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Measuring (dis)similarity between ecosystem states is a key theme in ecology. Much of community and ecosystem ecology is devoted to searching for patterns in ecosystem similarity from an external observer's viewpoint, using variables such as species abundances, measures of diversity and complexity. However, from the point of view of organisms in the ecosystem, proportional population growth rates are the only relevant aspect of ecosystem state, because natural selection acts on groups of organisms with different proportional population growth rates. We therefore argue that two ecosystem states are equivalent if and only if, for each species they contain, the proportional population growth rate does not differ between the states. Based on this result, we develop species-level and aggregated summary measures of ecosystem state and discuss their ecological meaning. We illustrate our approach using a long-term dataset on the plankton community from the Central European Lake Constance. We show that the first three principal components of proportional population growth rates describe most of the variation in ecosystem state in Lake Constance. We strongly recommend using proportional population growth rates and the derived equivalence classes for comparative ecosystem studies. This opens up new perspectives on important existing topics such as alternative stable ecosystem states, community assembly, and the processes generating regularities in ecosystems.
Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.