different areas of ecology. Authors, reviewers, and editors should use checklists to improve the quality of meta‐analyses in freshwater ecology.

Ecological studies are increasingly considering phylogenetic relationships among species. The phylogeny is used as a proxy or filter to improve statistical tests and retain evolutionary elements, such as niche conservation. We used the phylogenetic topology to improve the model for occurrence of Trichoptera genera in Cerrado (Brazilian Savanna) streams. We tested whether parameters generated by logistic models of occurrence, using phylogenetic signals, are better than models generated without phylogenetic information. We used a model with Bayesian updating to examine the influence of stream water pH and phylogenetic relationship among genera on the occurrence of Trichoptera genera. Then, we compared this model with the logistic model for each Trichoptera genus. The probability of occurrence of most genera increased with water pH, and the phylogeny‐based explicit logistic model improved the parameters estimated for observed genera. The inferred relationship between genera occurrence and stream pH improved, indicating that phylogeny adds relevant information when estimating ecological responses of organisms. Water with elevated acidity (low pH values) may be restrictive for the occurrence of Trichoptera larvae, especially if the regional streams exhibit neutral to alkaline water, as is observed in the Cerrado region. Using phylogeny‐based modeling to predict species occurrence is a prominent opportunity to extend our current statistical framework based on environmental conditions, as it enables a more precise estimation of ecological parameters.