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1. Organisms Composing an Experimental Coral Reef Community from Mo'orea, French Polynesia, Exhibit Taxon-Specific Net Production: Net Calcification Ratios

   https://doi.org/10.3389/fmars.2017.00298  

   Lantz, Coulson A. ; Carpenter, Robert C. ; Comeau, Steeve ; Edmunds, Peter J. ( September 2017 , Frontiers in Marine Science)
2. Taxon‐specific primary production rates on coral reefs in the Florida Keys

   https://doi.org/10.1002/lno.11627  

   Owen, Daniel P. ; Long, Matthew H. ; Fitt, William K. ; Hopkinson, Brian M. ( March 2021 , Limnology and Oceanography)

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3. Detecting Gene Ontology misannotations using taxon-specific rate ratio comparisons

   https://doi.org/10.1093/bioinformatics/btaa548  

   Wei, Xiaoqiong ; Zhang, Chengxin ; Freddolino, Peter L ; Zhang, Yang ; Lu, Zhiyong ( May 2020 , Bioinformatics)

   Abstract Motivation Many protein function databases are built on automated or semi-automated curations and can contain various annotation errors. The correction of such misannotations is critical to improving the accuracy and reliability of the databases. Results We proposed a new approach to detect potentially incorrect Gene Ontology (GO) annotations by comparing the ratio of annotation rates (RAR) for the same GO term across different taxonomic groups, where those with a relatively low RAR usually correspond to incorrect annotations. As an illustration, we applied the approach to 20 commonly-studied species in two recent UniProt-GOA releases and identified 250 potential misannotations in the 2018-11-6 release, where only 25% of them were corrected in the 2019-6-3 release. Importantly, 56% of the misannotations are “Inferred from Biological aspect of Ancestor (IBA)” which is in contradiction with previous observations that attributed misannotations mainly to “Inferred from Sequence or structural Similarity (ISS)”, probably reflecting an error source shift due to the new developments of function annotation databases. The results demonstrated a simple but efficient misannotation detection approach that is useful for large-scale comparative protein function studies. Availability https://zhanglab.ccmb.med.umich.edu/RAR Supplementary information Supplementary data are available at Bioinformatics online. 

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4. Manganese and iron deficiency in Southern Ocean Phaeocystis antarctica populations revealed through taxon-specific protein indicators

   https://doi.org/10.1038/s41467-019-11426-z  

   Wu, Miao ; McCain, J. Scott ; Rowland, Elden ; Middag, Rob ; Sandgren, Mats ; Allen, Andrew E. ; Bertrand, Erin M. ( December 2019 , Nature Communications)
5. Taxon-specific phytoplankton growth, nutrient utilization and light limitation in the oligotrophic Gulf of Mexico

   https://doi.org/10.1093/plankt/fbab028  

   Yingling, Natalia ; Kelly, Thomas B ; Shropshire, Taylor A ; Landry, Michael R ; Selph, Karen E ; Knapp, Angela N ; Kranz, Sven A ; Stukel, Michael R ( April 2021 , Journal of Plankton Research)

   Abstract The highly stratified, oligotrophic regions of the oceans are predominantly nitrogen limited in the surface ocean and light limited at the deep chlorophyll maximum (DCM). Hence, determining light and nitrogen co-limitation patterns for diverse phytoplankton taxa is crucial to understanding marine primary production throughout the euphotic zone. During two cruises in the deep-water Gulf of Mexico, we measured primary productivity (H13CO3−), nitrate uptake (15NO3−) and ammonium uptake (15NH4+) throughout the water column. Primary productivity declined with depth from the mixed layer to the DCM, averaging 27.1 mmol C m−2 d−1. The fraction of growth supported by NO3− was consistently low, with upper euphotic zone values ranging from 0.01 to 0.14 and lower euphotic zone values ranging from 0.03 to 0.44. Nitrate uptake showed strong diel patterns (maximum during the day), whereas ammonium uptake exhibited no diel variability. To parameterize taxon-specific phytoplankton nutrient and light utilization, we used a data assimilation approach (Bayesian Markov Chain Monte Carlo) including primary productivity, nutrient uptake and taxon-specific growth rate measurements. Parameters derived from this analysis define distinct niches for five phytoplankton taxa (Prochlorococcus, Synechococcus, diatoms, dinoflagellates and prymnesiophytes) and may be useful for constraining biogeochemical models of oligotrophic open-ocean systems. 

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