Thermal sensitivity of growth indicates heritable variation in 1-year-old rainbow trout (Oncorhynchus mykiss)

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Drastic demographic events triggered the Uralic spread

The widespread Uralic family offers several advantages for tracing prehistory: a firm absolute chronological anchor point in an ancient contact episode with well-dated Indo-Iranian; other points of intersection or diagnostic non-intersection with early Indo-European (the Late Proto-Indo-European-speaking Yamnaya culture of the western steppe, the Afanasievo culture of the upper Yenisei, and the Fatyanovo culture of the middle Volga); lexical and morphological reconstruction sufficient to establish critical absences of sharings and contacts. We add information on climate, linguistic geography, typology, and cognate frequency distributions to reconstruct the Uralic origin and spread. We argue that the Uralic homeland was east of the Urals and initially out of contact with Indo-European. The spread was rapid and without widespread shared substratal effects. We reconstruct its cause as the interconnected reactions of early Uralic and Indo-European populations to a catastrophic climate change episode and interregionalization opportunities which advantaged riverine hunter-fishers over herders.Peer reviewe

Genetic (co)variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environments

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Esiselvitys 3D-kameratekniikan ja koneoppimisen hyödyntämisestä suomalaisessa kalan- kasvatuksessa

Tehokas kalankasvatus edellyttää tarkkaa ajantasaista tietoa kalojen lukumäärästä, aktiivisuudesta, terveydentilasta ja kasvusta (biomassasta). Tämä mahdollistaa erilaisten toimenpiteiden, kuten ruokinnan, lajittelun, kasvatustiheyksien ja lopulta perkuuajankohdan optimaalisen hallinnan. Videovalvonnan ja -ohjauksen käyttö on lisääntynyt merkittävästi kalankasvatuksen seurannassa ja tuotannonohjauksessa. Myös tietokoneavusteiset kuva-tai videoanalyysit ovat kehittyneet viimeisten kolmen vuosikymmenen aikana, ja ne ovat avainroolissa kasvatettavien kalastojen automaattisessa, ilman ihmistä tapahtuvassa mittaamisessa ja analysoinnissa. Tärkeimmät käytännön sovellukset liittyvät kasvatettavan kalaston ruokinnan ja biomassan seuraamiseen mutta myös kalaterveyteen ja välineiden kunnossapitoon. Kalankasvatusmarkkinoille on kehitetty jonkin aikaa muun muassa 3D-videoseurantaan perustuvia biomassalaskureita, mutta toistaiseksi niiden tarkkuus ei ole ollut välttämättä riittävä. Suomen sameissa rannikkovesissä laskureista ei ole raportoituja käyttökokemuksia, eivätkä sovellukset huomioi kotimaassa kasvatettavia kalalajeja. Laskurit ovat myös verrattain kalliita, jolloin kilpailukykyisten sovellusten kehittäminen voisi lisätä niiden käyttöä ja hyötyjä. Tähän hankeraporttiin koottiin esiselvitys: • Tämänhetkisistä seurantasovelluksista kalankasvatuksessa • Kolmiulotteisen (3D) kuvamateriaalin soveltuvuudesta ja jatkokehitysmahdollisuuksista kasvatettavien kalojen seurannassa kotimaiset erityisolosuhdevaatimukset ja tekoälyn omat reunaehdot huomioiden. Kuvauskokeiluissa ruokailevista kaloista (kirjolohi ja siika) saatiin liikedataa, josta pystyttiin toteamaan niiden aktiviteetin muutoksia. Datamäärät jäivät kuitenkin pieneksi varsinaista mallintamista ajatellen. Myös kuhakasvatusta kuvattiin, mutta samean veden ja kuhan passiivisen ruokailukäyttäytymisen takia niiden liikeaktiivisuutta ei havaittu. Kalojen pituudesta saatiin luotettavia metrisiä mittaustuloksia 3D-kameradatan avulla, joskaan näytemäärä ei ollut tässäkään tapauksessa suuri suoraan biomassan arviointiin. Tulosten perusteella 3D-kuvasta saatuja pituusmittoja voitaisiin käyttää kalojen kasvun tarkempaan arviointiin ilman invasiivisia (stressaavia) välimittauksia. Hanke on toteutettu yritysyhteistyönä Luken koordinoimassa ja Euroopan meri-ja kalatalousrahaston rahoittamassa vesiviljelyn innovaatio-ohjelmassa vuonna 2018.201

Do whitefish (Coregonus lavaretus) larvae show adaptive variation in the avoidance of microplastic ingestion?

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Genetics of Microenvironmental Sensitivity of Body Weight in Rainbow Trout (Oncorhynchus mykiss) Selected for Improved Growth

Microenvironmental sensitivity of a genotype refers to the ability to buffer against non-specific environmental factors, and it can be quantified by the amount of residual variation in a trait expressed by the genotype’s offspring within a (macro)environment. Due to the high degree of polymorphism in behavioral, growth and life-history traits, both farmed and wild salmonids are highly susceptible to microenvironmental variation, yet the heritable basis of this characteristic remains unknown. We estimated the genetic (co)variance of body weight and its residual variation in 2-year-old rainbow trout (Oncorhynchus mykiss) using a multigenerational data of 45,900 individuals from the Finnish national breeding programme. We also tested whether or not microenvironmental sensitivity has been changed as a correlated genetic response when genetic improvement for growth has been practiced over five generations. The animal model analysis revealed the presence of genetic heterogeneity both in body weight and its residual variation. Heritability of residual variation was remarkably lower (0.02) than that for body weight (0.35). However, genetic coefficient of variation was notable in both body weight (14%) and its residual variation (37%), suggesting a substantial potential for selection responses in both traits. Furthermore, a significant negative genetic correlation (−0.16) was found between body weight and its residual variation, i.e., rapidly growing genotypes are also more tolerant to perturbations in microenvironment. The genetic trends showed that fish growth was successfully increased by selective breeding (an average of 6% per generation), whereas no genetic change occurred in residual variation during the same period. The results imply that genetic improvement for body weight does not cause a concomitant increase in microenvironmental sensitivity. For commercial production, however, there may be high potential to simultaneously improve weight gain and increase its uniformity if both criteria are included in a selection index

Pan-Eurasian Experiment (PEEX) : towards a holistic understanding of the feedbacks and interactions in the land–atmosphere–ocean–society continuum in the northern Eurasian region

Contributors: Hanna K. Lappalainen1,2, Veli-Matti Kerminen1, Tuukka Petäjä1, Theo Kurten3, Aleksander Baklanov4,5, Anatoly Shvidenko6, Jaana Bäck7, Timo Vihma2, Pavel Alekseychik1, Stephen Arnold8, Mikhail Arshinov9, Eija Asmi2, Boris Belan9, Leonid Bobylev10, Sergey Chalov11, Yafang Cheng12, Natalia Chubarova11, Gerrit de Leeuw1,2, Aijun Ding13, Sergey Dobrolyubov11, Sergei Dubtsov14, Egor Dyukarev15, Nikolai Elansky16, Kostas Eleftheriadis17, Igor Esau18, Nikolay Filatov19, Mikhail Flint20, Congbin Fu13, Olga Glezer21, Aleksander Gliko22, Martin Heimann23, Albert A. M. Holtslag24, Urmas Hõrrak25, Juha Janhunen26, Sirkku Juhola27, Leena Järvi1, Heikki Järvinen1, Anna Kanukhina28, Pavel Konstantinov11, Vladimir Kotlyakov29, Antti-Jussi Kieloaho1, Alexander S. Komarov30, Joni Kujansuu1, Ilmo Kukkonen31, Ella Kyrö1, Ari Laaksonen2, Tuomas Laurila2, Heikki Lihavainen2, Alexander Lisitzin32, Aleksander Mahura5, Alexander Makshtas33, Evgeny Mareev34, Stephany Mazon1, Dmitry Matishov35,†, Vladimir Melnikov36, Eugene Mikhailov37, Dmitri Moisseev1, Robert Nigmatulin33, Steffen M. Noe38, Anne Ojala7, Mari Pihlatie1, Olga Popovicheva39, Jukka Pumpanen40, Tatjana Regerand19, Irina Repina16, Aleksei Shcherbinin27, Vladimir Shevchenko33, Mikko Sipilä1, Andrey Skorokhod16, Dominick V. Spracklen8, Hang Su12, Dmitry A. Subetto19, Junying Sun41, Arkady Yu Terzhevik19, Yuri Timofeyev42, Yuliya Troitskaya34, Veli-Pekka Tynkkynen42, Viacheslav I. Kharuk43, Nina Zaytseva22, Jiahua Zhang44, Yrjö Viisanen2, Timo Vesala1, Pertti Hari7, Hans Christen Hansson45, Gennady G. Matvienko9, Nikolai S. Kasimov11, Huadong Guo44, Valery Bondur46, Sergej Zilitinkevich1,2,11,34, and Markku Kulmala1 1Department of Physics, University of Helsinki, 00014 Helsinki, Finland 2Finnish Meteorological Institute, Research and Development, 00101 Helsinki, Finland 3Department of Chemistry, University of Helsinki, 00014 Helsinki, Finland 4World Meteorological Organization, 1211 Genève, Switzerland 5Danish Meteorological Institute, Research and Development Department, 2100, Copenhagen 6International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria 7Department of Forest Sciences, University of Helsinki, 00014 Helsinki, Finland 8Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK 9Institute of Atmospheric Optics, Russian Academy of Sciences, Tomsk 634021, Russia 10Nansen International Environmental and Remote Sensing Center, St. Petersburg, Russia 11Lomonosov Moscow State University, Faculty of Geography, Moscow 119899, Russia 12Max Planck Institute for Chemistry, 55128 Mainz, Germany 13Institute for Climate and Global Change Research & School of Atmospheric Sciences, Nanjing University, 210023 Nanjing, China 14Institute of Chemical Kinetics & Combustion, Russian Academy of Sciences, 630090 Novosibirsk, Russia 15Institute of Monitoring of Climatic & Ecological Systems SB RAS, 634055 Tomsk, Russia 16A. M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Russia 17National Centre of Scientific Research "DEMOKRITOS", Greece 18Nansen Environmental and Remote Sensing Center/Bjerknes Centre for Climate Research, 5006 Bergen, Norway 19Northern Water Problems Institute, Karelian Research Center, Russian Academy of Sciences,185003 Petrozavodsk, Russia 20P. P. Shirshov, Institute of Oceanology, Russian Academy of Sciences, Russian Academy of Sciences, 117997 Moscow, Russia 21Institute of Geography, Russian Academy of Sciences, Moscow, Russia 22Depart ment of Earth Sciences of the Russian Academy of Sciences, Russian Academy of Sciences, 119991, Moscow, Russia 23Max-Planck-Institute for Biogeochemistry, 07745 Jena, Germany 24Wageningen University, 6708 Wageningen, Nederland 25Institute of Physics, University of Tartu, 18 Ülikooli St., 50090 Tartu, Estonia 26University of Helsinki, Department of World Cultures, 00014 Helsinki, Finland 27Department of Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland 28Russian State Hydrometeorological University, 195196 Saint Petersburg, Russia 29Institute of Geography, Russian Academy of Sciences, Moscow, Russia 30Institute of Physico-chemical & Biological Problems in Soil Science, Russian Academy of Sciences, 142290 Institutskaya, Russia 31University of Helsinki, Geophysics and Astronomy, 00014 Helsinki, Finland 32Shirshov Institute of Oceanology, Russian Academy of Sciences, 117997 Moscow, Russia 33Actic & Antarctic Research Institute, Russian Academy of Sciences, St. Petersburg 199397, Russia 34Department of Radiophysics, Nizhny Novgorod State University, Nizhny Novgorod, Russia 35Southern Center of Russian Academy of Sciences, Rostov on Don, Russia 36Tyumen Scientific Center, Siberian Branch, Russian Academy of Science, Russia 37Saint Petersburg State University, 7/9 Universitetskaya nab., St. Petersburg, 199034 Russia 38Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, 51014 Tartu, Estonia 39Skobeltsyn Institute of Nuclear Physics, Moscow State University, Department Microelectronics, Russia 40University of Eastern Finland, Department of Environmental Science, P.O. Box 1627, FI-70211 Kuopio, Finland 41Craduate University of Chinese Academy of Sciences, 100049 Beijing, China 42Aleksanteri Institute and Department of Social Research, 00014 University of Helsinki, Finland 43Sukachev Forest Institute, Russian Academy of Sciences, Krasnoyarsk 660036, Russia 44Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100094, China 45Environmental Science and Analytical Chemistry, Stockholm University, Sweden 46AEROCOSMOS Research Institute for Aerospace Monitoring, 105064, Moscow, Russia †deceased, 20 August 2015The Northern Eurasian regions and Arctic Ocean will very likely undergo substantial changes during the next decades. The arctic-boreal natural environments play a crucial role in the global climate via the albedo change, carbon sources and sinks, as well as atmospheric aerosol production via biogenic volatile organic compounds. Furthermore, it is expected that the global trade activities, demographic movement and use of natural resources will be increasing in the Arctic regions. There is a need for a novel research approach, which not only identifies and tackles the relevant multi-disciplinary research questions, but is also able to make a holistic system analysis of the expected feedbacks. In this paper, we introduce the research agenda of the Pan-Eurasian Experiment (PEEX), a multi-scale, multi-disciplinary and international program started in 2012 (https://www.atm.helsinki.fi/peex/). PEEX is setting a research approach where large-scale research topics are investigated from a system perspective and which aims to fill the key gaps in our understanding of the feedbacks and interactions between the land–atmosphere–aquatic–society continuum in the Northern Eurasian region. We introduce here the state of the art of the key topics in the PEEX research agenda and give the future prospects of the research which we see relevant in this context.Peer reviewe

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