Ecology

Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W. & Hunt, S. J. Global peatland dynamics since the Last Glacial Maximum. Geophys. Res. Lett. 37, 13 (2010).
Google Scholar 
Leifeld, J., Wüst-Galley, C. & Page, S. Intact and managed peatland soils as a source and sink of GHGs from 1850 to 2100. Nat. Clim. Change 9, 945–947 (2019).CAS 
Article 

Google Scholar 
Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020).CAS 
Article 

Google Scholar 
Evans, C. D. et al. Overriding water table control on managed peatland greenhouse gas emissions. Nature 593, 548–552 (2021).CAS 

Google Scholar 
Bonn, A. et al. Investing in nature: Developing ecosystem service markets for peatland restoration. Ecosyst. Serv. 9, 54–65 (2014).Article 

Google Scholar 
Martin-Ortega, J., Allott, T. E., Glenk, K. & Schaafsma, M. Valuing water quality improvements from peatland restoration: evidence and challenges. Ecosyst. Serv. 9, 34–43 (2014).Article 

Google Scholar 
Loisel, J. et al. Expert assessment of future vulnerability of the global peatland carbon sink. Nat. Clim. Change 11, 70–77 (2021).Article 

Google Scholar 
Chimner, R. A., Cooper, D. J., Wurster, F. C. & Rochefort, L. An overview of peatland restoration in North America: where are we after 25 years? Restor. Ecol. 25, 283–292 (2017).Article 

Google Scholar 
Andersen, R. et al. An overview of the progress and challenges of peatland restoration in Western Europe. Restor. Ecol. 25, 271–282 (2017).Article 

Google Scholar 
Bossio, D. A. et al. The role of soil carbon in natural climate solutions. Nat. Sustain. 3, 391–398 (2020).Article 

Google Scholar 
Humpenöder, F. et al. Peatland protection and restoration are key for climate change mitigation. Environ. Res. Lett. 15, 104093 (2020).Article 

Google Scholar 
Drever, C. R. et al. Natural climate solutions for Canada. Sci. Adv. 7, https://doi.org/10.1126/sciadv.abd6034 (2020).Leifeld, J. & Menichetti, L. The underappreciated potential of peatlands in global climate change mitigation strategies. Nat. Commun. 9, 1–7 (2018).CAS 
Article 

Google Scholar 
Gunderson, L. H. Ecological resilience—in theory and application. Annu. Rev. Ecol. Syst. 31, 425–439 (2000).Article 

Google Scholar 
Sasaki, T., Furukawa, T., Iwasaki, Y., Seto, M. & Mori, A. S. Perspectives for ecosystem management based on ecosystem resilience and ecological thresholds against multiple and stochastic disturbances. Ecol. Indic. 57, 395–408 (2015).Article 

Google Scholar 
Scheffer, M. Critical transitions in nature and society (Princeton University, 2009).Alexandrov, G. A., Brovkin, V. A., Kleinen, T. & Yu, Z. The capacity of northern peatlands for long-term carbon sequestration. Biogeosciences 17, 47–54 (2020).CAS 
Article 

Google Scholar 
Page, S. E. & Baird, A. J. Peatlands and global change: response and resilience. Annu. Rev. Environ. Resour. 41, 35–57 (2016).Article 

Google Scholar 
Rydin, H., Jeglum, J. K. & Bennett, K. D. The biology of peatlands, 2nd edition (Oxford University Press, 2013).Kim, J. et al. Water table fluctuation in peatlands facilitates fungal proliferation, impedes Sphagnum growth and accelerates decomposition. Front. Earth Sci. 8, 717 (2021).
Google Scholar 
IPCC. Climate Change 2022: Impacts, Adaptation, and Vulnerability (Cambridge University Press, In Press).Belyea, L. R. Non-linear dynamics of peatlands and potential feedbackson the climate system, in Northern Peatlands and Carbon Cycling (A, Baird. et al. eds), pp 5–18 (American Geophysical Union Monograph Series, 2009).Holden, J. et al. Overland flow velocity and roughness properties in peatlands. Water Resour. Res. 44, https://doi.org/10.1029/2007WR006052 (2008).Holden, J., Wallage, Z. E., Lane, S. N. & McDonald, A. T. Water table dynamics in undisturbed, drained and restored blanket peat. J. Hydrol. 402, 103–114 (2011).Article 

Google Scholar 
Glaser, P. H. et al. Surface deformations as indicators of deep ebullition fluxes in a large northern peatland. Glob. Biogeochem. Cycles 18, GB1003 (2004).Article 
CAS 

Google Scholar 
Belyea, L. R. & Baird, A. J. Beyond “the limits to peat bog growth”: cross‐scale feedback in peatland development. Ecol. Monogr. 76, 299–322 (2006).Article 

Google Scholar 
Waddington, J. M. et al. Hydrological feedbacks in northern peatlands. Ecohydrology 8, 113–127 (2015).Article 

Google Scholar 
Holden, J., Evans, M. G., Burt, T. P. & Horton, M. Impact of land drainage on peatland hydrology. J. Environ. Qual. 35, 1764–1778 (2006).CAS 
Article 

Google Scholar 
Liu, H. & Lennartz, B. Hydraulic properties of peat soils along a bulk density gradient—a meta study. Hydrol. Process. 33, 101–114 (2019).Article 

Google Scholar 
Gałka, M., Tobolski, K., Górska, A. & Lamentowicz, M. Resilience of plant and testate amoeba communities after climatic and anthropogenic disturbances in a Baltic bog in Northern Poland: implications for ecological restoration. Holocene 27, 130–141 (2017).Article 

Google Scholar 
Lamentowicz, M. et al. Unveiling tipping points in long-term ecological records from Sphagnum-dominated peatlands. Biol. Lett. 15, https://doi.org/10.1098/rsbl.2019.0043 (2019).van der Velde, Y. Emerging forest-peatland bistability and resilience of European peatland carbon stores. Proc. Natl Acad. Sci. 118, https://doi.org/10.1073/pnas.210174211 (2021).Ives, A. R. & Carpenter, S. R. Stability and diversity of ecosystems. Science 317, 58–62 (2007).CAS 
Article 

Google Scholar 
Minayeva, T. Y. & Sirin, A. A. Peatland biodiversity and climate change. Biol. Bull. Rev. 2, 164–175 (2012).Article 

Google Scholar 
Minayeva, T. Y., Bragg, O. & Sirin, A. A. Towards ecosystem-based restoration of peatland biodiversity. Mires Peat 19, 1–36 (2017).
Google Scholar 
Andersen, R., Chapman, S. J. & Artz, R. R. Microbial communities in natural and disturbed peatlands: a review. Soil Biol. Biochem. 1, 979–994 (2013).Article 
CAS 

Google Scholar 
van Breemen, N. How Sphagnum bogs down other plants. Trends Ecol. Evol. 10, 270–275 (1995).Article 

Google Scholar 
Hugron, S. & Rochefort, L. Sphagnum mosses cultivated in outdoor nurseries yield efficient plant material for peatland restoration. Mires Peat 20, 1–6 (2018).
Google Scholar 
Vitt, D. H. Peatlands: ecosystems dominated by bryophytes. In: Shaw A. J. & Goffinet B. (eds) Bryophyte biology, pp 312–343 (Cambridge University Press, 2002).Yu, Z. et al. Carbon sequestration in western Canadian peat highly sensitive to Holocene wet-dry climate cycles at millennial timescales. Holocene 13, 801–808 (2003).Article 

Google Scholar 
Chiapusio, G. et al. Sphagnum species module their phenolic profiles and mycorrhizal colonization of surrounding Andromeda polifolia along peatland microhabitats. J. Chem. Ecol. 44, 1146–1157 (2018).CAS 
Article 

Google Scholar 
Sherwood, J. H. et al. Effect of drainage and wildfire on peat hydrophysical properties. Hydrol. Process. 27, 1866–1874 (2013).Article 

Google Scholar 
Tanneberger, F., Flade, M., Preiksa, Z. & Schröder, B. Habitat selection of the globally threatened aquatic warbler Acrocephalus paludicola at the western margin of its breeding range and implications for management. Ibis 152, 347–358 (2010).Article 

Google Scholar 
Kreyling, J. Rewetting does not return drained fen peatlands to their old selves. Nat. Commun. 12, 1–8 (2021).Article 
CAS 

Google Scholar 
Ritson, J. P. et al. Towards a microbial process-based understanding of the resilience of peatland ecosystem service provisioning–a research agenda. Sci. Total Environ. 759, https://doi.org/10.1016/j.scitotenv.2020.143467 (2021).Secco, E. D., Haapalehto, T., Haimi, J., Meissner, K. & Tahvanainen, T. Do testate amoebae communities recover in concordance with vegetation after restoration of drained peatlands? Mires Peat 18, https://doi.org/10.19189/MaP.2016.OMB.231 (2016).Basiliko, N. et al. Controls on bacterial and archaeal community structure and greenhouse gas production in natural, mined, and restored Canadian peatlands. Front. Microbiol. 31, https://doi.org/10.3389/fmicb.2013.00215 (2013).Barber, K. E. Peat stratigraphy and climatic change. vol 219, (AA Balkema, 1981).Quinton, W. L. & Roulet, N. T. Spring and summer runoff hydrology of a subarctic patterned wetland. Arctic Alpine Res. 30, 285–294 (1998).Article 

Google Scholar 
Eppinga, M. B., Rietkerk, M., Wassen, M. J. & De Ruiter, P. C. Linking habitat modification to catastrophic shifts and vegetation patterns in bogs. Plant Ecol. 200, 53–68 (2009).Article 

Google Scholar 
Bragazza, L., Parisod, J., Buttler, A. & Bardgett, R. D. Biogeochemical plant– soil microbe feedback in response to climate warming in peatlands. Nat. Clim. Change 3, 273–277 (2013).CAS 
Article 

Google Scholar 
Fenton, N. J. Applied ecology in Canada’s boreal: a holistic view of the mitigation hierarchy and resilience theory. Botany 94, 1009–1014 (2016).Article 

Google Scholar 
Xu, L. X. et al. Maintain spatial heterogeneity, maintain biodiversity—a seed bank study in a grazed alpine fen meadow. Land Degrad. Dev. 28, 1376–1385 (2017).Article 

Google Scholar 
Laine, J., Vasander, H. & Laiho, R. Long-term effects of water level drawdown on the vegetation of drained pine mires in southern Finland. J. Appl. Ecol. 1, 785–802 (1995).
Google Scholar 
Gatis, N. et al. The effect of drainage ditches on vegetation diversity and CO2 fluxes in a Molinia caerulea‐dominated peatland. Ecohydrology 9, 407–420 (2016).CAS 
Article 

Google Scholar 
Swindles, G. T. et al. Resilience of peatland ecosystem services over millennial timescales: evidence from a degraded British bog. Journal of Ecology 104, 621–636 (2016).Article 

Google Scholar 
Liu, H., Gao, C. & Wang, G. Understand the resilience and regime shift of the wetland ecosystem after human disturbances. Sci. Total Environ. 643, 1031–1040 (2018).CAS 
Article 

Google Scholar 
Couwenberg, J. et al. Assessing greenhouse gas emissions from peatlands using vegetation as a proxy. Hydrobiologia 674, 67–89 (2011).CAS 
Article 

Google Scholar 
Tiemeyer, B. et al. High emissions of greenhouse gases from grasslands on peat and other organic soils. Glob. Change Biol. 22, 4134–4149 (2016).Article 

Google Scholar 
Strack, M. et al. Controls on plot-scale growing season CO2 and CH4 fluxes in restored peatlands: do they differ from unrestored and natural sites? Mires Peat 17, 1–18 (2016).
Google Scholar 
Nugent, K. A., Strachan, I. B., Strack, M., Roulet, N. T. & Rochefort, L. Multi-year net ecosystem carbon balance of a restored peatland reveals a return to carbon sink. Global Change Biol. 24, 5751–5768 (2018).Article 

Google Scholar 
Hambley, G. et al. Net ecosystem exchange from two formerly afforested peatlands undergoing restoration in the Flow Country of northern Scotland. Mires Peat 23, https://doi.org/10.19189/MaP.2018.DW.346 (2019).Schwieger, S. et al. Wetter is better: rewetting of minerotrophic peatlands increases plant production and moves them towards carbon sinks in a dry year. Ecosystems 24, 1093–1109 (2021).CAS 
Article 

Google Scholar 
Poulin, M., Andersen, R. & Rochefort, L. A new approach for tracking vegetation change after restoration: a case study with peatlands. Restor. Ecol. 21, 363–371 (2013).Article 

Google Scholar 
Gonzalez, E. & Rochefort, L. Drivers of success in 53 cutover bogs restored by a moss layer transfer technique. Ecol. Eng. 68, 279–290 (2014).Article 

Google Scholar 
Karofeld, E., Müür, M. & Vellak, K. Factors affecting re-vegetation dynamics of experimentally restored extracted peatland in Estonia. Environ. Sci. Pollut. Res. 23, 13706–13717 (2016).Article 

Google Scholar 
Karofeld, E., Kaasik, A. & Vellak, K. Growth characteristics of three Sphagnum species in restored extracted peatland. Restor. Ecol. 28, 1574–1583 (2020).Article 

Google Scholar 
Purre, A. H., Ilomets, M., Truus, L., Pajula, R. & Sepp, K. The effect of different treatments of moss layer transfer technique on plant functional types biomass in revegetated milled peatlands. Restor. Ecol. 28, 1584–1595 (2020).Article 

Google Scholar 
Beyer, F. et al. Drought years in peatland rewetting: rapid vegetation succession can maintain the net CO2 sink function. Biogeosciences 18, 917–935 (2021).CAS 
Article 

Google Scholar 
Ketcheson, S. J. & Price, J. S. The impact of peatland restoration on the site hydrology of an abandoned block-cut bog. Wetlands 31, 1263–1274 (2011).Article 

Google Scholar 
McCarter, C. P. R. & Price, J. S. The hydrology of the Bois-des-Bel bog peatland restoration: 10 years post-restoration. Ecol. Eng. 55, 73–81 (2013).Article 

Google Scholar 
Koebsch, F. et al. The impact of occasional drought periods on vegetation spread and greenhouse gas exchange in rewetted fens. Philos. Transac. R. Soc. B 375, https://doi.org/10.1098/rstb.2019.0685 (2020).Blier‐Langdeau, A., Guêné‐Nanchen, M., Hugron, S. & Rochefort, L. The resistance and short‐term resilience of a restored extracted peatland ecosystems post‐fire: an opportunistic study after a wildfire. Restor. Ecol. 30, https://doi.org/10.1111/rec.13545 (2022).Rochefort, L., Quinty, F., Campeau, S., Johnson, K. & Malterer, T. North American approach to the restoration of Sphagnum dominated peatlands. Wetlands Ecol. Manage. 11, 3–20 (2003).CAS 
Article 

Google Scholar 
Lavoie, C., St-Louis, A. & Lachance, D. Vegetation dynamics on an abandoned vacuum-mined peatland: Five years of monitoring. Wetlands Ecol. Manage. 13, 621–633 (2005).Article 

Google Scholar 
Poulin, M., Rochefort, L., Quinty, F. & Lavoie, C. Spontaneous revegetation of mined peatlands in eastern Canada. Can. J. Botany 83, 539–557 (2005).Article 

Google Scholar 
Quinty, F., LeBlanc, M.-C. & Rochefort, L. Peatland Restoration Guide—PERG, CSPMA and APTHQ (Université Laval, 2020).Wagner, D. J. & Titus, J. E. Comparative desiccation tolerance of two Sphagnum mosses. Oecologia 62, 182–187 (1984).Article 

Google Scholar 
Gonzalez, E. & Rochefort, L. Declaring success in Sphagnum peatland restoration: identifying outcomes from readily measurable vegetation descriptors. Mires Peat 24, 1–16 (2019).
Google Scholar 
Scotland National Peatland Plan. Working for our future. https://www.nature.scot/doc/scotlands-national-peatland-plan-working-our-future#:~:text=The%202020%20Challenge%20for%20Scotland’s,more%20resilient%20to%20climate%20change (2020).Wilkie, N. M. & Mayhew, P. W. The management and restoration of damaged blanket bog in the north of Scotland. Bot. J. Scotl. 55, 125–133 (2003).Article 

Google Scholar 
Hancock, M. H., Klein, D., Andersen, R. & Cowie, N. R. Vegetation response to restoration management of a blanket bog damaged by drainage and afforestation. Appl. Veg. Sci. 21, 167–178 (2018).Article 

Google Scholar 
Harris, A. & Baird, A. J. Microtopographic drivers of vegetation patterning in blanket peatlands recovering from erosion. Ecosystems 22, 1035–1054 (2019).Article 

Google Scholar 
Bradley, A. V., Andersen, R., Marshall, C., Sowter, A. & Large, D. J. Identification of typical ecohydrological behaviours using InSAR allows landscape-scale mapping of peatland condition. Earth Surf. Dyn. 10, 261–277 (2022).Article 

Google Scholar 
Gaffney, P. P., Hancock, M. H., Taggart, M. A. & Andersen, R. Measuring restoration progress using pore-and surface-water chemistry across a chronosequence of formerly afforested blanket bogs. J. Environ. Manage. 219, 239–251 (2018).CAS 
Article 

Google Scholar 
Hermans, R. et al. Climate benefits of forest-to-bog restoration on deep peat–Policy briefing. Climate X Change 1–5, https://www.climatexchange.org.uk/media/3654/climate-benefits-of-forest-to-bog-restoration-on-deep-peat.pdf (2019).Wilson, D. et al. Greenhouse gas emission factors associated with rewetting of organic soils. Mires Peat 17, 1–28 (2016).
Google Scholar 
Günther, A. et al. Prompt rewetting of drained peatlands reduces climate warming despite methane emissions. Nat. Commun. 11, 1–5 (2020).Article 
CAS 

Google Scholar 
Young, D. M. et al. Misinterpreting carbon accumulation rates in records from near-surface peat. Sci. Rep. 9, 1–8 (2019).Article 
CAS 

Google Scholar 
Young, D. M., Baird, A. J., Gallego-Sala, A. V. & Loisel, J. A cautionary tale about using the apparent carbon accumulation rate (aCAR) obtained from peat cores. Sci. Rep. 11, 9547 (2021).CAS 
Article 

Google Scholar 
Klimkowska, A. et al. Are we restoring functional fens? The outcomes of restoration projects in fens re-analysed with plant functional traits. PLoS One 14, https://doi.org/10.1371/journal.pone.0215645 (2019).Huth, V. et al. The climate benefits of topsoil removal and Sphagnum introduction in raised bog restoration. Restor. Ecol. 30, https://doi.org/10.1111/rec.13490 (2022).Schimelpfenig, D., Cooper, D. J. & Chimner, R. A. Effectiveness of ditch blockage for restoring hydrologic and soil processes in mountain peatlands. Restor. Ecol. 22, 257–265 (2014).Article 

Google Scholar 
Laine, A. M., Tolvanen, A., Mehtätalo, L. & Tuittila, E. S. Vegetation structure and photosynthesis respond rapidly to restoration in young coastal fens. Ecol. Evol. 6, 6880–6891 (2016).Article 

Google Scholar 
Gallego-Sala, A. V. & Prentice, I. C. Blanket peat biome endangered by climate change. Nat. Clim. Change 3, 152–155 (2013).Article 

Google Scholar 
Schneider, R. R., Devito, K., Kettridge, N. & Bayne, E. Moving beyond bioclimatic envelope models:50 integrating upland forest and peatland processes to predict ecosystem transitions under climate change in the51 western Canadian boreal plain: Western boreal ecosystem transitions under climate change. Ecohydrology 9, 899–908 (2016).Article 

Google Scholar 
Blundell, A. & Holden, J. Using palaeoecology to support blanket peatland management. Ecol. Indic. 49, 110–120 (2005).Article 

Google Scholar 
Newman, S. et al. Drivers of landscape evolution: multiple regimes and their influence on carbon sequestration in a sub‐tropical peatland. Ecol. Monogr. 87, 578–599 (2017).Article 

Google Scholar 
Wilkinson, S. L., Moore, P. A., Flannigan, M. D., Wotton, B. M. & Waddington, J. M. Did enhanced afforestation cause high severity peat burn in the Fort McMurray Horse River wildfire? Environ. Res. Lett. 13, https://doi.org/10.1088/1748-9326/aaa136 (2018).Hokanson, K. J. et al. A hydrogeological landscape framework to identify peatland wildfire smouldering hot spots. Ecohydrology 11, https://doi.org/10.1002/eco.1942 (2018).IPCC. Global warming of 1.5 °C (IPCC, 2018).Glenk, K., Faccioli, M., Martin-Ortega, J., Schulze, C. & Potts, J. The opportunity cost of delaying climate action: Peatland restoration and resilience to climate change. Glob. Environ. Change 70, https://doi.org/10.1016/j.gloenvcha.2021.102323 (2021).Tanneberger, F. et al. The power of nature‐based solutions: how peatlands can help us to achieve key EU sustainability objectives. Adv. Sustain. Syst. 5, https://doi.org/10.1002/adsu.202000146 (2021).Loisel, J. & Walenta, J. Carbon parks could secure essential ecosystems for climate stabilization. Nat. Ecol. Evol. 6, 486–488 (2022).Article 

Google Scholar 
Morecroft, M. D. et al. Measuring the success of climate change adaptation and mitigation in terrestrial ecosystems. Science 366, eaaw9256 (2019).Terzano, D. Community‐led peatland restoration in Southeast Asia: 5Rs approach. Restor. Ecol. 3, https://doi.org/10.1111/rec.13642 (2022). More

Galloway, J. N., Townsend, W. H., Erisman, J. W., Bekunda, M. & Cai, Z. Transformation of the nitrogen cycle: Recent trends, questions and potential solutions. Science 320, 889–892 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Phoenix, G. K. et al. Impacts of atmospheric nitrogen deposition: Responses of multiple plant and soil parameters across contrasting ecosystems in long-term field experiments. Glob. Change Biol. 18, 1197–1215 (2012).ADS 
Article 

Google Scholar 
Pons, T. L., van der, Werf, A. & Lambers, H. Photosynthetic nitrogen use efficiency of inherently slow- and fast-growing species: Possible explanations for observed differences. In A Whole Plant Perspective on Carbon-Nitrogen Interactions (eds Roy, J., Garnier, E.) 61–77 (SPB Academic Publishing, The Hague, 1994).Ai, Z. M., Xue, S., Wang, G. L. & Liu, G. B. Responses of Non-structural carbohydrates and C:N: P stoichiometry of Bothriochloa ischaemum to nitrogen addition on the Loess Plateau, China. J. Plant Growth Regul. 36, 714–722 (2017).CAS 
Article 

Google Scholar 
Marklein, A. R. & Houlton, B. Z. Nitrogen inputs accelerate phosphorus cycling rates across a wide variety of terrestrial ecosystems. New Phytol. 193, 696–704 (2012).CAS 
PubMed 
Article 

Google Scholar 
Dietze, M. C. et al. Nonstructural carbon in woody plants. Annu. Rev. Plant Biol. 65, 667–687 (2014).CAS 
PubMed 
Article 

Google Scholar 
Hartmann, H. & Trumbore, S. Understanding the roles of nonstructural carbohydrates in forest trees -from what we can measure to what we want to know. New Phytol. 211, 386–403 (2016).CAS 
PubMed 
Article 

Google Scholar 
Yang, Q. P. et al. Different responses of non-structural carbohydrates in above-ground tissues/organs and root to extreme drought and re-watering in Chinese fir (Cunninghamia lanceolata) saplings. Trees 30, 1863–1871 (2016).CAS 
Article 

Google Scholar 
Peng, Z. T. et al. Non-structural carbohydrates regulated by nitrogen and phosphorus fertilization varied with organs and fertilizer levels in Moringa oleifera Seedlings. J. Plant Growth Regul. 40, 1777–1786 (2021).CAS 
Article 

Google Scholar 
Nardini, A. et al. Rooting depth, water relations and non-structural carbohydrate dynamics in three woody angiosperms deferentially affected by an extreme summer drought. Plant Cell Environ. 39, 618–627 (2016).CAS 
PubMed 
Article 

Google Scholar 
Wang, F. C. et al. Effects of experimental nitrogen addition on nutrients and nonstructural carbohydrates of dominant understory plants in a Chinese Fir plantation. Forests 10, 155 (2019).Article 

Google Scholar 
Elser, J. J. et al. Growth rate-stoichiometry couplings in diverse biota. Ecol. Lett. 6, 936–943 (2003).Article 

Google Scholar 
Jinm, X. M. et al. Ecological stoichiometry and biomass response of Agropyron michnoi under simulated N deposition in a sandy grassland, China. J. Arid Land. 12, 741–751 (2020).Article 

Google Scholar 
Jing, H. et al. Nitrogen addition changes the stoichiometry and growth rate of different organsin pinus tabuliformis seedlings. Front. Plant Sci. 8, 1922 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Zhan, S. X., Wang, Y., Zhu, Z. C., Li, W. H. & Bai, Y. F. Nitrogen enrichment alters plant N: P stoichiometry and intensifies phosphorus limitation in a steppe ecosystem. Environ. Exp. Bot. 134, 21–32 (2017).CAS 
Article 

Google Scholar 
Stiling, P. & Cornelissen, T. How does elevated carbon dioxide (CO2) affect plant–herbivore interactions? A field experiment and meta-analysis of CO2 -mediated changes on plant chemistry and herbivore performance. Glob. Change Biol. 13, 1823–1842 (2007).ADS 
Article 

Google Scholar 
Wang, X. G. et al. Responses of C:N: P stoichiometry of plants from a Hulunbuir grassland to salt stress, drought and nitrogen addition. Phyton-Int. J. Exp. Bot. 87, 123–132 (2018).
Google Scholar 
Liu, X. J. et al. Enhanced nitrogen deposition over China. Nature 494, 459–462 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Huang, W. J., Houlton, B. Z., Marklein, A. R., Liu, J. X. & Zhou, G. Y. Plant stoichiometric responses to elevated CO2 vary with nitrogen and phosphorus inputs: Evidence from a global-scale meta-analysis. Sci. Rep. 5, 18225 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, X. et al. Effects of nutrient addition on nitrogen, phosphorus and non-structural carbohydrates concentrations in leaves of dominant plant species in a semiarid steppe. Chin. J. Ecol. 33, 1795–1802 (2014).
Google Scholar 
Yang, D. X., Song, L. & Jin, G. Z. The soil C:N: P stoichiometry is more sensitive than the leaf C:N: P stoichiometry to nitrogen addition: A four-year nitrogen addition experiment in a Pinus koraiensis plantation. Plant Soil 442, 183–198 (2019).CAS 
Article 

Google Scholar 
Chong, P. F., Zhan, J., Li, Y. & Jia, X. Y. Carbon dioxide and precipitation alter Reaumuria soongorica root morphology by regulating the levels of soluble sugars and phytohormones. Acta Physiol. Plant 41, 184 (2019).Article 
CAS 

Google Scholar 
Ma, X. Z. & Wang, X. P. Biomass partitioning and allometric relations of the Reaumuria soongorica shrub in Alxa steppe desert in NW China. For. Ecol. Manag. 468, 118–178 (2020).Article 

Google Scholar 
He, F. L., Bao, A. K., Wang, S. M. & Jin, H. X. NaCl stimulates growth and alleviates drought stress in the salt-secreting xerophyte Reaumuria soongorica. Environ. Exp. Bot. 162, 433–443 (2019).CAS 
Article 

Google Scholar 
Xu, D. H. et al. Photosynthetic parameters and carbon reserves of a resurrection plant Reaumuria soongorica during dehydration and rehydration. Plant Growth Regul. 60, 183–190 (2010).CAS 
Article 

Google Scholar 
Zhang, H. et al. miRNA–mRNA integrated analysis reveals roles for miRNAs in a typical halophyte, Reaumuria soongorica, during seed germination under salt stress. Plants 9, 351 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Bai, Y. M., Li, Y., Shan, L. S., Su, M. & Zhang, W. T. Effects of precipitation change and nitrogen addition on root morphological characteristics of Reaumuria soongorica. Arid Zone Res. 37, 1284–1292 (2020).
Google Scholar 
Hedwall, P. O., Nordin, A., Strengbom, J., Brunet, J. & Olsson, B. Does background nitrogen deposition affect the response of boreal vegetation to fertilization?. Oecologia 173, 615–624 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wang, G., Fahey, T. J., Xue, S. & Liu, F. Root morphology and architecture respond to N addition in Pinus tabuliformis, west China. Oecologia 171, 583–590 (2013).ADS 
PubMed 
Article 

Google Scholar 
Grechi, I. et al. Effect of light and nitrogen supply on internal C: N balance and control of root-to-shoot biomass allocation in grapevine. Environ. Exp. Bot. 59, 139–149 (2007).CAS 
Article 

Google Scholar 
Xiao, L., Liu, G., Li, P. & Xue, S. Nitrogen addition has a stronger effect on stoichiometries of non-structural carbohydrates, nitrogen and phosphorus in Bothriochloa ischaemum than elevated CO2. Plant Growth Regul. 83, 325–334 (2017).CAS 
Article 

Google Scholar 
Quentin, A. G. et al. Non-structural carbohydrates in woody plants compared among laboratories. Tree Physiol. 35, 1146–1165 (2015).CAS 
PubMed 

Google Scholar 
White, L. M. Carbohydrate reserves of grasses: A review. J. Range Manag. 26, 13–18 (1973).CAS 
Article 

Google Scholar 
Millard, P., Sommerkorn, M. & Grelet, G. A. Environmental change and carbon limitation in trees: A biochemical, ecophysiological and ecosystem appraisal. New Phytol. 175, 11–28 (2007).CAS 
PubMed 
Article 

Google Scholar 
Zhang, T., Cao, Y., Chen, Y. M. & Liu, G. B. Non-structural carbohydrate dynamics in Robinia pseudoacacia saplings under three levels of continuous drought stress. Trees 29, 1837–1849 (2015).CAS 
Article 

Google Scholar 
Chapin, F. S., Schulze, E. D. & Mooney, H. A. The ecology and economics of storage in plants. Annu. Rev. Ecol. Syst. 21, 423–447 (1990).Article 

Google Scholar 
Sardans, J., Rivas-Ubach, A. & Peñuelas, J. The C:N: P stoichiometry of organisms and ecosystems in a changing world: A review and perspectives. Perspect. Plant Ecol. Evolut. Syst. 14, 33–47 (2012).Article 

Google Scholar 
Xia, J. Y. & Wan, S. Q. Global response patterns of terrestrial plant species to nitrogen addition. New Phytol. 179, 428–439 (2008).CAS 
PubMed 
Article 

Google Scholar 
Yang, Y. H., Luo, Y. Q., Lu, M., Schädel, C. & Han, W. X. Terrestrial C: N stoichiometry in response to elevated CO2 and N addition: a synthesis of two meta-analyses. Plant Soil 343, 393–400 (2011).CAS 
Article 

Google Scholar 
Mayor, J. R., Wright, S. J. & Turner, B. L. Species-specific responses of foliar nutrients to long-term nitrogen and phosphorus additions in a lowland tropical forest. J. Ecol. 102, 36–44 (2014).CAS 
Article 

Google Scholar 
Koerselman, A. & Meuleman, A. F. The vegetation ratio: A new tool to detect the nature of nutrient limitation. J. Appl. Ecol. 33, 1441–1450 (1996).Article 

Google Scholar 
Gusewell, S. N: P ratios in terrestrial plants: variation and functional significance. New Phytol. 164, 243–266 (2004).PubMed 
Article 

Google Scholar 
Wang, S., Shan, L. S., Li, Y., Zhang, Z. Z. & Ma, J. Effect of Precipitation on the Stoichiometric Characteristics of Carbon, Nitrogen and Phosphorus of Reaumuria soongarica and Salsola passerina. Acta Bot. Boreal. Occident. Sin. 40, 0335–0344 (2020).ADS 

Google Scholar 
Niu, D. C., Li, Q., Jiang, S. G., Chang, P. J. & Fu, H. Seasonal variations of leaf C:N: P stoichiometry of six shrubs in desert of China’s Alxa Plateau. Chin. J. Plant Ecol. 37, 317–325 (2013).Article 

Google Scholar 
Kleyer, M. & Minden, V. Why functional ecology should consider all plant organs: An allocation-based perspective. Basic Appl. Ecol. 16, 1–9 (2015).Article 

Google Scholar 
Yemm, E. & Willis, A. The estimation of carbohydrates in plant extracts by anthrone. Biochem. J. 57, 508–514 (1954).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bao, S. D. Soil and Agricultural Chemistry Analysis 3rd edn. (China Agriculture Press, Beijing, 2000).
Google Scholar  More

Historical land use and cover changesExisting databases differed significantly in representing historical LUCC in China (Fig. 1). Generally, datasets agree on the direction of change in cropland area until 1980 in Liu and Tian18, Ramankutty19, Houghton20, and this study (Fig. 1b, c), while the magnitude of change varied greatly. Specifically, the total cropland expansion in China was comparable between our new data set and the LUH2-GCB from 1900 onwards (56 vs 60 Mha, Fig. 1b), but cropland area changes since 1980 diverged considerably (−14 vs 41 Mha, Fig. 1c). The differences were also evident across space and more distinct during the period of 1980 to 2019 (Fig. 2a–d), in which the cropland coverage was mainly declining in our reconstructed data but increasing in LUH2-GCB (Fig. 2b, d). We found that the distinct changes are derived from the abrupt cropland increases in the FAO data reported from China, upon which LUH2-GCB was based (see Supplementary Information 3).Fig. 1: Temporal, net changes of cropland and forest from 1900 (unit: Mha).Panel a–c: cropland; panel d–f: forest; the bar charts indicate the total accumulated areas (b, e) from 1900 and (c, f) from 1980 until the last available year; LUH2-GCB was the latest version of LUH2 data used in Global Carbon Budget assessments projects (LUH2 used in MsTMIP and TRENDY were showed in Supplementary Figs. S7 and S10); Houghton data were derived from Houghton and Nassikas20 and the data in 1900 were interpolated from 1850 and 1950; Liu&Tian and Ramankutty data were derived from the works of Liu and Tian16 and Ramankutty and Foley18; the open circles indicate the changes of cropland and forest areas derived from inventory-based benchmark data; details of the benchmark data for cropland and forest were presented in Yu et al.11 and Supplementary Information 1.2 of this study, respectively; error bars: one standard deviation from the mean.Full size imageFig. 2: Spatial distribution of the fractional coverage changes of cropland and forest in China (unit: %).Panels a–d: cropland; panel e–h: forest; panels a, b, e, and f indicate the results derived from this study; data in panels c, d, g, and h were from LUH2-GCB; panels a, c, e, and g show the changes from 1900 to 1980, whereas panels b, d, f, and h show the changes from 1980 to 2019; negative and positive values indicate coverage reduction and increment, respectively.Full size imageThe problems of cropland area expansion reported to FAO are likely caused by changes in the underlying database, in which the Chinese Agricultural Yearbook (CAY) was used prior to 1996, the China Land and Resources Statistical Yearbook (LRSY) from 1996 to 2007, and the National Land and Resources Bulletin (NLRB) after 2007 (Supplementary Information 3).These three datasets are not consistent with each other because surveying methods were distinct. For example, cropland area in CAY before 1982 used an extrapolation method (i.e. “production-to-acreage” approach) due to limited field survey data11. Specifically, the extrapolation method was widely adopted for convenience and for taxation purposes in the early period, such as in the framework of the first benchmark cropland survey conducted in 1953. Such methods assumed that low-productivity cropland occupied an area of 1/3–1/8 of a predetermined, “standard-productivity” cropland21, which greatly underestimates the acreages of low productivity cropland. Biases accumulated in this method persisted until the satellite era (1980s), while the 1953 surveying data were used as the baseline for CAY to update cropland area on an annual basis.Besides the survey method, policies also contributed to a bias of reported cropland area. To tackle rising food demands, cropland expansion was highly encouraged by the government before the 1980s, implementing an incentive policy to allow new tax-free cropland without reporting to the government for the first 3–5 years22,23. Even after the initial reporting free period, these newly cultivated croplands continued to be unreported due to political incentives to show increasing crop yield to the local authorities23,24.When the first comprehensive and systematic survey (i.e. the second national cropland survey conducted during 1985–1996) was completed, the cropland area was found to be larger than previously reported in CAY11. Similarly, the shift from the use of LRSY to NLRB also introduced a spurious cropland area increment from 2007 to 2010 as small, fragmented croplands were identified by better technologies adopted in NLRB, which had remained undetected previously (Supplementary Fig. S10).Thus, LUH2-GCB has inherited spurious temporal signals of abrupt cropland increment in FAO from the 1980s to 2010 (Fig. 1a and Supplementary Fig. S10). Therefore, if the areas of other land cover types (e.g. forest) are indirectly constrained from cropland area change, cropland area biases were mirrored in the area change of other land use types. This is the case for the LUH2-GCB and for Liu and Tian’s previous land use gridded datasets. Our new database, rebuilt from Yu et al.11, corrected these problems in temporal dynamics by assimilating multiple data sources (Fig. 1a). More specifically, we retrospectively reconstructed information about cropland and forest areas year by year, using tabular data from official agencies (Supplementary Information 1 and Supplementary Data 1). To further reduce the aforementioned biases, we used the most recent and authoritative record of provincial cropland and forest areas available as the benchmark, and then spatialized the cropland and forest distributions using gridded maps as ancillary data (Supplementary Information 1). The area changes were also validated using inventory-based benchmark data (Fig. 1a, d, details were presented in Yu et al.11 and Supplementary Information 1.2).Changes in forest area in China also varied dramatically among databases. Based on Ramankutty and Foley19 and LUH2-GCB, a net forest loss was found from 1900 to the last available year, at 33–108 Mha whereas Liu and Tian18 and Houghton and Nassikas20 reported a net increase of 15 Mha (1900–2005) and 70 Mha (1900–2015) in forest area, respectively (Fig. 1d, e).By assimilating multiple source records, reports, and national surveys, however, our newly reconstructed and intensively validated database (Supplementary Figs. S4, S5, and S8) with corrected biases suggests that the forest area increased by 58 Mha from 1900 to 2019 (Fig. 1e). In particular, our data suggest that there is a surprisingly large underestimation of forest expansion in all other databases (38–102 Mha) after 1980 (Fig. 1f). We performed spatial analyses and show that widespread forest expansion in our reconstructed data was represented as a forest decline in LUH2-GCB during the period 1980–2019 (Fig. 2f, h). These existing biases in the dataset during the last four decades can be simply removed using recently available and spatially explicit forest products (Supplementary Table S2).Bias in forest change might be explained by two reasons. First, gridded datasets inherited and transferred errors from the use of FAO-based cropland dataset in developing global land use databases such as HYDE and thus LUH2-GCB8. Second, the FAO forest area reported is an important reference data used in these databases. The FAO forest area is reported based on a “land use” definition, which underestimated gross “land cover” change signals between reported years (Supplementary Information 1.3). Specifically, the FAO forest area describes lands that have been forested and will continue to be used for forestry (e.g. cut-over area, fired-over area, unestablished afforestation land) (Supplementary Table S5). This approach overestimates forest area by including lands used for reforestation where no forest was yet created. Thus, for example, the FAO statistics reported a 157.2 Mha forest area in 1990 (Supplementary Fig. S7), which is ~30 Mha higher than officially released data.More importantly, newly established forests were underestimated in such an accounting approach. The forest area expansion in China reported in the FAO statistics was 61 Mha from 1990 to 2019, which is 30 Mha lower than the officially released data16. Our reconstructed dataset, in agreement with officially released forest area, uses a “land cover” definition that characterizes the distribution of annually established forests. Therefore, the FAO statistics – a data set with definition specified to describe the area of land use – should be used with caution for constraining the temporal evolution of forest cover distribution in gridded data reconstruction, and the modeling community should be alerted to treat the LUCC data appropriately.Nonetheless, the FAO and the related LUH2 products were the dominant LUCC forcing data used in multiple studies3,25, including various process-model-based intercomparison projects (e.g. MsTMIP, LUMIP, NMIP, TRENDY), annually released Global Carbon Budget reports2,26, and IPCC reports5, implying a potential bias of these assessments for the China region. In contrast, changes in forest area from our database were independently developed (Supplementary Information 1.2), intensively calibrated, and validated using officially released national forest inventories (NFIs, see Supplementary Figs. S4 and S5), which can help to reduce the potential bias of C balance assessment in China. More specifically, the total forest area and PF area in our database were compared with historical NFIs released by the National Forestry and Grassland Administration at provincial level since 1949 (Supplementary Figs. S4 and S5), which supports the reliability of our reconstructed data.Historical carbon stock changesTo illustrate the bias in the C balance of China when using previous LUCC dataset, we performed simulations with the DLEM model for the period 1900–2019 at a resolution of 0.5 × 0.5 degree forced by our new LUCC dataset. We validated the distribution and changes of C stock using published studies and previously reported inventory-based estimations (Supplementary Information 6 and 7). The model could capture well C dynamics in China using inventory-based forest C stock changes at both provincial and national levels as the validation data set (Supplementary Fig. S14).Our results show that the total C stock decreased by 6.9 ± 0.6 Pg from 1900 to 1980 and increased by 8.9 ± 0.8 Pg C from 1980 to 2019 (Fig. 3, derived from experiment S1 in Supplementary Table S10). Such a large C stock increment since the 1980s, which is dominated by vegetation biomass C accumulation, was not captured in the MsTMIP and TRENDY projects driven by different versions of the LUH2 data (Fig. 3). This is attributed to the fast expansion of forest area(s) that was not captured by this land use forcing (Fig. 1).Fig. 3: Temporal changes of carbon storage from 1900 to 2010s in China.Panel a–c indicate vegetation carbon, soil organic carbon, and total ecosystem carbon, respectively. Results derived from experiment designed to have all environmental factors vary historically from 1900 to the 2010s, for model design details of this study see Supplementary Information 8); pink color: MsTMIP (1900–2010); blue color: TRENDY (1900–2019); dark color: this study (1900–2019); the shade areas represent the ranges of 1 standard deviation; unit: Pg C.Full size imageWe found that the large-scale forest expansion in China alone has caused a substantial C accumulation since 1980 (0.21 ± 0.006 Pg C per year, Table 1). In contrast, the forest C sink of the TRENDY models is negligible (−0.02 ± 0.05 Pg C per year, Table 1). A moderate C source (0.10 ± 0.08 Pg C per year, Table 1) was even found in the MsTMIP models, since these models were driven by continuous forest area loss and cropland expansion since the 1980s (Supplementary Fig. S7).Table 1 Comparison of reported carbon fluxes from various biomes in ChinaFull size tableA recent atmospheric inversion-based study reported that China’s land ecosystems were a large CO2 sink of −1.11 ± 0.38 Pg C per year27, which seems to be ecologically implausible and critically sensitive to the assimilation of the CO2 record from one station28. The compilation of previous studies from inventory- and satellite-based estimation, atmospheric inversion, and process-based models suggested that the Chinese C sink was much smaller (−0.18– −0.45 Pg C per year; Table 1). Our model-simulated terrestrial sink (~−0.28 ± 0.06 Pg C per year) was in this range (Table 1).While our simulated C balance in different categories or biomes is close to previous estimations, three major differences are observed (Table 1). First, because the LUCC data used in previous global models suffered from biases as shown above, the national C sink was generally underestimated in these simulations (Table 1). Second, our estimation of the forest sink is around two to three times larger than the previous one during 1949–199829. This was mainly because forest area was underestimated by over 33% (53 Mha) in the previous study29 compared to the national forest inventory (NFI)16. This underestimation may stem from exclusion of economic and bamboo forests. The third major difference is the role of grassland soils in C balance during the period 1980–2000. China’s grassland soils were previously reported as a minor sink of −0.007–−0.022 Pg C per year from the 1980s to the 2000s (Table 1), while our simulations suggest that grassland soils were a C source of 0.062–0.066 Pg C per year. This discrepancy lies in the approaches used and the accounting boundaries between studies (i.e. whether the transitions of grassland were considered), in which LUCC impacts were represented differently. For example, impervious surfaces (part of urbanized area) expanded into ~15 Mha of natural lands in China from 1978 to 201730, which further drove redistribution of cropland into marginal lands with the majority converted from grassland, causing wind erosion, habitat loss, and more water and fertilizer consumption31. Earlier studies using a static grassland map exclude the C stock loss in the land-use transition32. Thus, the distinct roles of grassland soils (i.e. sink vs source) derived from our simulations and earlier studies are not contradictory but are due to differences in accounting boundaries.LUCC impacts on carbon stock changesOur DLEM simulation indicates that LUCC induced a C loss of 5.1 ± 0.7 Pg C from 1900 to 2010s (Fig. 4a), which is substantially lower than that from MsTMIP (13.8 ± 7.7 Pg C, 1900–2010) and TRENDY (9.4 ± 3.3 Pg C, 1900–2019; Fig. 4e, f and Supplementary Fig. S18d, g). From 1980 onward, LUCC increased C storage by 4.3 ± 0.7 Pg C, with the major contribution from vegetation biomass C increment in the southwestern and northeastern regions (Fig. 4d and Supplementary Fig. S19a). Nonetheless, this C increase in biomass was not captured in MsTMIP and TRENDY models (Fig. 4e, f and Supplementary Fig. S19d, g), which simulated that LUCC continued to reduce C stock by 7.5 ± 1.6 and 5.3 ± 2.3 Pg C during the period 1980 to the 2010s, respectively (Fig. 4 and Supplementary Fig. S20).Fig. 4: Spatial distribution of LUCC impacts on ecosystem carbon storage.Panel a–c: LUCC impacts for period of 1900–2019; panel d–f: LUCC impacts for period of 1980–2019 (d–f). Panels a and d are from this study; data in panels b and e are from MsTMIP; data in panels c and f are from TRENDY; negative and positive values indicate sink and source, respectively; green and yellow bar stacked in the insert indicate LUCC impacts on vegetation and soil organic carbon in Pg C; spatial map unit: g C m−2; error bars: one standard deviation from the mean of LUCC impacts on total carbon storage.Full size imageTo confirm that such discrepancy was induced by LUCC data but not the DLEM model, we set up additional DLEM simulations using the LUH2-GCB database (Supplementary Information 8). The simulated C losses induced by LUCC when DLEM was driven with LUH2-GCB were 6.5 ± 0.4 and 11.4 ± 0.6 Pg C during the periods of 1980–2019 and 1900–2019, which are close to MsTMIP and TRENDY simulations. These results confirm that the LUCC forcing database is the major contributor to the difference between our simulations and the MsTMIP and TRENDY projects. An earlier study reported that global LUCC-induced C emissions are substantially underestimated due to underrepresented tree harvesting and land clearing from shifting cultivation33. Our simulation revealed that regional LUCC-induced C emission could also be overestimated in China due to a bias in the LUCC data.There are also disputes over whether the LUCC induced a C sink in China since the 1990s or not (Supplementary Table S8). By using an updated LUCC database, our simulations revealed that LUCC was a strong C sink in China, and that its magnitude was larger than previous estimates since the 1990s (Supplementary Table S8). Our results using an improved LUCC forcing data can facilitate narrowing down the well-known, large uncertainty in LUCC-induced C change at regional scale.Attributions of different factors on C stock changes since 1980By using the DLEM model with factorial simulations (see Supplementary Information 8 for details), we examined the direct and interactive contributions of different drivers to terrestrial C stock change in China for the period 1980–2019, including LUCC, climate, forest management, N deposition, and CO2 fertilization (see Methods, Fig. 5). Note that historical C stock change is not equivalent to the sum of factorial attributions as the baseline conditions differ (see Supplementary Information 8).Fig. 5: Attributions of different environmental factors on carbon stock change in China from 1980 to 2019.Panels a–c indicate attributions of impacts on the changes of vegetation carbon, soil organic carbon, and total ecosystem carbon, respectively; CLM: climate; CO2: rising atmospheric CO2 concentration; Ndep: N deposition; Man: forest management; Nfer: N fertilizer and manure application.Full size imageOverall, 81.9% (6.5 Pg C) of the terrestrial C sink during this period was attributed to direct impacts of all major factors, while the interactive effect contributed 18.1% (1.43 Pg C; Fig. 5c). Among all the factors examined, LUCC was the dominant driver accounting for 50.3% (3.96 Pg C) of the total C increment during the period 1980–2019 (Fig. 5c), which was largely attributed to biomass C accumulation (70.0%; Fig. 5a, c). Tian et al.13 reported that LUCC’s contribution to the sink in China was at 0.05 Pg C yr−1 since the 1980s – an amount that is only about 30% of our simulations. The discrepancy is attributed to the different representation of forest expansion in model simulations, which was 65 Mha from 1980 to 2005 in our database but only ~14 Mha in Tian et al.13. Similarly, the increase in the global land sink during the recent period (1998–2012) was also mainly attributed to LUCC (i.e. decreased tropical forest area loss and increased afforestation in northern temperate regions), instead of CO2 or climate change34.Climate change enhanced biomass C stocks by 1.63 Pg but caused a soil C loss of 0.30 Pg, thus contributing to land sink of 1.41 Pg C (18.0% of the total with all factors) since 1980 (Fig. 5). Other global change factors, such as N fertilizer application, atmospheric N deposition, and rising CO2, had a relatively minor contribution (0.1–9.54%) to the terrestrial C sink. Therefore, conversely to previous studies13,35,36,37, we showed that LUCC was the dominant driver of the recent land C sink in China, and other factors including climate change, rising CO2, and N deposition, contributed much less (0.1–18.0%) to the C stock increment in China (Fig. 5c). Tian et al.13 pointed out that LUCC effects in China should not be ignored and that the CO2 fertilization effect might be overestimated in Piao et al.38.Our simulations confirm these statements, and further show that LUCC was actually the largest contributor to land sink in China since 1980 (Fig. 5). In those studies which did not account for the influence of LUCC separately, the effects of other global change factors may have been overestimated by including LUCC impacts. For example, Chen et al.39 and He et al.37 attributed China’s C sink into different components including climate change, leaf area index (LAI) change, rising CO2, and N deposition. Such partition inevitably masked the separate contribution from LUCC, because LAI changes are closely related to land-cover changes. Thus, the accurate representation of the LUCC should be prioritized in future modeling attribution studies.Carbon stock changes in each land cover type since 1980The contribution of the establishment of young and new forest plantations to C sink has received increasing attention3,40,41,42. Our simulation (experiment S1, see Methods section) revealed that the increase in terrestrial C stock was dominantly contributed by biomass C accumulation (76.3%) (Fig. 5), in which the natural and planted forests accounted for 65% (2.9 Pg C) and 35% (1.6 Pg C) during the last four decades. We examined the LUCC effect (i.e. the largest contributor of C stock increment in Fig. 5) on the C stock of different biomes and confirmed that forest was the major contributor of the net C accumulation in China since 1980, while other biomes, including cropland, grassland, shrubland, and wetland, were relatively stable, varying from −0.3 to 0.3 Pg C during the same period (Fig. 6). A recent study documented that forest expansion was essential for a large C sink in southern China during 2002–2017, where newly-established and existing forests contributed to 32% and 34% of land C sink in the region43. In comparison to the large biomass C increase since 1980 (3.0 Pg C, Fig. 6a), the SOC increase was much lower (0.7 Pg C) during the concurrent period, although SOC changes in each biome varied greatly (–3.4–8.6 Pg C; Fig. 6b) due to area change from land conversions. The biome-level analyses further revealed that the LUCC-induced C stock increment was dominantly contributed from forest and by area expansion, while C storage in grassland and shrubland was reduced by LUCC (Fig. 6).Fig. 6: LUCC-induced carbon storage changes by land cover types based on model simulations during 1980–2019.Panel a–c indicate vegetation carbon, soil organic carbon, and total ecosystem carbon, respectively; the widths of the red blocks indicate the estimation ranges of net changes in model simulations; purple error bars indicate one standard deviation of multiple model runs; negative and positive changes indicate carbon loss and gain, respectively.Full size imageThis study highlights the dominant role of LUCC in determining the terrestrial C sink in China. Because of inaccurate representations of land cover change in China, previous estimates of the terrestrial C sink have been strongly underestimated. In contrast, forest expansion and cropland abandonment have been overestimated in the U.S., resulting in an underestimated C emission since 19807. Hence, we highlighted that the global LUCC database should be further improved, which could potentially narrow down the C imbalance reported in global C budget accounting2. In contrast to the previous studies, we showed that the contributions of factors including rising CO2, N deposition, and climate change to the land C sink in China were much smaller than LUCC over the past four decades (1980-present time). Thus, reforestation projects could represent important climate change mitigation pathways, with co-benefits for biodiversity33. To achieve the ‘C neutrality’ goal as the Chinese government declared, future climate policy should be directed to improve land management, especially forest ecosystems.Implications for future LUCC data improvementsThis study provides a novel reconstruction of recent land use change in China and assesses its implications in quantifying for terrestrial C storage dynamics. The improved dataset more accurately depicts the spatiotemporal dynamics of LUCC in China because the historically contradictory surveying records were identified, which helped to correct the biased temporal signals. Specifically, the improved surveying methods and the socioeconomic factors have greatly shaped the LUCC signals. We advocate that these impacts should be considered in the reconstruction of the national and global LUCC dataset, especially in the areas that have been intensively disturbed by human activities as is the case of China. These endeavours will be worthwhile, as demonstrated by the large impact that these bias corrections have on China’s C dynamic assessments since 1900. Thus, accurate delineation of LUCC forcing should be stressed in global simulations, including C budget accounting, biodiversity assessments, and ecosystem services evaluations. More

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Chocolate, a serious contender for the world’s most beloved food, is made from the seed kernels of the cacao tree (Theobroma cacao). But despite its popularity, Justine Vansynghel at the University of Würzburg in Germany and her colleagues found that nobody had quantified how species living on small-scale cacao farms collectively affect production1.

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BirdLife International. Red List Update: Parrots of the Americas in Peril. https://www.birdlife.org/news/2021/02/08/red-list-update-parrots-of-the-americas-in-peril/ (2020).Berkunsky, I. et al. Current threats faced by Neotropical parrot populations. Biol. Cons. 214, 278–287. https://doi.org/10.1016/j.biocon.2017.08.016 (2017).Article 

Google Scholar 
ICMBIO—Instituto Chico Mendes de Conservação da Biodiversidade (Org.). Livro Vermelho da Fauna Brasileira Ameaçada de Extinção: Volume III-Aves 709. https://www.icmbio.gov.br/portal/images/stories/comunicacao/publicacoes/publicacoes-diversas/livro_vermelho_2018_vol3.pdf (Ministério do Meio Ambiente, 2018).CBRO—Comitê Brasileiro de Registros Ornitológicos. Listas das Aves do Brasil. 11th ed. http://www.cbro.org.br/wp-content/uploads/2020/06/avesbrasil_2014jan1.pdf (CBRO, 2014).Pacheco, J. F. et al. Annotated checklist of the birds of Brazil by the Brazilian Ornithological Records Committee—second edition. Ornithol. Res. 29(2), 94–105. https://doi.org/10.1007/s43388-021-00058-x (2021).Article 

Google Scholar 
IUCN—International Union for Conservation of Nature. The IUCN Red List of Threatened Species www.iucnredlist.org (2018).Guedes, N. M. R. Biologia reprodutiva da arara azul (Anodorhynchus hyacinthinus) no Pantanal—MS, Brasil. (Dissertação de Mestrado Universidade de São Paulo, São Paulo (1993).Guedes, N. M. R. et al. Technical Report Assessing the Impact of Fire on Blue Macaws, Pantanal, Mato Grosso do Sul, Brazil, p 13, Campo Grande, Instituto Arara Azul (2019).Guedes, N. M. R. Araras azuis: 15 anos de estudos no Pantanal. In Paper presented at IV Simpósio Sobre Recursos Naturais e Sócio-Econômicos do Pantanal, Corumbá: Embrapa Pantanal (2004).Guedes, N. M. R. Sucesso reprodutivo, mortalidade e crescimento de filhotes de araras azuis Anodorhynchus hyacinthinus (Aves, Psittacidae), no Pantanal, Brasil (Tese de doutorado Universidade Estadual Paulista, Botucatu, 2009)Guedes, N. M. R. & Harper, L. H. Hyacinth macaws in the Pantanal. In The Large Macaws (eds Abramson, J. et al.) 394–421 (Raintree Publications, 1995).
Google Scholar 
Vicente, E. C. & Guedes, N. M. Organophosphate poisoning of Hyacinth Macaws in the Southern Pantanal, Brazil. Sci. Rep. 11, 1–6. https://doi.org/10.1038/s41598-021-84228-3 (2021).CAS 
Article 

Google Scholar 
Guedes, N. M. R. et al. Assessment of fire impact on Hyacinth Macaws in Perigara, Pantanal—MT, Brazil, p 35, Campo Grande, Instituto Arara Azul (2020).Guedes, N. M. R. et al. Macaws survive fires and provide hope for resilience—Stubborn survivors. Pantanal Sci. Mag. 6, 36–41 (2021).
Google Scholar 
Oliveira, M. D. R. et al. Lack of protected areas and future habitat loss threaten the Hyacinth Macaw Anodorhynchus hyacinthinus and its main food and nesting resources. Ibis 163, 1217–1234 (2021).Article 

Google Scholar 
Ricklefs, R. E. Patterns of growth in birds. Ibis 110, 419–451. https://doi.org/10.1111/j.1474-919X.1968.tb00058.x (1968).Article 

Google Scholar 
Gebhardt-Henrich, S. & Richner, H. Causes of growth variation and its consequences for fitness. Oxford Ornithol. Ser. 8, 324–339 (1998).
Google Scholar 
Masello, J. F. & Quillfeldt, P. Body size, body condition and ornamental feathers of Burrowing Parrots: Variation between years and sexes, assortative mating and influences on breeding success. Emu Austral Ornithol. 103, 149–161. https://doi.org/10.1071/MU02036 (2003).Article 

Google Scholar 
Renton, K. Influence of environmental variability on the growth of Lilac-crowned Parrot nestlings. Ibis 144, 331–339. https://doi.org/10.1046/j.1474-919X.2002.00015.x (2002).Article 

Google Scholar 
Masello, J. F. & Quillfeldt, P. Chick growth and breeding success of the Burrowing Parrot. Condor 104, 574–586. https://doi.org/10.1650/0010-5422 (2002).Article 

Google Scholar 
Pacheco, M. A., Beissinger, S. R. & Bosque, C. Why grow slowly in a dangerous place? Postnatal growth, thermoregulation, and energetics of nestling green-rumped parrotlets (Forpus passerinus). Auk 127, 558–570. https://doi.org/10.1525/auk.2009.09190 (2010).Article 

Google Scholar 
Vigo, G., Williams, M. & Brightsmith, D. J. Growth of Scarlet Macaw (Ara macao) chicks in southeastern Peru. Neotrop. Ornithol. 22, 143–153 (2011).
Google Scholar 
Lyon, J. P. et al. Reintroduction success of threatened Australian trout cod (Maccullochella macquariensis) based on growth and reproduction. Mar. Freshw. Res. 63, 598–605. https://doi.org/10.1071/MF12034 (2012).Article 

Google Scholar 
Vigo-Trauco, G., Garcia-Anleu, R. & Brightsmith, D. J. Increasing survival of wild macaw chicks using foster parents and supplemental feeding. Diversity 13, 121. https://doi.org/10.3390/d13030121 (2021).Article 

Google Scholar 
Tellería, J. L., De La Hera, I. & Perez-Tris, J. Morphological variation as a tool for monitoring bird populations: A review. Ardeola 60, 191–224. https://doi.org/10.13157/arla.60.2.2013.191 (2013).Article 

Google Scholar 
Silva, J. S. V. Elementos fisiográficos para delimitação do ecossistema Pantanal: Discussão e proposta. Oecol. Brasil. 1, 349–458. https://doi.org/10.4257/OECO.1995.0101.22 (1995).Article 

Google Scholar 
Silva, J. S. V. & Abdon, M. M. Delimitação do Pantanal Brasileiro e suas Sub-Regiões. Pesq. Agropec. Bras. 33, 1703–1711 (1998).
Google Scholar 
Keuroghlian, A., Eaton, D. & Desbiez, A. L. J. The response of a landscape species, white-lipped peccaries, to seasonal resource fluctuations in a tropical wetland, the Brazilian Pantanal. Int. J. Biodivers. Conserv. 1, 87–97 (2009).
Google Scholar 
Donatelli, R. J., Posso, S. R. & Toledo, M. C. B. D. Distribution, composition and seasonality of aquatic birds in the Nhecolândia sub-region of South Pantanal, Brazil. Braz. J. Biol. 74, 844–853 (2014).CAS 
Article 

Google Scholar 
Donatelli, R. J. et al. Temporal and spatial variation of richness and abundance of the community of birds in the Pantanal wetlands of Nhecolândia (Mato Grosso do Sul, Brazil). Rev. Biol. Trop. 65, 1358–1380 (2017).Article 

Google Scholar 
Tomas, W. M. et al. Sustainability agenda for the Pantanal Wetland: Perspectives on a collaborative interface for science, policy, and decision-making. Trop. Conserv. Sci. 12, 1–30. https://doi.org/10.1177/1940082919872634 (2019).ADS 
Article 

Google Scholar 
Harris, M. B. et al. Safeguarding the Pantanal wetlands: Threats and conservation initiatives. Conserv. Biol. 19, 714–720. https://doi.org/10.1111/j.1523-1739.2005.00708.x (2005).Article 

Google Scholar 
Santos Júnior, A. D., Aspectos populacionais de Sterculia apetala (Jacq.) Karst (Sterculiaceae) como subsídios ao plano de conservação da arara-azul no Sul do Pantanal, Mato Grosso do Sul, Brasil. (2006). https://repositorio.ufms.br/handle/123456789/521.Ricklefs, R. E. The optimization of growth rate in altricial birds. Ecology 65, 1602–1616 (1984).Article 

Google Scholar 
Bruford, M. W., Hanotte, O., Brookfield, J. F. Y. & Burke, T. Single-locus and multilocus DNA fingerprinting. In Molecular Genetic Analysis of Populations: A Practical Approach (ed. Hoelzel, A. R.) 225–269 (Oxford University Press, 1992).
Google Scholar 
Miyaki, C. Y. et al. Sex identification of parrots, toucans, and curassows by PCR: Perspectives for wild and captive population studies. Zoo Biol. 17(5), 415–423 (1998).Article 

Google Scholar 
Cavanaugh, J. E. & Neath, A. A. The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. Wiley Interdiscip. Rev. Comput. Stat. 11, 1460. https://doi.org/10.1002/wics.1460 (2019).MathSciNet 
Article 

Google Scholar 
Motulsky H. J. GraphPad curve fitting guide. 2021. http://www.graphpad.com/guides/prism/7/curve-fitting/index.htm. Accessed 18 September.Saunders, D. A., Smith, G. T. & Rowley, I. The availability and dimensions of tree hollows that provide nest sites for cockatoos (Psittaciformes) in Western Australia. Wildl. Res. 9, 541–556. https://doi.org/10.1071/WR9820541 (1982).Article 

Google Scholar 
Navarro, J. L. & Bucher, E. H. Growth of monk parakeets. Wilson Bull. 102, 520–525 (1990).
Google Scholar 
Murtaugh, P. A. Performance of several variable-selection methods applied to real ecological data. Ecol. Lett. 12, 1061–1068 (2009).Article 

Google Scholar 
Waltman, J. R. & Beissinger, S. R. Breeding behavior of the Green-rumped Parrotlet. Wilson Bull. 104, 65–84 (1992).
Google Scholar 
Enkerlin-Hoeflich, E. C., Packard, J. M. & González-Elizondo, J. J. Safe field techniques for nest inspections and nestling crop sampling of parrots. J. Field Ornithol. 70, 8–17 (1999).
Google Scholar 
Barros, Y. de M. Biologia comportamental de Propyrrhura maracana (Aves, Psittacidae): Fundamentos para conservação in situ de Cyanopsitta spixii (Aves, Psittacidae) na Caatinga. (Tese de Doutorado Universidade Estadual de São Paulo, Rio Claro, 2001).Seixas, G. H. F. & Mourão, G. M. Growth of nestlings of the BlueFronted Amazon (Amazona aestiva) raised in the wild or in captivity. Ornitol. Neotrop. 14, 295–305 (2003).
Google Scholar 
Vigo-Trauco, G. Crecimiento de pichones de Guacamayo Escarlata, Ara macao (Linneus: 1758) en la Reserva Nacional Tambopata-Madre de Dios-Peru (Tese Universidad Nacional Agraria La Molina, 2007).
Google Scholar 
Tjørve, K. M. & Tjørve, E. The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family. PLoS One https://doi.org/10.1371/journal.pone.0178691 (2017).Article 
PubMed 
PubMed Central 
MATH 

Google Scholar 
Reed, J. M. The role of behavior in recent avian extinctions and endangerments. Conserv. Biol. 13, 232–241. https://doi.org/10.1046/j.1523-1739.1999.013002232.x (1999).Article 

Google Scholar 
Tjørve, K. M., Underhill, L. G. & Visser, G. H. Energetics of growth in semi-precocial shorebird chicks in a warm environment: The African black oystercatcher, Haematopus moquini. Zoology 110, 176–188. https://doi.org/10.1016/j.zool.2007.01.002 (2007).Article 
PubMed 

Google Scholar 
Tjørve, K. M., Underhill, L. G. & Visser, G. H. The energetic implications of precocial development for three shorebird species breeding in a warm environment. Ibis 150, 125–138 (2008).Article 

Google Scholar 
Ricklefs, R. E. Weight recession in nestling birds. Auk 85, 30–35. https://doi.org/10.2307/4083621 (1968).Article 

Google Scholar 
Huin, N. & Prince, P. A. Chick growth in albatrosses: Curve fitting with a twist. J. Avian Biol. 31, 418–425. https://doi.org/10.1034/j.1600-048X.2000.310318.x (2000).Article 

Google Scholar 
Corsini, M. et al. Growing in the city: Urban evolutionary ecology of avian growth rates. Evol. Appl. 14, 69–84. https://doi.org/10.1111/eva.13081 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Barbosa, L. T. Avaliação do sucesso reprodutivo da arara-canindé (Ara ararauna—Psittacidae) e o desenvolvimento urbano de Campo Grande, Mato Grosso do Sul (Dissertação de mestrado Universidade Anhanguera Uniderp, Campo Grande, 2015).Giraldo-Deck, L. M. et al. Development of intraspecific size variation in black coucals, white-browed coucals and ruffs from hatching to fledging. J. Avian Biol. 51, e02440. https://doi.org/10.1111/jav.02440 (2020).Article 

Google Scholar 
Guedes et al. Annual Technical Report from the Instituto Arara Azul., Pantanal-MS, Brazil. 35p, Campo Grande, Instituto Arara Azul (2022). More

Cliffs present a unique flora that has been little studied until now mainly because of the inherent difficulties to access this unique environment, as shown in Fig. 2. The techniques currently used to access plants on steep slopes and cliffs (e.g., abseiling, helicopter) are generally dangerous, costly and time consuming. Using a small aerial manipulator to sample plants on the cliffs can represent many advantages, including safety and portability, as well as the capability of reaching otherwise inaccessible locations easily, quickly and at low cost.Figure 2Examples of the cliff habitats of some critically endangered species on the Kauaʻi Island along with the count of known individuals as of February 2022.Full size imageHowever, several technical challenges make it difficult to develop suitable aerial manipulators for this task. Indeed, the sampling of plants on cliffs necessarily leads to significant collision risks, as well as contact forces and moments during sampling that can destabilize the drone. The samples collected would also need to be accessed from the side of the aerial platform22. Any weight (e.g., sampling tool, collected samples) located horizontally away from the center of mass of the drone creates large additional demands on the propulsion system of most drones. To collect specific plant parts in windy conditions (e.g., scion, flowers, seeds, etc.), precise and fast motion is required even in degraded Global Navigation Satellite System (GNSS) coverage near the cliffs. The great diversity of plant species and morphology found on cliffs, as well as the variety of targeted sections of plant, also represent a major design challenge. Finally, to maximize the adoption of this tool, it is also desirable that scientists with minimal training could use this platform. The next sections describe how these challenges were addressed through the development of the Mamba.Suspended sampling platformThere are a multitude of configurations that could have been explored to sample plants on cliffs. Some drones have manipulators rigidly attached to their structure20,23. However, these manipulators tend to have a limited reach to keep the center of mass within the propeller footprint and to minimize the inertia of the system. This could result in a high collision risk with the propellers in the uneven terrain found on cliffs. The contact forces created during the sampling operation also generate destabilizing moments through manipulators rigidly attached to the drone. To address these challenges, concepts involving a compliant manipulator operated from specialized drones were also explored10. Alternatively, some aerial manipulators were also passively suspended under the drone through a long rod21,24. This keeps the drone above potential obstacles within the environment, significantly reducing the operator’s mental demand and stress while also reducing the disturbances transmitted to the drone to a downward force aligned with the rod and yaw torque. To maintain these advantages while providing better precision, some projects have developed cable suspended platforms equipped with thrusters25,26. As these platforms do not have to counter gravity, the thrusters can be positioned to fight external disturbances more efficiently (e.g., wind, contact forces, drone movements). Existing systems however only stabilize the suspended platform close to its equilibrium point.The chosen concept for the Mamba, illustrated at Fig. 3, consists of a suspended platform that can stabilize itself far from its natural equilibrium to provide a large workspace. The lifting drone in this system stays safely away and above from steep cliff faces, while supporting the platform and providing rough positioning in space through better GNSS coverage. The platform is suspended 10 m below the lifting drone using four attachment points to prevent pitch and roll motions. The cable also acts as a low pass filter, isolating the platform from the fast drone movements required to fight wind disturbances. The suspended platform design can then focus on fast and precise positioning, while also being tolerant to contacts during sampling. To do so, four pairs of bidirectional actuators are used to control the motion in the plane of the pendulum (i.e., x and y translation, as well as yaw). Two pairs of actuators are installed in the x-direction to provide sufficient force to reach plants as far as 4 m from the equilibrium position. This corresponds to roughly 3.3 m from the tip of the lifting drone’s propellers.Figure 3(a) General concept of the Mamba and lifting drone during transit and sampling on cliffs. (b) Side view of the Mamba showing the components and cable installations. (c) Top view showing the antagonist thrusters configuration. (d) Close-up of the sampling tool and 2 degrees of freedom (DOF) wrist specifically designed to sample small fragile plants.Full size imageSince the Mamba is self-powered and has its own communication system, the lifting drone function is simply to lift the platform and hold it in place. This made it possible to select amongst the many commercially available products to accelerate the development of the Mamba. The DJI M300 was chosen as it comes equipped with a 360° optical obstacle avoidance vision system, an IP45 rating, and a flight time of 20 min with the Mamba attached (3.3 kg). It also advertised a four constellation GNSS receiver for better coverage around buildings, structures, and cliffs.Precise control in windsWinds under 20 km/h represent a gentle breeze on the Beaufort scale. At this level, the wind only moves the leaves, and not the branches, which allows for ideal sampling conditions. According to historical weather data from 2020, daily maximum winds are less than 20 km/h for 40 to 70% of the year, depending on the exact location on Kauaʻi Island (i.e., Lihuʻe International airport, as reported by the National Oceanic and Atmospheric Administration, and the Makaha Ridge Weather Station, as reported in the MesoWest database). This also implies that Kauaʻi experiences stronger winds on certain days which would make precise sampling difficult. Wind conditions are also more challenging near cliff faces, with increased turbulence and vertical airflow along the cliff.To allow operations on most days, while providing precise positioning and fast rejection of wind disturbances, the actuators of the Mamba are oriented in the horizontal plane. This allows the actuator forces to directly affect the motion of the suspended platform. Each actuator of the Mamba consists of a pair of brushless DC motors and 23 cm propellers capable of producing 7 N of force. The motors are installed in opposite directions, are always idling at their minimum rotation speed, and are commanded to only create force in their preferred direction. This antagonistic configuration avoids the low-velocity dead zone of a brushless motor during thrust reversal. This makes it possible to quickly revert the direction of the thrust and nearly triples the bandwidth of the actuators to approximately 2.5 Hz27. This configuration, however, comes at the expense of added mass and components.The Mamba is equipped with a flight controller that includes a control system, and a state estimator. To avoid degraded GNSS coverage issues, the state estimator only uses data from a high accuracy inertial measurement unit (IMU) to estimate the attitude of the platform. This provides the relative position of the platform with respect to the drone and is sufficient for teleoperation. Three separated proportional-derivative controllers are used for each of the DOF controlled by the actuators. This control system also provides attitude-hold assistance (i.e., pitch and roll, which correspond to x and y displacements, as well as yaw). This implies that if the user does not send any commands, the suspended platform maintains its current state.Figure 4 illustrates the stabilization accuracy of the Mamba when moving along a representative trajectory when suspended indoors from a 5.7 m cable (limited by ceiling height). This experiment confirmed that the sampling tool can maintain a position at a horizontal reach of 2.25 m with a precision of about 5 cm for 30 s. As the horizontal reach and precision are limited by the cable angular displacements (e.g., component of weight acting on the pendulum, IMU angular resolution), the resulting workspace when operating with a 10 m long cable would reach a radius of 4 m with a positioning accuracy of about 9 cm. To account for potential external disturbances like wind, the sampling tool was designed with an opening of 15 cm. This creates some margin for the pilot to align the target with the sampling mechanism. Field trials detailed below demonstrated that the Mamba actuators and controller could maintain a sufficiently stable position to sample plants in winds During the sampling phase, wind speed averaged 15.7 km/h with a standard deviation of 6.8 km/h, while wind gusts reached an average of 20.1 km/h with a standard deviation of 6.5 km/h. The maximum average wind speed recorded during sampling was 28 km/h with gusts up to 37 km/h. This represents a lower bound of the system performance, as no failure resulted from the wind conditions experienced during the trials. The a ttached Supplementary Video also demonstrates the stability of the system.Figure 4Representative motion of the sampling tool within its workspace based only on feedback from a high accuracy IMU and recorded using a motion capture system. The natural equilibrium point is at (0,0). The experiment starts with a 90° rotation around the z axis, followed by a forward movement along the x-axis of the Mamba and a lateral movement along its y-axis. The system then maintains this position for 30 s without any user inputs. Produced in MATLAB R2021a.Full size imageTeleoperated sampling of cliffs habitatsPlants growing on Kauaʻi cliffs exhibit a wide morphological variety. For this project, targets ranged from small herbaceous plants such as Euphorbia eleanoriae (plants  More

Test animals and husbandryWild type adult zebrafish (AB line) were initially obtained from China Zebrafish Resource Center (CZRC, China) and reared at the zebrafish facility of the University of Saint Joseph, Macao. Fish were maintained in 10 L tanks in a standalone housing system (model AAB-074-AA-A, Yakos 65, Taiwan) with filtered and aerated water (pH balanced 7–8; 400–550 μS conductivity) at 28 ± 1 °C and under a 12:12 light: dark cycle. Animals were fed twice daily with live artemia and dry powder food (Zeigler, PA, USA). The fish used in this study were 6–8 months old, both males and females (1:1), with a total length of 2.2–3.1 cm. The total number of specimens tested was 30 for the auditory sensitivity measurements and inner ear morphological analysis (6 fish per experimental group), and 78 for the Novel Tank Diving assay (15-18 fish per group).All experimental procedures complied with the ethical guidelines regarding animal research and welfare enforced at the Institute of Science and Environment, University of Saint Joseph, and approved by the Division of Animal Control and Inspection of the Civic and Municipal Affairs Bureau of Macao (IACM), license AL017/DICV/SIS/2016. This study was conducted in compliance with the ARRIVE guidelines60.Noise treatmentsPrior to acoustic treatments, all subjects were transferred to 4 L isolation glass tanks that were placed in a quiet lab environment (Sound Pressure Level, SPL: ranging between 103 and 108 dB re 1 μPa) for a minimum of 7 days. These tanks had no filtering system but were subject to frequent water changes, and the light, temperature and water quality were kept similar to the stock conditions. This adaptation period was important to reduce potential effects of noise conditions from the zebrafish housing system.After this period, groups of six zebrafish were transferred into separate acoustic treatment glass tanks (dimensions: 59 cm length × 29 cm width × 47 cm height; 70 L)—Fig. 1 Supplementary, where they remained 24 h in acclimation. Each tank was equipped with an underwater speaker (UW30, Electro-Voice, MN, USA) housed between two styrofoam boards (dimensions: 3 cm thick × 29 cm width × 47 cm height) with a hole in the centre, positioned vertically in one side of the tank. Another similar sized board was positioned in the opposite side of the tank and fine sand was placed in the bottom to minimize transmission of playback vibrations into the tank walls. Each treatment tank was mounted on top of styrofoam boards placed over two granite plates spaced by rubber pads to reduce non-controlled vibrations.Four acoustic treatment tanks were prepared for this study to be used alternately between trials and cleaning procedures, but only two were used simultaneously. When two tanks were being used, one contained specimens under acclimation and the other fish under a specific acoustic treatment. The tanks were housed in a custom-made rack and placed at least 1 m apart to minimize acoustic interferences. The tanks were used randomly for the different treatments across the various trials.The speakers were connected to audio amplifiers (ST-50, Ai Shang Ke, China) that were connected to laptops running Adobe Audition 3.0 for windows (Adobe Systems Inc., USA). After the acclimation period, specimens were exposed to white noise playbacks (bandwidth: 100–3000 Hz) at 150 dB re 1 µPa for 24 h, starting in the morning between 10 and 11 a.m. The bandwidth adopted covered the best hearing range of zebrafish27, as well as the frequency range of most anthropogenic noise sources, such as pile driving and vessels2.Sound recordings and SPL measurements were made with a hydrophone (Brüel & Kjær type 8104, Naerum, Denmark; frequency range: 0.1 Hz–120 kHz, sensitivity of − 205 dB re 1 V/μPa) connected to a hand-held sound level meter (Brüel & Kjær type 2270). Noise level was adjusted with the speaker amplifier so that the intended amplitude (LZS, RMS sound level obtained with slow time and linear frequency weightings: 6.3 Hz–20 kHz) was achieved at the centre of the tanks before each treatment. A variation in SPL of ±10 dB was registered in the closest and farthest points (in relation to the speaker). The sound spectra of the noise treatments were relatively flat similar to the setup described in a prior study by Breitzler et al.27.Moreover, the acoustic treatments were calibrated with a tri-axial accelerometer (M20-040, frequency range 1–3 kHz, GeoSpectrum Technologies, NS, Canada) with the acoustic centre placed in the middle of the tank. The sound playback generated was about 120 dB re 1 m/s2, with most energy in the horizontal axis perpendicular to the speaker, which was verified based on previously described methods using a MATLAB script paPAM16.In this study four sound treatments were used with varying temporal patterns similar to Sabet et al.18—Fig. 1: continuous noise (CN); intermittent regular noise with a fast pulse rate—1 s pulses interspersed with 1 s silence (IN1,1); intermittent regular noise with a slow pulse rate—1 s pulses interspersed with 4 s silence (IN1,4) and intermittent random noise—1 s pulses interspersed with 1, 2, 3, 4, 5, 6 or 7 s silent intervals in randomized sequence (RN1,7) leading to a mean interval of 4 s. All intermittent patterns had 5 ms ramps to fade in and fade out pulses for smooth transitions. In the “control” treatment tank, the amplifier connected to the speaker was switched on but without playback.After each treatment, two specimens were tested for audiometry, two were tested with the NTD assay and another two were euthanized and dissected for inner ear morphological analysis.Auditory sensitivity measurementsAuditory Evoked Potential (AEP) recordings were conducted immediately after noise treatments. The AEP recording technique adopted followed previously described procedures27. The recordings were conducted in a rectangular plastic tank (50 cm length × 35 cm width × 23 cm height) equipped with an underwater speaker (UW30) positioned in the bottom and surrounded by fine sand. A custom-built sound stimulation system with enhanced performance at lower frequencies ( More

Morris, G. L. & Fan, J. Reservoir Sedimentation Handbook: Design and Management of Dams, Reservoirs, and Watersheds for Sustainable Use (McGraw Hill Professional, 1998).
Google Scholar 
White, R. Evacuation of Sediments from Reservoirs, HR Wallingford, http://www.thomastelford.com (Thomas Telford Publishing, 2001).Kondolf, G. M. et al. Sustainable sediment management in reservoirs and regulated rivers: Experiences from five continents. Earth’s Future 2, 256–280 (2014).ADS 
Article 

Google Scholar 
Schleiss, A. J., Franca, M. J., Juez, C. & De Cesare, G. Reservoir sedimentation. J. Hydraul. Res. 54, 595–614 (2016).Article 

Google Scholar 
Dahal, S., Crosato, A., Omer, A. Y. A. & Lee, A. A. Validation of model-based optimization of reservoir sediment releases by dam removal. J. Water Resour. Plan. Manag. 147, 04021033 (2021).Article 

Google Scholar 
Williams, G. P. & Wolman, M. G. Effects of dams and reservoirs on surface water hydrology—Changes in rivers downstream from dams. Natl. Water Summ. Hydrol. Events Surf. Water Resour. 2300, 83 (1986).
Google Scholar 
Toffolon, M., Siviglia, A. & Zolezzi, G. Thermal wave dynamics in rivers affected by hydropeaking. Water Resour. Res. https://doi.org/10.1029/2009WR008234 (2010).Article 

Google Scholar 
Stewart, G. B. Patterns and Processes of Sediment Transport Following Sediment-Filled Dam Removal in Gravel Bed Rivers. (PhD Thesis, Oregon State University, Oregon USA, 2006).Major, J. J. et al. Geomorphic Response of the Sandy River, Oregon, to Removal of Marmot Dam. U.S. Geological Survey Professional Paper, 64p https://pubs.usgs.gov/pp/1792/ (2012).Espa, P., Castelli, E., Crosa, G. & Gentili, G. Environmental effects of storage preservation practices: Controlled flushing of fine sediment from a small hydropower reservoir. Environ. Manag. 52, 261–276 (2013).ADS 
Article 

Google Scholar 
Tena, A., Vericat, D. & Batalla, R. J. Suspended sediment dynamics during flushing flows in a large impounded river (the lower River Ebro). J Soils Sediments 14, 2057–2069 (2014).Article 

Google Scholar 
Antoine, G., Camenen, B., Jodeau, M., Némery, J. & Esteves, M. Downstream erosion and deposition dynamics of fine suspended sediments due to dam flushing. J. Hydrol. 585, 124763 (2020).Article 

Google Scholar 
Power, M., Dietrich, W. & Finlay, J. Dams and downstream aquatic biodiversity: Potential food web consequences of hydrologic and geomorphic change. Environ. Manag. 20, 887–895 (1996).ADS 
CAS 
Article 

Google Scholar 
Clarke, K. D., Pratt, T. C., Randall, R. G., Scruton, D. A. & Smokorowski, K. E. Validation of the flow management pathway: Effects of altered flow on fish habitat and fishes downstream from a hydropower dam. Can. Tech. Rep. Fish. Aquat. Sci. 2784, 111 (2008).
Google Scholar 
Poff, N. L. & Zimmerman, J. K. H. Ecological responses to altered flow regimes: A literature review to inform the science and management of environmental flows. Freshw. Biol. 55, 194–205 (2010).Article 

Google Scholar 
Juracek, K. E. The aging of America’s reservoirs: In-reservoir and downstream physical changes and habitat implications. JAWRA J. Am. Water Resour. Assoc. 51, 168–184 (2015).ADS 
Article 

Google Scholar 
Brandt, S. A. & Swenning, J. Sedimentological and geomorphological effects of reservoir flushing: the Cachí Reservoir, Costa Rica, 1996. Geogr. Ann. Ser. B 81, 391–407 (1999).Article 

Google Scholar 
Grant, G. E., Schmidt, J. C. & Lewis, S. L. A geological framework for interpreting downstream effects of dams on rivers. In Water Science and Application (eds O’Connor, J. E. & Grant, G. E.) 203–219 (American Geophysical Union, 2003). https://doi.org/10.1029/007WS13.Chapter 

Google Scholar 
Petts, G. E. & Gurnell, A. M. Dams and geomorphology: Research progress and future directions. Geomorphology 71, 27–47 (2005).ADS 
Article 

Google Scholar 
Newcombe, C. & MacDonald, D. D. Effects of suspended sediments on aquatic ecosystems. J. N. Am. J. Fish. Manag. 11, 72–82 (1991).Article 

Google Scholar 
Gilles, B. & Le Bail, P.-Y. Does light have an influence on fish growth?. Aquaculture 177, 129–152 (1999).Article 

Google Scholar 
Carolli, M., Bruno, M. C., Siviglia, A. & Maiolini, B. Responses of benthic invertebrates to abrupt changes of temperature in flume simulations. River Res. Appl. 28, 678–691 (2012).Article 

Google Scholar 
Bennel, D. H., Connor, W. P. & Eaton, C. A. Substrate composition and emergence success of fall Chinook salmon in the Snake river. Northwest Sci. 77, 93–99 (2003).
Google Scholar 
Jensen, D. W., Steel, E. A., Fullerton, A. H. & Pess, G. R. Impact of fine sediment on egg-to-fry survival of Pacific salmon: A meta-analysis of published studies. Rev. Fish. Sci. 17, 348–359 (2009).CAS 
Article 

Google Scholar 
Bjornn, T. C. & Reiser, D. W. Habitat requirements of salmonids in streams. Am. Fish. Soc. Spec. Publ. 19, 83–138 (1991).
Google Scholar 
ASCE, N. Sediment and aquatic habitat in river systems. J. Hydraul. Eng. 118, 669–687 (1992).Article 

Google Scholar 
Louhi, P., Mäki-Petäys, A. & Erkinaro, J. Spawning habitat of Atlantic salmon and brown trout: General criteria and intragravel factors. River Res. Appl. 24, 330–339 (2008).Article 

Google Scholar 
Baxter, C. V. & Hauer, F. R. Geomorphology, hyporheic exchange, and selection of spawning habitat by bull trout (Salvelinus confluentus). Can. J. Fish. Aquat. Sci. 57, 1470–1481 (2000).Article 

Google Scholar 
Peviani, M., Saccardo, I., Crosato, A. & Gentili, G. Natural and artificial floods connected with river habitat. in Ecohydraulics 2000. Proceedings
of the 2nd International Symposium on Habitat Hydraulics, IAHR- Que´bec,
Canada, vol. B 175–186 (1996).Crosa, G., Castelli, E., Gentili, G. & Espa, P. Effects of suspended sediments from reservoir flushing on fish and macroinvertebrates in an alpine stream. Aquat. Sci. 72, 85 (2009).Article 
CAS 

Google Scholar 
Espa, P., Crosa, G., Gentili, G., Quadroni, S. & Petts, G. Downstream ecological impacts of controlled sediment flushing in an Alpine valley river: A case study. River Res. Appl. 31, 931–942 (2014).Article 

Google Scholar 
Lee, A. Modelling Salmon Spawning Habitat Response to Dam Removal. (MSc Thesis, IHE Delft, the Netherlands, 2017).van Oorschot, M. et al. Impact of dam operations on the habitat suitability of Plecoglossus altivelis downstream of the Funagira dam, Japan. In River Flow 2020 (eds Uijttewaal et al.) (2020 Taylor & Francis Group, CRC Press, 2020).
Google Scholar 
Newcombe, C. P. Suspended sediments in acquatic ecosystem: III effects as a function of concentration and duration of exposure. (1994).Newcombe, C. P. & Jensen, J. O. T. Channel suspended sediment and fisheries: A synthesis for quantitative assessment of risk and impact. N. Am. J. Fish. Manag. 16, 693–727 (1996).Article 

Google Scholar 
Hubert, W. A., Helzner, R. S., Lee, L. A. & Nelson, P. C. Habitat suitability index models and instream flow suitability curves: Arctic grayling riverine populations. Western Energy and Land Use Team, Division of Biological Services, Research and Development, Fish and Wildlife Service, US Department of Interior, Biological report 82 (10.110) (1985).Raleigh, R. F., Miller, W. J. & Nelson, P. C. Habitat suitability index models and instream flow suitability curves: Chinook salmon. Fish and Wildlife Service, US Department of the Interior, Biological Report 82(10.122) www.nwrc.usgs.gov/wdb/pub/hsi/hsi-122.pdf (1986).Fisher, S., Gray, L., Grimm, N. & Busch, D. E. Temporal succession in a desert stream ecosystem following flash flooding. Ecol. Monogr. https://doi.org/10.2307/2937346 (1982).Article 

Google Scholar 
Lapointe, M., Eaton, B., Driscoll, S. & Latulippe, C. Modelling the probability of salmonid egg pocket scour due to floods. Can. J. Fish. Aquat. Sci. 57, 11 (2000).Article 

Google Scholar 
Baldwin, D. & Mitchell, A. M. The effects of drying and re-flooding on the sediment and soil nutrient dynamics of lowland river–floodplain systems: A synthesis. River Res. Appl. 16, 457–467 (2000).
Google Scholar 
Kowalski, D. The effects of stream flow on the trout populations of the Gunnison river. (2007).Konard, C. P. Effects of urban development on floods. U.S. Geological Survey—Water Resources Fact Sheet 076-03 https://pubs.usgs.gov/fs/fs07603/ (2016).Miller, J. D. & Hutchins, M. The impacts of urbanisation and climate change on urban flooding and urban water quality: A review of the evidence concerning the United Kingdom. J. Hydrol. Reg. Stud. 12, 345–362 (2017).Article 

Google Scholar 
Poff, N. L. & Ward, J. V. Implications of streamflow variability and predictability for lotic community structure: A regional analysis of streamflow patterns. Can. J. Fish. Aquat. Sci. 46, 1805–1818 (1989).Article 

Google Scholar 
George, S. D., Baldigo, B. P., Smith, A. J. & Robinson, G. R. Effects of extreme floods on trout populations and fish communities in a Catskill Mountain river. Freshw. Biol. 60, 2511–2522 (2015).Article 

Google Scholar 
Carlson, A. K., Fincel, M. J., Longhenry, C. M. & Graeb, B. D. Effects of historic flooding on fishes and aquatic habitats in a Missouri river delta. J. Freshw. Ecol. 31, 271–288 (2016).Article 

Google Scholar 
Ríos-Pulgarín, M. I., Barletta, M. & Mancera-Rodríguez, N. J. The role of the hydrological cycle on the distribution patterns of fish assemblages in an Andean stream. J. Fish Biol. 89, 102–130 (2016).PubMed 
Article 
CAS 

Google Scholar 
United States Federal energy regulatory Commission (FERC). Application for Surrender of License, Bull Run Hydropower Project: Environmental Impact Statement. https://catalog.hathitrust.org/Record/100940309 (2003).Squier Associates. Sandy river sediment study, Bull Run Hydroelectric Project. (2000).Taylor, B. Salmon and Steelhead Runs and Related Events of the Clackamas River Basin–A Historical Perspective. Portland General Electric Company, 64 https://www.eaglecreekfriends.org/links-references (1999).Trimble, D. E. Geology of Portland, Oregon, and Adjacent Areas. Bulletin U.S. G.P.O. https://pubs.er.usgs.gov/publication/b1119. https://doi.org/10.3133/b1119. (1963).Sandy River basin Working Group. Sandy River basin aquatic habitat restoration strategy: An anchor habitat-based prioritization of restoration opportunities Oregon Trout. Portland, Oregon. Preprint at https://www.fs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb5325660.pdf (2007).Lee, A., Crosato, A., Omer, A. Y. A. & Bregoli, F. Applying a two-dimensional morphodynamic model to assess impacts to Chinook salmon spawning habitat from dam removal. in AGU Fall meeting (2017).Healey, M. C. Life history of Chinook salmon (Oncorhynchus tshawytscha). Pacific Salmon Life Histories 311–394 (1991).Bourret, S. L., Caudill, C. C. & Keefer, M. L. Diversity of juvenile Chinook salmon life history pathways. Rev. Fish. Biol. Fish. 26, 375–403 (2016).Article 

Google Scholar 
Alderdice, D. & Velsen, F. Relation between temperature and incubation time for eggs of Chinook salmon (Oncorhynchus tshawytscha). J. Fish. Res. Board Can. 35, 69–75 (1978).Article 

Google Scholar 
Seattle Aquarium. Redd alert: Our Chinook salmon are hatching! |. Seattle Aquarium https://www.seattleaquarium.org/blog/redd-alert-our-chinook-salmon-are-hatching (2015).Whitman, L., Cannon, B. & Hart, S. Spring Chinook salmon in the Willamette and Sandy rivers: Sandy river basin Spring Chinook salmon spawning surveys. Oregon Department of Fish and Wildlife 4034 Fairview Industrial Drive SE Salem, Oregon 97302, 30 https://odfw.forestry.oregonstate.edu/willamettesalmonidrme/sites/default/files/2016_sandy_basin_spring_chinook_spawning_survey.pdf (2016).Cramer, S. P. Fish and habitat surveys of the lower Sandy and Bull Run rivers. Report of SP Cramer&Associates, Inc. to Portland General Electric and Portland Water bureau, Portland, Oregon (1998).Westley, P. A. Documentation of en route mortality of summer chum salmon in the Koyukuk river, Alaska and its potential linkage to the heatwave of 2019. Ecol. Evol. 10, 10296–10304 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Bowerman, T. E., Keefer, M. L. & Caudill, C. C. Elevated stream temperature, origin, and individual size influence Chinook salmon prespawn mortality across the Columbia River Basin. Fish. Res. 237, 105874 (2021).Article 

Google Scholar 
Beechie, T. J. et al. Process-based principles for restoring river ecosystems. Bioscience 60, 209–222 (2010).Article 

Google Scholar 
Crosato, A. & Saleh, M. S. Numerical study on the effects of floodplain vegetation on river planform style. Earth Surf. Process. Landf. 36, 711–720 (2011).ADS 
Article 

Google Scholar 
Schuurman, F., Marra, W. A. & Kleinhans, M. G. Physics-based modeling of large braided sand-bed rivers: Bar pattern formation, dynamics, and sensitivity. J. Geophys. Res. Earth Surf. 118, 2509–2527 (2013).ADS 
Article 

Google Scholar 
Singh, U., Crosato, A., Giri, S. & Hicks, M. Sediment heterogeneity and mobility in the morphodynamic modelling of gravel-bed braided rivers. Adv. Water Resour. 104, 127–144 (2017).ADS 
Article 

Google Scholar 
van ledden, M. Sand-Mud Segregation in Estuaries and Tidal Basins. (PhD Thesis, University of Technology Delft, 2003).Kandiah, A. Fundamental Aspects of Surface Erosion of Cohesive Soils. (University of California, Davis, 1974).Partheniades, E. Cohesive Sediments in Open Channels: Erosion, Transport and Deposition (Butterworth-Heinemann, 2009).
Google Scholar 
Jiang, J. An Examination of Estuarine Lutocline Dynamics. (PhD Thesis, University of Florida, USA, 1999).Exner, F. M. Uber die wechselwirkung zwischen wasser und geschiebe in flussen (about the interaction between water and bedload in rivers). Akad. Wiss. Wien Math. Naturwiss. Kl. 134, 165–204 (1925).
Google Scholar 
Ikeda, S. Incipient motion of sand particles on side slopes. J. Hydraul. Div. 108, 95–114 (1982).Article 

Google Scholar 
Bagnold, R. A. An approach to the sediment transport problem from general physics. Physiographic and hydraulic studies of rivers in US Geological Survey Professional Paper, vol. 422 I, 231–291 (1966).Stillwater Sciences. Numerical modeling of sediment transport in the Sandy river, Oregon following removal of Marmot dam. (2000).Ashida, K. & Michiue, M. Study on hydraulic resistance and bed transport rate in alluvial stream. Proc. Jpn. Soc. Civ. Eng. 201, 59–69 (1972).Article 

Google Scholar 
Panthi, M. Generation and Fate of Fine Sediment from Dam Flushing. (MSc Thesis, IHE Delft, Institute for Water Education, the Netherlands, 2020).Meyer-Peter, E. & Müller, R. Formulas for bed-load transport. in IAHSR 2nd Meeting, Stockholm, Appendix 2 (IAHR, 1948).Podolak, C. & Pittman, S. Marmot Dam Removal Geomorphic Monitoring & Modeling Project: Final Report. Sandy river basin watershed council. (2011).Keith, M. K. Reservoir Evolution Following the Removal of Marmot Dam on the Sandy River, Oregon. (MSc Thesis, Portland State University, Oregon, USA, 2012).Redding, J. M., Schreck, C. B. & Everest, F. H. Physiological effects on coho salmon and steelhead of exposure to suspended solids. Trans. Am. Fish. Soc. 116, 737–744 (1987).Article 

Google Scholar 
Lisle, T. E. Sediment transport and resulting deposition in spawning gravels, north coastal California. Water Resour. Res. 25, 1303–1319 (1989).ADS 
Article 

Google Scholar 
Pitlick, J. & Wilcock, P. Relations between streamflow, sediment transport, and aquatic habitat in regulated rivers. In Geomorphic Processes and Riverine Habitat (eds Dorava, J. M. et al.) 185–198 (American Geophysical Union, 2001).Chapter 

Google Scholar 
Toupin, L. Freshwater Habitats: Life in Freshwater Ecosystems. (Franklin Watts, Watts Library, 2005).Bovee, K. D. A guide to Stream Habitat Analysis Using the Instream Flow Incremental Methodology. IFIP No. 12. FWS/OBS https://pubs.er.usgs.gov/publication/fwsobs82_26 (1982).Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).Article 

Google Scholar 
Vadas, R. L. & Orth, D. J. Formulation of habitat suitability models for stream fish guilds: Do the standard methods work?. Trans. Am. Fish. Soc. 130, 217–235 (2001).Article 

Google Scholar 
Moir, H. J., Gibbins, C. N., Soulsby, C. & Youngson, A. F. PHABSIM modelling of Atlantic salmon spawning habitat in an upland stream: Testing the influence of habitat suitability indices on model output. River Res. Appl. 21, 1021–1034 (2005).Article 

Google Scholar 
Hauer, C. et al. State of the art, shortcomings and future challenges for a sustainable sediment management in hydropower: A review. Renew. Sustain. Energy Rev. 98, 40–55 (2018).Article 

Google Scholar 
Koizumi, I., Kanazawa, Y. & Tanaka, Y. The fishermen were right: Experimental evidence for tributary refuge hypothesis during floods. Zool. Sci. 30, 375–379 (2013).Article 

Google Scholar 
Quadroni, S. et al. Effects of sediment flushing from a small Alpine reservoir on downstream aquatic fauna. Ecohydrology 9, 1276–1288 (2016).Article 

Google Scholar 
Bond, M. H., Nodine, T. G., Beechie, T. J. & Zabel, R. W. Estimating the benefits of widespread floodplain reconnection for Columbia river Chinook salmon. Can. J. Fish. Aquat. Sci. 76, 1212–1226 (2019).Article 

Google Scholar 
Stanford, J. A., Lorang, M. S. & Hauer, F. R. The shifting habitat mosaic of river ecosystems. Int. Ver. Theor. Angew. Limnol. Verh. 29, 123–136 (2005).
Google Scholar  More