Variation in carbon and nitrogen concentrations among peatland categories at the global scale

Publisher Copyright: © 2022 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.Peatlands account for 15 to 30% of the world's soil carbon (C) stock and are important controls over global nitrogen (N) cycles. However, C and N concentrations are known to vary among peatlands contributing to the uncertainty of global C inventories, but there are few global studies that relate peatland classification to peat chemistry. We analyzed 436 peat cores sampled in 24 countries across six continents and measured C, N, and organic matter (OM) content at three depths down to 70 cm. Sites were distinguished between northern (387) and tropical (49) peatlands and assigned to one of six distinct broadly recognized peatland categories that vary primarily along a pH gradient. Peat C and N concentrations, OM content, and C:N ratios differed significantly among peatland categories, but few differences in chemistry with depth were found within each category. Across all peatlands C and N concentrations in the 10-20 cm layer, were 440 ± 85.1 g kg-1 and 13.9 ± 7.4 g kg-1, with an average C:N ratio of 30.1 ± 20.8. Among peatland categories, median C concentrations were highest in bogs, poor fens and tropical swamps (446-532 g kg-1) and lowest in intermediate and extremely rich fens (375-414 g kg-1). The C:OM ratio in peat was similar across most peatland categories, except in deeper samples from ombrotrophic tropical peat swamps that were higher than other peatlands categories. Peat N concentrations and C:N ratios varied approximately two-fold among peatland categories and N concentrations tended to be higher (and C:N lower) in intermediate fens compared with other peatland types. This study reports on a unique data set and demonstrates that differences in peat C and OM concentrations among broadly classified peatland categories are predictable, which can aid future studies that use land cover assessments to refine global peatland C and N stocks.Peer reviewe

Plant ecology meets animal cognition: impacts of animal memory on seed dispersal

We propose that an understanding of animal learning and memory is critical to predicting the impacts of animals on plant populations through processes such as seed dispersal, pollination and herbivory. Focussing on endozoochory, we review the evidence that animal memory plays a role in seed dispersal, and present a model which allows us to explore the fundamental consequences of memory for this process. We demonstrate that decision-making by animals based on their previous experiences has the potential to determine which plants are visited, which fruits are selected to be eaten from the plant and where seeds are subsequently deposited, as well as being an important determinant of animal survival. Collectively, these results suggest that the impact of animal learning and memory on seed dispersal is likely to be extremely important, although to date our understanding of these processes suffers from a conspicuous lack of empirical support. This is partly because of the difficulty of conducting appropriate experiments but is also the result of limited interaction between plant ecologists and those who work on animal cognition

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

Monitoring the effects of oil contamination on dissolved organic matter using fluorescence and UV-Vis spectroscopy

Fluorescence and ultraviolet-visible spectroscopy were used to analyze dissolved organic matter (DOM) in Barataria Bay, Louisiana for fluorophores and chromophores that might be derived from crude oil or decomposing vegetation. DOM is the water-soluble portion of natural organic matter (NOM), and it interacts chemically with trace metals and hydrophobic organic compounds in aquatic ecosystems. The present study was undertaken to investigate both the short- and mid-term responses of a coastal marsh ecosystem to contamination from the Deepwater Horizon (DWH MC252) oil spill. Samples were collected from twenty-six oil contaminated and uncontaminated sites in Barataria Bay. Fluorescence excitation emission matrices (EEMs) and UV-Vis spectroscopy were used to determine the overall distribution of fluorophores and chromophores at each site. These data were complemented by dissolved organic carbon measurements

Candidatus Frankia Datiscae Dg1, the Actinobacterial Microsymbiont of Datisca glomerata, Expresses the Canonical nod Genes nodABC in Symbiosis with Its Host Plant

Frankia strains are nitrogen-fixing soil actinobacteria that can form root symbioses with actinorhizal plants. Phylogenetically, symbiotic frankiae can be divided into three clusters, and this division also corresponds to host specificity groups. The strains of cluster II which form symbioses with actinorhizal Rosales and Cucurbitales, thus displaying a broad host range, show suprisingly low genetic diversity and to date can not be cultured. The genome of the first representative of this cluster, Candidatus Frankia datiscae Dg1 (Dg1), a microsymbiont of Datisca glomerata, was recently sequenced. A phylogenetic analysis of 50 different housekeeping genes of Dg1 and three published Frankia genomes showed that cluster II is basal among the symbiotic Frankia clusters. Detailed analysis showed that nodules of Datisca glomerata, independent of the origin of the inoculum, contain several closely related cluster II Frankia operational taxonomic units. Actinorhizal plants and legumes both belong to the nitrogen-fixing plant clade, and bacterial signaling in both groups involves the common symbiotic pathway also used by arbuscular mycorrhizal fungi. However, so far, no molecules resembling rhizobial Nod factors could be isolated from Frankia cultures. Alone among Frankia genomes available to date, the genome of Dg1 contains the canonical nod genes nodA, nodB and nodC known from rhizobia, and these genes are arranged in two operons which are expressed in Datisca glomerata nodules. Furthermore, Frankia Dg1 nodC was able to partially complement a Rhizobium leguminosarum A34 nodC::Tn5 mutant. Phylogenetic analysis showed that Dg1 Nod proteins are positioned at the root of both alpha- and beta-rhizobial NodABC proteins. NodA-like acyl transferases were found across the phylum Actinobacteria, but among Proteobacteria only in nodulators. Taken together, our evidence indicates an Actinobacterial origin of rhizobial Nod factors

Tundra Trait Team : A database of plant traits spanning the tundra biome

Motivation The Tundra Trait Team (TTT) database includes field-based measurements of key traits related to plant form and function at multiple sites across the tundra biome. This dataset can be used to address theoretical questions about plant strategy and trade-offs, trait-environment relationships and environmental filtering, and trait variation across spatial scales, to validate satellite data, and to inform Earth system model parameters. Main types of variable contained Spatial location and grain The database contains 91,970 measurements of 18 plant traits. The most frequently measured traits (> 1,000 observations each) include plant height, leaf area, specific leaf area, leaf fresh and dry mass, leaf dry matter content, leaf nitrogen, carbon and phosphorus content, leaf C:N and N:P, seed mass, and stem specific density. Measurements were collected in tundra habitats in both the Northern and Southern Hemispheres, including Arctic sites in Alaska, Canada, Greenland, Fennoscandia and Siberia, alpine sites in the European Alps, Colorado Rockies, Caucasus, Ural Mountains, Pyrenees, Australian Alps, and Central Otago Mountains (New Zealand), and sub-Antarctic Marion Island. More than 99% of observations are georeferenced. Time period and grain Major taxa and level of measurement All data were collected between 1964 and 2018. A small number of sites have repeated trait measurements at two or more time periods. Trait measurements were made on 978 terrestrial vascular plant species growing in tundra habitats. Most observations are on individuals (86%), while the remainder represent plot or site means or maximums per species. Software format csv file and GitHub repository with data cleaning scripts in R; contribution to TRY plant trait database (www.try-db.org) to be included in the next version release.Peer reviewe