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, 9 (24), 13991-14004

Accumulating Evidence in Ecology: Once Is Not Enough


Accumulating Evidence in Ecology: Once Is Not Enough

James D Nichols et al. Ecol Evol.


Many published studies in ecological science are viewed as stand-alone investigations that purport to provide new insights into how ecological systems behave based on single analyses. But it is rare for results of single studies to provide definitive results, as evidenced in current discussions of the "reproducibility crisis" in science. The key step in science is the comparison of hypothesis-based predictions with observations, where the predictions are typically generated by hypothesis-specific models. Repeating this step allows us to gain confidence in the predictive ability of a model, and its corresponding hypothesis, and thus to accumulate evidence and eventually knowledge. This accumulation may occur via an ad hoc approach, via meta-analyses, or via a more systematic approach based on the anticipated evolution of an information state. We argue the merits of this latter approach, provide an example, and discuss implications for designing sequences of studies focused on a particular question. We conclude by discussing current data collection programs that are preadapted to use this approach and argue that expanded use would increase the rate of learning in ecology, as well as our confidence in what is learned.

Keywords: Bayes theorem; ecology; evidence; information state; knowledge; replication; reproducibility; science.

Conflict of interest statement

None declared.


Figure 1
Figure 1
Upper panel: population estimates of mid‐continent mallards (in millions) compared to predictions of each member of the model set (SaRw = additive mortality and weakly density‐dependent reproduction, ScRw = compensatory mortality and weakly density‐dependent reproduction, SaRs = additive mortality and strongly density‐dependent reproduction, ScRs = compensatory mortality and strongly density‐dependent reproduction). The gray shading represents 95% confidence intervals for observed population estimates. The arrow represents a weighted mean annual prediction based on the entire model set. Lower panel: annual changes in model weights for each member of the mid‐continent mallard model set; weights were assumed to be equal in 1995
Figure 2
Figure 2
Shannon entropy (Equation 3) computed for varying model weights in the case of two models
Figure 3
Figure 3
The results of an optimal design approach to treatment selection for one time step of an experiment on distributional dynamics. Hypotheses about system dynamics are expressed using four models with differing values for extinction (e) and colonization (c) probabilities: Model 1: e = 0.3, c = 0.3; Model 2: e = 0.3, c = 0.4; Model 3: e = 0.1, c = 0.3; Model 4: e = 0.1, c = 0.4. There are 50 experimental sites, 40 of which are currently occupied. Two treatments are considered: (a) do nothing; or (b) eradicate the species from 20 of the occupied sites. Figure depicts optimal treatment for all possible initial weights for each model, in increments of 0.1

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