Quasireplication differs from true replication in that the latter is, at its most basic level, performed using the same species to test the same hypothesis. Exact replications - also called direct, literal, operational, or constructive - generally entail some notion that the study is a duplication of another study Schmidt, ; Simons, However, this is nearly impossible to achieve for obvious reasons e.
Partial replications involve some procedural modifications while conceptual replications also called instrumental replication test the same hypothesis and predictions using markedly different experimental approaches Schmidt, It is useful to think of exact replications being at one end of the replication spectrum with quasi-replications at the other; partial and conceptual replications occupy the space between these extremes.
The replication of research studies or at least the publication of replications has been rather poor across disciplines in the social and natural sciences despite its need. Not only are scientists failing to conduct or publish replications, but more worryingly, we are failing to replicate original research findings when studies are repeated.
Other bioscience fields of research have shown equally poor replication success. Camerer et al. Perhaps the apparent low success of replication studies stems from the manner in which success is judged. There is no single standard for evaluating replication success Open Science Collaboration, but most replications are deemed successful if they find a result that is statistically significant in the same direction as the result from the original study Simonsohn, This approach has several shortcomings Cumming, not least of which is that our confidence in the original study is unnecessarily undermined when replications are underpowered i.
Rather than asking whether the replication differs from zero, this approach asks whether it differs from the original estimate. This method, however, is poor at detecting false-positives Simonsohn, Simonsohn proposed an alternative approach based on the premise that if an original effect size was seen with a small sample size then it should also be seen with a larger sample size in a replicate study.
If the effect size of the replicate study could not be detected with the sample size of the original study, then the effect is too small to have been reliably detected by the original experiment, and doubt is cast on the original observation.
However, in order to make informed decisions on whether we need to change our views and behaviour toward replications, we need, as a first step, an empirical assessment of their frequency and success in the published literature. My aim in this paper is two-fold. First, I attempt to quantify the frequency of Ecology, Evolution, Behavior, and Systematics studies claiming to be true replications and then compare this rate with that of a general biology open access publication PeerJ.
Second, I calculate the success rate of replications found in these journals. On 4 June , I downloaded as. This resulted in a subset of 38, papers from journals see Supplemental Information for list of journals. Each of these instances was added as a row to a. I eliminated from this group papers published in PLoS Computational Biology because these studies did not empirically test ecological or evolutionary hypotheses with living systems.
Text-mined papers were from non-open access journals e. In order to compare rates of study replication in discipline-specific i. I did not include quasireplications Palmer, or studies that re-analyzed previously published data e. If the article reported on a replicated study, I retrieved the original study and the replication from the literature. By reading the replication study I was able to ascertain whether the authors of the replication deemed their study a successful replication of the original.
I also extracted from the original and replication, where possible, the statistical information e. Therefore, I found that only 0. Examination of PeerJ revealed one replication Holman, out of 3, papers, giving a replication rate of 0. My analysis of the Ecology, Evolution, Behavior, and Systematics literature suggests that approximately 0. I did not subdivide true replications in the current study and so direct comparisons with Kelly are not possible.
I do not know of any study examining whether exact replications are more likely to be called a replication within the paper than a conceptual replication or the likelihhod that an author conducting a true replication will even label their study as such. However, perhaps authors are reticent to claim their study as a replication because the stigma of study replication persists and thus reduces publication success.
Alternatively, perhaps the low rate observed here is due to a higher likelihood of replications being published in multidisciplinary biology journals rather than more targeted sources such as those in Ecology, Evolution, Behavior, and Systematics. Contrary to this prediction, I found no difference between the rate of publication in PeerJ a multidisciplinary bioscience journal and that in Ecology, Evolution, Behavior, and Systematics journals.
Finally, perhaps the rate of replication observed here is not representative of the field as a whole because publication rates of replications might be higher in non-open access Ecology, Evolution, Behavior, and Systematics journals. This seems counter-intuitive, however, as anecdotal evidence suggests that open access journals are expected to have the highest likelihood of publishing a replication.
Given these caveats it is important to emphasize that 0. In the other eight cases, both the replication and original were experiments with qualitative outcomes and did not record quantitative data, or either the original study or replication did not provide the data required to calculate an effect size Table 1. Pasukonis et al. Assessing the effect sizes and confidence intervals supports their conclusion as both effect sizes were positive and neither of the confidence intervals included zero Fig.
These data suggest that father frogs can indeed anticipate the distance they need to travel to deposit their offspring. They found that females from Colorado accepted as mates males from Colorado and Vancouver with equal probability. Figure 2A supports this conclusion as the effect sizes for both the original and replication overlap zero. This formal analysis of three replicated effect sizes shows that the replicate study supported the original conclusion in two-thirds of cases.
This sample size is too small to support sweeping generalizations of the efficacy of replications in Ecology, Evolution, Behavior, and Systematics ; however, that authors provided the relevant information to calculate an effect size in only three of 11 cases is cause for concern.
Abundance varies naturally and is further influenced by differences in sampling technique and effort. Richness differences can result from differences in the number of organisms sampled Figure 2. Thirty nine macroinvertebrate families were recorded from Snipe Island Brook; 29 families from Potash Brook, and 10 families from Centennial Brook.
But over 1, individual macroinvertebrates were sampled from two of the brooks while the Centennial Brook samples contained just individuals.
Are richness differences just sampling artifacts? The Snipe Island Brook and Potash Brook samples were quite comparable and individuals respectively. Having worked hard at identifying macroinvertebrates, most scientists would like to be similarly comfortable comparing the Centennial Brook dataset! This common problem can be addressed using rarefaction Simberloff Removing abundance effects: Rarefaction. Rarefaction answers the question "how many species would a smaller sample include?
The process can be repeated for multiple abundance levels. Resulting curves with abundance on the horizontal axis and taxonomic richness on the vertical axis Figure 4 display the richness expected in sub-samples of any size.
The macroinvertebrates from Centennial Brook included 10 families. Subsamples of individuals from larger samples included 17 families from Potash Brook, and 29 families from Snipe Island Brook. Figure 4. Solid lines are rarefaction curves of macroinvertebrate communities from three Vermont brooks. Just individuals were sampled from Centennial Brook. The vertical line intersects each rarefaction curve at individuals sampled; horizontal lines from those intersections show how many invertebrate families would be sampled if just individuals were sampled from each brook.
Graphing numbers of individuals also facilitates comparisons with rarefaction curves see above. A sample from a community dominated by one species may contain only that dominant species and we might sample many individuals before observing more species.
In even communities, early samples add more species and the graph steps up rapidly before leveling off when most species are accumulated.
Rare species contribute uncertainty to all estimates of overall diversity. Adding three more samples did not add a single new taxon. But the 17th sample added a new group, and three additional families were added by subsequent samples. The families added by later samples were rare groups missed by earlier samples and represented by just one or two individuals.
The frequency of rare species relative to abundant species is used to assess confidence in overall estimates of taxonomic richness Chao Figure 5. Taxonomic richness increases as samples are added. Each symbol represents a new sample; horizontal stretches of graph indicate that no additional families were added by samples. Differences in horizontal spacing of symbols reflect samples with different abundances. Curves from more even communities climb more rapidly.
Log-normal plots. Natural community samples typically include a small number of very common species and a small number of very rare species. The majority of the species in the samples are moderately abundant.
We can represent communities using a histogram with abundance categories on the horizontal axis and numbers of species in each category on the vertical axis. Preston plotted many such histograms using the log2 abundance scale and observed that many communities had a normal distribution of species—hence the lognormal distribution Figure 6.
Very common species e. In a well-sampled community we might expect most species to occur in the middle of the distribution. Even with extensive sampling, the very rarest species may remain undetected. Figure 6. Four species are in the most abundant range with between and individuals recorded. Similarly, there are 4 species represented by 1 or 2 individuals. Most species are moderately abundant as indicated by the peak of the curve occurring near the individuals observed range.
Redrawn from Preston Figure 7. Unlike the example in Figure 6, this plot peaks at the left, indicating that rare families are quite common. Rank abundance curves. Ranking species from most abundant to least provides another useful way to visualize community data.
Using proportions allows for comparisons of samples of different sizes. Plotting on a log scale allows for better data visualization. Rank abundance curves are plotted with rank from most abundant to least on the horizontal axis; log of proportional abundance is on the vertical axis. The last data point corresponds to the number of species observed; the first point shows the degree to which the community is dominated by the most abundant species.
The slope of the declining graph indicates evenness with more even communities producing flatter graphs. Rank abundance curves for Vermont streams Figure 8 show that Snipe Island Brook has more families than Centennial Brook and that the relative abundances of those species are more evenly distributed. We can also see that 0. The most abundant family in Snipe Island Brook represents 0.
Figure 8. Rank abundance curves for contrasting Vermont brooks. Centennial Brook drains mixed urban areas, is impacted by runoff from impervious surfaces, and has just 10 macroinvertebrate families sampled. Snipe Island Brook drains forested land and has 40 macroinvertebrate families sampled. The steeper curve from Centennial Brook indicates a very uneven distribution of relative abundances of the families collected. Potash Brook drains mixed land uses and was sampled downstream from a wooded area.
It is of intermediate species richness and evenness. Appropriately-collected community data can be presented in a number of useful ways to reveal patterns, address questions, and make comparisons.
Data collected to compare sample-scale questions and treatments can later be combined to address habitat-wide questions.
Sampling effort and differential abundance of individuals in samples affects the number of species observed. Rarefaction of data can separate sampling artifacts from real patterns in community data. References and Recommended Reading Chao, A. Estimating the population size for capture-recapture data with unequal catchability. Biometrics 43 , — Colwell, R. Estimating terrestrial biodiversity through extrapolation. Connor, E. Species number and compositional similarity of the Galapagos flora and avifauna.
Ecological Monographs 48 , — Eckblad, J. How many samples should be taken? Bioscience 41 , — Gotelli, N. Quantifying biodiversity: Procedures and pitfalls in the measurement and comparison of species richness.
Ecology Letters 4 , — A Primer of Ecological Statistics. Ecosim: Null models software for ecology. Version 7. Range expansion of a habitat-modifying species leads to loss of taxonomic diversity: A new and impoverished reef state. Oecologia , — Magurran, A. Ecological Diversity and its Measurement. Preston, F. The commonness, and rarity, of species. Ecology 29 , — Pyron, M. Characterizing communities.
Nature Education Knowledge 1 , 20 Simberloff, D. Properties of the rarefaction diversity measurement. The American Naturalist , — Article History Close. Share Cancel. Revoke Cancel. Keywords Keywords for this Article. Save Cancel. Flag Inappropriate The Content is: Objectionable. Flag Content Cancel. Email your Friend. Submit Cancel. This content is currently under construction.
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