59 research outputs found
Phytoplankton evolution during the creation of a biofloc system for shrimp culture
[EN] Microalgae play a key role in the dynamics of biofloc technology aquaculture systems. Some phytoplankton groups, such as diatoms, are desired for their high nutritional value and contribution to water quality. Other groups, such as cyanobacteria, are undesired because of their low nutritional value and capacity of producing toxins. So, monitoring the phytoplankton community structure and succession is key for managing biofloc systems. However, research on phytoplankton in these systems is scarce and mostly done by microscopy. The primary objective of this research was to estimate phytoplankton community structure in shrimp biofloc system water samples, using high-performance liquid chromatography methods and CHEMTAX software. The major groups present in our system were diatoms, euglenophytes, cyanobacteria and chlorophytes, while dinoflagellates were only remarkable at the initial period. We observed a clear dominance of diatoms all along the 5 months that comprised a complete biofloc system culture. The characteristic succession of autotrophic processes by heterotrophs of the biofloc systems, was observed by the reduction of net primary production. Light intensity played a key role in determining the phytoplankton composition and abundance. Algal pigment analyses using high-performance liquid chromatography and subsequent CHEMTAX analysis in water samples was useful for estimating the phytoplankton community structure in the biofloc systems. However, we found some limitations when the biofloc system was in heterotrophic mode. Under these conditions, some dinoflagellates and cyanobacteria behaved as heterotrophs and lost or decreased their biomarkers pigments. So, further research is needed to increase knowledge on the accuracy of high-performance liquid chromatography /CHEMTAX under these conditions.Financial support for this research was provided by Conselleria d’Educació, Investigació, Cultura i Esport of the Generalitat Valenciana, through the program VALi+D, fle number ACIF/2014/244. We would like to express our deepest thanks to Professor Luis Henrique da Silva Poersch of FURG (Universidade Federal do Rio Grande) and Ivan Vidal (Langostinos el Real) for his support. Finally, the authors wish to thank Le Gouessant and Michaël Metz for providing the commercial feed.Llario-Sempere, F.; Rodilla, M.; Escrivá-Perales, J.; Falco, S.; Sebastiá-Frasquet, M. (2018). Phytoplankton evolution during the creation of a biofloc system for shrimp culture. International Journal of Environmental Science and Technology. 1-12. https://doi.org/10.1007/s13762-018-1655-5S112Ahmed A, Kurian S, Gauns M, Chndrasekhararao AV, Mulla A, Naik B, Naik H, Naqvi SWA (2016) Spatial variability in phytoplankton community structure along the eastern Arabian Sea during the onset of south-west monsoon. Cont Shelf Res 119:30–39. https://doi.org/10.1016/j.csr.2016.03.005Avnimelech Y (1999) Carbon/nitrogen ratio as a control element in aquaculture systems. Aquaculture 176:227–235. https://doi.org/10.1016/S0044-8486(99)00085-XAvnimelech Y (2007) Feeding with microbial flocs by tilapia in minimal discharge bio-flocs technology ponds. Aquaculture 264:140–147. https://doi.org/10.1016/j.aquaculture.2006.11.025Avnimelech Y (2009) Biofloc technology. A practical guide book. The World Aquaculture Society, Baton RougeAzim ME, Little DC (2008) The biofloc technology (BFT) in indoor tanks: water quality, biofloc composition, and growth and welfare of Nile tilapia (Oreochromis niloticus). Aquaculture 283:29–35. https://doi.org/10.1016/j.aquaculture.2008.06.036Ballester ELC, Abreu PC, Cavalli RO, Emerenciano M, de Abreu L, Wasielesky WJ (2010) Effect of practical diets with different protein levels on the performance of Farfantepenaeus paulensis juveniles nursed in a zero exchange suspended microbial flocs intensive system. Aquac Nutr 16:163–172. https://doi.org/10.1111/j.1365-2095.2009.00648.xBaloi M, Arantes R, Schveitzer R, Magnotti C, Vinatea L (2013) Performance of Pacific white shrimp Litopenaeus vannamei raised in biofloc systems with varying levels of light exposure. Aquac Eng 52:39–44. https://doi.org/10.1016/j.aquaeng.2012.07.003Baumgarten MGZ, Wallner-Kersanach M, Niencheski LFH (2010) Manual de análises em oceanografia química. Furg, Rio GrandeBecerra-Dórame MJ, Martínez-Córdova LR, Martínez-Porchas M, Lopez-Elías JA (2011) Evaluation of autotrophic and heterotrophic microcosm- based systems on the production response of Litopenaeus vannamei intensively nursed without Artemia and with zero water exchange. Isr J Aquac Bamidgeh 63:7Brito LO, dos Santos IGS, de Abreu JL, de Araújo MT, Severi W, Gàlvez AO (2016) Effect of the addition of diatoms (Navicula spp.) and rotifers (Brachionus plicatilis) on water quality and growth of the Litopenaeus vannamei postlarvae reared in a biofloc system. Aquac Res 47:3990–3997. https://doi.org/10.1111/are.12849Campa-Córdova AI, Núñez-Vázquez EJ, Luna-González A, Romero-Geraldo MJ, Ascencio F (2009) Superoxide dismutase activity in juvenile Litopenaeus vannamei and Nodipecten subnodosus exposed to the toxic dinoflagellate Prorocentrum lima. Comp Biochem Physiol C Toxicol Pharmacol 149:317–322. https://doi.org/10.1016/j.cbpc.2008.08.006Casé M, Leça EE, Leitão SN, SantAnna EE, Schwamborn R, de Moraes Junior AT (2008) Plankton community as an indicator of water quality in tropical shrimp culture ponds. Mar Pollut Bull 56:1343–1352. https://doi.org/10.1016/j.marpolbul.2008.02.008Chen YC (2001) Immobilized microalga Scenedesmus quadricauda (Chlorophyta, Chlorococcales) for long-term storage and for application for water quality control in fish culture. Aquaculture 195:71–80. https://doi.org/10.1016/S0044-8486(00)00540-8Correia ES, Wilkenfeld JS, Morris TC, Wei L, Prangnell DI, Samocha TM (2014) Intensive nursery production of the Pacific white shrimp Litopenaeus vannamei using two commercial feeds with high and low protein content in a biofloc-dominated system. Aquac Eng 59:48–54. https://doi.org/10.1016/j.aquaeng.2014.02.002Duarte CM, Marrasé C, Vaqué D, Estrada M (1990) Counting error and the quantitative analysis of phytoplankton communities. J Plankton Res 12:295–304. https://doi.org/10.1093/plankt/12.2.295Ebeling J, Timmons M, Bisogni J (2006) Engineering analysis of the stoichiometry of photoautotrophic, autotrophic, and heterotrophic removal of ammonia–nitrogen in aquaculture systems. Aquaculture 257:346–358. https://doi.org/10.1016/j.aquaculture.2006.03.019El-Dahhar AA, Salama M, Elebiary EH (2015) Effect of energy to protein ratio in biofloc technology on water quality, survival and growth of mullet (Mugil cephalus). J Arab Aquac Soc 10:15–32. https://doi.org/10.12816/0026633Emerenciano MGC, Martínez-Córdova LR, Martínez-Porchas M, Miranda-Baeza A (2017) Biofloc technology (BFT): a tool for water quality management. In: Tutu H (ed) water quality. InTech, Rijeka. https://doi.org/10.5772/66416Figueroa F, Niell F, Figueiras F, Villarino M (1998) Diel migration of phytoplankton and spectral light field in the Ria de Vigo (NW Spain). Mar Biol 130:491–499Gaona CAP, Poersch LH, Krummenauer D, Foes GK, Wasielesky WJ (2011) The effect of solids removal on water quality, growth and survival of Litopenaeus vannamei in a biofloc technology culture system. Int J Recirc Aquac. https://doi.org/10.21061/ijra.v12i1.1354Garrido JL, Airs RL, Rodríguez F, Van Heukelem L, Zapata M (2011) New HPLC separation techniques. In: Roy S, Llewellyn CA, Egeland ES, Johnsen G (eds) Phytoplankton pigments: characterization, chemotaxonomy, and applications in oceanography. University Press, Cambridge, pp 165–194Ge H, Li J, Chang Z, Chen P, Shen M, Zhao F (2016) Effect of microalgae with semicontinuous harvesting on water quality and zootechnical performance of white shrimp reared in the zero water exchange system. Aquac Eng 72–73:70–76. https://doi.org/10.1016/j.aquaeng.2016.04.006Godoy LC, Odebrecht C, Ballester E, Martins TG, Wasielesky WJ (2012) Effect of diatom supplementation during the nursery rearing of Litopenaeus vannamei (Boone, 1931) in a heterotrophic culture system. Aquac Int 20:559–569. https://doi.org/10.1007/s10499-011-9485-1Grasshoff K (1976) Methods of seawater analysis. Verlag Chemie: Weinstei, New YorkGreen BW, Schrader KK, Perschbacher PW (2014) Effect of stocking biomass on solids, phytoplankton communities, common off-flavors, and production parameters in a channel catfish biofloc technology production system. Aquac Res 45:1442–1458. https://doi.org/10.1111/are.12096Gris B, Sforza E, Morosinotto T, Bertucco A, La Rocca N (2017) Influence of light and temperature on growth and high-value molecules productivity from Cyanobacterium aponinum. J Appl Phycol 29:1781–1790. https://doi.org/10.1007/s10811-017-1133-3Higgins HW, Wright SW, Schlüter L (2011) Quantitative interpretation of chemotaxonomic pigment data. In: Roy S, Llewellyn CA, Egeland ES, Johnsen G (eds) Phytoplankton pigments: characterization, chemotaxonomy, and applications in oceanography. Cambridge University Press, Cambridge, pp 257–313Hooker S, Firestone E, Claustre H, Ras J (2001) The first SeaWiFS HPLC analysis round-robin experiment (SeaHARRE-1). https://ntrs.nasa.gov/search.jsp?R=20010072242 . Accessed 19 July 2017Horabun T (1997) Relationships between water quality and phytoplankton in the Bangpakong river. http://agris.fao.org/agris-search/search.do?recordID=TH2000001898 . Accessed 19 July 2017Ismael AA (2003) Succession of heterotrophic and mixotrophic dinoflagellates as well as autotrophic microplankton in the harbour of Alexandria, Egypt. J Plankton Res 25:193–202. https://doi.org/10.1093/plankt/25.2.193Jeffrey SW, Sielicki M, Haxo FT (1975) Chloroplast pigment patterns in dinoflagellates. J Phycol 11:374–384. https://doi.org/10.1111/j.1529-8817.1975.tb02799.xJeong HJ, Yoo YD, Kim JS, Seong KA, Kang NS, Kim TH (2010) Growth, feeding and ecological roles of the mixotrophic and heterotrophic dinoflagellates in marine planktonic food webs. Ocean Sci J 45:65–91. https://doi.org/10.1007/s12601-010-0007-2Jory DE, Cabrera TR, Dugger DM, Fegan D, Lee PG, Lawrence L, Jackson C, Mcintosh R, Castañeda J, International B, Park H, Hwy N, Pierce F (2001) A global review of shrimp feed management: status and perspectives. Aquaculture 318:104–152Ju ZY, Forster I, Conquest L, Dominy W, Kuo WC, Horgen FD (2008) Determination of microbial community structures of shrimp floc cultures by biomarkers and analysis of floc amino acid profiles. Aquac Res 39:118–133. https://doi.org/10.1111/j.1365-2109.2007.01856.xKingston MB (1999) Effect of light on vertical migration and photosynthesis of Euglena proxima (euglenophyta). J Phycol 35:245–253. https://doi.org/10.1046/j.1529-8817.1999.3520245.xLatasa M, Scharek R, Vidal M, Vila-Reixach G (2010) Preferences of phytoplankton groups for waters of different trophic status in the northwestern Mediterranean Sea. Mar Ecol Prog Ser 40:27–42. https://doi.org/10.3354/meps08559Li Y, Swift E, Buskey EJ (1996) Photoinhibition of mechanically stimulable bioluminescence in the heterotrophic dinoflagellate Protoperidinium depressum (pyrrophyta). J Phycol 32:974–982. https://doi.org/10.1111/j.0022-3646.1996.00974.xLi A, Stoecker D, Adolf J (1999) Feeding, pigmentation, photosynthesis and growth of the mixotrophic dinoflagellate Gyrodinium galatheanum. Aquat Microb Ecol 19:163–176. https://doi.org/10.3354/ame019163Lin YC, Chen JC (2001) Acute toxicity of ammonia on Litopenaeus vannamei (Boone) juveniles at different salinity levels. J Exp Mar Biol Ecol 259:109–119. https://doi.org/10.1016/S0022-0981(01)00227-1Lin YC, Chen JC (2003) Acute toxicity of nitrite on Litopenaeus vannamei (Boone) juveniles at different salinity levels. Aquaculture 224:93–201. https://doi.org/10.1016/S0044-8486(03)00220-5Lohscheider JN, Strittmatter M, Küpper H, Adamska I, Heaney S, Cunningham C (2011) Vertical distribution of epibenthic freshwater cyanobacterial Synechococcus spp. Strains depends on their ability for photoprotection. PLoS ONE. https://doi.org/10.1371/journal.pone.0020134Lukwambe B, Qiuqian L, Wu J, Zhang D, Wang K, Zheng Z (2015) The effects of commercial microbial agents (probiotics) on phytoplankton community structure in intensive white shrimp (Litopenaeus vannamei) aquaculture ponds. Aquac Int 23:1443–1455. https://doi.org/10.1007/s10499-015-9895-6Mackey MD, Mackey DJ, Higgins HW, Wright SW (1996) CHEMTAX—a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Mar Ecol Prog Ser 144:265–283Maicá PF, de Borba MR, Wasielesky WJ (2012) Effect of low salinity on microbial floc composition and performance of Litopenaeus vannamei (Boone) juveniles reared in a zero-water-exchange super-intensive system. Aquac Res 43:361–370. https://doi.org/10.1111/j.1365-2109.2011.02838.xManan H, Moh JHZ, Kasan NA, Suratman S, Ikhwanuddin M (2016) Identification of biofloc microscopic composition as the natural bioremediation in zero water exchange of Pacific white shrimp, Penaeus vannamei, culture in closed hatchery system. Appl Water Sci. https://doi.org/10.1007/s13201-016-0421-4Marinho YF, Brito LO, Campos S, Severi W, Andrade HA, Galvez AO (2016) Effect of the addition of Chaetoceros calcitrans, Navicula sp. and Phaeodactylum tricornutum (diatoms) on phytoplankton composition and growth of Litopenaeus vannamei (Boone) postlarvae reared in a biofloc system. Aquac Res 48:4155–4164. https://doi.org/10.1111/are.13235Martins TG, Odebrecht C, Jensen LV, D’Oca MG, Wasielesky WJ (2016) The contribution of diatoms to bioflocs lipid content and the performance of juvenile Litopenaeus vannamei (Boone, 1931) in a BFT culture system. Aquac Res 47:1315–1326. https://doi.org/10.1111/are.12592Murphy J, Riley JP (1962) A modified single solution method for the determination of phosphate in natural waters. Anal Chim Acta 27:31–36. https://doi.org/10.1016/S0003-2670(00)88444-5Natrah FMI, Bossier P, Sorgeloos P, Yusoff FM, Defoirdt T (2014) Significance of microalgal-bacterial interactions for aquaculture. Rev Aquac 6:48–61. https://doi.org/10.1111/raq.12024Niemi G, Wardrop D, Brooks R, Anderson S, Brady V, Paerl H, Rakocinski C, Brouwer M, Levinson B, McDonald M (2004) Rationale for a new generation of indicators for coastal waters. Environ Health Perspect 112:979–986. https://doi.org/10.1289/ehp.6903Paerl H, Tucker C (1995) Ecology of blue-green algae in aquaculture ponds. J World Aquac 26:109–131. https://doi.org/10.1111/j.1749-7345.1995.tb00235.xPérez-Linares J, Ochoa JL, GagoMartínez A (2008) Effect of PSP toxins in white leg shrimp Litopenaeus vannamei Boone, 1931. J Food Sci 73:T69–T73. https://doi.org/10.1111/j.1750-3841.2008.00710.xPérez-Morales A, Band-Schmidt CJ, Martínez-Díaz SF (2017) Mortality on zoea stage of the Pacific white shrimp Litopenaeus vannamei caused by Cochlodinium polykrikoides (Dinophyceae) and Chattonella spp. (Raphidophyceae). Mar Biol 164:57. https://doi.org/10.1007/s00227-017-3083-3Ray AJ, Dillon KS, Lotz JM (2011) Water quality dynamics and shrimp (Litopenaeus vannamei) production in intensive, mesohaline culture systems with two levels of biofloc management. Aquac Eng 45:127–136. https://doi.org/10.1016/j.aquaeng.2011.09.001Schlüter L, Lauridsen T, Krogh G (2006) Identification and quantification of phytoplankton groups in lakes using new pigment ratios–a comparison between pigment analysis by HPLC and microscopy. Freshwater 51:1474–1485. https://doi.org/10.1111/j.1365-2427.2006.01582.x/fullSchlüter L, Behl S, Striebel M, Stibor H (2016) Comparing microscopic counts and pigment analyses in 46 phytoplankton communities from lakes of different trophic state. Freshw Biol 61:1627–1639. https://doi.org/10.1111/fwb.12803Schrader KK, Green BW, Perschbacher PW (2011) Development of phytoplankton communities and common off-flavors in a biofloc technology system used for the culture of channel catfish (Ictalurus punctatus). Aquac Eng 45:118–126. https://doi.org/10.1016/j.aquaeng.2011.08.004Sebastiá M, Rodilla M (2013) Nutrient and phytoplankton analysis of a Mediterranean Coastal area. Environ Manage 51:225–240. https://doi.org/10.1007/s00267-012-9986-3Sebastiá M, Rodilla M, Sanchis J, Altur V (2012) Influence of nutrient inputs from a wetland dominated by agriculture on the phytoplankton community in a shallow harbour at the Spanish Mediterranean coast. Agric Ecosyst Environ 152:10–20. https://doi.org/10.1016/j.agee.2012.02.006Seoane S, Garmendia M, Revilla M, Borja Á, Franco J, Orive E, Valencia V (2011) Phytoplankton pigments and epifluorescence microscopy as tools for ecological status assessment in coastal and estuarine waters, within the Water Framework. Mar Pollut 62:1484–1497. https://doi.org/10.1016/j.marpolbul.2011.04.010Sinden A, Sinang SC (2016) Cyanobacteria in aquaculture systems: linking the occurrence, abundance and toxicity with rising temperatures. Int J Environ Sci Technol 13:2855–2862. https://doi.org/10.1007/s13762-016-1112-2Sospedra J, Niencheski LFH, Falco S, Andrade CF, Attisano KK, Rodilla M (2017) Identifying the main sources of silicate in coastal waters of the Southern Gulf of Valencia (Western Mediterranean Sea). Oceanologia. https://doi.org/10.1016/j.oceano.2017.07.004Strickland J (1960) Measuring the production of marine phytoplankton. Bull Fish Res Bd Canada 122:172Ter Braak CJF (1994) Canonical community ordination. Part I: basic theory and linear methods. Écoscience 1:127–140. https://doi.org/10.1080/11956860.1994.11682237Ter Braak C, Smilauer P (2002) CANOCO reference manual and CanoDraw for Windows user’s guide: software for canonical community ordination (version 4.5). http://library.wur.nl/WebQuery/wurpubs/wever/341885 . Accessed 19 July 2017Utermohl M (1985) Zur Vervollkommnung der quantitative Phytoplankton-Methodik. Limnologie 9:1–38Van Wyk P, Scarpa J (1999) Water quality requirements and management. In: Institution Harbor Branch Oceanographic (ed) Farming marine shrimp in recirculating freshwater systems. Florida Department of Agriculture and Consumer Services, Florida, pp 128–138Vinatea L, Gálvez AO, Browdy CL, Stokes A, Venero J, Haveman J, Lewis BL, Lawson A, Shuler A, Leffler JW (2010) Photosynthesis, water respiration and growth performance of Litopenaeus vannamei in a super-intensive raceway culture with zero water exchange: interaction of water quality variables. Aquac Eng 42:17–24. https://doi.org/10.1016/j.aquaeng.2009.09.001Wright S, Jeffrey S, Mantoura R (1991) Improved HPLC method for the analysis of chlorophylls and carotenoids from marine phytoplankton. Mar Ecol Prog Ser 77:186–196Yu H, Jia S, Dai Y (2009) Growth characteristics of the cyanobacterium Nostoc flagelliforme in photoautotrophic, mixotrophic and heterotrophic cultivation. J Appl Phycol 21:127–133. https://doi.org/10.1007/s10811-008-9341-5Yusoff FM, Zubaidah MS, Matias HB, Kwan TS (2002) Phytoplankton succession in intensive marine shrimp culture ponds treated with a commercial bacterial product. Aquac Res 33:269–278. https://doi.org/10.1046/j.1355-557x.2002.00671.
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks
The United States COVID-19 Forecast Hub dataset
Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages
Recognizing Affiliation: Using Behavioural Traces to Predict the Quality of Social Interactions in Online Games
Online social interactions in multiplayer games can be supportive and
positive or toxic and harmful; however, few methods can easily assess
interpersonal interaction quality in games. We use behavioural traces to
predict affiliation between dyadic strangers, facilitated through their social
interactions in an online gaming setting. We collected audio, video, in-game,
and self-report data from 23 dyads, extracted 75 features, trained Random
Forest and Support Vector Machine models, and evaluated their performance
predicting binary (high/low) as well as continuous affiliation toward a
partner. The models can predict both binary and continuous affiliation with up
to 79.1% accuracy (F1) and 20.1% explained variance (R2) on unseen data, with
features based on verbal communication demonstrating the highest potential. Our
findings can inform the design of multiplayer games and game communities, and
guide the development of systems for matchmaking and mitigating toxic behaviour
in online games.Comment: CHI '2
The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data
The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe
TRY plant trait database – enhanced coverage and open access
Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives
Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease
BACKGROUND:
Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes.
METHODS:
We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization.
RESULTS:
During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events.
CONCLUSIONS:
Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)
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