Comparison of Small Mammal Communities in Ephemeral Wetlands and Wet Meadows during Drought

Ephemeral wetlands are characterized by a cyclical hydropattern, ranging from complete inundation to a total absence of surface water. This cycle between wet and dry phases is necessary for the flora of ephemeral wetlands to perpetuate. However, little research has been done to study the response of the non-avian fauna to these cycles, particularly during the dry phase. I live trapped small mammals by using Sherman live traps and conducted vegetation surveys monthly (May–August in 2012 and May–July in 2013) in the ephemeral wetlands and the surrounding wet meadows of the Cheyenne Bottoms basin in central Kansas. Drought occurred both years, leaving the wetlands dry; this allowed small mammal use of wetlands in the dry phase to be documented. Small mammal species richness in the 2 habitats differed by 1 the first year but was equal the second year, although species composition differed. In 2012, population estimates were higher in the wetlands than the wet meadows for Peromyscus maniculatus, Sigmodon hispidus, and Mus musculus, as well as in 2013 for P. maniculatus. Overall small mammal community estimates were higher in the wetlands than the wet meadows in both years. Small mammal survival rates varied by species and habitat. The survival rates of the overall small mammal communities were greater in the wetlands than the wet meadows in 2012, but were comparable between habitats in 2013. In both years, forbs had a higher aerial cover in the wetlands, while grasses had a higher aerial cover in the wet meadows. The height of the standing dead vegetation was taller in the wetlands than the wet meadows in 2012, but showed no difference between habitat types in 2013. These vegetational cover types, coupled with small mammal species interactions, influenced small mammal population estimates and survival rates in the 2 habitats. The vegetational cover types were also the likely reason for finding M. musculus, the additional species, in the wetlands. The process of wetland drawdown in a southern mixed-grass prairie ephemeral wetland greatly affected small mammal communities locally. With small mammals playing a pivotal role in many food webs, it is critical that managers understand the effects of processes, whether natural or man-induced, on small mammal communities

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