University of Bayreuth
UniversityBayreuth, Germany
Research output, citation impact, and the most-cited recent papers from University of Bayreuth (Germany). Aggregated across the NobleBlocks index of 300M+ scholarly works.
Top-cited papers from University of Bayreuth
Collinearity refers to the non independence of predictor variables, usually in a regression‐type analysis. It is a common feature of any descriptive ecological data set and can be a problem for parameter estimation because it inflates the variance of regression parameters and hence potentially leads to the wrong identification of relevant predictors in a statistical model. Collinearity is a severe problem when a model is trained on data from one region or time, and predicted to another with a different or unknown structure of collinearity. To demonstrate the reach of the problem of collinearity in ecology, we show how relationships among predictors differ between biomes, change over spatial scales and through time. Across disciplines, different approaches to addressing collinearity problems have been developed, ranging from clustering of predictors, threshold‐based pre‐selection, through latent variable methods, to shrinkage and regularisation. Using simulated data with five predictor‐response relationships of increasing complexity and eight levels of collinearity we compared ways to address collinearity with standard multiple regression and machine‐learning approaches. We assessed the performance of each approach by testing its impact on prediction to new data. In the extreme, we tested whether the methods were able to identify the true underlying relationship in a training dataset with strong collinearity by evaluating its performance on a test dataset without any collinearity. We found that methods specifically designed for collinearity, such as latent variable methods and tree based models, did not outperform the traditional GLM and threshold‐based pre‐selection. Our results highlight the value of GLM in combination with penalised methods (particularly ridge) and threshold‐based pre‐selection when omitted variables are considered in the final interpretation. However, all approaches tested yielded degraded predictions under change in collinearity structure and the ‘folk lore’‐thresholds of correlation coefficients between predictor variables of |r| >0.7 was an appropriate indicator for when collinearity begins to severely distort model estimation and subsequent prediction. The use of ecological understanding of the system in pre‐analysis variable selection and the choice of the least sensitive statistical approaches reduce the problems of collinearity, but cannot ultimately solve them.
AUTORES: Daniel J Klionsky1745,1749*, Kotb Abdelmohsen840, Akihisa Abe1237, Md Joynal Abedin1762, Hagai Abeliovich425, \nAbraham Acevedo Arozena789, Hiroaki Adachi1800, Christopher M Adams1669, Peter D Adams57, Khosrow Adeli1981, \nPeter J Adhihetty1625, Sharon G Adler700, Galila Agam67, Rajesh Agarwal1587, Manish K Aghi1537, Maria Agnello1826, \nPatrizia Agostinis664, Patricia V Aguilar1960, Julio Aguirre-Ghiso784,786, Edoardo M Airoldi89,422, Slimane Ait-Si-Ali1376, \nTakahiko Akematsu2010, Emmanuel T Akporiaye1097, Mohamed Al-Rubeai1394, Guillermo M Albaiceta1294, \nChris Albanese363, Diego Albani561, Matthew L Albert517, Jesus Aldudo128, Hana Alg€ul1164, Mehrdad Alirezaei1198, \nIraide Alloza642,888, Alexandru Almasan206, Maylin Almonte-Beceril524, Emad S Alnemri1212, Covadonga Alonso544, \nNihal Altan-Bonnet848, Dario C Altieri1205, Silvia Alvarez1497, Lydia Alvarez-Erviti1395, Sandro Alves107, \nGiuseppina Amadoro860, Atsuo Amano930, Consuelo Amantini1554, Santiago Ambrosio1458, Ivano Amelio756, \nAmal O Amer918, Mohamed Amessou2089, Angelika Amon726, Zhenyi An1538, Frank A Anania291, Stig U Andersen6, \nUsha P Andley2079, Catherine K Andreadi1690, Nathalie Andrieu-Abadie502, Alberto Anel2027, David K Ann58, \nShailendra Anoopkumar-Dukie388, Manuela Antonioli832,858, Hiroshi Aoki1791, Nadezda Apostolova2007, \nSaveria Aquila1500, Katia Aquilano1876, Koichi Araki292, Eli Arama2098, Agustin Aranda456, Jun Araya591, \nAlexandre Arcaro1472, Esperanza Arias26, Hirokazu Arimoto1225, Aileen R Ariosa1749, Jane L Armstrong1930, \nThierry Arnould1773, Ivica Arsov2120, Katsuhiko Asanuma675, Valerie Askanas1924, Eric Asselin1867, Ryuichiro Atarashi794, \nSally S Atherton369, Julie D Atkin713, Laura D Attardi1131, Patrick Auberger1787, Georg Auburger379, Laure Aurelian1727, \nRiccardo Autelli1992, Laura Avagliano1029,1755, Maria Laura Avantaggiati364, Limor Avrahami1166, Suresh Awale1986, \nNeelam Azad404, Tiziana Bachetti568, Jonathan M Backer28, Dong-Hun Bae1933, Jae-sung Bae677, Ok-Nam Bae409, \nSoo Han Bae2117, Eric H Baehrecke1729, Seung-Hoon Baek17, Stephen Baghdiguian1368, \nAgnieszka Bagniewska-Zadworna2, Hua Bai90, Jie Bai667, Xue-Yuan Bai1133, Yannick Bailly884, \nKithiganahalli Narayanaswamy Balaji473, Walter Balduini2002, Andrea Ballabio316, Rena Balzan1711, Rajkumar Banerjee239, \nG abor B anhegyi1052, Haijun Bao2109, Benoit Barbeau1363, Maria D Barrachina2007, Esther Barreiro467, Bonnie Bartel997, \nAlberto Bartolom e222, Diane C Bassham550, Maria Teresa Bassi1046, Robert C Bast Jr1273, Alakananda Basu1798, \nMaria Teresa Batista1578, Henri Batoko1336, Maurizio Battino970, Kyle Bauckman2085, Bradley L Baumgarner1909, \nK Ulrich Bayer1594, Rupert Beale1553, Jean-Fran¸cois Beaulieu1360, George R. Beck Jr48,294, Christoph Becker336, \nJ David Beckham1595, Pierre-Andr e B edard749, Patrick J Bednarski301, Thomas J Begley1135, Christian Behl1419, \nChristian Behrends757, Georg MN Behrens406, Kevin E Behrns1627, Eloy Bejarano26, Amine Belaid490, \nFrancesca Belleudi1041, Giovanni B enard497, Guy Berchem706, Daniele Bergamaschi983, Matteo Bergami1401, \nBen Berkhout1441, Laura Berliocchi714, Am elie Bernard1749, Monique Bernard1354, Francesca Bernassola1880, \nAnne Bertolotti791, Amanda S Bess272, S ebastien Besteiro1351, Saverio Bettuzzi1828, Savita Bhalla913, \nShalmoli Bhattacharyya973, Sujit K Bhutia838, Caroline Biagosch1159, Michele Wolfe Bianchi520,1378,1381, \nMartine Biard-Piechaczyk210, Viktor Billes298, Claudia Bincoletto1314, Baris Bingol350, Sara W Bird1128, Marc Bitoun1112, \nIvana Bjedov1258, Craig Blackstone843, Lionel Blanc1183, Guillermo A Blanco1496, Heidi Kiil Blomhoff1812, \nEmilio Boada-Romero1297, Stefan B€ockler1464, Marianne Boes1423, Kathleen Boesze-Battaglia1835, Lawrence H Boise286,287, \nAlessandra Bolino2063, Andrea Boman693, Paolo Bonaldo1823, Matteo Bordi897, J€urgen Bosch608, Luis M Botana1308, \nJoelle Botti1375, German Bou1405, Marina Bouch e1038, Marion Bouchecareilh1331, Marie-Jos ee Boucher1901, \nMichael E Boulton481, Sebastien G Bouret1926, Patricia Boya133, Micha€el Boyer-Guittaut1345, Peter V Bozhkov1141, \nNathan Brady374, Vania MM Braga469, Claudio Brancolini1997, Gerhard H Braus353, Jos e M Bravo-San Pedro299,393,508,1374, \nLisa A Brennan322, Emery H Bresnick2022, Patrick Brest490, Dave Bridges1939, Marie-Agn es Bringer124, Marisa Brini1822, \nGlauber C Brito1311, Bertha Brodin631, Paul S Brookes1872, Eric J Brown352, Karen Brown1690, Hal E Broxmeyer480, \nAlain Bruhat486,1339, Patricia Chakur Brum1893, John H Brumell446, Nicola Brunetti-Pierri315,1171, \nRobert J Bryson-Richardson781, Shilpa Buch1777, Alastair M Buchan1819, Hikmet Budak1022, Dmitry V Bulavin118,505,1789, \nScott J Bultman1792, Geert Bultynck665, Vladimir Bumbasirevic1470, Yan Burelle1356, Robert E Burke216,217, \nMargit Burmeister1750, Peter B€utikofer1473, Laura Caberlotto1987, Ken Cadwell896, Monika Cahova112, Dongsheng Cai24, \nJingjing Cai2099, Qian Cai1018, Sara Calatayud2007, Nadine Camougrand1343, Michelangelo Campanella1700, \nGrant R Campbell1525, Matthew Campbell1249, Silvia Campello556,1876, Robin Candau1769, Isabella Caniggia1983, \nLavinia Cantoni560, Lizhi Cao116, Allan B Caplan1656, Michele Caraglia1051, Claudio Cardinali1043, Sandra Morais Cardoso1579, Jennifer S Carew208, Laura A Carleton874, Cathleen R Carlin101, Silvia Carloni2002, \nSven R Carlsson1267, Didac Carmona-Gutierrez1643, Leticia AM Carneiro312, Oliana Carnevali971, Serena Carra1318, \nAlice Carrier120, Bernadette Carroll900, Caty Casas1324, Josefina Casas1116, Giuliana Cassinelli324, Perrine Castets1462, \nSusana Castro-Obregon214, Gabriella Cavallini1841, Isabella Ceccherini568, Francesco Cecconi253,555,1884, \nArthur I Cederbaum459, Valent ın Ce~na199,1281, Simone Cenci1323,2064, Claudia Cerella444, Davide Cervia1996, \nSilvia Cetrullo1478, Hassan Chaachouay2028, Han-Jung Chae187, Andrei S Chagin634, Chee-Yin Chai626,628, \nGopal Chakrabarti1502, Georgios Chamilos1601, Edmond YW Chan1142, Matthew TV Chan181, Dhyan Chandra1003, \nPallavi Chandra548, Chih-Peng Chang818, Raymond Chuen-Chung Chang1653, Ta Yuan Chang345, John C Chatham1434, \nSaurabh Chatterjee1910, Santosh Chauhan527, Yongsheng Che62, Michael E Cheetham1263, Rajkumar Cheluvappa1783, \nChun-Jung Chen1153, Gang Chen598,1676, Guang-Chao Chen9, Guoqiang Chen1078, Hongzhuan Chen1077, Jeff W Chen1514, \nJian-Kang Chen370,371, Min Chen249, Mingzhou Chen2104, Peiwen Chen1823, Qi Chen1674, Quan Chen172, \nShang-Der Chen138, Si Chen325, Steve S-L Chen10, Wei Chen2125, Wei-Jung Chen829, Wen Qiang Chen979, Wenli Chen1113, \nXiangmei Chen1133, Yau-Hung Chen1157, Ye-Guang Chen1250, Yin Chen1447, Yingyu Chen953,955, Yongshun Chen2135, \nYu-Jen Chen712, Yue-Qin Chen1145, Yujie Chen1208, Zhen Chen339, Zhong Chen2123, Alan Cheng1702, \nChristopher HK Cheng184, Hua Cheng1728, Heesun Cheong814, Sara Cherry1836, Jason Chesney1703, \nChun Hei Antonio Cheung817, Eric Chevet1359, Hsiang Cheng Chi140, Sung-Gil Chi656, Fulvio Chiacchiera308, \nHui-Ling Chiang958, Roberto Chiarelli1826, Mario Chiariello235,567,577, Marcello Chieppa835, Lih-Shen Chin290, \nMario Chiong1285, Gigi NC Chiu878, Dong-Hyung Cho676, Ssang-Goo Cho650, William C Cho982, Yong-Yeon Cho105, \nYoung-Seok Cho1064, Augustine MK Choi2095, Eui-Ju Choi656, Eun-Kyoung Choi387,400,685, Jayoung Choi1563, \nMary E Choi2093, Seung-Il Choi2116, Tsui-Fen Chou412, Salem Chouaib395, Divaker Choubey1574, Vinay Choubey1936, \nKuan-Chih Chow822, Kamal Chowdhury730, Charleen T Chu1856, Tsung-Hsien Chuang827, Taehoon Chun657, \nHyewon Chung652, Taijoon Chung978, Yuen-Li Chung1194, Yong-Joon Chwae18, Valentina Cianfanelli254, \nRoberto Ciarcia1775, Iwona A Ciechomska886, Maria Rosa Ciriolo1876, Mara Cirone1042, Sofie Claerhout1694, \nMichael J Clague1698, Joan Cl aria1457, Peter GH Clarke1687, Robert Clarke361, Emilio Clementi1045,1398, C edric Cleyrat1781, \nMiriam Cnop1366, Eliana M Coccia574, Tiziana Cocco1459, Patrice Codogno1375, J€orn Coers271, Ezra EW Cohen1533, \nDavid Colecchia235,567,577, Luisa Coletto25, N uria S Coll123, Emma Colucci-Guyon516, Sergio Comincini1829, \nMaria Condello578, Katherine L Cook2073, Graham H Coombs1929, Cynthia D Cooper2076, J Mark Cooper1395, \nIsabelle Coppens601, Maria Tiziana Corasaniti1387, Marco Corazzari485,1884, Ramon Corbalan1566, \nElisabeth Corcelle-Termeau251, Mario D Cordero1899, Cristina Corral-Ramos1289, Olga Corti507,1109, Andrea Cossarizza1767, \nPaola Costelli1993, Safia Costes1518, Susan L Cotman721, Ana Coto-Montes946, Sandra Cottet566,1688, Eduardo Couve1301, \nLori R Covey1015, L Ashley Cowart762, Jeffery S Cox1536, Fraser P Coxon1427, Carolyn B Coyne1846, Mark S Cragg1919, \nRolf J Craven1679, Tiziana Crepaldi1995, Jose L Crespo1300, Alfredo Criollo1285, Valeria Crippa558, Maria Teresa Cruz1576, \nAna Maria Cuervo26, Jose M Cuezva1277, Taixing Cui1907, Pedro R Cutillas987, Mark J Czaja27, Maria F Czyzyk-Krzeska1572, \nRuben K Dagda2068, Uta Dahmen1404, Chunsun Dai800, Wenjie Dai1187, Yun Dai2059, Kevin N Dalby1940, \nLuisa Dalla Valle1822, Guillaume Dalmasso1340, Marcello D’Amelio557, Markus Damme188, Arlette Darfeuille-Michaud1340, \nCatherine Dargemont950, Victor M Darley-Usmar1433, Srinivasan Dasarathy205, Biplab Dasgupta202, Srikanta Dash1254, \nCrispin R Dass242, Hazel Marie Davey8, Lester M Davids1560, David D avila227, Roger J Davis1731, Ted M Dawson604, \nValina L Dawson606, Paula Daza1898, Jackie de Belleroche470, Paul de Figueiredo1180,1182, \nRegina Celia Bressan Queiroz de Figueiredo135, Jos e de la Fuente1023, Luisa De Martino1775, \nAntonella De Matteis1171, Guido RY De Meyer1443, Angelo De Milito631, Mauro De Santi2002,
Abstract This paper discusses the advantages and disadvantages of the different methods that separate net ecosystem exchange (NEE) into its major components, gross ecosystem carbon uptake (GEP) and ecosystem respiration ( R eco ). In particular, we analyse the effect of the extrapolation of night‐time values of ecosystem respiration into the daytime; this is usually done with a temperature response function that is derived from long‐term data sets. For this analysis, we used 16 one‐year‐long data sets of carbon dioxide exchange measurements from European and US‐American eddy covariance networks. These sites span from the boreal to Mediterranean climates, and include deciduous and evergreen forest, scrubland and crop ecosystems. We show that the temperature sensitivity of R eco , derived from long‐term (annual) data sets, does not reflect the short‐term temperature sensitivity that is effective when extrapolating from night‐ to daytime. Specifically, in summer active ecosystems the long‐term temperature sensitivity exceeds the short‐term sensitivity. Thus, in those ecosystems, the application of a long‐term temperature sensitivity to the extrapolation of respiration from night to day leads to a systematic overestimation of ecosystem respiration from half‐hourly to annual time‐scales, which can reach >25% for an annual budget and which consequently affects estimates of GEP. Conversely, in summer passive (Mediterranean) ecosystems, the long‐term temperature sensitivity is lower than the short‐term temperature sensitivity resulting in underestimation of annual sums of respiration. We introduce a new generic algorithm that derives a short‐term temperature sensitivity of R eco from eddy covariance data that applies this to the extrapolation from night‐ to daytime, and that further performs a filling of data gaps that exploits both, the covariance between fluxes and meteorological drivers and the temporal structure of the fluxes. While this algorithm should give less biased estimates of GEP and R eco , we discuss the remaining biases and recommend that eddy covariance measurements are still backed by ancillary flux measurements that can reduce the uncertainties inherent in the eddy covariance data.
FLUXNET is a global network of micrometeorological flux measurement sites that measure the exchanges of carbon dioxide, water vapor, and energy between the biosphere and atmosphere. At present over 140 sites are operating on a long-term and continuous basis. Vegetation under study includes temperate conifer and broadleaved (deciduous and evergreen) forests, tropical and boreal forests, crops, grasslands, chaparral, wetlands, and tundra. Sites exist on five continents and their latitudinal distribution ranges from 70°N to 30°S. FLUXNET has several primary functions. First, it provides infrastructure for compiling, archiving, and distributing carbon, water, and energy flux measurement, and meteorological, plant, and soil data to the science community. (Data and site information are available online at the FLUXNET Web site, http://www-eosdis.ornl.gov/FLUXNET/.) Second, the project supports calibration and flux intercomparison activities. This activity ensures that data from the regional networks are intercomparable. And third, FLUXNET supports the synthesis, discussion, and communication of ideas and data by supporting project scientists, workshops, and visiting scientists. The overarching goal is to provide information for validating computations of net primary productivity, evaporation, and energy absorption that are being generated by sensors mounted on the NASA Terra satellite. Data being compiled by FLUXNET are being used to quantify and compare magnitudes and dynamics of annual ecosystem carbon and water balances, to quantify the response of stand-scale carbon dioxide and water vapor flux densities to controlling biotic and abiotic factors, and to validate a hierarchy of soil–plant–atmosphere trace gas exchange models. Findings so far include 1) net CO2 exchange of temperate broadleaved forests increases by about 5.7 g C m−2 day−1 for each additional day that the growing season is extended; 2) the sensitivity of net ecosystem CO2 exchange to sunlight doubles if the sky is cloudy rather than clear; 3) the spectrum of CO2 flux density exhibits peaks at timescales of days, weeks, and years, and a spectral gap exists at the month timescale; 4) the optimal temperature of net CO2 exchange varies with mean summer temperature; and 5) stand age affects carbon dioxide and water vapor flux densities.
Summary Mechanisms for C stabilization in soils have received much interest recently due to their relevance in the global C cycle. Here we review the mechanisms that are currently, but often contradictorily or inconsistently, considered to contribute to organic matter (OM) protection against decomposition in temperate soils: (i) selective preservation due to recalcitrance of OM, including plant litter, rhizodeposits, microbial products, humic polymers, and charred OM; (ii) spatial inaccessibility of OM against decomposer organisms due to occlusion, intercalation, hydrophobicity and encapsulation; and (iii) stabilization by interaction with mineral surfaces (Fe‐, Al‐, Mn‐oxides, phyllosilicates) and metal ions. Our goal is to assess the relevance of these mechanisms to the formation of soil OM during different stages of decomposition and under different soil conditions. The view that OM stabilization is dominated by the selective preservation of recalcitrant organic components that accumulate in proportion to their chemical properties can no longer be accepted. In contrast, our analysis of mechanisms shows that: (i) the soil biotic community is able to disintegrate any OM of natural origin; (ii) molecular recalcitrance of OM is relative, rather than absolute; (iii) recalcitrance is only important during early decomposition and in active surface soils; while (iv) during late decomposition and in the subsoil, the relevance of spatial inaccessibility and organo‐mineral interactions for SOM stabilization increases. We conclude that major difficulties in the understanding and prediction of SOM dynamics originate from the simultaneous operation of several mechanisms. We discuss knowledge gaps and promising directions of future research.
Dissolved organic matter (DOM) in soils plays an important role in the biogeochemistry of carbon, nitrogen, and phosphorus, in pedogenesis, and in the transport of pollutants in soils. The aim of this review is to summarize the recent literature about controls on DOM concentrations and fluxes in soils. We focus on comparing results between laboratory and field investigations and on the differences between the dynamics of dissolved organic carbon (DOC), nitrogen (DON), and phosphorus (DOP). Both laboratory and field studies show that litter and humus are the most important DOM sources in soils. However, it is impossible to quantify the individual contributions of each of these sources to DOM release. In addition, it is not clear how changes in the pool sizes of litter or humus may affect DOM release. High microbial activity, high fungal abundance, and any conditions that enhance mineralization all promote high DOM concentrations. However, under field conditions, hydrologic variability in soil horizons with high carbon contents may be more important than biotic controls. In subsoil horizons with low carbon contents, DOM may be adsorbed strongly to mineral surfaces, resulting in low DOM concentrations in the soil solution. There are strong indications that microbial degradation of DOM also controls the fate of DOM in the soil. Laboratory experiments on controls of DOM dynamics have often contradicted field observations, primarily because hydrology has not been taken into account. For example, laboratory findings on the effects of plant species (conifer vs. deciduous) on DOM release from forest floors and on the effects of substrate quality (e.g.: C/N ratio) or pH on DOC concentrations were often not confirmed in field studies. The high adsorption capacity of soil clay minerals and oxides for DOM shown in laboratory studies may not control the transport of DOM in soils in the field if macropore fluxes dominate under field conditions. Laboratory findings about the biodegradability of DOM also await verification under field conditions. Studies that include DON and DOP dynamics in addition to DOC are few. The rate of release and the fate of DOC, DON, and DOP in soils may differ to a far greater extent than previously assumed. Controls established for DOC might thus be not valid for DON and DOP. Despite intensive research in the last decade, our knowledge of the formation and fate of DOM in soils and its response to changing environmental conditions is still fragmented and often inconsistent. Predictions at the field scale are still very uncertain, and most of the information available today is the result of studies on temperate soils and forest ecosystems. Thus, future research on controls of DOM dynamics should be extended to soils under different land uses and in other climate zones. Emphasis should also be given to: (i) the effects of soil organic matter properties on the release of DOM (ii) environmental factors controlling DOM quantity and quality (iii) the assessment of biological versus physico-chemical controls on the release and retention of DOM in soils, and (iv) the differences between DOC, DON, and DOP. Finally, if our goal is to predict DOM concentrations and fluxes in soils, future research on the controls of DOM dynamics should have a strong focus on field studies.
At eight European field sites, the impact of loss of plant diversity on primary productivity was simulated by synthesizing grassland communities with different numbers of plant species. Results differed in detail at each location, but there was an overall log-linear reduction of average aboveground biomass with loss of species. For a given number of species, communities with fewer functional groups were less productive. These diversity effects occurred along with differences associated with species composition and geographic location. Niche complementarity and positive species interactions appear to play a role in generating diversity-productivity relationships within sites in addition to sampling from the species pool.
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.
Maintained at the Universitat Bayreuth, Bayreuth, Germany, the Compilation of tRNA Sequences and Sequences of tRNA Genes is accessible at the URL http://www.tRNA.uni-bayreuth.de with mirror site located at the Institute of Protein Research, Pushchino, Russia (http://alpha.protres.ru/trnadbase). The compilation is a searchable, periodically updated database of currently available tRNA sequences. The present version of the database contains a new Genomic tRNA Compilation including the sequences of tRNA genes from genomic sequences published up to July 2003. It consists of about 5800 tRNA gene sequences from 111 organisms covering archaea, bacteria, higher and lower eukarya. The former Compilation of tRNA Genes (up to the end of 1998) and the updated Compilation tRNA Sequences (561 entries) are also supported by the new software. The database can be explored by using multiple search criteria and sequence templates. The database provides a service that allows to obtain statistical information on the occurrences of certain bases at given positions of the tRNA sequences. This allows phylogenic studies and search for identity elements in respect to interactions of tRNAs with various enzymes.
The cubic complex Ginzburg-Landau equation is one of the most-studied nonlinear equations in the physics community. It describes a vast variety of phenomena from nonlinear waves to second-order phase transitions, from superconductivity, superfluidity, and Bose-Einstein condensation to liquid crystals and strings in field theory. The authors give an overview of various phenomena described by the complex Ginzburg-Landau equation in one, two, and three dimensions from the point of view of condensed-matter physicists. Their aim is to study the relevant solutions in order to gain insight into nonequilibrium phenomena in spatially extended systems.
, 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.
The mid‐, near‐, and far‐infrared (IR) spectra of synthetic, single‐phase calcium silicate hydrates (C‐S‐H) with Ca/Si ratios (C/S) of 0.41–1.85, 1.4 nm tobermorite, 1.1 nm tobermorite, and jennite confirm the similarity of the structure of these phases and provide important new insight into their H 2 O and OH environments. The main mid‐IR bands occur at 950–1100, 810–830, 660–670, and 440–450 cm −1 , consistent with single silicate chain structures. For the C‐S‐H samples, the mid‐IR bands change systematically with increasing C/S ratio, consistent with decreasing silicate polymerization and with an increasing content of jennite‐like structural environments of C/S ratios >1.2. The 950–1100 cm −1 group of bands due to Si‐O stretching shifts first to lower wave number due to decreasing polymerization and then to higher wave numbers, possibly reflecting an increase in jennite‐like structural environments. Because IR spectroscopy is a local structural probe, the spatial distribution of the jennite‐like domains cannot be determined from these data. A shoulder at ∼1200 cm −1 due to Si‐O stretching vibrations in Q 3 sites occurs only at C/S lessthan equal to 0.7. The 660–670 cm −1 band due to Si‐O‐Si bending broadens and decreases in intensity for samples with C/S > 0.88, consistent with depolymerization and decreased structural order. In the near‐IR region, the combination band at 4567 cm −1 due to Si‐OH stretching plus O‐H stretching decreases in intensity and is absent at C/S greater than ∼1.2, indicating the absence of Si‐OH linkages at C/S ratios greater than this. The primary Si‐OH band at 3740 cm ‐1 decreases in a similar way. In the far‐IR region, C‐S‐H samples with C/S ratio greater than ∼1.3 have increased absorption intensity at ∼300 cm −1 , indicating the presence of CaOH environments, even though portlandite cannot be detected by X‐ray diffraction for C/S ratios <1.5. These results, in combination with our previous NMR and Raman spectroscopic studies of the same samples, provide the basis for a more complete structural model for this type of C‐S‐H, which is described.
Global biogeochemical models have improved dramatically in the last decade in their representation of the biosphere. Although leaf area data are an important input to such models and are readily available globally, global root distributions for modeling water and nutrient uptake and carbon cycling have not been available. This analysis provides global distributions for fine root biomass, length, and surface area with depth in the soil, and global estimates of nutrient pools in fine roots. Calculated root surface area is almost always greater than leaf area, more than an order of magnitude so in grasslands. The average C:N:P ratio in living fine roots is 450:11:1, and global fine root carbon is more than 5% of all carbon contained in the atmosphere. Assuming conservatively that fine roots turn over once per year, they represent 33% of global annual net primary productivity.
Abstract Aims Vegetation classification consistent with the Braun‐Blanquet approach is widely used in Europe for applied vegetation science, conservation planning and land management. During the long history of syntaxonomy, many concepts and names of vegetation units have been proposed, but there has been no single classification system integrating these units. Here we (1) present a comprehensive, hierarchical, syntaxonomic system of alliances, orders and classes of Braun‐Blanquet syntaxonomy for vascular plant, bryophyte and lichen, and algal communities of Europe; (2) briefly characterize in ecological and geographic terms accepted syntaxonomic concepts; (3) link available synonyms to these accepted concepts; and (4) provide a list of diagnostic species for all classes. Location European mainland, Greenland, Arctic archipelagos (including Iceland, Svalbard, Novaya Zemlya), Canary Islands, Madeira, Azores, Caucasus, Cyprus. Methods We evaluated approximately 10 000 bibliographic sources to create a comprehensive list of previously proposed syntaxonomic units. These units were evaluated by experts for their floristic and ecological distinctness, clarity of geographic distribution and compliance with the nomenclature code. Accepted units were compiled into three systems of classes, orders and alliances (EuroVegChecklist, EVC ) for communities dominated by vascular plants ( EVC 1), bryophytes and lichens ( EVC 2) and algae ( EVC 3). Results EVC 1 includes 109 classes, 300 orders and 1108 alliances; EVC 2 includes 27 classes, 53 orders and 137 alliances, and EVC 3 includes 13 classes, 24 orders and 53 alliances. In total 13 448 taxa were assigned as indicator species to classes of EVC 1, 2087 to classes of EVC 2 and 368 to classes of EVC 3. Accepted syntaxonomic concepts are summarized in a series of appendices, and detailed information on each is accessible through the software tool EuroVegBrowser. Conclusions This paper features the first comprehensive and critical account of European syntaxa and synthesizes more than 100 yr of classification effort by European phytosociologists. It aims to document and stabilize the concepts and nomenclature of syntaxa for practical uses, such as calibration of habitat classification used by the European Union, standardization of terminology for environmental assessment, management and conservation of nature areas, landscape planning and education. The presented classification systems provide a baseline for future development and revision of European syntaxonomy.
Many ecosystem services are delivered by organisms that depend on habitats that are segregated spatially or temporally from the location where services are provided. Management of mobile organisms contributing to ecosystem services requires consideration not only of the local scale where services are delivered, but also the distribution of resources at the landscape scale, and the foraging ranges and dispersal movements of the mobile agents. We develop a conceptual model for exploring how one such mobile-agent-based ecosystem service (MABES), pollination, is affected by land-use change, and then generalize the model to other MABES. The model includes interactions and feedbacks among policies affecting land use, market forces and the biology of the organisms involved. Animal-mediated pollination contributes to the production of goods of value to humans such as crops; it also bolsters reproduction of wild plants on which other services or service-providing organisms depend. About one-third of crop production depends on animal pollinators, while 60-90% of plant species require an animal pollinator. The sensitivity of mobile organisms to ecological factors that operate across spatial scales makes the services provided by a given community of mobile agents highly contextual. Services vary, depending on the spatial and temporal distribution of resources surrounding the site, and on biotic interactions occurring locally, such as competition among pollinators for resources, and among plants for pollinators. The value of the resulting goods or services may feed back via market-based forces to influence land-use policies, which in turn influence land management practices that alter local habitat conditions and landscape structure. Developing conceptual models for MABES aids in identifying knowledge gaps, determining research priorities, and targeting interventions that can be applied in an adaptive management context.
The global presence of microplastic (MP) in aquatic ecosystems has been shown by various studies. However, neither MP concentrations nor their sources or sinks are completely known. Waste water treatment plants (WWTPs) are considered as significant point sources discharging MP to the environment. This study investigated MP in the effluents of 12 WWTPs in Lower Saxony, Germany. Samples were purified by a plastic-preserving enzymatic-oxidative procedure and subsequent density separation using a zinc chloride solution. For analysis, attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FT-IR) and focal plane array (FPA)-based transmission micro-FT-IR imaging were applied. This allowed the identification of polymers of all MP down to a size of 20 μm. In all effluents MP was found with quantities ranging from 0 to 5 × 101 m−3 MP > 500 μm and 1 × 101 to 9 × 103 m−3 MP < 500 μm. By far, polyethylene was the most frequent polymer type in both size classes. Quantities of synthetic fibres ranged from 9 × 101 to 1 × 103 m−3 and were predominantly made of polyester. Considering the annual effluxes of tested WWTPs, total discharges of 9 × 107 to 4 × 109 MP particles and fibres per WWTP could be expected. Interestingly, one tertiary WWTP had an additionally installed post-filtration that reduced the total MP discharge by 97%. Furthermore, the sewage sludge of six WWTPs was examined and the existence of MP, predominantly polyethylene, revealed. Our findings suggest that WWTPs could be a sink but also a source of MP and thus can be considered to play an important role for environmental MP pollution.
This paper gives an overview of 20 years of research on the energy balance closure problem. It will be shown that former assumptions that measuring errors or storage terms are the reason for the unclosed energy balance do not stand up because even turbulent fluxes derived from documented methods and calibrated sensors, net radiation, and ground heat fluxes cannot close the energy balance. Instead, exchange processes on larger scales of the heterogeneous landscape have a significant influence. By including these fluxes, the energy balance can be approximately closed. Therefore, the problem is a scale problem and has important consequences to the measurement and modeling of turbulent fluxes.
Pollination by bees and other animals increases the size, quality, or stability of harvests for 70% of leading global crops. Because native species pollinate many of these crops effectively, conserving habitats for wild pollinators within agricultural landscapes can help maintain pollination services. Using hierarchical Bayesian techniques, we synthesize the results of 23 studies - representing 16 crops on five continents - to estimate the general relationship between pollination services and distance from natural or semi-natural habitats. We find strong exponential declines in both pollinator richness and native visitation rate. Visitation rate declines more steeply, dropping to half of its maximum at 0.6 km from natural habitat, compared to 1.5 km for richness. Evidence of general decline in fruit and seed set - variables that directly affect yields - is less clear. Visitation rate drops more steeply in tropical compared with temperate regions, and slightly more steeply for social compared with solitary bees. Tropical crops pollinated primarily by social bees may therefore be most susceptible to pollination failure from habitat loss. Quantifying these general relationships can help predict consequences of land use change on pollinator communities and crop productivity, and can inform landscape conservation efforts that balance the needs of native species and people.
Abstract We summarize progress with respect to (1) different approaches to isolate, extract, and quantify organo‐mineral compounds from soils, (2) types of mineral surfaces and associated interactions, (3) the distribution and function of soil biota at organo‐mineral surfaces, (4) the distribution and content of organo‐mineral associations, and (5) the factors controlling the turnover of organic matter (OM) in organo‐mineral associations from temperate soils. Physical fractionation achieves a rough separation between plant residues and mineral‐associated OM, which makes density or particle‐size fractionation a useful pretreatment for further differentiation of functional fractions. A part of the OM in organo‐mineral associations resists different chemical treatments, but the data obtained cannot readily be compared among each other, and more research is necessary on the processes underlying resistance to treatments for certain OM components. Studies using physical‐fractionation procedures followed by soil‐microbiological analyses revealed that organo‐mineral associations spatially isolate C sources from soil biota, making quantity and quality of OM in microhabitats an important factor controlling community composition. The distribution and activity of soil microorganisms at organo‐mineral surfaces can additionally be modified by faunal activities. Composition of OM in organo‐mineral associations is highly variable, with loamy soils having generally a higher contribution of polysaccharides, whereas mineral‐associated OM in sandy soils is often more aliphatic. Though highly reactive towards Fe oxide surfaces, lignin and phenolic components are usually depleted in organo‐mineral associations. Charred OM associated with the mineral surface contributes to a higher aromaticity in heavy fractions. The relative proportion of OC bound in organo‐mineral fractions increases with soil depth. Likewise does the strength of the bonding. Organic molecules sorbed to the mineral surfaces or precipitated by Al are effectively stabilized, indicated by reduced susceptibility towards oxidative attack, higher thermal stability, and lower bioavailability. At higher surface loading, organic C is much better bioavailable, also indicated by little 14 C age. In the subsurface horizons of the soils investigated in this study, Fe oxides seem to be the most important sorbents, whereas phyllosilicate surfaces may be comparatively more important in topsoils. Specific surface area of soil minerals is not always a good predictor for C‐stabilization potentials because surface coverage is discontinuous. Recalcitrance and accessibility/aggregation seem to determine the turnover dynamics in fast and intermediate cycling OM pools, but for long‐term OC preservation the interactions with mineral surfaces, and especially with Fe oxide surfaces, are a major control in all soils investigated here.
Animal movement is fundamental for ecosystem functioning and species survival, yet the effects of the anthropogenic footprint on animal movements have not been estimated across species. Using a unique GPS-tracking database of 803 individuals across 57 species, we found that movements of mammals in areas with a comparatively high human footprint were on average one-half to one-third the extent of their movements in areas with a low human footprint. We attribute this reduction to behavioral changes of individual animals and to the exclusion of species with long-range movements from areas with higher human impact. Global loss of vagility alters a key ecological trait of animals that affects not only population persistence but also ecosystem processes such as predator-prey interactions, nutrient cycling, and disease transmission.