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"Ruxton, Graeme D."
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Nature's giants : the biology and evolution of the world's largest lifeforms
\"The colossal plants and animals of our world-dinosaurs, whales, and even trees-are a source of unending fascination, and their sheer scale can be truly impressive. Size is integral to the way that organisms experience the world: a puddle that a human being would step over without thinking is an entire world to thousands of microscopic rotifers. But why are creatures the size that they are? Why aren't bugs the size of elephants, or whales the size of goldfish? In this lavishly illustrated new book, biologist Graeme Ruxton explains how and why nature's giants came to be so big-for example, how decreased oxygen levels limited the size of insects and how island isolation allowed small-bodied animals to evolve larger body sizes. Through a diverse array of examples, from huge butterflies to giant squid, Ruxton explores the physics, biology, and evolutionary drivers behind organism size, showing what it's like to live large\"-- Provided by publisher.
Book
Linking the evolution and form of warning coloration in nature
Many animals are toxic or unpalatable and signal this to predators with warning signals (aposematism). Aposematic appearance has long been a classical system to study predator–prey interactions, communication and signalling, and animal behaviour and learning. The area has received considerable empirical and theoretical investigation. However, most research has centred on understanding the initial evolution of aposematism, despite the fact that these studies often tell us little about the form and diversity of real warning signals in nature. In contrast, less attention has been given to the mechanistic basis of aposematic markings; that is, ‘what makes an effective warning signal?’, and the efficacy of warning signals has been neglected. Furthermore, unlike other areas of adaptive coloration research (such as camouflage and mate choice), studies of warning coloration have often been slow to address predator vision and psychology. Here, we review the current understanding of warning signal form, with an aim to comprehend the diversity of warning signals in nature. We present hypotheses and suggestions for future work regarding our current understanding of several inter-related questions covering the form of warning signals and their relationship with predator vision, learning, and links to broader issues in evolutionary ecology such as mate choice and speciation.
Journal Article
Circular data in biology: advice for effectively implementing statistical procedures
Circular data are common in biological studies. The most fundamental question that can be asked of a sample of circular data is whether it suggests that the underlying population is uniformly distributed around the circle, or whether it is concentrated around at least one preferred direction (e.g. a migratory goal or activity phase). We compared the statistical power of five commonly used tests (the Rayleigh test, the V-test, Watson's test, Kuiper's test and Rao's spacing test) across a range of different unimodal scenarios. The V-test showed higher power for symmetrical distributions, Rao's spacing performed worst for all explored unimodal distributions tested and the remaining three tests showed very similar performance. However, the V-test only applies if the hypothesis is restricted to one (pre-specified) direction of interest. In all other unimodal cases, we recommend using the Rayleigh test. Much less explored is the multimodal case with data concentrated around several directions. We performed power simulations for a variety of multimodal situations, testing the performance of the widely used Rayleigh, Rao's, Watson, and Kuiper's tests as well as the more recent Bogdan and Hermans-Rasson tests. Our analyses of alternative statistical methods show that the commonly used tests lack statistical power in many of multimodal cases. Transformation of the raw data (e.g. doubling the angles) can overcome some of the issues, but only in the case of perfect f-fold symmetry. However, the Hermans-Rasson method, which is not yet implemented in any software package, outcompetes the alternative tests (often by substantial margins) in most of the multimodal situations explored. We recommend the wider uptake of the powerful but hitherto neglected Hermans-Rasson method. In summary, we provide guidance for biologists helping them to make decisions when testing circular data for single or multiple departures from uniformity.
Journal Article
Advice on comparing two independent samples of circular data in biology
Many biological variables are recorded on a circular scale and therefore need different statistical treatment. A common question that is asked of such circular data involves comparison between two groups: Are the populations from which the two samples are drawn differently distributed around the circle? We compared 18 tests for such situations (by simulation) in terms of both abilities to control Type-I error rate near the nominal value, and statistical power. We found that only eight tests offered good control of Type-I error in all our simulated situations. Of these eight, we were able to identify the Watson’s U
2
test and a MANOVA approach, based on trigonometric functions of the data, as offering the best power in the overwhelming majority of our test circumstances. There was often little to choose between these tests in terms of power, and no situation where either of the remaining six tests offered substantially better power than either of these. Hence, we recommend the routine use of either Watson’s U
2
test or MANOVA approach when comparing two samples of circular data.
Journal Article
A note on the Wilcoxon-Mann-Whitney test and tied observations
Recently, it was recommended to omit tied observations before applying the two-sample Wilcoxon-Mann-Whitney test McGee M. et al. (2018). Using a simulation study, we argue for exact tests using all the data (including tied values) as a preferable approach. Exact tests, with tied observations included guarantee the type I error rate with a better exploitation of the significance level and a larger power than the corresponding tests after the omission of tied observations. The omission of ties can produce a considerable change in the shape of the sample, and so can violate underlying test assumptions. Thus, on both theoretical and practical grounds, the recommendation to omit tied values cannot be supported, relative to analysing the whole data set in the same way whether or not ties occur, preferably with an exact permutation test.
Journal Article
The Hermans–Rasson test as a powerful alternative to the Rayleigh test for circular statistics in biology
Background
Circular data are gathered in diverse fields of science where measured traits are cyclical in nature: such as compass directions or times of day. The most common statistical question asked of a sample of circular data is whether the data seems to be drawn from a uniform distribution or one that is concentrated around one or more preferred directions. The overwhelmingly most-popular test of the null hypothesis of uniformity is the Rayleigh test, even though this test is known to have very low power in some circumstances. Here we present simulation studies evaluating the performance of tests developed as alternatives to the Rayleigh test.
Results
The results of our simulations demonstrate that a single test, the Hermans and Rasson test is almost as powerful as the Rayleigh test in unimodal situations (when the Rayleigh test does well) but substantially outperforms the Rayleigh test in multimodal situations.
Conclusion
We recommend researchers switch to routine use of the new Hermans and Rasson test. We also demonstrate that all available tests have low power to detect departures from uniformity involving more than two concentrated regions: we recommend that where researchers suspect such complex departures that they collect substantially-sized samples and apply another recent test due to Pycke that was designed specifically for such complex cases. We provide clear textual descriptions of how to implement each of these recommended tests and encode them in R functions that we provide.
Journal Article
Functional and evolutionary synergy of trait components can explain the existence of leaf masquerade in katydids
One of the most enduring mysteries in biology concerns the evolution of complex adaptations made up of interacting component traits. When these component traits do not enhance fitness independently of one another, their origin requires that they evolve sequentially through intermediate steps that do not produce their full adaptive value as a combined trait, or alternatively, that they arise via simultaneous, synergistic evolution. We tested these alternatives using the powerful but accessible example of leaf masquerade in katydids, where in some species, highly modified wings strikingly mimic vegetation to avoid predator recognition. Combining a field predation experiment with a phylogenetic comparative analysis of wing morphology in 58 Neotropical katydid species, we show that color and shape synergistically interact to enhance survival in the wild, and modifications in both traits evolved concurrently during diversification of this clade. Our findings identify the adaptive value of masquerade camouflage in the wild and show how concordant evolutionary change in separate traits—evolutionary synergy—can generate extraordinarily specialized, multi-component adaptations.
Journal Article
On the variety of methods for calculating confidence intervals by bootstrapping
1. Researchers often want to place a confidence interval around estimated parameter values calculated from a sample. This is commonly implemented by bootstrapping. There are several different frequently used bootstrapping methods for this purpose. 2. Here we demonstrate that authors of recent papers frequently do not specify the method they have used and that different methods can produce markedly different confidence intervals for the same sample and parameter estimate. 3. We encourage authors to be more explicit about the method they use (and number of bootstrap resamples used). 4. We recommend the bias corrected and accelerated method as giving generally good performance; although researchers should be warned that coverage of bootstrap confidence intervals is characteristically less than the specified nominal level, and confidence interval evaluation by any method can be unreliable for small samples in some situations.
Journal Article
Advanced circular statistics in biology: Multiple factors, interactions and repeated measures
Circular data is common across biology and the wider sciences, but presents unique analytical challenges due to their wrapped structure, where endpoints coincide (e.g. 360° = 0°). This requires the use of specific statistical methods. Traditional tests like the Rayleigh and Watson U2 tests remain widely used, but lack flexibility in handling multiple linear and categorical factors or repeated measures. This study aims to fill this gap by leveraging trigonometric transformations of a circular response variable to enable the use of Multivariate Analysis of Variance (MANOVA) and linear mixed models (LMMs) with circular data. Approach and Methods. We compared the performance of MANOVA and LMM against traditional methods across various simulated and real‐world scenarios, including data with multiple co‐factors and repeated measures. Simulations were conducted using different circular distributions, sample sizes and configurations to evaluate type 1 error rates, and statistical power to detect group differences, overall non‐uniform orientation and interactions. In practical examples, we re‐analysed published data of shark predatory approaches and Emlen funnel experiments on bird orientation. Main Results. The MANOVA and LMM demonstrated robust control of type 1 errors across scenarios. The MANOVA consistently exhibited higher power than traditional methods for detecting non‐uniform orientation and group interactions in complex designs, particularly for directional differences. LMMs showed strengths in detecting deviations from uniformity, especially for unimodal distributions or symmetrical group differences. Practical examples highlighted the superiority of these methods over traditional tests in identifying nuanced effects, such as the combined influences of sun and wind direction on shark predatory angles and the effects of the test sequence on bird orientation. Conclusions and Implications. This study provides evidence supportive of a transformative expansion of the circular statistical toolkit by adapting linear statistical frameworks for circular data. MANOVA and LMMs enable researchers to model multiple random factors, covariates and grouping variables, offering substantial improvements over traditional methods. These advancements hold promise for broader applications, enhancing the power and versatility of circular data analysis in diverse biological and ecological contexts.
Journal Article