Tradeoffs limit the evolution of male traits that are attractive to females

Tradeoffs occur between a variety of traits in a diversity of organisms, and these tradeoffs can have major effects on ecological and evolutionary processes. Far less is known, however, about tradeoffs between male traits that affect mate attraction than about tradeoffs between other types of traits. Previous results indicate that females of the variable field cricket, Gryllus lineaticeps, prefer male songs with higher chirp rates and longer chirp durations. In the current study, we tested the hypothesis that a tradeoff between these traits affects the evolution of male song. The two traits were negatively correlated among full-sibling families, consistent with a genetically based tradeoff, and the tradeoff was stronger when nutrients were limiting. In addition, for males from 12 populations reared in a common environment, the traits were negatively correlated within populations, the strength of the tradeoff was largely invariant across populations, and the within-population tradeoff predicted how the traits have evolved among populations. A widespread tradeoff thus affects male trait evolution. Finally, for males from four populations assayed in the field, the traits were negatively correlated within and among populations. The tradeoff is thus robust to the presence of environmental factors that might mask its effects. Together, our results indicate there is a fundamental tradeoff between male traits that: (i) limits the ability of males to produce multiple attractive traits; (ii) limits how male traits evolve; and (iii) might favor plasticity in female mating preferences. Includes Supplementary Materials

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press