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1. OFraMP: a fragment-based tool to facilitate the parametrization of large molecules

Author: Stroet, M. (Martin), Caron, B. (Bertrand), Engler, M.S. (Martin), Woning, J.M. (Jimi) van der, Kauffmann, A. (Aude), van Dijk, M. (Marc), El-Kebir, M. (Mohammed), Visscher, K.M. (Koen), Holownia, J. (Josef), Macfarlane, C. (Callum), Bennion, B.J. (Brian), Gelpi-Dominguez, S. (Svetlana), Lightstone, F.C. (Felice), Storm, T. (Tijs) van der, Geerke, D.P., Mark, A.E. (Alan), Klau, G.W. (Gunnar), Stroet, M. (Martin), Caron, B. (Bertrand), Engler, M.S. (Martin), Woning, J.M. (Jimi) van der, Kauffmann, A. (Aude), van Dijk, M. (Marc), El-Kebir, M. (Mohammed), Visscher, K.M. (Koen), Holownia, J. (Josef), Macfarlane, C. (Callum), Bennion, B.J. (Brian), Gelpi-Dominguez, S. (Svetlana), Lightstone, F.C. (Felice), Storm, T. (Tijs) van der, Geerke, D.P., Mark, A.E. (Alan), and Klau, G.W. (Gunnar)
Abstract: An Online tool for Fragment-based Molecule Parametrization (OFraMP) is described. OFraMP is a web application for assigning atomic interaction parameters to large molecules by matching sub-fragments within the target molecule to equivalent sub-fragments within the Automated Topology Builder (ATB, atb.uq.edu.au) database. OFraMP identifies and compares alternative molecular fragments from the ATB database, which contains over 890,000 pre-parameterized molecules, using a novel hierarchical matching procedure. Atoms are considered within the context of an extended local environment (buffer region) with the degree of similarity between an atom in the target molecule and that in the proposed match controlled by varying the size of the buffer region. Adjacent matching atoms are combined into progressively larger matched sub-structures. The user then selects the most appropriate match. OFraMP also allows users to manually alter interaction parameters and automates the submission of missing substructures to the ATB in order to generate parameters for atoms in environments not represented in the existing database. The utility of OFraMP is illustrated using the anti-cancer agent paclitaxel and a dendrimer used in organic semiconductor devices. Graphical abstract: OFraMP applied to paclitaxel (ATB ID 35922).[Figure not available: see fulltext.]
Published: 2023
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2. The three major axes of terrestrial ecosystem function

Author: Migliavacca, M, Musavi, T, Mahecha, M, Nelson, J, Knauer, J, Baldocchi, D, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, T, Blanken, P, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, A, El-Madany, T, Farella, M, Fernandez-Martinez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, C, Gockede, M, Ibrom, A, Ikawa, H, Janssens, I, Jung, M, Kattge, J, Keenan, T, Knohl, A, Kobayashi, H, Kraemer, G, Law, B, Liddell, M, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, D, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, R, Reich, P, Rossini, M, Rotenberg, E, Scott, R, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, I, Yakir, D, Zaehle, S, Reichstein, M, Migliavacca M., Musavi T., Mahecha M. D., Nelson J. A., Knauer J., Baldocchi D. D., Perez-Priego O., Christiansen R., Peters J., Anderson K., Bahn M., Black T. A., Blanken P. D., Bonal D., Buchmann N., Caldararu S., Carrara A., Carvalhais N., Cescatti A., Chen J., Cleverly J., Cremonese E., Desai A. R., El-Madany T. S., Farella M. M., Fernandez-Martinez M., Filippa G., Forkel M., Galvagno M., Gomarasca U., Gough C. M., Gockede M., Ibrom A., Ikawa H., Janssens I. A., Jung M., Kattge J., Keenan T. F., Knohl A., Kobayashi H., Kraemer G., Law B. E., Liddell M. J., Ma X., Mammarella I., Martini D., Macfarlane C., Matteucci G., Montagnani L., Pabon-Moreno D. E., Panigada C., Papale D., Pendall E., Penuelas J., Phillips R. P., Reich P. B., Rossini M., Rotenberg E., Scott R. L., Stahl C., Weber U., Wohlfahrt G., Wolf S., Wright I. J., Yakir D., Zaehle S., Reichstein M., Migliavacca, M, Musavi, T, Mahecha, M, Nelson, J, Knauer, J, Baldocchi, D, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, T, Blanken, P, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, A, El-Madany, T, Farella, M, Fernandez-Martinez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, C, Gockede, M, Ibrom, A, Ikawa, H, Janssens, I, Jung, M, Kattge, J, Keenan, T, Knohl, A, Kobayashi, H, Kraemer, G, Law, B, Liddell, M, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, D, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, R, Reich, P, Rossini, M, Rotenberg, E, Scott, R, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, I, Yakir, D, Zaehle, S, Reichstein, M, Migliavacca M., Musavi T., Mahecha M. D., Nelson J. A., Knauer J., Baldocchi D. D., Perez-Priego O., Christiansen R., Peters J., Anderson K., Bahn M., Black T. A., Blanken P. D., Bonal D., Buchmann N., Caldararu S., Carrara A., Carvalhais N., Cescatti A., Chen J., Cleverly J., Cremonese E., Desai A. R., El-Madany T. S., Farella M. M., Fernandez-Martinez M., Filippa G., Forkel M., Galvagno M., Gomarasca U., Gough C. M., Gockede M., Ibrom A., Ikawa H., Janssens I. A., Jung M., Kattge J., Keenan T. F., Knohl A., Kobayashi H., Kraemer G., Law B. E., Liddell M. J., Ma X., Mammarella I., Martini D., Macfarlane C., Matteucci G., Montagnani L., Pabon-Moreno D. E., Panigada C., Papale D., Pendall E., Penuelas J., Phillips R. P., Reich P. B., Rossini M., Rotenberg E., Scott R. L., Stahl C., Weber U., Wohlfahrt G., Wolf S., Wright I. J., Yakir D., Zaehle S., and Reichstein M.
Abstract: The leaf economics spectrum and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability. Here we derive a set of ecosystem functions from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems.
Published: 2021

3. Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network

Author: Beringer, J, Moore, CE, Cleverly, J, Campbell, D, Cleugh, H, De Kauwe, MG, Kirschbaum, MUF, Griebel, A, Grover, S, Huete, A, Hutley, LB, Laubach, J, Van Niel, T, Arndt, SK, Bennett, AC, Cernusak, LA, Eamus, D, Ewenz, CM, Goodrich, JP, Jiang, M, Hinko-Najera, N, Isaac, P, Hobeichi, S, Knauer, J, Koerber, GR, Liddell, M, Ma, X, Macfarlane, C, McHugh, ID, Medlyn, BE, Meyer, WS, Norton, AJ, Owens, J, Pitman, A, Pendall, E, Prober, SM, Ray, RL, Restrepo-Coupe, N, Rifai, SW, Rowlings, D, Schipper, L, Silberstein, RP, Teckentrup, L, Thompson, SE, Ukkola, AM, Wall, A, Wang, Y-P, Wardlaw, TJ, Woodgate, W, Beringer, J, Moore, CE, Cleverly, J, Campbell, D, Cleugh, H, De Kauwe, MG, Kirschbaum, MUF, Griebel, A, Grover, S, Huete, A, Hutley, LB, Laubach, J, Van Niel, T, Arndt, SK, Bennett, AC, Cernusak, LA, Eamus, D, Ewenz, CM, Goodrich, JP, Jiang, M, Hinko-Najera, N, Isaac, P, Hobeichi, S, Knauer, J, Koerber, GR, Liddell, M, Ma, X, Macfarlane, C, McHugh, ID, Medlyn, BE, Meyer, WS, Norton, AJ, Owens, J, Pitman, A, Pendall, E, Prober, SM, Ray, RL, Restrepo-Coupe, N, Rifai, SW, Rowlings, D, Schipper, L, Silberstein, RP, Teckentrup, L, Thompson, SE, Ukkola, AM, Wall, A, Wang, Y-P, Wardlaw, TJ, and Woodgate, W
Abstract: In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists
Published: 2022

4. Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network.

Author: Beringer, J, Moore, CE, Cleverly, J, Campbell, DI, Cleugh, H, De Kauwe, MG, Kirschbaum, MUF, Griebel, A, Grover, S, Huete, A, Hutley, LB, Laubach, J, Van Niel, T, Arndt, SK, Bennett, AC, Cernusak, LA, Eamus, D, Ewenz, CM, Goodrich, JP, Jiang, M, Hinko-Najera, N, Isaac, P, Hobeichi, S, Knauer, J, Koerber, GR, Liddell, M, Ma, X, Macfarlane, C, McHugh, ID, Medlyn, BE, Meyer, WS, Norton, AJ, Owens, J, Pitman, A, Pendall, E, Prober, SM, Ray, RL, Restrepo-Coupe, N, Rifai, SW, Rowlings, D, Schipper, L, Silberstein, RP, Teckentrup, L, Thompson, SE, Ukkola, AM, Wall, A, Wang, Y-P, Wardlaw, TJ, Woodgate, W, Beringer, J, Moore, CE, Cleverly, J, Campbell, DI, Cleugh, H, De Kauwe, MG, Kirschbaum, MUF, Griebel, A, Grover, S, Huete, A, Hutley, LB, Laubach, J, Van Niel, T, Arndt, SK, Bennett, AC, Cernusak, LA, Eamus, D, Ewenz, CM, Goodrich, JP, Jiang, M, Hinko-Najera, N, Isaac, P, Hobeichi, S, Knauer, J, Koerber, GR, Liddell, M, Ma, X, Macfarlane, C, McHugh, ID, Medlyn, BE, Meyer, WS, Norton, AJ, Owens, J, Pitman, A, Pendall, E, Prober, SM, Ray, RL, Restrepo-Coupe, N, Rifai, SW, Rowlings, D, Schipper, L, Silberstein, RP, Teckentrup, L, Thompson, SE, Ukkola, AM, Wall, A, Wang, Y-P, Wardlaw, TJ, and Woodgate, W
Abstract: In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists
Published: 2022

5. Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network.

Author: Beringer, J, Moore, CE, Cleverly, J, Campbell, DI, Cleugh, H, De Kauwe, MG, Kirschbaum, MUF, Griebel, A, Grover, S, Huete, A, Hutley, LB, Laubach, J, Van Niel, T, Arndt, SK, Bennett, AC, Cernusak, LA, Eamus, D, Ewenz, CM, Goodrich, JP, Jiang, M, Hinko-Najera, N, Isaac, P, Hobeichi, S, Knauer, J, Koerber, GR, Liddell, M, Ma, X, Macfarlane, C, McHugh, ID, Medlyn, BE, Meyer, WS, Norton, AJ, Owens, J, Pitman, A, Pendall, E, Prober, SM, Ray, RL, Restrepo-Coupe, N, Rifai, SW, Rowlings, D, Schipper, L, Silberstein, RP, Teckentrup, L, Thompson, SE, Ukkola, AM, Wall, A, Wang, Y-P, Wardlaw, TJ, Woodgate, W, Beringer, J, Moore, CE, Cleverly, J, Campbell, DI, Cleugh, H, De Kauwe, MG, Kirschbaum, MUF, Griebel, A, Grover, S, Huete, A, Hutley, LB, Laubach, J, Van Niel, T, Arndt, SK, Bennett, AC, Cernusak, LA, Eamus, D, Ewenz, CM, Goodrich, JP, Jiang, M, Hinko-Najera, N, Isaac, P, Hobeichi, S, Knauer, J, Koerber, GR, Liddell, M, Ma, X, Macfarlane, C, McHugh, ID, Medlyn, BE, Meyer, WS, Norton, AJ, Owens, J, Pitman, A, Pendall, E, Prober, SM, Ray, RL, Restrepo-Coupe, N, Rifai, SW, Rowlings, D, Schipper, L, Silberstein, RP, Teckentrup, L, Thompson, SE, Ukkola, AM, Wall, A, Wang, Y-P, Wardlaw, TJ, and Woodgate, W
Abstract: In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists
Published: 2022

6. Land surface phenology retrievals for arid and semi-arid ecosystems

Author: Xie, Q, Cleverly, J, Moore, CE, Ding, Y, Hall, CC, Ma, X, Brown, LA, Wang, C, Beringer, J, Prober, SM, Macfarlane, C, Meyer, WS, Yin, G, Huete, A, Xie, Q, Cleverly, J, Moore, CE, Ding, Y, Hall, CC, Ma, X, Brown, LA, Wang, C, Beringer, J, Prober, SM, Macfarlane, C, Meyer, WS, Yin, G, and Huete, A
Abstract: Land surface phenology (LSP) plays a critical role in the regulation of photosynthesis, evapotranspiration, and energy fluxes. Significant progress has been made in extracting LSP information over large areas using satellite data, yet LSP retrievals remain a challenge over vast arid and semi-arid ecosystems because of sparse greenness, high variability and the lack of distinct annual patterns; for example, the MODerate Imaging Spectrometer (MODIS) Land Cover Dynamics Product MCD12Q2 that provides LSP metrics globally often failed to provide LSP information in these ecosystems. In this study, we used a modified threshold algorithm to extract LSP timing metrics, including the start, peak, and end of growing seasons, using the 16-day composite Enhanced Vegetation Index (EVI) time series from MODIS data. We applied this regionally customized algorithm across all arid and semi-arid climate regions of Australia (75% of the continental land area) encompassing shrublands, grasslands, savannas, woodlands, and croplands, extracting LSP metrics annually from 2003 to 2018, with up to two (phenology) seasons accounted for in each year. Our algorithm yielded an average of 64.9% successful rate of retrieval (proportion of pixels with retrieved LSP metrics) across 16 years in Arid and Semi-arid AUStralia (AS-AUS), which was a significant increase compared to the 14.5% rate of retrieval yielded in our study area by the global product and the major cause of the different performances between these two approaches was the different EVI amplitude restrictions utilized to avoid spurious peaks (i.e. EVI amplitude ≥ 0.1 used by the global product and peak EVI ≥ time series average EVI used by our algorithm). Gross primary productivity (GPP) measurements at OzFlux eddy covariance (EC) tower sites were used to cross-compare with the presence/absence of growing seasons detected by our algorithm, and 97% of our retrieved seasons matched with those extracted using EC data. Preliminary tests at
Published: 2022

7. Land surface phenology retrievals for arid and semi-arid ecosystems

Author: Xie, Q, Cleverly, J, Moore, CE, Ding, Y, Hall, CC, Ma, X, Brown, LA, Wang, C, Beringer, J, Prober, SM, Macfarlane, C, Meyer, WS, Yin, G, Huete, A, Xie, Q, Cleverly, J, Moore, CE, Ding, Y, Hall, CC, Ma, X, Brown, LA, Wang, C, Beringer, J, Prober, SM, Macfarlane, C, Meyer, WS, Yin, G, and Huete, A
Abstract: Land surface phenology (LSP) plays a critical role in the regulation of photosynthesis, evapotranspiration, and energy fluxes. Significant progress has been made in extracting LSP information over large areas using satellite data, yet LSP retrievals remain a challenge over vast arid and semi-arid ecosystems because of sparse greenness, high variability and the lack of distinct annual patterns; for example, the MODerate Imaging Spectrometer (MODIS) Land Cover Dynamics Product MCD12Q2 that provides LSP metrics globally often failed to provide LSP information in these ecosystems. In this study, we used a modified threshold algorithm to extract LSP timing metrics, including the start, peak, and end of growing seasons, using the 16-day composite Enhanced Vegetation Index (EVI) time series from MODIS data. We applied this regionally customized algorithm across all arid and semi-arid climate regions of Australia (75% of the continental land area) encompassing shrublands, grasslands, savannas, woodlands, and croplands, extracting LSP metrics annually from 2003 to 2018, with up to two (phenology) seasons accounted for in each year. Our algorithm yielded an average of 64.9% successful rate of retrieval (proportion of pixels with retrieved LSP metrics) across 16 years in Arid and Semi-arid AUStralia (AS-AUS), which was a significant increase compared to the 14.5% rate of retrieval yielded in our study area by the global product and the major cause of the different performances between these two approaches was the different EVI amplitude restrictions utilized to avoid spurious peaks (i.e. EVI amplitude ≥ 0.1 used by the global product and peak EVI ≥ time series average EVI used by our algorithm). Gross primary productivity (GPP) measurements at OzFlux eddy covariance (EC) tower sites were used to cross-compare with the presence/absence of growing seasons detected by our algorithm, and 97% of our retrieved seasons matched with those extracted using EC data. Preliminary tests at
Published: 2022

8. The radio loudness of SDSS quasars from the LOFAR Two-metre Sky Survey: ubiquitous jet activity and constraints on star formation

Author: Macfarlane, C., Best, P. N., Sabater, J., Gurkan, G., Jarvis, M. J., Rottgering, H. J. A., Baldi, R. D., Rivera, G. Calistro, Duncan, K. J., Morabito, L. K., Prandoni, I., Retana-Montenegro, E., Macfarlane, C., Best, P. N., Sabater, J., Gurkan, G., Jarvis, M. J., Rottgering, H. J. A., Baldi, R. D., Rivera, G. Calistro, Duncan, K. J., Morabito, L. K., Prandoni, I., and Retana-Montenegro, E.
Abstract: We examine the distribution of radio emission from ~42,000 quasars from the Sloan Digital Sky Survey, as measured in the LOFAR Two-Metre Sky Survey (LoTSS). We present a model of the radio luminosity distribution of the quasars that assumes that every quasar displays a superposition of two sources of radio emission: active galactic nuclei (jets) and star-formation. Our two-component model provides an excellent match to the observed radio flux density distributions across a wide range of redshifts and quasar optical luminosities; this suggests that the jet-launching mechanism operates in all quasars but with different powering efficiency. The wide distribution of jet powers allows for a smooth transition between the 'radio-quiet' and 'radio-loud' quasar regimes, without need for any explicit bimodality. The best-fit model parameters indicate that the star-formation rate of quasar host galaxies correlates strongly with quasar luminosity and also increases with redshift at least out to z~2. For a model where star-formation rate scales as $SFR \propto L_{bol}^\alpha (1+z)^\beta$, we find $\alpha = 0.47 \pm 0.01$ and $\beta = 1.61 \pm 0.05$, in agreement with far-infrared studies. Quasars contribute ~0.15 per cent of the cosmic star-formation rate density at z=0.5, rising to 0.4 per cent by z=2. The typical radio jet power is seen to increase with both increasing optical luminosity and black hole mass independently, but does not vary with redshift, suggesting intrinsic properties govern the production of the radio jets. We discuss the implications of these results for the triggering of quasar activity and the launching of jets., Comment: Accepted for publication in MNRAS. 21 pages
Published: 2021
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9. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data (vol 7, 225, 2020)

Author: Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brummer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grunwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hortnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Luers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brummer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grunwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hortnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Luers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D
Abstract: A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00851-9.
Published: 2021

10. The three major axes of terrestrial ecosystem function.

Author: Migliavacca, M, Musavi, T, Mahecha, MD, Nelson, JA, Knauer, J, Baldocchi, DD, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, TA, Blanken, PD, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, AR, El-Madany, TS, Farella, MM, Fernández-Martínez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, CM, Göckede, M, Ibrom, A, Ikawa, H, Janssens, IA, Jung, M, Kattge, J, Keenan, TF, Knohl, A, Kobayashi, H, Kraemer, G, Law, BE, Liddell, MJ, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, DE, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, RP, Reich, PB, Rossini, M, Rotenberg, E, Scott, RL, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, IJ, Yakir, D, Zaehle, S, Reichstein, M, Migliavacca, M, Musavi, T, Mahecha, MD, Nelson, JA, Knauer, J, Baldocchi, DD, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, TA, Blanken, PD, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, AR, El-Madany, TS, Farella, MM, Fernández-Martínez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, CM, Göckede, M, Ibrom, A, Ikawa, H, Janssens, IA, Jung, M, Kattge, J, Keenan, TF, Knohl, A, Kobayashi, H, Kraemer, G, Law, BE, Liddell, MJ, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, DE, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, RP, Reich, PB, Rossini, M, Rotenberg, E, Scott, RL, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, IJ, Yakir, D, Zaehle, S, and Reichstein, M
Abstract: The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8.
Published: 2021

11. The three major axes of terrestrial ecosystem function.

Author: Migliavacca, M, Musavi, T, Mahecha, MD, Nelson, JA, Knauer, J, Baldocchi, DD, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, TA, Blanken, PD, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, AR, El-Madany, TS, Farella, MM, Fernández-Martínez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, CM, Göckede, M, Ibrom, A, Ikawa, H, Janssens, IA, Jung, M, Kattge, J, Keenan, TF, Knohl, A, Kobayashi, H, Kraemer, G, Law, BE, Liddell, MJ, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, DE, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, RP, Reich, PB, Rossini, M, Rotenberg, E, Scott, RL, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, IJ, Yakir, D, Zaehle, S, Reichstein, M, Migliavacca, M, Musavi, T, Mahecha, MD, Nelson, JA, Knauer, J, Baldocchi, DD, Perez-Priego, O, Christiansen, R, Peters, J, Anderson, K, Bahn, M, Black, TA, Blanken, PD, Bonal, D, Buchmann, N, Caldararu, S, Carrara, A, Carvalhais, N, Cescatti, A, Chen, J, Cleverly, J, Cremonese, E, Desai, AR, El-Madany, TS, Farella, MM, Fernández-Martínez, M, Filippa, G, Forkel, M, Galvagno, M, Gomarasca, U, Gough, CM, Göckede, M, Ibrom, A, Ikawa, H, Janssens, IA, Jung, M, Kattge, J, Keenan, TF, Knohl, A, Kobayashi, H, Kraemer, G, Law, BE, Liddell, MJ, Ma, X, Mammarella, I, Martini, D, Macfarlane, C, Matteucci, G, Montagnani, L, Pabon-Moreno, DE, Panigada, C, Papale, D, Pendall, E, Penuelas, J, Phillips, RP, Reich, PB, Rossini, M, Rotenberg, E, Scott, RL, Stahl, C, Weber, U, Wohlfahrt, G, Wolf, S, Wright, IJ, Yakir, D, Zaehle, S, and Reichstein, M
Abstract: The leaf economics spectrum1,2 and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species2. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities4. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability4,5. Here we derive a set of ecosystem functions6 from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems7,8.
Published: 2021

12. The three major axes of terrestrial ecosystem function

Author: Migliavacca, M., Musavi, T., Mahecha, Miguel Dario, Nelson, J.A., Knauer, J., Baldocchi, D.D., Perez-Priego, O., Christiansen, R., Peters, J., Anderson, K., Bahn, M., Black, T.A., Blanken, P.D., Bonal, D., Buchmann, N., Caldararu, S., Carrara, A., Carvalhais, N., Cescatti, A., Chen, J., Cleverly, J., Cremonese, E., Desai, A.R., El-Madany, T.S., Farella, M.M., Fernández-Martínez, M., Filippa, G., Forkel, M., Galvagno, M., Gomarasca, U., Gough, C.M., Göckede, M., Ibrom, A., Ikawa, H., Janssens, I.A., Jung, M., Kattge, J., Keenan, T.F., Knohl, A., Kobayashi, H., Kraemer, G., Law, B.E., Liddell, M.J., Ma, X., Mammarella, I., Martini, D., Macfarlane, C., Matteucci, G., Montagnani, L., Pabon-Moreno, D.E., Panigada, C., Papale, D., Pendall, E., Penuelas, J., Phillips, R.P., Reich, P.B., Rossini, M., Rotenberg, E., Scott, R.L., Stahl, C., Weber, U., Wohlfahrt, G., Wolf, S., Wright, I.J., Yakir, D., Zaehle, S., Reichstein, M., Migliavacca, M., Musavi, T., Mahecha, Miguel Dario, Nelson, J.A., Knauer, J., Baldocchi, D.D., Perez-Priego, O., Christiansen, R., Peters, J., Anderson, K., Bahn, M., Black, T.A., Blanken, P.D., Bonal, D., Buchmann, N., Caldararu, S., Carrara, A., Carvalhais, N., Cescatti, A., Chen, J., Cleverly, J., Cremonese, E., Desai, A.R., El-Madany, T.S., Farella, M.M., Fernández-Martínez, M., Filippa, G., Forkel, M., Galvagno, M., Gomarasca, U., Gough, C.M., Göckede, M., Ibrom, A., Ikawa, H., Janssens, I.A., Jung, M., Kattge, J., Keenan, T.F., Knohl, A., Kobayashi, H., Kraemer, G., Law, B.E., Liddell, M.J., Ma, X., Mammarella, I., Martini, D., Macfarlane, C., Matteucci, G., Montagnani, L., Pabon-Moreno, D.E., Panigada, C., Papale, D., Pendall, E., Penuelas, J., Phillips, R.P., Reich, P.B., Rossini, M., Rotenberg, E., Scott, R.L., Stahl, C., Weber, U., Wohlfahrt, G., Wolf, S., Wright, I.J., Yakir, D., Zaehle, S., and Reichstein, M.
Abstract: The leaf economics spectrum(1,2) and the global spectrum of plant forms and functions(3) revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species(2). Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities(4). However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability(4,5). Here we derive a set of ecosystem functions(6) from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems(7,8).
Published: 2021

13. Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author: Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardö, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brümmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D’Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrêne, E, Dunn, A, Dušek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grünwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hörtnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janouš, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, López-Ballesteros, A, López-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lüers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, Ü, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sánchez-Cañete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlák, P, Serrano-Ortíz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Šigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardö, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brümmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D’Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrêne, E, Dunn, A, Dušek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grünwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hörtnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janouš, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, López-Ballesteros, A, López-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lüers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, Ü, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sánchez-Cañete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlák, P, Serrano-Ortíz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Šigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D
Abstract: The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions.
Published: 2021

14. The radio loudness of SDSS quasars from the LOFAR Two-metre Sky Survey: ubiquitous jet activity and constraints on star formation

Author: Macfarlane, C., Best, P. N., Sabater, J., Gurkan, G., Jarvis, M. J., Rottgering, H. J. A., Baldi, R. D., Rivera, G. Calistro, Duncan, K. J., Morabito, L. K., Prandoni, I., Retana-Montenegro, E., Macfarlane, C., Best, P. N., Sabater, J., Gurkan, G., Jarvis, M. J., Rottgering, H. J. A., Baldi, R. D., Rivera, G. Calistro, Duncan, K. J., Morabito, L. K., Prandoni, I., and Retana-Montenegro, E.
Abstract: We examine the distribution of radio emission from ~42,000 quasars from the Sloan Digital Sky Survey, as measured in the LOFAR Two-Metre Sky Survey (LoTSS). We present a model of the radio luminosity distribution of the quasars that assumes that every quasar displays a superposition of two sources of radio emission: active galactic nuclei (jets) and star-formation. Our two-component model provides an excellent match to the observed radio flux density distributions across a wide range of redshifts and quasar optical luminosities; this suggests that the jet-launching mechanism operates in all quasars but with different powering efficiency. The wide distribution of jet powers allows for a smooth transition between the 'radio-quiet' and 'radio-loud' quasar regimes, without need for any explicit bimodality. The best-fit model parameters indicate that the star-formation rate of quasar host galaxies correlates strongly with quasar luminosity and also increases with redshift at least out to z~2. For a model where star-formation rate scales as $SFR \propto L_{bol}^\alpha (1+z)^\beta$, we find $\alpha = 0.47 \pm 0.01$ and $\beta = 1.61 \pm 0.05$, in agreement with far-infrared studies. Quasars contribute ~0.15 per cent of the cosmic star-formation rate density at z=0.5, rising to 0.4 per cent by z=2. The typical radio jet power is seen to increase with both increasing optical luminosity and black hole mass independently, but does not vary with redshift, suggesting intrinsic properties govern the production of the radio jets. We discuss the implications of these results for the triggering of quasar activity and the launching of jets., Comment: Accepted for publication in MNRAS. 21 pages
Published: 2021
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15. Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author: Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardö, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brümmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D’Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrêne, E, Dunn, A, Dušek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grünwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hörtnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janouš, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, López-Ballesteros, A, López-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lüers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, Ü, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sánchez-Cañete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlák, P, Serrano-Ortíz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Šigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Reichstein, M, Ribeca, A, van Ingen, C, Vuichard, N, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardö, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Brümmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D’Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrêne, E, Dunn, A, Dušek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Grünwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hörtnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janouš, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, López-Ballesteros, A, López-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lüers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, Ü, Raz-Yaseef, N, Rebmann, C, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sánchez-Cañete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlák, P, Serrano-Ortíz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Šigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D
Abstract: The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions.
Published: 2021

16. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author: Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Ribeca, A, van Ingen, C, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Bruemmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Gruenwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hoertnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lueers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tiedemann, F, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, Papale, D, Pastorello, G, Trotta, C, Canfora, E, Chu, H, Christianson, D, Cheah, Y-W, Poindexter, C, Chen, J, Elbashandy, A, Humphrey, M, Isaac, P, Polidori, D, Ribeca, A, van Ingen, C, Zhang, L, Amiro, B, Ammann, C, Arain, MA, Ardo, J, Arkebauer, T, Arndt, SK, Arriga, N, Aubinet, M, Aurela, M, Baldocchi, D, Barr, A, Beamesderfer, E, Marchesini, LB, Bergeron, O, Beringer, J, Bernhofer, C, Berveiller, D, Billesbach, D, Black, TA, Blanken, PD, Bohrer, G, Boike, J, Bolstad, PV, Bonal, D, Bonnefond, J-M, Bowling, DR, Bracho, R, Brodeur, J, Bruemmer, C, Buchmann, N, Burban, B, Burns, SP, Buysse, P, Cale, P, Cavagna, M, Cellier, P, Chen, S, Chini, I, Christensen, TR, Cleverly, J, Collalti, A, Consalvo, C, Cook, BD, Cook, D, Coursolle, C, Cremonese, E, Curtis, PS, D'Andrea, E, da Rocha, H, Dai, X, Davis, KJ, De Cinti, B, de Grandcourt, A, De Ligne, A, De Oliveira, RC, Delpierre, N, Desai, AR, Di Bella, CM, di Tommasi, P, Dolman, H, Domingo, F, Dong, G, Dore, S, Duce, P, Dufrene, E, Dunn, A, Dusek, J, Eamus, D, Eichelmann, U, ElKhidir, HAM, Eugster, W, Ewenz, CM, Ewers, B, Famulari, D, Fares, S, Feigenwinter, I, Feitz, A, Fensholt, R, Filippa, G, Fischer, M, Frank, J, Galvagno, M, Gharun, M, Gianelle, D, Gielen, B, Gioli, B, Gitelson, A, Goded, I, Goeckede, M, Goldstein, AH, Gough, CM, Goulden, ML, Graf, A, Griebel, A, Gruening, C, Gruenwald, T, Hammerle, A, Han, S, Han, X, Hansen, BU, Hanson, C, Hatakka, J, He, Y, Hehn, M, Heinesch, B, Hinko-Najera, N, Hoertnagl, L, Hutley, L, Ibrom, A, Ikawa, H, Jackowicz-Korczynski, M, Janous, D, Jans, W, Jassal, R, Jiang, S, Kato, T, Khomik, M, Klatt, J, Knohl, A, Knox, S, Kobayashi, H, Koerber, G, Kolle, O, Kosugi, Y, Kotani, A, Kowalski, A, Kruijt, B, Kurbatova, J, Kutsch, WL, Kwon, H, Launiainen, S, Laurila, T, Law, B, Leuning, R, Li, Y, Liddell, M, Limousin, J-M, Lion, M, Liska, AJ, Lohila, A, Lopez-Ballesteros, A, Lopez-Blanco, E, Loubet, B, Loustau, D, Lucas-Moffat, A, Lueers, J, Ma, S, Macfarlane, C, Magliulo, V, Maier, R, Mammarella, I, Manca, G, Marcolla, B, Margolis, HA, Marras, S, Massman, W, Mastepanov, M, Matamala, R, Matthes, JH, Mazzenga, F, McCaughey, H, McHugh, I, McMillan, AMS, Merbold, L, Meyer, W, Meyers, T, Miller, SD, Minerbi, S, Moderow, U, Monson, RK, Montagnani, L, Moore, CE, Moors, E, Moreaux, V, Moureaux, C, Munger, JW, Nakai, T, Neirynck, J, Nesic, Z, Nicolini, G, Noormets, A, Northwood, M, Nosetto, M, Nouvellon, Y, Novick, K, Oechel, W, Olesen, JE, Ourcival, J-M, Papuga, SA, Parmentier, F-J, Paul-Limoges, E, Pavelka, M, Peichl, M, Pendall, E, Phillips, RP, Pilegaard, K, Pirk, N, Posse, G, Powell, T, Prasse, H, Prober, SM, Rambal, S, Rannik, U, Raz-Yaseef, N, Reed, D, de Dios, VR, Restrepo-Coupe, N, Reverter, BR, Roland, M, Sabbatini, S, Sachs, T, Saleska, SR, Sanchez-Canete, EP, Sanchez-Mejia, ZM, Schmid, HP, Schmidt, M, Schneider, K, Schrader, F, Schroder, I, Scott, RL, Sedlak, P, Serrano-Ortiz, P, Shao, C, Shi, P, Shironya, I, Siebicke, L, Sigut, L, Silberstein, R, Sirca, C, Spano, D, Steinbrecher, R, Stevens, RM, Sturtevant, C, Suyker, A, Tagesson, T, Takanashi, S, Tang, Y, Tapper, N, Thom, J, Tiedemann, F, Tomassucci, M, Tuovinen, J-P, Urbanski, S, Valentini, R, van der Molen, M, van Gorsel, E, van Huissteden, K, Varlagin, A, Verfaillie, J, Vesala, T, Vincke, C, Vitale, D, Vygodskaya, N, Walker, JP, Walter-Shea, E, Wang, H, Weber, R, Westermann, S, Wille, C, Wofsy, S, Wohlfahrt, G, Wolf, S, Woodgate, W, Zampedri, R, Zhang, J, Zhou, G, Zona, D, Agarwal, D, Biraud, S, Torn, M, and Papale, D
Abstract: 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.
Published: 2020

17. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Author: Pastorello, G. (Gilberto), Trotta, C. (Carlo), Canfora, E. (Eleonora), Chu, H. (Housen), Christianson, D. (Danielle), Cheah, Y.-W. (You-Wei), Poindexter, C. (Cristina), Chen, J. (Jiquan), Elbashandy, A. (Abdelrahman), Humphrey, M. (Marty), Isaac, P. (Peter), Polidori, D. (Diego), Ribeca, A. (Alessio), van Ingen, C. (Catharine), Zhang, L. (Leiming), Amiro, B. (Brian), Ammann, C. (Christof), Arain, M. A. (M. Altaf), Ardo, J. (Jonas), Arkebauer, T. (Timothy), Arndt, S. K. (Stefan K.), Arriga, N. (Nicola), Aubinet, M. (Marc), Aurela, M. (Mika), Baldocchi, D. (Dennis), Barr, A. (Alan), Beamesderfer, E. (Eric), Marchesini, L. B. (Luca Belelli), Bergeron, O. (Onil), Beringer, J. (Jason), Bernhofer, C. (Christian), Berveiller, D. (Daniel), Billesbach, D. (Dave), Black, T. A. (Thomas Andrew), Blanken, P. D. (Peter D.), Bohrer, G. (Gil), Boike, J. (Julia), Bolstad, P. V. (Paul V.), Bonal, D. (Damien), Bonnefond, J.-M. (Jean-Marc), Bowling, D. R. (David R.), Bracho, R. (Rosvel), Brodeur, J. (Jason), Bruemmer, C. (Christian), Buchmann, N. (Nina), Burban, B. (Benoit), Burns, S. P. (Sean P.), Buysse, P. (Pauline), Cale, P. (Peter), Cavagna, M. (Mauro), Cellier, P. (Pierre), Chen, S. (Shiping), Chini, I. (Isaac), Christensen, T. R. (Torben R.), Cleverly, J. (James), Collalti, A. (Alessio), Consalvo, C. (Claudia), Cook, B. D. (Bruce D.), Cook, D. (David), Coursolle, C. (Carole), Cremonese, E. (Edoardo), Curtis, P. S. (Peter S.), D'Andrea, E. (Ettore), da Rocha, H. (Humberto), Dai, X. (Xiaoqin), Davis, K. J. (Kenneth J.), De Cinti, B. (Bruno), de Grandcourt, A. (Agnes), De Ligne, A. (Anne), De Oliveira, R. C. (Raimundo C.), Delpierre, N. (Nicolas), Desai, A. R. (Ankur R.), Di Bella, C. M. (Carlos Marcelo), di Tommasi, P. (Paul), Dolman, H. (Han), Domingo, F. (Francisco), Dong, G. (Gang), Dore, S. (Sabina), Duce, P. (Pierpaolo), Dufrene, E. (Eric), Dunn, A. (Allison), Dusek, J. (Jiri), Eamus, D. (Derek), Eichelmann, U. (Uwe), ElKhidir, H. A. (Hatim Abdalla M.), Eugster, W. (Werner), Ewenz, C. M. (Cacilia M.), Ewers, B. (Brent), Famulari, D. (Daniela), Fares, S. (Silvano), Feigenwinter, I. (Iris), Feitz, A. (Andrew), Fensholt, R. (Rasmus), Filippa, G. (Gianluca), Fischer, M. (Marc), Frank, J. (John), Galvagno, M. (Marta), Gharun, M. (Mana), Gianelle, D. (Damiano), Gielen, B. (Bert), Gioli, B. (Beniamino), Gitelson, A. (Anatoly), Goded, I. (Ignacio), Goeckede, M. (Mathias), Goldstein, A. H. (Allen H.), Gough, C. M. (Christopher M.), Goulden, M. L. (Michael L.), Graf, A. (Alexander), Griebel, A. (Anne), Gruening, C. (Carsten), Gruenwald, T. (Thomas), Hammerle, A. (Albin), Han, S. (Shijie), Han, X. (Xingguo), Hansen, B. U. (Birger Ulf), Hanson, C. (Chad), Hatakka, J. (Juha), He, Y. (Yongtao), Hehn, M. (Markus), Heinesch, B. (Bernard), Hinko-Najera, N. (Nina), Hoertnagl, L. (Lukas), Hutley, L. (Lindsay), Ibrom, A. (Andreas), Ikawa, H. (Hiroki), Jackowicz-Korczynski, M. (Marcin), Janous, D. (Dalibor), Jans, W. (Wilma), Jassal, R. (Rachhpal), Jiang, S. (Shicheng), Kato, T. (Tomomichi), Khomik, M. (Myroslava), Klatt, J. (Janina), Knohl, A. (Alexander), Knox, S. (Sara), Kobayashi, H. (Hideki), Koerber, G. (Georgia), Kolle, O. (Olaf), Kosugi, Y. (Yoshiko), Kotani, A. (Ayumi), Kowalski, A. (Andrew), Kruijt, B. (Bart), Kurbatova, J. (Julia), Kutsch, W. L. (Werner L.), Kwon, H. (Hyojung), Launiainen, S. (Samuli), Laurila, T. (Tuomas), Law, B. (Bev), Leuning, R. (Ray), Li, Y. (Yingnian), Liddell, M. (Michael), Limousin, J.-M. (Jean-Marc), Lion, M. (Marryanna), Liska, A. J. (Adam J.), Lohila, A. (Annalea), Lopez-Ballesteros, A. (Ana), Lopez-Blanco, E. (Efren), Loubet, B. (Benjamin), Loustau, D. (Denis), Lucas-Moffat, A. (Antje), Lueers, J. (Johannes), Ma, S. (Siyan), Macfarlane, C. (Craig), Magliulo, V. (Vincenzo), Maier, R. (Regine), Mammarella, I. (Ivan), Manca, G. (Giovanni), Marcolla, B. (Barbara), Margolis, H. A. (Hank A.), Marras, S. (Serena), Massman, W. (William), Mastepanov, M. (Mikhail), Matamala, R. (Roser), Matthes, J. H. (Jaclyn Hatala), Mazzenga, F. (Francesco), McCaughey, H. (Harry), McHugh, I. (Ian), McMillan, A. M. (Andrew M. S.), Merbold, L. (Lutz), Meyer, W. (Wayne), Meyers, T. (Tilden), Miller, S. D. (Scott D.), Minerbi, S. (Stefano), Moderow, U. (Uta), Monson, R. K. (Russell K.), Montagnani, L. (Leonardo), Moore, C. E. (Caitlin E.), Moors, E. (Eddy), Moreaux, V. (Virginie), Moureaux, C. (Christine), Munger, J. W. (J. William), Nakai, T. (Taro), Neirynck, J. (Johan), Nesic, Z. (Zoran), Nicolini, G. (Giacomo), Noormets, A. (Asko), Northwood, M. (Matthew), Nosetto, M. (Marcelo), Nouvellon, Y. (Yann), Novick, K. (Kimberly), Oechel, W. (Walter), Olesen, J. E. (Jorgen Eivind), Ourcival, J.-M. (Jean-Marc), Papuga, S. A. (Shirley A.), Parmentier, F.-J. (Frans-Jan), Paul-Limoges, E. (Eugenie), Pavelka, M. (Marian), Peichl, M. (Matthias), Pendall, E. (Elise), Phillips, R. P. (Richard P.), Pilegaard, K. (Kim), Pirk, N. (Norbert), Posse, G. (Gabriela), Powell, T. (Thomas), Prasse, H. (Heiko), Prober, S. M. (Suzanne M.), Rambal, S. (Serge), Rannik, U. (Ullar), Raz-Yaseef, N. (Naama), Reed, D. (David), de Dios, V. R. (Victor Resco), Restrepo-Coupe, N. (Natalia), Reverter, B. R. (Borja R.), Roland, M. (Marilyn), Sabbatini, S. (Simone), Sachs, T. (Torsten), Saleska, S. R. (Scott R.), Sanchez-Canete, E. P. (Enrique P.), Sanchez-Mejia, Z. M. (Zulia M.), Schmid, H. P. (Hans Peter), Schmidt, M. (Marius), Schneider, K. (Karl), Schrader, F. (Frederik), Schroder, I. (Ivan), Scott, R. L. (Russell L.), Sedlak, P. (Pavel), Serrano-Ortiz, P. (Penelope), Shao, C. (Changliang), Shi, P. (Peili), Shironya, I. (Ivan), Siebicke, L. (Lukas), Sigut, L. (Ladislav), Silberstein, R. (Richard), Sirca, C. (Costantino), Spano, D. (Donatella), Steinbrecher, R. (Rainer), Stevens, R. M. (Robert M.), Sturtevant, C. (Cove), Suyker, A. (Andy), Tagesson, T. (Torbern), Takanashi, S. (Satoru), Tang, Y. (Yanhong), Tapper, N. (Nigel), Thom, J. (Jonathan), Tiedemann, F. (Frank), Tomassucci, M. (Michele), Tuovinen, J.-P. (Juha-Pekka), Urbanski, S. (Shawn), Valentini, R. (Riccardo), van der Molen, M. (Michiel), van Gorsel, E. (Eva), van Huissteden, K. (Ko), Varlagin, A. (Andrej), Verfaillie, J. (Joseph), Vesala, T. (Timo), Vincke, C. (Caroline), Vitale, D. (Domenico), Vygodskaya, N. (Natalia), Walker, J. P. (Jeffrey P.), Walter-Shea, E. (Elizabeth), Wang, H. (Huimin), Weber, R. (Robin), Westermann, S. (Sebastian), Wille, C. (Christian), Wofsy, S. (Steven), Wohlfahrt, G. (Georg), Wolf, S. (Sebastian), Woodgate, W. (William), Li, Y. (Yuelin), Zampedri, R. (Roberto), Zhang, J. (Junhui), Zhou, G. (Guoyi), Zona, D. (Donatella), Agarwal, D. (Deb), Biraud, S. (Sebastien), Torn, M. (Margaret), Papale, D. (Dario), Pastorello, G. (Gilberto), Trotta, C. (Carlo), Canfora, E. (Eleonora), Chu, H. (Housen), Christianson, D. (Danielle), Cheah, Y.-W. (You-Wei), Poindexter, C. (Cristina), Chen, J. (Jiquan), Elbashandy, A. (Abdelrahman), Humphrey, M. (Marty), Isaac, P. (Peter), Polidori, D. (Diego), Ribeca, A. (Alessio), van Ingen, C. (Catharine), Zhang, L. (Leiming), Amiro, B. (Brian), Ammann, C. (Christof), Arain, M. A. (M. Altaf), Ardo, J. (Jonas), Arkebauer, T. (Timothy), Arndt, S. K. (Stefan K.), Arriga, N. (Nicola), Aubinet, M. (Marc), Aurela, M. (Mika), Baldocchi, D. (Dennis), Barr, A. (Alan), Beamesderfer, E. (Eric), Marchesini, L. B. (Luca Belelli), Bergeron, O. (Onil), Beringer, J. (Jason), Bernhofer, C. (Christian), Berveiller, D. (Daniel), Billesbach, D. (Dave), Black, T. A. (Thomas Andrew), Blanken, P. D. (Peter D.), Bohrer, G. (Gil), Boike, J. (Julia), Bolstad, P. V. (Paul V.), Bonal, D. (Damien), Bonnefond, J.-M. (Jean-Marc), Bowling, D. R. (David R.), Bracho, R. (Rosvel), Brodeur, J. (Jason), Bruemmer, C. (Christian), Buchmann, N. (Nina), Burban, B. (Benoit), Burns, S. P. (Sean P.), Buysse, P. (Pauline), Cale, P. (Peter), Cavagna, M. (Mauro), Cellier, P. (Pierre), Chen, S. (Shiping), Chini, I. (Isaac), Christensen, T. R. (Torben R.), Cleverly, J. (James), Collalti, A. (Alessio), Consalvo, C. (Claudia), Cook, B. D. (Bruce D.), Cook, D. (David), Coursolle, C. (Carole), Cremonese, E. (Edoardo), Curtis, P. S. (Peter S.), D'Andrea, E. (Ettore), da Rocha, H. (Humberto), Dai, X. (Xiaoqin), Davis, K. J. (Kenneth J.), De Cinti, B. (Bruno), de Grandcourt, A. (Agnes), De Ligne, A. (Anne), De Oliveira, R. C. (Raimundo C.), Delpierre, N. (Nicolas), Desai, A. R. (Ankur R.), Di Bella, C. M. (Carlos Marcelo), di Tommasi, P. (Paul), Dolman, H. (Han), Domingo, F. (Francisco), Dong, G. (Gang), Dore, S. (Sabina), Duce, P. (Pierpaolo), Dufrene, E. (Eric), Dunn, A. (Allison), Dusek, J. (Jiri), Eamus, D. (Derek), Eichelmann, U. (Uwe), ElKhidir, H. A. (Hatim Abdalla M.), Eugster, W. (Werner), Ewenz, C. M. (Cacilia M.), Ewers, B. (Brent), Famulari, D. (Daniela), Fares, S. (Silvano), Feigenwinter, I. (Iris), Feitz, A. (Andrew), Fensholt, R. (Rasmus), Filippa, G. (Gianluca), Fischer, M. (Marc), Frank, J. (John), Galvagno, M. (Marta), Gharun, M. (Mana), Gianelle, D. (Damiano), Gielen, B. (Bert), Gioli, B. (Beniamino), Gitelson, A. (Anatoly), Goded, I. (Ignacio), Goeckede, M. (Mathias), Goldstein, A. H. (Allen H.), Gough, C. M. (Christopher M.), Goulden, M. L. (Michael L.), Graf, A. (Alexander), Griebel, A. (Anne), Gruening, C. (Carsten), Gruenwald, T. (Thomas), Hammerle, A. (Albin), Han, S. (Shijie), Han, X. (Xingguo), Hansen, B. U. (Birger Ulf), Hanson, C. (Chad), Hatakka, J. (Juha), He, Y. (Yongtao), Hehn, M. (Markus), Heinesch, B. (Bernard), Hinko-Najera, N. (Nina), Hoertnagl, L. (Lukas), Hutley, L. (Lindsay), Ibrom, A. (Andreas), Ikawa, H. (Hiroki), Jackowicz-Korczynski, M. (Marcin), Janous, D. (Dalibor), Jans, W. (Wilma), Jassal, R. (Rachhpal), Jiang, S. (Shicheng), Kato, T. (Tomomichi), Khomik, M. (Myroslava), Klatt, J. (Janina), Knohl, A. (Alexander), Knox, S. (Sara), Kobayashi, H. (Hideki), Koerber, G. (Georgia), Kolle, O. (Olaf), Kosugi, Y. (Yoshiko), Kotani, A. (Ayumi), Kowalski, A. (Andrew), Kruijt, B. (Bart), Kurbatova, J. (Julia), Kutsch, W. L. (Werner L.), Kwon, H. (Hyojung), Launiainen, S. (Samuli), Laurila, T. (Tuomas), Law, B. (Bev), Leuning, R. (Ray), Li, Y. (Yingnian), Liddell, M. (Michael), Limousin, J.-M. (Jean-Marc), Lion, M. (Marryanna), Liska, A. J. (Adam J.), Lohila, A. (Annalea), Lopez-Ballesteros, A. (Ana), Lopez-Blanco, E. (Efren), Loubet, B. (Benjamin), Loustau, D. (Denis), Lucas-Moffat, A. (Antje), Lueers, J. (Johannes), Ma, S. (Siyan), Macfarlane, C. (Craig), Magliulo, V. (Vincenzo), Maier, R. (Regine), Mammarella, I. (Ivan), Manca, G. (Giovanni), Marcolla, B. (Barbara), Margolis, H. A. (Hank A.), Marras, S. (Serena), Massman, W. (William), Mastepanov, M. (Mikhail), Matamala, R. (Roser), Matthes, J. H. (Jaclyn Hatala), Mazzenga, F. (Francesco), McCaughey, H. (Harry), McHugh, I. (Ian), McMillan, A. M. (Andrew M. S.), Merbold, L. (Lutz), Meyer, W. (Wayne), Meyers, T. (Tilden), Miller, S. D. (Scott D.), Minerbi, S. (Stefano), Moderow, U. (Uta), Monson, R. K. (Russell K.), Montagnani, L. (Leonardo), Moore, C. E. (Caitlin E.), Moors, E. (Eddy), Moreaux, V. (Virginie), Moureaux, C. (Christine), Munger, J. W. (J. William), Nakai, T. (Taro), Neirynck, J. (Johan), Nesic, Z. (Zoran), Nicolini, G. (Giacomo), Noormets, A. (Asko), Northwood, M. (Matthew), Nosetto, M. (Marcelo), Nouvellon, Y. (Yann), Novick, K. (Kimberly), Oechel, W. (Walter), Olesen, J. E. (Jorgen Eivind), Ourcival, J.-M. (Jean-Marc), Papuga, S. A. (Shirley A.), Parmentier, F.-J. (Frans-Jan), Paul-Limoges, E. (Eugenie), Pavelka, M. (Marian), Peichl, M. (Matthias), Pendall, E. (Elise), Phillips, R. P. (Richard P.), Pilegaard, K. (Kim), Pirk, N. (Norbert), Posse, G. (Gabriela), Powell, T. (Thomas), Prasse, H. (Heiko), Prober, S. M. (Suzanne M.), Rambal, S. (Serge), Rannik, U. (Ullar), Raz-Yaseef, N. (Naama), Reed, D. (David), de Dios, V. R. (Victor Resco), Restrepo-Coupe, N. (Natalia), Reverter, B. R. (Borja R.), Roland, M. (Marilyn), Sabbatini, S. (Simone), Sachs, T. (Torsten), Saleska, S. R. (Scott R.), Sanchez-Canete, E. P. (Enrique P.), Sanchez-Mejia, Z. M. (Zulia M.), Schmid, H. P. (Hans Peter), Schmidt, M. (Marius), Schneider, K. (Karl), Schrader, F. (Frederik), Schroder, I. (Ivan), Scott, R. L. (Russell L.), Sedlak, P. (Pavel), Serrano-Ortiz, P. (Penelope), Shao, C. (Changliang), Shi, P. (Peili), Shironya, I. (Ivan), Siebicke, L. (Lukas), Sigut, L. (Ladislav), Silberstein, R. (Richard), Sirca, C. (Costantino), Spano, D. (Donatella), Steinbrecher, R. (Rainer), Stevens, R. M. (Robert M.), Sturtevant, C. (Cove), Suyker, A. (Andy), Tagesson, T. (Torbern), Takanashi, S. (Satoru), Tang, Y. (Yanhong), Tapper, N. (Nigel), Thom, J. (Jonathan), Tiedemann, F. (Frank), Tomassucci, M. (Michele), Tuovinen, J.-P. (Juha-Pekka), Urbanski, S. (Shawn), Valentini, R. (Riccardo), van der Molen, M. (Michiel), van Gorsel, E. (Eva), van Huissteden, K. (Ko), Varlagin, A. (Andrej), Verfaillie, J. (Joseph), Vesala, T. (Timo), Vincke, C. (Caroline), Vitale, D. (Domenico), Vygodskaya, N. (Natalia), Walker, J. P. (Jeffrey P.), Walter-Shea, E. (Elizabeth), Wang, H. (Huimin), Weber, R. (Robin), Westermann, S. (Sebastian), Wille, C. (Christian), Wofsy, S. (Steven), Wohlfahrt, G. (Georg), Wolf, S. (Sebastian), Woodgate, W. (William), Li, Y. (Yuelin), Zampedri, R. (Roberto), Zhang, J. (Junhui), Zhou, G. (Guoyi), Zona, D. (Donatella), Agarwal, D. (Deb), Biraud, S. (Sebastien), Torn, M. (Margaret), and Papale, D. (Dario)
Abstract: 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.
Published: 2020

18. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.

Author: Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah Y-W, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Ribeca A, van Ingen C, Zhang L, Amiro B, Ammann C, Arain MA, Ardö J, Arkebauer T, Arndt SK, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini LB, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black TA, Blanken PD, Bohrer G, Boike J, Bolstad PV, Bonal D, Bonnefond J-M, Bowling DR, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns SP, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen TR, Cleverly J, Collalti A, Consalvo C, Cook BD, Cook D, Coursolle C, Cremonese E, Curtis PS, D'Andrea E, da Rocha H, Dai X, Davis KJ, De Cinti B, de Grandcourt A, De Ligne A, De Oliveira RC, Delpierre N, Desai AR, Di Bella CM, di Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir HAM, Eugster W, Ewenz CM, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein AH, Gough CM, Goulden ML, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S, Han X, Hansen BU, Hanson C, Hatakka J, He Y, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch WL, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y, Liddell M, Limousin J-M, Lion M, Liska AJ, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis HA, Marras S, Massman W, Mastepanov M, Matamala R, Matthes JH, Mazzenga F, McCaughey H, McHugh I, McMillan AMS, Merbold L, Meyer W, Meyers T, Miller SD, Minerbi S, Moderow U, Monson RK, Montagnani L, Moore CE, Moors E, Moreaux V, Moureaux C, Munger JW, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen JE, Ourcival J-M, Papuga SA, Parmentier F-J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips RP, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober SM, Rambal S, Rannik Ü, Raz-Yaseef N, Reed D, de Dios VR, Restrepo-Coupe N, Reverter BR, Roland M, Sabbatini S, Sachs T, Saleska SR, Sánchez-Cañete EP, Sanchez-Mejia ZM, Schmid HP, Schmidt M, Schneider K, Schrader F, Schroder I, Scott RL, Sedlák P, Serrano-Ortíz P, Shao C, Shi P, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens RM, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y, Tapper N, Thom J, Tiedemann F, Tomassucci M, Tuovinen J-P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker JP, Walter-Shea E, Wang H, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Zampedri R, Zhang J, Zhou G, Zona D, Agarwal D, Biraud S, Torn M, Papale D, Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah Y-W, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Ribeca A, van Ingen C, Zhang L, Amiro B, Ammann C, Arain MA, Ardö J, Arkebauer T, Arndt SK, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini LB, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black TA, Blanken PD, Bohrer G, Boike J, Bolstad PV, Bonal D, Bonnefond J-M, Bowling DR, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns SP, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen TR, Cleverly J, Collalti A, Consalvo C, Cook BD, Cook D, Coursolle C, Cremonese E, Curtis PS, D'Andrea E, da Rocha H, Dai X, Davis KJ, De Cinti B, de Grandcourt A, De Ligne A, De Oliveira RC, Delpierre N, Desai AR, Di Bella CM, di Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir HAM, Eugster W, Ewenz CM, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein AH, Gough CM, Goulden ML, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S, Han X, Hansen BU, Hanson C, Hatakka J, He Y, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch WL, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y, Liddell M, Limousin J-M, Lion M, Liska AJ, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis HA, Marras S, Massman W, Mastepanov M, Matamala R, Matthes JH, Mazzenga F, McCaughey H, McHugh I, McMillan AMS, Merbold L, Meyer W, Meyers T, Miller SD, Minerbi S, Moderow U, Monson RK, Montagnani L, Moore CE, Moors E, Moreaux V, Moureaux C, Munger JW, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen JE, Ourcival J-M, Papuga SA, Parmentier F-J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips RP, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober SM, Rambal S, Rannik Ü, Raz-Yaseef N, Reed D, de Dios VR, Restrepo-Coupe N, Reverter BR, Roland M, Sabbatini S, Sachs T, Saleska SR, Sánchez-Cañete EP, Sanchez-Mejia ZM, Schmid HP, Schmidt M, Schneider K, Schrader F, Schroder I, Scott RL, Sedlák P, Serrano-Ortíz P, Shao C, Shi P, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens RM, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y, Tapper N, Thom J, Tiedemann F, Tomassucci M, Tuovinen J-P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker JP, Walter-Shea E, Wang H, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Zampedri R, Zhang J, Zhou G, Zona D, Agarwal D, Biraud S, Torn M, and Papale D
Abstract: 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.
Published: 2020

19. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.

Author: Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah Y-W, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Ribeca A, van Ingen C, Zhang L, Amiro B, Ammann C, Arain MA, Ardö J, Arkebauer T, Arndt SK, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini LB, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black TA, Blanken PD, Bohrer G, Boike J, Bolstad PV, Bonal D, Bonnefond J-M, Bowling DR, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns SP, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen TR, Cleverly J, Collalti A, Consalvo C, Cook BD, Cook D, Coursolle C, Cremonese E, Curtis PS, D'Andrea E, da Rocha H, Dai X, Davis KJ, De Cinti B, de Grandcourt A, De Ligne A, De Oliveira RC, Delpierre N, Desai AR, Di Bella CM, di Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir HAM, Eugster W, Ewenz CM, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein AH, Gough CM, Goulden ML, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S, Han X, Hansen BU, Hanson C, Hatakka J, He Y, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch WL, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y, Liddell M, Limousin J-M, Lion M, Liska AJ, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis HA, Marras S, Massman W, Mastepanov M, Matamala R, Matthes JH, Mazzenga F, McCaughey H, McHugh I, McMillan AMS, Merbold L, Meyer W, Meyers T, Miller SD, Minerbi S, Moderow U, Monson RK, Montagnani L, Moore CE, Moors E, Moreaux V, Moureaux C, Munger JW, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen JE, Ourcival J-M, Papuga SA, Parmentier F-J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips RP, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober SM, Rambal S, Rannik Ü, Raz-Yaseef N, Reed D, de Dios VR, Restrepo-Coupe N, Reverter BR, Roland M, Sabbatini S, Sachs T, Saleska SR, Sánchez-Cañete EP, Sanchez-Mejia ZM, Schmid HP, Schmidt M, Schneider K, Schrader F, Schroder I, Scott RL, Sedlák P, Serrano-Ortíz P, Shao C, Shi P, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens RM, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y, Tapper N, Thom J, Tiedemann F, Tomassucci M, Tuovinen J-P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker JP, Walter-Shea E, Wang H, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Zampedri R, Zhang J, Zhou G, Zona D, Agarwal D, Biraud S, Torn M, Papale D, Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah Y-W, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Ribeca A, van Ingen C, Zhang L, Amiro B, Ammann C, Arain MA, Ardö J, Arkebauer T, Arndt SK, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini LB, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black TA, Blanken PD, Bohrer G, Boike J, Bolstad PV, Bonal D, Bonnefond J-M, Bowling DR, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns SP, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen TR, Cleverly J, Collalti A, Consalvo C, Cook BD, Cook D, Coursolle C, Cremonese E, Curtis PS, D'Andrea E, da Rocha H, Dai X, Davis KJ, De Cinti B, de Grandcourt A, De Ligne A, De Oliveira RC, Delpierre N, Desai AR, Di Bella CM, di Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir HAM, Eugster W, Ewenz CM, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein AH, Gough CM, Goulden ML, Graf A, Griebel A, Gruening C, Grünwald T, Hammerle A, Han S, Han X, Hansen BU, Hanson C, Hatakka J, He Y, Hehn M, Heinesch B, Hinko-Najera N, Hörtnagl L, Hutley L, Ibrom A, Ikawa H, Jackowicz-Korczynski M, Janouš D, Jans W, Jassal R, Jiang S, Kato T, Khomik M, Klatt J, Knohl A, Knox S, Kobayashi H, Koerber G, Kolle O, Kosugi Y, Kotani A, Kowalski A, Kruijt B, Kurbatova J, Kutsch WL, Kwon H, Launiainen S, Laurila T, Law B, Leuning R, Li Y, Liddell M, Limousin J-M, Lion M, Liska AJ, Lohila A, López-Ballesteros A, López-Blanco E, Loubet B, Loustau D, Lucas-Moffat A, Lüers J, Ma S, Macfarlane C, Magliulo V, Maier R, Mammarella I, Manca G, Marcolla B, Margolis HA, Marras S, Massman W, Mastepanov M, Matamala R, Matthes JH, Mazzenga F, McCaughey H, McHugh I, McMillan AMS, Merbold L, Meyer W, Meyers T, Miller SD, Minerbi S, Moderow U, Monson RK, Montagnani L, Moore CE, Moors E, Moreaux V, Moureaux C, Munger JW, Nakai T, Neirynck J, Nesic Z, Nicolini G, Noormets A, Northwood M, Nosetto M, Nouvellon Y, Novick K, Oechel W, Olesen JE, Ourcival J-M, Papuga SA, Parmentier F-J, Paul-Limoges E, Pavelka M, Peichl M, Pendall E, Phillips RP, Pilegaard K, Pirk N, Posse G, Powell T, Prasse H, Prober SM, Rambal S, Rannik Ü, Raz-Yaseef N, Reed D, de Dios VR, Restrepo-Coupe N, Reverter BR, Roland M, Sabbatini S, Sachs T, Saleska SR, Sánchez-Cañete EP, Sanchez-Mejia ZM, Schmid HP, Schmidt M, Schneider K, Schrader F, Schroder I, Scott RL, Sedlák P, Serrano-Ortíz P, Shao C, Shi P, Shironya I, Siebicke L, Šigut L, Silberstein R, Sirca C, Spano D, Steinbrecher R, Stevens RM, Sturtevant C, Suyker A, Tagesson T, Takanashi S, Tang Y, Tapper N, Thom J, Tiedemann F, Tomassucci M, Tuovinen J-P, Urbanski S, Valentini R, van der Molen M, van Gorsel E, van Huissteden K, Varlagin A, Verfaillie J, Vesala T, Vincke C, Vitale D, Vygodskaya N, Walker JP, Walter-Shea E, Wang H, Weber R, Westermann S, Wille C, Wofsy S, Wohlfahrt G, Wolf S, Woodgate W, Zampedri R, Zhang J, Zhou G, Zona D, Agarwal D, Biraud S, Torn M, and Papale D
Abstract: 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.
Published: 2020

20. The validity of optimal leaf traits modelled on environmental conditions

Author: Bloomfield, KJ, Prentice, IC, Cernusak, LA, Eamus, D, Medlyn, BE, Rumman, R, Wright, IJ, Boer, MM, Cale, P, Cleverly, J, Egerton, JJG, Ellsworth, DS, Evans, BJ, Hayes, LS, Hutchinson, MF, Liddell, MJ, Macfarlane, C, Meyer, WS, Togashi, HF, Wardlaw, T, Zhu, L, Atkin, OK, Bloomfield, KJ, Prentice, IC, Cernusak, LA, Eamus, D, Medlyn, BE, Rumman, R, Wright, IJ, Boer, MM, Cale, P, Cleverly, J, Egerton, JJG, Ellsworth, DS, Evans, BJ, Hayes, LS, Hutchinson, MF, Liddell, MJ, Macfarlane, C, Meyer, WS, Togashi, HF, Wardlaw, T, Zhu, L, and Atkin, OK
Published: 2019

21. The validity of optimal leaf traits modelled on environmental conditions

Author: Bloomfield, KJ, Prentice, IC, Cernusak, LA, Eamus, D, Medlyn, BE, Rumman, R, Wright, IJ, Boer, MM, Cale, P, Cleverly, J, Egerton, JJG, Ellsworth, DS, Evans, BJ, Hayes, LS, Hutchinson, MF, Liddell, MJ, Macfarlane, C, Meyer, WS, Togashi, HF, Wardlaw, T, Zhu, L, Atkin, OK, Bloomfield, KJ, Prentice, IC, Cernusak, LA, Eamus, D, Medlyn, BE, Rumman, R, Wright, IJ, Boer, MM, Cale, P, Cleverly, J, Egerton, JJG, Ellsworth, DS, Evans, BJ, Hayes, LS, Hutchinson, MF, Liddell, MJ, Macfarlane, C, Meyer, WS, Togashi, HF, Wardlaw, T, Zhu, L, and Atkin, OK
Published: 2019

22. A continental-scale assessment of variability in leaf traits: Within species, across sites and between seasons

Author: Bloomfield, KJ, Cernusak, LA, Eamus, D, Ellsworth, DS, Colin Prentice, I, Wright, IJ, Boer, MM, Bradford, MG, Cale, P, Cleverly, J, Egerton, JJG, Evans, BJ, Hayes, LS, Hutchinson, MF, Liddell, MJ, Macfarlane, C, Meyer, WS, Prober, SM, Togashi, HF, Wardlaw, T, Zhu, L, Atkin, OK, Bloomfield, KJ, Cernusak, LA, Eamus, D, Ellsworth, DS, Colin Prentice, I, Wright, IJ, Boer, MM, Bradford, MG, Cale, P, Cleverly, J, Egerton, JJG, Evans, BJ, Hayes, LS, Hutchinson, MF, Liddell, MJ, Macfarlane, C, Meyer, WS, Prober, SM, Togashi, HF, Wardlaw, T, Zhu, L, and Atkin, OK
Abstract: © 2018 The Authors. Functional Ecology © 2018 British Ecological Society Plant species show considerable leaf trait variability that should be accounted for in dynamic global vegetation models (DGVMs). In particular, differences in the acclimation of leaf traits during periods more and less favourable to growth have rarely been examined. We conducted a field study of leaf trait variation at seven sites spanning a range of climates and latitudes across the Australian continent; 80 native plant species were included. We measured key traits associated with leaf structure, chemistry and metabolism during the favourable and unfavourable growing seasons. Leaf traits differed widely in the degree of seasonal variation displayed. Leaf mass per unit area (Ma) showed none. At the other extreme, seasonal variation accounted for nearly a third of total variability in dark respiration (Rdark). At the non-tropical sites, carboxylation capacity (Vcmax) at the prevailing growth temperature was typically higher in summer than in winter. When Vcmax was normalized to a common reference temperature (25°C), however, the opposite pattern was observed for about 30% of the species. This suggests that metabolic acclimation is possible, but far from universal. Intraspecific variation—combining measurements of individual plants repeated at contrasting seasons, different leaves from the same individual, and multiple conspecific plants at a given site—dominated total variation for leaf metabolic traits Vcmax and Rdark. By contrast, site location was the major source of variation (53%) for Ma. Interspecific trait variation ranged from only 13% of total variation for Vcmax up to 43% for nitrogen content per unit leaf area. These findings do not support a common practice in DGVMs of assigning fixed leaf trait values to plant functional types. Trait-based models should allow for interspecific differences, together with spatial and temporal plasticity in leaf structural, chemical and metabolic trait
Published: 2018

23. A continental-scale assessment of variability in leaf traits: Within species, across sites and between seasons

Author: Bloomfield, KJ, Cernusak, LA, Eamus, D, Ellsworth, DS, Colin Prentice, I, Wright, IJ, Boer, MM, Bradford, MG, Cale, P, Cleverly, J, Egerton, JJG, Evans, BJ, Hayes, LS, Hutchinson, MF, Liddell, MJ, Macfarlane, C, Meyer, WS, Prober, SM, Togashi, HF, Wardlaw, T, Zhu, L, Atkin, OK, Bloomfield, KJ, Cernusak, LA, Eamus, D, Ellsworth, DS, Colin Prentice, I, Wright, IJ, Boer, MM, Bradford, MG, Cale, P, Cleverly, J, Egerton, JJG, Evans, BJ, Hayes, LS, Hutchinson, MF, Liddell, MJ, Macfarlane, C, Meyer, WS, Prober, SM, Togashi, HF, Wardlaw, T, Zhu, L, and Atkin, OK
Abstract: © 2018 The Authors. Functional Ecology © 2018 British Ecological Society Plant species show considerable leaf trait variability that should be accounted for in dynamic global vegetation models (DGVMs). In particular, differences in the acclimation of leaf traits during periods more and less favourable to growth have rarely been examined. We conducted a field study of leaf trait variation at seven sites spanning a range of climates and latitudes across the Australian continent; 80 native plant species were included. We measured key traits associated with leaf structure, chemistry and metabolism during the favourable and unfavourable growing seasons. Leaf traits differed widely in the degree of seasonal variation displayed. Leaf mass per unit area (Ma) showed none. At the other extreme, seasonal variation accounted for nearly a third of total variability in dark respiration (Rdark). At the non-tropical sites, carboxylation capacity (Vcmax) at the prevailing growth temperature was typically higher in summer than in winter. When Vcmax was normalized to a common reference temperature (25°C), however, the opposite pattern was observed for about 30% of the species. This suggests that metabolic acclimation is possible, but far from universal. Intraspecific variation—combining measurements of individual plants repeated at contrasting seasons, different leaves from the same individual, and multiple conspecific plants at a given site—dominated total variation for leaf metabolic traits Vcmax and Rdark. By contrast, site location was the major source of variation (53%) for Ma. Interspecific trait variation ranged from only 13% of total variation for Vcmax up to 43% for nitrogen content per unit leaf area. These findings do not support a common practice in DGVMs of assigning fixed leaf trait values to plant functional types. Trait-based models should allow for interspecific differences, together with spatial and temporal plasticity in leaf structural, chemical and metabolic trait
Published: 2018

24. Australian osteopathic students' perceptions of interprofessional relationships

Author: Vaughan, B, Moore, K, Macfarlane, C, Grace, S, Vaughan, B, Moore, K, Macfarlane, C, and Grace, S
Abstract: Background Interprofessional education is increasingly included in health profession education programs as governments strive to improve patient care and outcomes through interprofessional teams. Objective To investigate Australian osteopathy students perceptions of interprofessional relationships. Design Single administration of a questionnaire to students explore perceptions of interprofessional relationships. Setting Two Australian university-based osteopathy programs. Methods Students completed the Interdisciplinary Education Perception Scale (IEPS) and a demographic questionnaire. Descriptive data were generated for each of the IEPS items, total and factor scores. Ordinal logistic regression was used to evaluate the association between the IEPS items and the demographic variables: age; gender; university; and year level. Cronbach's alpha was calculated as the reliability estimate. Participants Students enrolled in the osteopathy programs at Victoria University (Melbourne, Australia) and Southern Cross University (Lismore, Australia). Results Responses from 319 students were analysed (63% response rate). The mean total IEPS score was 246.46 (±23.79). No association was observed for the demographic variables and eleven of the IEPS items in the regression models. Moderate odds ratios were observed for year 3 students in relation to their perception of autonomy and respect, where these perceptions were less positive compared to students in other year levels. Age, gender and university were significant in the regression models for other IEPS items however these odds ratios were either trivial or small. Cronbach's alpha was 0.818 indicating an acceptable internal structure for the 18-item IEPS. Conclusions The perception of Australian osteopathy students towards interprofessional relationships was largely positive, and not associated with demographics such as age, gender, program year level and the university they were attending. Students identified positive aspects
Published: 2017

25. The SMAP Level 4 Carbon Product for Monitoring Ecosystem Land-Atmosphere CO2 Exchange

Author: Jones, LA, Kimball, JS, Reichle, RH, Madani, N, Glassy, J, Ardizzone, JV, Colliander, A, Cleverly, J, Desai, AR, Eamus, D, Euskirchen, ES, Hutley, L, Macfarlane, C, Scott, RL, Jones, LA, Kimball, JS, Reichle, RH, Madani, N, Glassy, J, Ardizzone, JV, Colliander, A, Cleverly, J, Desai, AR, Eamus, D, Euskirchen, ES, Hutley, L, Macfarlane, C, and Scott, RL
Abstract: © 1980-2012 IEEE. The National Aeronautics and Space Administration's Soil Moisture Active Passive (SMAP) mission Level 4 Carbon (L4C) product provides model estimates of the Net Ecosystem CO2 exchange (NEE) incorporating SMAP soil moisture information. The L4C product includes NEE, computed as total ecosystem respiration less gross photosynthesis, at a daily time step posted to a 9-km global grid by plant functional type. Component carbon fluxes, surface soil organic carbon stocks, underlying environmental constraints, and detailed uncertainty metrics are also included. The L4C model is driven by the SMAP Level 4 Soil Moisture data assimilation product, with additional inputs from the Goddard Earth Observing System, Version 5 weather analysis, and Moderate Resolution Imaging Spectroradiometer satellite vegetation data. The L4C data record extends from March 31, 2015 to present with ongoing production and 8-12 day latency. Comparisons against concurrent global CO2 eddy flux tower measurements, satellite solar-induced canopy florescence, and other independent observation benchmarks show favorable L4C performance and accuracy, capturing the dynamic biosphere response to recent weather anomalies. Model experiments and L4C spatiotemporal variability were analyzed to understand the independent value of soil moisture and SMAP observations relative to other sources of input information. This analysis highlights the potential for microwave observations to inform models where soil moisture strongly controls land CO2 flux variability; however, skill improvement relative to flux towers is not yet discernable within the relatively short validation period. These results indicate that SMAP provides a unique and promising capability for monitoring the linked global terrestrial water and carbon cycles.
Published: 2017

26. The SMAP Level 4 Carbon Product for Monitoring Ecosystem Land-Atmosphere CO2 Exchange

Author: Jones, LA, Kimball, JS, Reichle, RH, Madani, N, Glassy, J, Ardizzone, JV, Colliander, A, Cleverly, J, Desai, AR, Eamus, D, Euskirchen, ES, Hutley, L, Macfarlane, C, Scott, RL, Jones, LA, Kimball, JS, Reichle, RH, Madani, N, Glassy, J, Ardizzone, JV, Colliander, A, Cleverly, J, Desai, AR, Eamus, D, Euskirchen, ES, Hutley, L, Macfarlane, C, and Scott, RL
Abstract: © 1980-2012 IEEE. The National Aeronautics and Space Administration's Soil Moisture Active Passive (SMAP) mission Level 4 Carbon (L4C) product provides model estimates of the Net Ecosystem CO2 exchange (NEE) incorporating SMAP soil moisture information. The L4C product includes NEE, computed as total ecosystem respiration less gross photosynthesis, at a daily time step posted to a 9-km global grid by plant functional type. Component carbon fluxes, surface soil organic carbon stocks, underlying environmental constraints, and detailed uncertainty metrics are also included. The L4C model is driven by the SMAP Level 4 Soil Moisture data assimilation product, with additional inputs from the Goddard Earth Observing System, Version 5 weather analysis, and Moderate Resolution Imaging Spectroradiometer satellite vegetation data. The L4C data record extends from March 31, 2015 to present with ongoing production and 8-12 day latency. Comparisons against concurrent global CO2 eddy flux tower measurements, satellite solar-induced canopy florescence, and other independent observation benchmarks show favorable L4C performance and accuracy, capturing the dynamic biosphere response to recent weather anomalies. Model experiments and L4C spatiotemporal variability were analyzed to understand the independent value of soil moisture and SMAP observations relative to other sources of input information. This analysis highlights the potential for microwave observations to inform models where soil moisture strongly controls land CO2 flux variability; however, skill improvement relative to flux towers is not yet discernable within the relatively short validation period. These results indicate that SMAP provides a unique and promising capability for monitoring the linked global terrestrial water and carbon cycles.
Published: 2017

27. Carbon uptake and water use in woodlands and forests in southern Australia during an extreme heat wave event in the 'angry Summer' of 2012/2013

Author: Van Gorsel, E, Van Gorsel, E, Wolf, S, Cleverly, J, Isaac, P, Haverd, V, Ewenz, C, Arndt, S, Beringer, J, De Dios, VR, Evans, BJ, Griebel, A, Hutley, LB, Keenan, T, Kljun, N, Macfarlane, C, Meyer, WS, McHugh, I, Pendall, E, Prober, SM, Silberstein, R, Van Gorsel, E, Van Gorsel, E, Wolf, S, Cleverly, J, Isaac, P, Haverd, V, Ewenz, C, Arndt, S, Beringer, J, De Dios, VR, Evans, BJ, Griebel, A, Hutley, LB, Keenan, T, Kljun, N, Macfarlane, C, Meyer, WS, McHugh, I, Pendall, E, Prober, SM, and Silberstein, R
Abstract: As a result of climate change warmer temperatures are projected through the 21st century and are already increasing above modelled predictions. Apart from increases in the mean, warm/hot temperature extremes are expected to become more prevalent in the future, along with an increase in the frequency of droughts. It is crucial to better understand the response of terrestrial ecosystems to such temperature extremes for predicting land-surface feedbacks in a changing climate. While land-surface feedbacks in drought conditions and during heat waves have been reported from Europe and the US, direct observations of the impact of such extremes on the carbon and water cycles in Australia have been lacking. During the 2012/2013 summer, Australia experienced a record-breaking heat wave with an exceptional spatial extent that lasted for several weeks. In this study we synthesised eddy-covariance measurements from seven woodlands and one forest site across three biogeographic regions in southern Australia. These observations were combined with model results from BIOS2 (Haverd et al., 2013a, b) to investigate the effect of the summer heat wave on the carbon and water exchange of terrestrial ecosystems which are known for their resilience toward hot and dry conditions. We found that water-limited woodland and energy-limited forest ecosystems responded differently to the heat wave. During the most intense part of the heat wave, the woodlands experienced decreased latent heat flux (23ĝ€% of background value), increased Bowen ratio (154ĝ€%) and reduced carbon uptake (60ĝ€%). At the same time the forest ecosystem showed increased latent heat flux (151ĝ€%), reduced Bowen ratio (19ĝ€%) and increased carbon uptake (112ĝ€%). Higher temperatures caused increased ecosystem respiration at all sites (up to 139ĝ€%). During daytime all ecosystems remained carbon sinks, but carbon uptake was reduced in magnitude. The number of hours during which the ecosystem acted as a carbon sink was also red
Published: 2016

28. An introduction to the Australian and New Zealand flux tower network - OzFlux

Author: Beringer, J, Hutley, LB, McHugh, I, Arndt, SK, Campbell, D, Cleugh, HA, Cleverly, J, Resco de Dios, V, Eamus, D, Evans, B, Ewenz, C, Grace, P, Griebel, A, Haverd, V, Hinko-Najera, N, Huete, A, Isaac, P, Kanniah, K, Leuning, R, Liddell, MJ, Macfarlane, C, Meyer, W, Moore, C, Pendall, E, Phillips, A, Phillips, RL, Prober, SM, Restrepo-Coupe, N, Rutledge, S, Schroder, I, Silberstein, R, Southall, P, Yee, MS, Tapper, NJ, van Gorsel, E, Vote, C, Walker, J, Wardlaw, T, Beringer, J, Hutley, LB, McHugh, I, Arndt, SK, Campbell, D, Cleugh, HA, Cleverly, J, Resco de Dios, V, Eamus, D, Evans, B, Ewenz, C, Grace, P, Griebel, A, Haverd, V, Hinko-Najera, N, Huete, A, Isaac, P, Kanniah, K, Leuning, R, Liddell, MJ, Macfarlane, C, Meyer, W, Moore, C, Pendall, E, Phillips, A, Phillips, RL, Prober, SM, Restrepo-Coupe, N, Rutledge, S, Schroder, I, Silberstein, R, Southall, P, Yee, MS, Tapper, NJ, van Gorsel, E, Vote, C, Walker, J, and Wardlaw, T
Abstract: OzFlux is the regional Australian and New Zealand flux tower network that aims to provide a continental-scale national research facility to monitor and assess trends, and improve predictions, of Australia's terrestrial biosphere and climate. This paper describes the evolution, design, and current status of OzFlux as well as provides an overview of data processing. We analyse measurements from all sites within the Australian portion of the OzFlux network and two sites from New Zealand. The response of the Australian biomes to climate was largely consistent with global studies except that Australian systems had a lower ecosystem water-use efficiency. Australian semi-arid/arid ecosystems are important because of their huge extent (70 %) and they have evolved with common moisture limitations. We also found that Australian ecosystems had a similar radiation-use efficiency per unit leaf area compared to global values that indicates a convergence toward a similar biochemical efficiency. The two New Zealand sites represented extremes in productivity for a moist temperate climate zone, with the grazed dairy farm site having the highest GPP of any OzFlux site (2620 gC m−2 yr−1) and the natural raised peat bog site having a very low GPP (820 gC m−2 yr−1). The paper discusses the utility of the flux data and the synergies between flux, remote sensing, and modelling. Lastly, the paper looks ahead at the future direction of the network and concludes that there has been a substantial contribution by OzFlux, and considerable opportunities remain to further advance our understanding of ecosystem response to disturbances, including drought, fire, land-use and land-cover change, land management, and climate change, which are relevant both nationally and internationally. It is suggested that a synergistic approach is required to address all of the spatial, ecological, human, and cultural challenges of managing the delicately balanced ecosystems in Australasia.
Published: 2016

29. Carbon uptake and water use in woodlands and forests in southern Australia during an extreme heat wave event in the 'angry Summer' of 2012/2013

Author: Van Gorsel, E, Van Gorsel, E, Wolf, S, Cleverly, J, Isaac, P, Haverd, V, Ewenz, C, Arndt, S, Beringer, J, De Dios, VR, Evans, BJ, Griebel, A, Hutley, LB, Keenan, T, Kljun, N, Macfarlane, C, Meyer, WS, McHugh, I, Pendall, E, Prober, SM, Silberstein, R, Van Gorsel, E, Van Gorsel, E, Wolf, S, Cleverly, J, Isaac, P, Haverd, V, Ewenz, C, Arndt, S, Beringer, J, De Dios, VR, Evans, BJ, Griebel, A, Hutley, LB, Keenan, T, Kljun, N, Macfarlane, C, Meyer, WS, McHugh, I, Pendall, E, Prober, SM, and Silberstein, R
Abstract: As a result of climate change warmer temperatures are projected through the 21st century and are already increasing above modelled predictions. Apart from increases in the mean, warm/hot temperature extremes are expected to become more prevalent in the future, along with an increase in the frequency of droughts. It is crucial to better understand the response of terrestrial ecosystems to such temperature extremes for predicting land-surface feedbacks in a changing climate. While land-surface feedbacks in drought conditions and during heat waves have been reported from Europe and the US, direct observations of the impact of such extremes on the carbon and water cycles in Australia have been lacking. During the 2012/2013 summer, Australia experienced a record-breaking heat wave with an exceptional spatial extent that lasted for several weeks. In this study we synthesised eddy-covariance measurements from seven woodlands and one forest site across three biogeographic regions in southern Australia. These observations were combined with model results from BIOS2 (Haverd et al., 2013a, b) to investigate the effect of the summer heat wave on the carbon and water exchange of terrestrial ecosystems which are known for their resilience toward hot and dry conditions. We found that water-limited woodland and energy-limited forest ecosystems responded differently to the heat wave. During the most intense part of the heat wave, the woodlands experienced decreased latent heat flux (23ĝ€% of background value), increased Bowen ratio (154ĝ€%) and reduced carbon uptake (60ĝ€%). At the same time the forest ecosystem showed increased latent heat flux (151ĝ€%), reduced Bowen ratio (19ĝ€%) and increased carbon uptake (112ĝ€%). Higher temperatures caused increased ecosystem respiration at all sites (up to 139ĝ€%). During daytime all ecosystems remained carbon sinks, but carbon uptake was reduced in magnitude. The number of hours during which the ecosystem acted as a carbon sink was also red
Published: 2016

30. Carbon uptake and water use in woodlands and forests in southern Australia during an extreme heat wave event in the ‘Angry Summer’ of 2012/2013

Author: van Gorsel, E, Wolf, S, Isaac, P, Cleverly, J, Haverd, V, Ewenz, C, Arndt, S, Beringer, J, de Dios, VR, Evans, BJ, Griebel, A, Hutley, LB, Keenan, T, Kljun, N, Macfarlane, C, Meyer, WS, McHugh, I, Pendall, E, Prober, S, Silberstein, R, van Gorsel, E, Wolf, S, Isaac, P, Cleverly, J, Haverd, V, Ewenz, C, Arndt, S, Beringer, J, de Dios, VR, Evans, BJ, Griebel, A, Hutley, LB, Keenan, T, Kljun, N, Macfarlane, C, Meyer, WS, McHugh, I, Pendall, E, Prober, S, and Silberstein, R
Published: 2016

31. Carbon uptake and water use in woodlands and forests in southern Australia during an extreme heat wave event in the 'angry Summer' of 2012/2013

Author: Van Gorsel, E, Wolf, S, Cleverly, J, Isaac, P, Haverd, V, Ewenz, C, Arndt, S, Beringer, J, De Dios, VR, Evans, BJ, Griebel, A, Hutley, LB, Keenan, T, Kljun, N, Macfarlane, C, Meyer, WS, McHugh, I, Pendall, E, Prober, SM, Silberstein, R, Van Gorsel, E, Wolf, S, Cleverly, J, Isaac, P, Haverd, V, Ewenz, C, Arndt, S, Beringer, J, De Dios, VR, Evans, BJ, Griebel, A, Hutley, LB, Keenan, T, Kljun, N, Macfarlane, C, Meyer, WS, McHugh, I, Pendall, E, Prober, SM, and Silberstein, R
Abstract: As a result of climate change warmer temperatures are projected through the 21st century and are already increasing above modelled predictions. Apart from increases in the mean, warm/hot temperature extremes are expected to become more prevalent in the future, along with an increase in the frequency of droughts. It is crucial to better understand the response of terrestrial ecosystems to such temperature extremes for predicting land-surface feedbacks in a changing climate. While land-surface feedbacks in drought conditions and during heat waves have been reported from Europe and the US, direct observations of the impact of such extremes on the carbon and water cycles in Australia have been lacking. During the 2012/2013 summer, Australia experienced a record-breaking heat wave with an exceptional spatial extent that lasted for several weeks. In this study we synthesised eddy-covariance measurements from seven woodlands and one forest site across three biogeographic regions in southern Australia. These observations were combined with model results from BIOS2 (Haverd et al., 2013a, b) to investigate the effect of the summer heat wave on the carbon and water exchange of terrestrial ecosystems which are known for their resilience toward hot and dry conditions. We found that water-limited woodland and energy-limited forest ecosystems responded differently to the heat wave. During the most intense part of the heat wave, the woodlands experienced decreased latent heat flux (23ĝ€% of background value), increased Bowen ratio (154ĝ€%) and reduced carbon uptake (60ĝ€%). At the same time the forest ecosystem showed increased latent heat flux (151ĝ€%), reduced Bowen ratio (19ĝ€%) and increased carbon uptake (112ĝ€%). Higher temperatures caused increased ecosystem respiration at all sites (up to 139ĝ€%). During daytime all ecosystems remained carbon sinks, but carbon uptake was reduced in magnitude. The number of hours during which the ecosystem acted as a carbon sink was also red
Published: 2016

32. The Australian SuperSite Network: A continental, long-term terrestrial ecosystem observatory

Author: Karan, M, Liddell, M, Prober, SM, Arndt, S, Beringer, J, Boer, M, Cleverly, J, Eamus, D, Grace, P, Van Gorsel, E, Hero, JM, Hutley, L, Macfarlane, C, Metcalfe, D, Meyer, W, Pendall, E, Sebastian, A, Wardlaw, T, Karan, M, Liddell, M, Prober, SM, Arndt, S, Beringer, J, Boer, M, Cleverly, J, Eamus, D, Grace, P, Van Gorsel, E, Hero, JM, Hutley, L, Macfarlane, C, Metcalfe, D, Meyer, W, Pendall, E, Sebastian, A, and Wardlaw, T
Abstract: © 2016 Elsevier B.V. Ecosystem monitoring networks aim to collect data on physical, chemical and biological systems and their interactions that shape the biosphere. Here we introduce the Australian SuperSite Network that, along with complementary facilities of Australia's Terrestrial Ecosystem Research Network (TERN), delivers field infrastructure and diverse, ecosystem-related datasets for use by researchers, educators and policy makers. The SuperSite Network uses infrastructure replicated across research sites in different biomes, to allow comparisons across ecosystems and improve scalability of findings to regional, continental and global scales. This conforms with the approaches of other ecosystem monitoring networks such as Critical Zone Observatories, the U.S. National Ecological Observatory Network; Analysis and Experimentation on Ecosystems, Europe; Chinese Ecosystem Research Network; International Long Term Ecological Research network and the United States Long Term Ecological Research Network. The Australian SuperSite Network currently involves 10 SuperSites across a diverse range of biomes, including tropical rainforest, grassland and savanna; wet and dry sclerophyll forest and woodland; and semi-arid grassland, woodland and savanna. The focus of the SuperSite Network is on using vegetation, faunal and biophysical monitoring to develop a process-based understanding of ecosystem function and change in Australian biomes; and to link this with data streams provided by the series of flux towers across the network. The Australian SuperSite Network is also intended to support a range of auxiliary researchers who contribute to the growing body of knowledge within and across the SuperSite Network, public outreach and education to promote environmental awareness and the role of ecosystem monitoring in the management of Australian environments.
Published: 2016

33. Carbon uptake and water use in woodlands and forests in southern Australia during an extreme heat wave event in the 'angry Summer' of 2012/2013

Author: Van Gorsel, E, Wolf, S, Cleverly, J, Isaac, P, Haverd, V, Ewenz, C, Arndt, S, Beringer, J, De Dios, VR, Evans, BJ, Griebel, A, Hutley, LB, Keenan, T, Kljun, N, Macfarlane, C, Meyer, WS, McHugh, I, Pendall, E, Prober, SM, Silberstein, R, Van Gorsel, E, Wolf, S, Cleverly, J, Isaac, P, Haverd, V, Ewenz, C, Arndt, S, Beringer, J, De Dios, VR, Evans, BJ, Griebel, A, Hutley, LB, Keenan, T, Kljun, N, Macfarlane, C, Meyer, WS, McHugh, I, Pendall, E, Prober, SM, and Silberstein, R
Abstract: As a result of climate change warmer temperatures are projected through the 21st century and are already increasing above modelled predictions. Apart from increases in the mean, warm/hot temperature extremes are expected to become more prevalent in the future, along with an increase in the frequency of droughts. It is crucial to better understand the response of terrestrial ecosystems to such temperature extremes for predicting land-surface feedbacks in a changing climate. While land-surface feedbacks in drought conditions and during heat waves have been reported from Europe and the US, direct observations of the impact of such extremes on the carbon and water cycles in Australia have been lacking. During the 2012/2013 summer, Australia experienced a record-breaking heat wave with an exceptional spatial extent that lasted for several weeks. In this study we synthesised eddy-covariance measurements from seven woodlands and one forest site across three biogeographic regions in southern Australia. These observations were combined with model results from BIOS2 (Haverd et al., 2013a, b) to investigate the effect of the summer heat wave on the carbon and water exchange of terrestrial ecosystems which are known for their resilience toward hot and dry conditions. We found that water-limited woodland and energy-limited forest ecosystems responded differently to the heat wave. During the most intense part of the heat wave, the woodlands experienced decreased latent heat flux (23ĝ€% of background value), increased Bowen ratio (154ĝ€%) and reduced carbon uptake (60ĝ€%). At the same time the forest ecosystem showed increased latent heat flux (151ĝ€%), reduced Bowen ratio (19ĝ€%) and increased carbon uptake (112ĝ€%). Higher temperatures caused increased ecosystem respiration at all sites (up to 139ĝ€%). During daytime all ecosystems remained carbon sinks, but carbon uptake was reduced in magnitude. The number of hours during which the ecosystem acted as a carbon sink was also red
Published: 2016

34. An introduction to the Australian and New Zealand flux tower network - OzFlux

Author: Beringer, J, Hutley, LB, McHugh, I, Arndt, SK, Campbell, D, Cleugh, HA, Cleverly, J, De Dios, VR, Eamus, D, Evans, B, Ewenz, C, Grace, P, Griebel, A, Haverd, V, Hinko-Najera, N, Huete, A, Isaac, P, Kanniah, K, Leuning, R, Liddell, MJ, MacFarlane, C, Meyer, W, Moore, C, Pendall, E, Phillips, A, Phillips, RL, Prober, SM, Restrepo-Coupe, N, Rutledge, S, Schroder, I, Silberstein, R, Southall, P, Sun Yee, M, Tapper, NJ, Van Gorsel, E, Vote, C, Walker, J, Wardlaw, T, Beringer, J, Hutley, LB, McHugh, I, Arndt, SK, Campbell, D, Cleugh, HA, Cleverly, J, De Dios, VR, Eamus, D, Evans, B, Ewenz, C, Grace, P, Griebel, A, Haverd, V, Hinko-Najera, N, Huete, A, Isaac, P, Kanniah, K, Leuning, R, Liddell, MJ, MacFarlane, C, Meyer, W, Moore, C, Pendall, E, Phillips, A, Phillips, RL, Prober, SM, Restrepo-Coupe, N, Rutledge, S, Schroder, I, Silberstein, R, Southall, P, Sun Yee, M, Tapper, NJ, Van Gorsel, E, Vote, C, Walker, J, and Wardlaw, T
Abstract: © Author(s) 2016. OzFlux is the regional Australian and New Zealand flux tower network that aims to provide a continental-scale national research facility to monitor and assess trends, and improve predictions, of Australia's terrestrial biosphere and climate. This paper describes the evolution, design, and current status of OzFlux as well as provides an overview of data processing. We analyse measurements from all sites within the Australian portion of the OzFlux network and two sites from New Zealand. The response of the Australian biomes to climate was largely consistent with global studies except that Australian systems had a lower ecosystem water-use efficiency. Australian semi-arid/arid ecosystems are important because of their huge extent (70 %) and they have evolved with common moisture limitations. We also found that Australian ecosystems had a similar radiation-use efficiency per unit leaf area compared to global values that indicates a convergence toward a similar biochemical efficiency. The two New Zealand sites represented extremes in productivity for a moist temperate climate zone, with the grazed dairy farm site having the highest GPP of any OzFlux site (2620 gC m-2 yr-1) and the natural raised peat bog site having a very low GPP (820 gC m-2 yr-1). The paper discusses the utility of the flux data and the synergies between flux, remote sensing, and modelling. Lastly, the paper looks ahead at the future direction of the network and concludes that there has been a substantial contribution by OzFlux, and considerable opportunities remain to further advance our understanding of ecosystem response to disturbances, including drought, fire, land-use and land-cover change, land management, and climate change, which are relevant both nationally and internationally. It is suggested that a synergistic approach is required to address all of the spatial, ecological, human, and cultural challenges of managing the delicately balanced ecosystems in Australasia.
Published: 2016

35. The Australian SuperSite Network: A continental, long-term terrestrial ecosystem observatory

Author: Karan, M, Liddell, M, Prober, SM, Arndt, S, Beringer, J, Boer, M, Cleverly, J, Eamus, D, Grace, P, Van Gorsel, E, Hero, JM, Hutley, L, Macfarlane, C, Metcalfe, D, Meyer, W, Pendall, E, Sebastian, A, Wardlaw, T, Karan, M, Liddell, M, Prober, SM, Arndt, S, Beringer, J, Boer, M, Cleverly, J, Eamus, D, Grace, P, Van Gorsel, E, Hero, JM, Hutley, L, Macfarlane, C, Metcalfe, D, Meyer, W, Pendall, E, Sebastian, A, and Wardlaw, T
Abstract: © 2016 Elsevier B.V. Ecosystem monitoring networks aim to collect data on physical, chemical and biological systems and their interactions that shape the biosphere. Here we introduce the Australian SuperSite Network that, along with complementary facilities of Australia's Terrestrial Ecosystem Research Network (TERN), delivers field infrastructure and diverse, ecosystem-related datasets for use by researchers, educators and policy makers. The SuperSite Network uses infrastructure replicated across research sites in different biomes, to allow comparisons across ecosystems and improve scalability of findings to regional, continental and global scales. This conforms with the approaches of other ecosystem monitoring networks such as Critical Zone Observatories, the U.S. National Ecological Observatory Network; Analysis and Experimentation on Ecosystems, Europe; Chinese Ecosystem Research Network; International Long Term Ecological Research network and the United States Long Term Ecological Research Network. The Australian SuperSite Network currently involves 10 SuperSites across a diverse range of biomes, including tropical rainforest, grassland and savanna; wet and dry sclerophyll forest and woodland; and semi-arid grassland, woodland and savanna. The focus of the SuperSite Network is on using vegetation, faunal and biophysical monitoring to develop a process-based understanding of ecosystem function and change in Australian biomes; and to link this with data streams provided by the series of flux towers across the network. The Australian SuperSite Network is also intended to support a range of auxiliary researchers who contribute to the growing body of knowledge within and across the SuperSite Network, public outreach and education to promote environmental awareness and the role of ecosystem monitoring in the management of Australian environments.
Published: 2016

36. An introduction to the Australian and New Zealand flux tower network - OzFlux

Author: Beringer, J, Hutley, LB, McHugh, I, Arndt, SK, Campbell, D, Cleugh, HA, Cleverly, J, De Dios, VR, Eamus, D, Evans, B, Ewenz, C, Grace, P, Griebel, A, Haverd, V, Hinko-Najera, N, Huete, A, Isaac, P, Kanniah, K, Leuning, R, Liddell, MJ, MacFarlane, C, Meyer, W, Moore, C, Pendall, E, Phillips, A, Phillips, RL, Prober, SM, Restrepo-Coupe, N, Rutledge, S, Schroder, I, Silberstein, R, Southall, P, Sun Yee, M, Tapper, NJ, Van Gorsel, E, Vote, C, Walker, J, Wardlaw, T, Beringer, J, Hutley, LB, McHugh, I, Arndt, SK, Campbell, D, Cleugh, HA, Cleverly, J, De Dios, VR, Eamus, D, Evans, B, Ewenz, C, Grace, P, Griebel, A, Haverd, V, Hinko-Najera, N, Huete, A, Isaac, P, Kanniah, K, Leuning, R, Liddell, MJ, MacFarlane, C, Meyer, W, Moore, C, Pendall, E, Phillips, A, Phillips, RL, Prober, SM, Restrepo-Coupe, N, Rutledge, S, Schroder, I, Silberstein, R, Southall, P, Sun Yee, M, Tapper, NJ, Van Gorsel, E, Vote, C, Walker, J, and Wardlaw, T
Abstract: © Author(s) 2016. OzFlux is the regional Australian and New Zealand flux tower network that aims to provide a continental-scale national research facility to monitor and assess trends, and improve predictions, of Australia's terrestrial biosphere and climate. This paper describes the evolution, design, and current status of OzFlux as well as provides an overview of data processing. We analyse measurements from all sites within the Australian portion of the OzFlux network and two sites from New Zealand. The response of the Australian biomes to climate was largely consistent with global studies except that Australian systems had a lower ecosystem water-use efficiency. Australian semi-arid/arid ecosystems are important because of their huge extent (70 %) and they have evolved with common moisture limitations. We also found that Australian ecosystems had a similar radiation-use efficiency per unit leaf area compared to global values that indicates a convergence toward a similar biochemical efficiency. The two New Zealand sites represented extremes in productivity for a moist temperate climate zone, with the grazed dairy farm site having the highest GPP of any OzFlux site (2620 gC m-2 yr-1) and the natural raised peat bog site having a very low GPP (820 gC m-2 yr-1). The paper discusses the utility of the flux data and the synergies between flux, remote sensing, and modelling. Lastly, the paper looks ahead at the future direction of the network and concludes that there has been a substantial contribution by OzFlux, and considerable opportunities remain to further advance our understanding of ecosystem response to disturbances, including drought, fire, land-use and land-cover change, land management, and climate change, which are relevant both nationally and internationally. It is suggested that a synergistic approach is required to address all of the spatial, ecological, human, and cultural challenges of managing the delicately balanced ecosystems in Australasia.
Published: 2016

37. Geochemical Dispersion Through Transported Cover in Regolith-Dominated Terrains—Toward an Understanding of Process

Author: Anand, R., Lintern, M., Noble, R., Aspandiar, Mehrooz, MacFarlane, C., Hough, R., Stewart, A., Wakelin, S., Townley, B., Reid, N., Anand, R., Lintern, M., Noble, R., Aspandiar, Mehrooz, MacFarlane, C., Hough, R., Stewart, A., Wakelin, S., Townley, B., and Reid, N.
Abstract: As mineral exploration moves into regions dominated by transported cover, conventional techniques (e.g., lag gravel) may not be applicable and thus, increasingly, there is a need for new, innovative approaches. To develop these approaches, potential mechanisms that transfer metals from buried mineral deposits through cover to the surface need to be identified. This paper presents an overview of some of the experimental and field trials conducted in Australia as part of an industry-supported three-year CSIRO/AMIRA project. The objective was to define vadose zone processes that might form elemental anomalies at surface over buried deposits in semiarid and arid terrains, and to compare methods that detect these anomalies. Studies were conducted at seven sites representing orogenic Au, volcanogenic massive sulfide (VMS; Cu-Zn-Ag), and magmatic Ni mineralization with transported cover ranging in thickness from 2 to 30 m. Three vertical metal migration mechanisms are important in vadose environments: (1) biological, (2) gaseous, and (3) capillary. An integrated approach, combining different mechanisms with the nature and evolution of transported regolith and climatic settings, was considered to obtain the best prediction of metal transfer.Upward element transfer by vegetation (Acacia aneura and Eucalyptus spp.) occurs in areas of transported cover up to 30 m thick, but not in environments which lack supergene enrichment and have hypersaline acid groundwater. Microbial populations are different in soil over mineral deposits than in those from background sites. Metals, detected by gas collectors, are transferred to surface as gases. Soil pit experiments show that strong geochemical anomalies can form rapidly (over 7 months) through 2 m of transported cover, and assist in understanding the genesis of natural geochemical anomalies. Seasonal variations suggest that migration of elements from source to surface may vary in time and intensity. Anomaly formation in the pit experim
Published: 2015

38. The DREEM, part 1: measurement of the educational environment in an osteopathy teaching program

Author: Vaughan, B, Carter, A, Macfarlane, C, Morrison, T, Vaughan, B, Carter, A, Macfarlane, C, and Morrison, T
Abstract: BACKGROUND: Measurement of the educational environment has become more common in health professional education programs. Information gained from these investigations can be used to implement and measure changes to the curricula, educational delivery and the physical environment. A number of questionnaires exist to measure the educational environment, and the most commonly utilised of these is the Dundee Ready Educational Environment Measure (DREEM). METHODS: The DREEM was administered to students in all year levels of the osteopathy program at Victoria University (VU), Melbourne, Australia. Students also completed a demographic survey. Inferential and correlational statistics were employed to investigate the educational environment based on the scores obtained from the DREEM. RESULTS: A response rate of 90% was achieved. The mean total DREEM score was 135.37 (+/- 19.33) with the scores ranging from 72 to 179. Some subscales and items demonstrated differences for gender, clinical phase, age and whether the student was in receipt of a government allowance. CONCLUSIONS: There are a number of areas in the program that are performing well, and some aspects that could be improved. Overall students rated the VU osteopathy program as more positive than negative. The information obtained in the present study has identified areas for improvement and will enable the program leaders to facilitate changes. It will also provide other educational institutions with data on which they can make comparisons with their own programs.
Published: 2014

39. Facilitating adaptation of biodiversity to climate change: a conceptual framework applied to the world’s largest Mediterranean-climate woodland

Author: Prober, S.M., Thiele, K.R., Rundel, P. W., Yates, C.J., Berry, S. L., Byrne, M., Christidis, L., Gosper, C.R., Grierson, P.F., Lemson, K., Lyons, T., Macfarlane, C., O’Connor, M.H., Scott, J.K., Standish, R.J., Stock, W.D., Etten, E.J.B., Wardell-Johnson, G.W., Watson, A., Prober, S.M., Thiele, K.R., Rundel, P. W., Yates, C.J., Berry, S. L., Byrne, M., Christidis, L., Gosper, C.R., Grierson, P.F., Lemson, K., Lyons, T., Macfarlane, C., O’Connor, M.H., Scott, J.K., Standish, R.J., Stock, W.D., Etten, E.J.B., Wardell-Johnson, G.W., and Watson, A.
Abstract: The importance of ecological management for reducing the vulnerability of biodiversity to climate change is increasingly recognized, yet frameworks to facilitate a structured approach to climate adaptation management are lacking. We developed a conceptual framework that can guide identification of climate change impacts and adaptive management options in a given region or biome. The framework focuses on potential points of early climate change impact, and organizes these along two main axes. First, it recognizes that climate change can act at a range of ecological scales. Secondly, it emphasizes that outcomes are dependent on two potentially interacting and countervailing forces: (1) changes to environmental parameters and ecological processes brought about by climate change, and (2) responses of component systems as determined by attributes of resistance and resilience. Through this structure, the framework draws together a broad range of ecological concepts, with a novel emphasis on attributes of resistance and resilience that can temper the response of species, ecosystems and landscapes to climate change. We applied the framework to the world's largest remaining Mediterranean-climate woodland, the 'Great Western Woodlands' of south-western Australia. In this relatively intact region, maintaining inherent resistance and resilience by preventing anthropogenic degradation is of highest priority and lowest risk. Limited, higher risk options such as fire management, protection of refugia and translocation of adaptive genes may be justifiable under more extreme change, hence our capacity to predict the extent of change strongly impinges on such management decisions. These conclusions may contrast with similar analyses in degraded landscapes, where natural integrity is already compromised, and existing investment in restoration may facilitate experimentation with higher risk options.
Published: 2012

40. Facilitating adaptation of biodiversity to climate change: a conceptual framework applied to the world’s largest Mediterranean-climate woodland

Author: Prober, S.M., Thiele, K.R., Rundel, P. W., Yates, C.J., Berry, S. L., Byrne, M., Christidis, L., Gosper, C.R., Grierson, P.F., Lemson, K., Lyons, T., Macfarlane, C., O’Connor, M.H., Scott, J.K., Standish, R.J., Stock, W.D., Etten, E.J.B., Wardell-Johnson, G.W., Watson, A., Prober, S.M., Thiele, K.R., Rundel, P. W., Yates, C.J., Berry, S. L., Byrne, M., Christidis, L., Gosper, C.R., Grierson, P.F., Lemson, K., Lyons, T., Macfarlane, C., O’Connor, M.H., Scott, J.K., Standish, R.J., Stock, W.D., Etten, E.J.B., Wardell-Johnson, G.W., and Watson, A.
Abstract: The importance of ecological management for reducing the vulnerability of biodiversity to climate change is increasingly recognized, yet frameworks to facilitate a structured approach to climate adaptation management are lacking. We developed a conceptual framework that can guide identification of climate change impacts and adaptive management options in a given region or biome. The framework focuses on potential points of early climate change impact, and organizes these along two main axes. First, it recognizes that climate change can act at a range of ecological scales. Secondly, it emphasizes that outcomes are dependent on two potentially interacting and countervailing forces: (1) changes to environmental parameters and ecological processes brought about by climate change, and (2) responses of component systems as determined by attributes of resistance and resilience. Through this structure, the framework draws together a broad range of ecological concepts, with a novel emphasis on attributes of resistance and resilience that can temper the response of species, ecosystems and landscapes to climate change. We applied the framework to the world's largest remaining Mediterranean-climate woodland, the 'Great Western Woodlands' of south-western Australia. In this relatively intact region, maintaining inherent resistance and resilience by preventing anthropogenic degradation is of highest priority and lowest risk. Limited, higher risk options such as fire management, protection of refugia and translocation of adaptive genes may be justifiable under more extreme change, hence our capacity to predict the extent of change strongly impinges on such management decisions. These conclusions may contrast with similar analyses in degraded landscapes, where natural integrity is already compromised, and existing investment in restoration may facilitate experimentation with higher risk options.
Published: 2012

41. Facilitating adaptation of biodiversity to climate change: a conceptual framework applied to the world’s largest Mediterranean-climate woodland

Author: Prober, S., Thiele, K., Rundel, P., Yates, C., Berry, S., Byrne, M., Christidis, L., Gosper, C., Grierson, P., Lemson, K., Lyons, T., Macfarlane, C., O'Connor, M., Scott, J., Standish, R., Stock, W., Van Etten, E j, Wardell-Johnson, Grant, Watson, A., Prober, S., Thiele, K., Rundel, P., Yates, C., Berry, S., Byrne, M., Christidis, L., Gosper, C., Grierson, P., Lemson, K., Lyons, T., Macfarlane, C., O'Connor, M., Scott, J., Standish, R., Stock, W., Van Etten, E j, Wardell-Johnson, Grant, and Watson, A.
Abstract: The importance of ecological management for reducing the vulnerability of biodiversity to climate change is increasingly recognized, yet frameworks to facilitate a structured approach to climate adaptation management are lacking. We developed a conceptual framework that can guide identification of climate change impacts and adaptive management options in a given region or biome. The framework focuses on potential points of early climate change impact, and organizes these along two main axes. First, it recognizes that climate change can act at a range of ecological scales. Secondly, it emphasizes that outcomes are dependent on two potentially interacting and countervailing forces: (1) changes to environmental parameters and ecological processes brought about by climate change, and (2) responses of component systems as determined by attributes of resistance and resilience. Through this structure, the framework draws together a broad range of ecological concepts, with a novel emphasis on attributes of resistance and resilience that can temper the response of species, ecosystems and landscapes to climate change.We applied the framework to the world’s largest remaining Mediterranean-climate woodland, the ‘Great Western Woodlands’ of south-western Australia. In this relatively intact region, maintaining inherent resistance and resilience by preventing anthropogenic degradation is of highest priority and lowest risk. Limited, higher risk options such as fire management, protection of refugia and translocation of adaptive genes may be justifiable under more extreme change, hence our capacity to predict the extent of change strongly impinges on such management decisions. These conclusions may contrast with similar analyses in degraded landscapes, where natural integrity is already compromised, and existing investment in restoration may facilitate experimentation with higher risk options.
Published: 2012

42. Does initial spacing influence crown and hydraulic architecture of Eucalyptus marginata?

Author: Grigg, AH, Macfarlane, C, Evangelista, C, Eamus, D, Adams, MA, Grigg, AH, Macfarlane, C, Evangelista, C, Eamus, D, and Adams, MA
Abstract: Long-term declines in rainfall in south-western Australia have resulted in increased interest in the hydraulic characteristics of jarrah (Eucalyptus marginata Donn ex Smith) forest established in the region's drinking water catchments on rehabilitated bauxite mining sites. We hypothesized that in jarrah forest established on rehabilitated mine sites: (1) leaf area index (L) is independent of initial tree spacing; and (2) more densely planted trees have less leaf area for the same leaf mass, or the same sapwood area, and have denser sapwood. Initial stand densities ranged from about 600 to 9000 stems ha -1, and trees were 18 years old at the time of sampling. Leaf area index was unaffected by initial stand density, except in the most sparsely stocked stands where L was 1.2 compared with 2.0-2.5 in stands at other spacings. The ratio of leaf area to sapwood area (Al:As) was unaffected by tree spacing or tree size and was 0.2 at 1.3 m height and 0.25 at the crown base. There were small increases in sapwood density and decreases in leaf specific area with increased spacing. Tree diameter or basal area was a better predictor of leaf area than sapwood area. At the stand scale, basal area was a good predictor of L (r2 = 0.98, n = 15) except in the densest stands. We conclude that the hydraulic attributes of this forest type are largely independent of initial tree spacing, thus simplifying parameterization of stand and catchment water balance models. © 2008 Heron Publishing.
Published: 2008

43. Does initial spacing influence crown and hydraulic architecture of Eucalyptus marginata?

Author: Grigg, AH, Macfarlane, C, Evangelista, C, Eamus, D, Adams, MA, Grigg, AH, Macfarlane, C, Evangelista, C, Eamus, D, and Adams, MA
Abstract: Long-term declines in rainfall in south-western Australia have resulted in increased interest in the hydraulic characteristics of jarrah (Eucalyptus marginata Donn ex Smith) forest established in the region's drinking water catchments on rehabilitated bauxite mining sites. We hypothesized that in jarrah forest established on rehabilitated mine sites: (1) leaf area index (L) is independent of initial tree spacing; and (2) more densely planted trees have less leaf area for the same leaf mass, or the same sapwood area, and have denser sapwood. Initial stand densities ranged from about 600 to 9000 stems ha -1, and trees were 18 years old at the time of sampling. Leaf area index was unaffected by initial stand density, except in the most sparsely stocked stands where L was 1.2 compared with 2.0-2.5 in stands at other spacings. The ratio of leaf area to sapwood area (Al:As) was unaffected by tree spacing or tree size and was 0.2 at 1.3 m height and 0.25 at the crown base. There were small increases in sapwood density and decreases in leaf specific area with increased spacing. Tree diameter or basal area was a better predictor of leaf area than sapwood area. At the stand scale, basal area was a good predictor of L (r2 = 0.98, n = 15) except in the densest stands. We conclude that the hydraulic attributes of this forest type are largely independent of initial tree spacing, thus simplifying parameterization of stand and catchment water balance models. © 2008 Heron Publishing.
Published: 2008

44. Estimation of leaf area index in eucalypt forest using digital photography

Author: Macfarlane, C, Hoffman, M, Eamus, D, Kerp, N, Higginson, S, McMurtrie, R, Adams, M, Macfarlane, C, Hoffman, M, Eamus, D, Kerp, N, Higginson, S, McMurtrie, R, and Adams, M
Abstract: We tested whether leaf area index (L) in eucalypt vegetation could be accurately estimated from gap fraction measurements made using both fisheye and non-fisheye digital photography. We compared methods that measure the gap fraction at a single zenith angle (0° or 57°), with fisheye photography that measures the gap fraction at multiple zenith angles. We applied these methods in an unthinned stand of the broadleaf tree species Eucalyptus marginata that had an initial L of 3. We removed one-third of the trees and reapplied the methods, and then removed another one-third of the trees and applied the methods a third time. L from the photographic methods was compared to L obtained from destructive sampling and allometry. We found that L was accurately estimated from non-fisheye images taken at the zenith, providing that the total gap fraction was divided into large, between-crown gaps and smaller, within-crown gaps, prior to using the Beer-Lambert law to estimate L. This rapid and simple method corrected for foliage clumping and provided estimates of crown porosity, crown cover, foliage cover and the foliage clumping index at the zenith, but required an assumption about the light extinction coefficient at the zenith. Fisheye photography also provided good estimates of L but only if the images were corrected for the gamma function of the digital camera, and the combined Chen-Cihlar and Lang-Xiang method of correcting for foliage clumping was used. The clumping index derived from fisheye images was insensitive to thinning but the calculated foliage projection coefficient was. Methods of obtaining and analysing gap fraction and gap size distributions from fisheye photography need further improvement to separate the effects of foliage clumping and leaf angle distribution. © 2006 Elsevier B.V. All rights reserved.
Published: 2007

45. Estimation of leaf area index in eucalypt forest with vertical foliage, using cover and fullframe fisheye photography

Author: Macfarlane, C, Arndt, SK, Livesley, SJ, Edgar, AC, White, DA, Adams, MA, Eamus, D, Macfarlane, C, Arndt, SK, Livesley, SJ, Edgar, AC, White, DA, Adams, MA, and Eamus, D
Abstract: This study compared fullframe fisheye photography and cover photography with destructive leaf area index (L) estimation and the Licor LAI-2000 plant canopy analyser (PCA) in plantations of the vertical leaved species Eucalyptus globulus. Fullframe fisheye photography differs from circular fisheye photography in that the images have reduced field of view such that the zenithal range of 0-90° extends to the corners of the rectangular image, roughly doubling image resolution compared to circular images. Cover images instead are obtained by pointing a 70 mm equivalent focal length lens (in 35 mm format) straight upwards. Measurements of cover and indirect estimates of plant area index (Lt) were made in 12 stands of 6-8 years old Eucalyptus globulus. L was measured using destructive sampling and allometry in nine of these stands and ranged from 2.5 to 6.6. Both foliage cover and Lt from the PCA were well correlated with L from allometry, but fullframe fisheye photography provided poor estimates of L despite corrections for foliage clumping. Sampling location had a significant effect on estimates of crown porosity, crown cover and zenithal clumping index from cover photography. The zenithal extinction coefficient (k), calculated from L, crown porosity and cover, ranged from 0.14 to 0.25 and appeared to decrease as L increased; hence, we were unable to obtain an unambiguous estimate of k for E. globulus stands. Nonetheless, the study showed that L can be estimated from foliage cover with similar certainty to that of the PCA. We conclude that the greatest challenge facing indirect estimation of L in forests using photographic methods is to separate the effects of foliage angle from those of foliage clumping. © 2007 Elsevier B.V. All rights reserved.
Published: 2007

46. Estimation of leaf area index in eucalypt forest using digital photography

Author: Macfarlane, C, Hoffman, M, Eamus, D, Kerp, N, Higginson, S, McMurtrie, R, Adams, M, Macfarlane, C, Hoffman, M, Eamus, D, Kerp, N, Higginson, S, McMurtrie, R, and Adams, M
Abstract: We tested whether leaf area index (L) in eucalypt vegetation could be accurately estimated from gap fraction measurements made using both fisheye and non-fisheye digital photography. We compared methods that measure the gap fraction at a single zenith angle (0° or 57°), with fisheye photography that measures the gap fraction at multiple zenith angles. We applied these methods in an unthinned stand of the broadleaf tree species Eucalyptus marginata that had an initial L of 3. We removed one-third of the trees and reapplied the methods, and then removed another one-third of the trees and applied the methods a third time. L from the photographic methods was compared to L obtained from destructive sampling and allometry. We found that L was accurately estimated from non-fisheye images taken at the zenith, providing that the total gap fraction was divided into large, between-crown gaps and smaller, within-crown gaps, prior to using the Beer-Lambert law to estimate L. This rapid and simple method corrected for foliage clumping and provided estimates of crown porosity, crown cover, foliage cover and the foliage clumping index at the zenith, but required an assumption about the light extinction coefficient at the zenith. Fisheye photography also provided good estimates of L but only if the images were corrected for the gamma function of the digital camera, and the combined Chen-Cihlar and Lang-Xiang method of correcting for foliage clumping was used. The clumping index derived from fisheye images was insensitive to thinning but the calculated foliage projection coefficient was. Methods of obtaining and analysing gap fraction and gap size distributions from fisheye photography need further improvement to separate the effects of foliage clumping and leaf angle distribution. © 2006 Elsevier B.V. All rights reserved.
Published: 2007

47. Estimation of leaf area index in eucalypt forest using digital photography

Author: Macfarlane, C, Hoffman, M, Eamus, D, Kerp, N, Higginson, S, McMurtrie, R, Adams, M, Macfarlane, C, Hoffman, M, Eamus, D, Kerp, N, Higginson, S, McMurtrie, R, and Adams, M
Published: 2007

48. Estimation of leaf area index in eucalypt forest with vertical foliage, using cover and fullframe fisheye photography

Author: Macfarlane, C, Arndt, SK, Livesley, SJ, Edgar, AC, White, DA, Adams, MA, Eamus, D, Macfarlane, C, Arndt, SK, Livesley, SJ, Edgar, AC, White, DA, Adams, MA, and Eamus, D
Abstract: This study compared fullframe fisheye photography and cover photography with destructive leaf area index (L) estimation and the Licor LAI-2000 plant canopy analyser (PCA) in plantations of the vertical leaved species Eucalyptus globulus. Fullframe fisheye photography differs from circular fisheye photography in that the images have reduced field of view such that the zenithal range of 0-90° extends to the corners of the rectangular image, roughly doubling image resolution compared to circular images. Cover images instead are obtained by pointing a 70 mm equivalent focal length lens (in 35 mm format) straight upwards. Measurements of cover and indirect estimates of plant area index (Lt) were made in 12 stands of 6-8 years old Eucalyptus globulus. L was measured using destructive sampling and allometry in nine of these stands and ranged from 2.5 to 6.6. Both foliage cover and Lt from the PCA were well correlated with L from allometry, but fullframe fisheye photography provided poor estimates of L despite corrections for foliage clumping. Sampling location had a significant effect on estimates of crown porosity, crown cover and zenithal clumping index from cover photography. The zenithal extinction coefficient (k), calculated from L, crown porosity and cover, ranged from 0.14 to 0.25 and appeared to decrease as L increased; hence, we were unable to obtain an unambiguous estimate of k for E. globulus stands. Nonetheless, the study showed that L can be estimated from foliage cover with similar certainty to that of the PCA. We conclude that the greatest challenge facing indirect estimation of L in forests using photographic methods is to separate the effects of foliage angle from those of foliage clumping. © 2007 Elsevier B.V. All rights reserved.
Published: 2007

49. Estimation of leaf area index in eucalypt forest using digital photography

Author: Macfarlane, C, Hoffman, M, Eamus, D, Kerp, N, Higginson, S, McMurtrie, R, Adams, M, Macfarlane, C, Hoffman, M, Eamus, D, Kerp, N, Higginson, S, McMurtrie, R, and Adams, M
Published: 2007

50. Nurses' beliefs about the conditions that hinder or support evidence-based nursing.

Author: Nagy, S, Lumby, J, McKinley, S, Macfarlane, C, Nagy, S, Lumby, J, McKinley, S, and Macfarlane, C
Abstract: The aim of this study was to identify the conditions that Australian nurses believe support or hinder the development of evidence-based nursing. A survey instrument was developed from the results of content analysis of the UK and the USA research and of interviews with 12 Australian nurses. Eight hundred and sixteen nurses working in three large hospitals responded representing a response rate of 65%. Principal axis factor analysis of the survey results revealed six conditions that the participants believed were necessary for evidence-based nursing to take place. The results of this study may be used to develop models for the improvement of evidence-based nursing in Australia.
Published: 2001

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