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Peer review report 2 on “Critical climate periods for grassland productivity on China’s Loess Plateau”
Agricultural and Forest Meteorology 217 (2016) 551–552
Contents lists available at ScienceDirect
Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet
Peer Review Report
Peer review report 2 on “Critical climate periods for grassland productivity on China’s Loess Plateau”
Original Submission Recommendation Minor Revision Comments to Author In this manuscript, the authors use ∼20 years of ANPP, air temperature, and rainfall data from the Loess Plateua in China to identify linkages among grassland growth patterns and climate drivers. Assessed at the annual scale, air temperature and rainfall were poor predictors of ANPP for this grassland ecosystem. Using a partial least squares regression technique, the authors found significant predictive roles for air temperature and rainfall amount for distinct annual periods. In the early Spring, air temperature had a positive influence on growth, while the same relationship was negative during the mid-summer. In addition, the authors found unique, strong relationships between climate and growth during the dormant season for this grassland. The premise of this research − identification of critical climate periods to predict ecosystem productivity − is not terribly novel, as this approach has been used with long-term data for many ecosystems in the North America, South America, and Europe. Regardless, this contribution is well organized and technically sound, with the presentation of logical arguments, strong data, analyses, and interpretation. In addition, the writing contains few mistakes, is easy to understand, and requires minimal further editing. Taken as a whole, this manuscript makes a nice contribution to ecosystem ecology and highlights a novel statistical method that is likely to be duplicated by others in the future. My most significant suggestion for future revision is to downplay the implications for climate change in the Discussion section (and the end of the Abstract). Given the data here and the lack of modeling or model forecasting, the ability to forecast potential impacts of climate change for this region are relatively weak. Forecast changes for this specific region are not clarified in detail here, making it ambiguous to link climate-productivity relationships from the long-term data to future climate patterns. My suggestion would be to minimize vague text in this regard, particularly in the Discussion and Conclusions sections of the manuscript.
DOI of published article: http://dx.doi.org/10.1016/j.agrformet.2016.11.006. http://dx.doi.org/10.1016/j.agrformet.2016.11.272 0168-1923/
Section 4.1 refers to “annual climate change”. But ‘climate change’ isn’t measured here − rather its variability associated with annual climate. I think this is an important distinction. Your 20 year time record is not of sufficient length to assess ‘climate change’. The term ‘climate’ inherently includes associated variability, which you measured. Climate change would imply some change in mean amounts, change in associated variability, or both (and you don’t have the data to assess this). The authors missed several citations with direct relevance to their topic. I don’t expect the authors to scour the literature, but many of these manuscripts should be referenced here. Gherardi, L. A., & Sala, O. E. (2015). Enhanced interannual precipitation variability increases plant functional diversity that in turn ameliorates negative impact on productivity. Ecology Letters, 18(12), 1293-1300. http://doi.org/10.1111/ele.12523 Gherardi, L. A., & Sala, O. E. (2015). Enhanced precipitation variability decreases grass- and increases shrub-productivity. Proceedings of the National Academy of Sciences, 112(41), 1273512740. http://doi.org/10.1073/pnas.1506433112 Hoover, D. L. and Rogers, B. M. (2016), Not all droughts are created equal: the impacts of interannual drought pattern and magnitude on grassland carbon cycling. Glob Change Biol, 22: 18091820 Hovenden, Newton, Willis (2014) Seasonal not annual rainfall determines grassland biomass response to carbon dioxide. Nature 511 583-586 Hsu, J. S., & Adler, P. B. (2014). Anticipating changes in variability of grassland production due to increases in interannual precipitation variability. Ecosphere, 5(5), art58. http://doi.org/10.1890/ ES13-00210.1 Susan Moran, M., Ponce-Campos, G. E., Huete, A., McClaran, M. P., Zhang, Y., Hamerlynck, E. P., . . . Hernandez, M. (2014). Functional response of U.S. grasslands to the early 21st-century drought. Ecology, 95(8), 2121-2133. Peters, D. P. C., Yao, J., Browning, D., & Rango, A. (2014). Mechanisms of grass response in grasslands and shrublands during dry or wet periods. Oecologia, 174(4), 1323-34. http://doi.org/10.1007/ s00442-013-2837-y Sala, Gherardi, Reichmann, Jobbagy, Peters (2012) Legacies of precipitation fluctuations on primary production: theory and data synthesis. Philosophical Transactions of the Royal Society B 367: 3135-3144 Fig. 1 seems unnecessary. This material can be mentioned in text, or added to the Appendix (or both).
552
Peer Review Report / Agricultural and Forest Meteorology 217 (2016) 551–552
The sentence on line 333-335 belongs in the Discussion section, not the Results. Line 371 – Nippert et al., 2006 was conducted at the Konza Prairie which has MAP of ∼850 mm. Thus, this isn’t an arid or semiarid grassland.
Guo Cheng Luedeling Koerner He Xu Gang Li Luo Peng Jesse Nippert, 106 Ackert Hall, Manhattaan, KS 66506, United States Available online 7 December 2016
Contents lists available at ScienceDirect
Agricultural and Forest Meteorology journal homepage: www.elsevier.com/locate/agrformet
Peer Review Report
Peer review report 2 on “Critical climate periods for grassland productivity on China’s Loess Plateau”
Original Submission Recommendation Minor Revision Comments to Author In this manuscript, the authors use ∼20 years of ANPP, air temperature, and rainfall data from the Loess Plateua in China to identify linkages among grassland growth patterns and climate drivers. Assessed at the annual scale, air temperature and rainfall were poor predictors of ANPP for this grassland ecosystem. Using a partial least squares regression technique, the authors found significant predictive roles for air temperature and rainfall amount for distinct annual periods. In the early Spring, air temperature had a positive influence on growth, while the same relationship was negative during the mid-summer. In addition, the authors found unique, strong relationships between climate and growth during the dormant season for this grassland. The premise of this research − identification of critical climate periods to predict ecosystem productivity − is not terribly novel, as this approach has been used with long-term data for many ecosystems in the North America, South America, and Europe. Regardless, this contribution is well organized and technically sound, with the presentation of logical arguments, strong data, analyses, and interpretation. In addition, the writing contains few mistakes, is easy to understand, and requires minimal further editing. Taken as a whole, this manuscript makes a nice contribution to ecosystem ecology and highlights a novel statistical method that is likely to be duplicated by others in the future. My most significant suggestion for future revision is to downplay the implications for climate change in the Discussion section (and the end of the Abstract). Given the data here and the lack of modeling or model forecasting, the ability to forecast potential impacts of climate change for this region are relatively weak. Forecast changes for this specific region are not clarified in detail here, making it ambiguous to link climate-productivity relationships from the long-term data to future climate patterns. My suggestion would be to minimize vague text in this regard, particularly in the Discussion and Conclusions sections of the manuscript.
DOI of published article: http://dx.doi.org/10.1016/j.agrformet.2016.11.006. http://dx.doi.org/10.1016/j.agrformet.2016.11.272 0168-1923/
Section 4.1 refers to “annual climate change”. But ‘climate change’ isn’t measured here − rather its variability associated with annual climate. I think this is an important distinction. Your 20 year time record is not of sufficient length to assess ‘climate change’. The term ‘climate’ inherently includes associated variability, which you measured. Climate change would imply some change in mean amounts, change in associated variability, or both (and you don’t have the data to assess this). The authors missed several citations with direct relevance to their topic. I don’t expect the authors to scour the literature, but many of these manuscripts should be referenced here. Gherardi, L. A., & Sala, O. E. (2015). Enhanced interannual precipitation variability increases plant functional diversity that in turn ameliorates negative impact on productivity. Ecology Letters, 18(12), 1293-1300. http://doi.org/10.1111/ele.12523 Gherardi, L. A., & Sala, O. E. (2015). Enhanced precipitation variability decreases grass- and increases shrub-productivity. Proceedings of the National Academy of Sciences, 112(41), 1273512740. http://doi.org/10.1073/pnas.1506433112 Hoover, D. L. and Rogers, B. M. (2016), Not all droughts are created equal: the impacts of interannual drought pattern and magnitude on grassland carbon cycling. Glob Change Biol, 22: 18091820 Hovenden, Newton, Willis (2014) Seasonal not annual rainfall determines grassland biomass response to carbon dioxide. Nature 511 583-586 Hsu, J. S., & Adler, P. B. (2014). Anticipating changes in variability of grassland production due to increases in interannual precipitation variability. Ecosphere, 5(5), art58. http://doi.org/10.1890/ ES13-00210.1 Susan Moran, M., Ponce-Campos, G. E., Huete, A., McClaran, M. P., Zhang, Y., Hamerlynck, E. P., . . . Hernandez, M. (2014). Functional response of U.S. grasslands to the early 21st-century drought. Ecology, 95(8), 2121-2133. Peters, D. P. C., Yao, J., Browning, D., & Rango, A. (2014). Mechanisms of grass response in grasslands and shrublands during dry or wet periods. Oecologia, 174(4), 1323-34. http://doi.org/10.1007/ s00442-013-2837-y Sala, Gherardi, Reichmann, Jobbagy, Peters (2012) Legacies of precipitation fluctuations on primary production: theory and data synthesis. Philosophical Transactions of the Royal Society B 367: 3135-3144 Fig. 1 seems unnecessary. This material can be mentioned in text, or added to the Appendix (or both).
552
Peer Review Report / Agricultural and Forest Meteorology 217 (2016) 551–552
The sentence on line 333-335 belongs in the Discussion section, not the Results. Line 371 – Nippert et al., 2006 was conducted at the Konza Prairie which has MAP of ∼850 mm. Thus, this isn’t an arid or semiarid grassland.
Guo Cheng Luedeling Koerner He Xu Gang Li Luo Peng Jesse Nippert, 106 Ackert Hall, Manhattaan, KS 66506, United States Available online 7 December 2016