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    Anthropogenic impacts on lowland tropical peatland biogeochemistry

    Dargie, G. C. et al. Age, extent and carbon storage of the central Congo Basin peatland complex. Nature 542, 86–90 (2017). This study describes the large extent and huge carbon stocks of the Congo Basin peatlands.Article 

    Google Scholar 
    Page, S. E., Rieley, J. O. & Banks, C. J. Global and regional importance of the tropical peatland carbon pool. Glob. Change Biol. 17, 798–818 (2011). This is a comprehensive assessment of the extent, volume and carbon stocks of peatlands across the tropics, highlighting their importance in the global carbon cycle and key uncertainties.Article 

    Google Scholar 
    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, L13402 (2010).
    Google Scholar 
    Gumbricht, T. et al. An expert system model for mapping tropical wetlands and peatlands reveals South America as the largest contributor. Glob. Change Biol. 23, 3581–3599 (2017).Article 

    Google Scholar 
    Olsson, L. et al. Climate change and land (eds Shukla, P. R. et al.) 345–436 (IPCC, 2019).Leifeld, J. & Menichetti, L. The underappreciated potential of peatlands in global climate change mitigation strategies. Nat. Commun. 9, 1071 (2018).Article 

    Google Scholar 
    Smith, P. et al. Climate change 2014: mitigation of climate change. Contribution of Working Group III to the fifth assessment report of the Intergovernmental Panel on Climate Change (eds Edenhofer, O. et al.) 811–922 (Cambridge Univ. Press, 2014).Goldstein, A. et al. Protecting irrecoverable carbon in Earth’s ecosystems. Nat. Clim. Chang. 10, 287–295 (2020). This study evaluates ecosystems on the basis of the size of carbon stocks that are vulnerable to release upon land-use conversion and not recoverable on timescales relevant to avoiding dangerous climate impacts; it emphasizes the high density of irrecoverable carbon in tropical peatlands.Article 

    Google Scholar 
    Griscom, B. W. et al. Natural climate solutions. Proc. Natl. Acad. Sci. USA 114, 11645–11650 (2017).Article 

    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. Chang. 9, 945–947 (2019).Article 

    Google Scholar 
    Intergovernmental Panel on Climate Change. Climate change and land (IPCC, 2019).Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).Article 

    Google Scholar 
    Page, S., Wüst, R. & Banks, C. Past and present carbon accumulation and loss in Southeast Asian peatlands. PAGES News 18, 25–27 (2010).Article 

    Google Scholar 
    Page, S. E. et al. A record of Late Pleistocene and Holocene carbon accumulation and climate change from an equatorial peat bog (Kalimantan, Indonesia): implications for past, present and future carbon dynamics. J. Quat. Sci. 19, 625–635 (2004).Article 

    Google Scholar 
    Dommain, R., Couwenberg, J. & Joosten, H. Development and carbon sequestration of tropical peat domes in south-east Asia: links to post-glacial sea-level changes and Holocene climate variability. Quat. Sci. Rev. 30, 999–1010 (2011). This is a comprehensive assessment of peatland development in Southeast Asia, exploring regional differences in rates of peat formation and carbon accumulation.Article 

    Google Scholar 
    Ruwaimana, M., Anshari, G. Z., Silva, L. C. R. & Gavin, D. G. The oldest extant tropical peatland in the world: a major carbon reservoir for at least 47,000 years. Environ. Res. Lett. 15, 114027 (2020). This study compares the development of coastal and inland peatlands in West Kalimantan, Indonesia, and provides a description of the oldest known peat deposit in Southeast Asia.Article 

    Google Scholar 
    Anshari, G., Kershaw, A. P., Kaars, S. V. D. & Jacobsen, G. Environmental change and peatland forest dynamics in the Lake Sentarum area, West Kalimantan, Indonesia. J. Quat. Sci. 19, 637–655 (2004).Article 

    Google Scholar 
    Dommain, R., Couwenberg, J. & Joosten, H. Hydrological self-regulation of domed peatlands in south-east Asia and consequences for conservation and restoration Mires Peat 6, 1–17 2010).
    Google Scholar 
    Jones, M. B. & Muthuri, F. M. Standing biomass and carbon distribution in a papyrus (Cyperus papyrus L.) swamp on Lake Naivasha, Kenya. J. Trop. Ecol. 13, 347–356 (1997).Article 

    Google Scholar 
    Saunders, M. J., Jones, M. B. & Kansiime, F. Carbon and water cycles in tropical papyrus wetlands. Wetl. Ecol. Manag. 15, 489–498 (2007).Article 

    Google Scholar 
    Burrough, S. L., Thomas, D. S. G., Orijemie, E. A. & Willis, K. J. Landscape sensitivity and ecological change in western Zambia: the long-term perspective from dambo cut-and-fill sediments. J. Quat. Sci. 30, 44–58 (2015).Article 

    Google Scholar 
    Davenport, I. J. et al. First evidence of peat domes in the Congo Basin using LiDAR from a fixed-wing drone. Remote Sens. 12, 2196 (2020).Article 

    Google Scholar 
    Alsdorf, D. et al. Opportunities for hydrologic research in the Congo Basin. Rev. Geophys. 54, 378–409 (2016).Article 

    Google Scholar 
    Biddulph, G. E. et al. Current knowledge on the Cuvette Centrale peatland complex and future research directions. Bois For. Trop. 350, 3–14 (2021).Article 

    Google Scholar 
    Lähteenoja, O. et al. The large Amazonian peatland carbon sink in the subsiding Pastaza–Marañón foreland basin, Peru. Glob. Change Biol. 18, 164–178 (2012).Article 

    Google Scholar 
    Kelly, T. J. et al. The vegetation history of an Amazonian domed peatland. Palaeogeogr. Palaeoclimatol. Palaeoecol. 468, 129–141 (2017).Article 

    Google Scholar 
    Draper, F. C. et al. The distribution and amount of carbon in the largest peatland complex in Amazonia. Environ. Res. Lett. 9, 124017 (2014). Using a combination of remote sensing and field data, this study provides an assessment of the distribution of above- and belowground peatland carbon stocks in the Pastaza–Marañon foreland basin in Peruvian Amazonia.Article 

    Google Scholar 
    Phillips, S., Rouse, G. E. & Bustin, R. M. Vegetation zones and diagnostic pollen profiles of a coastal peat swamp, Bocas del Toro, Panamá. Palaeogeogr. Palaeoclimatol. Palaeoecol. 128, 301–338 (1997).Article 

    Google Scholar 
    Sjögersten, S. et al. Coastal wetland ecosystems deliver large carbon stocks in tropical Mexico. Geoderma 403, 115173 (2021).Article 

    Google Scholar 
    Joosten, H. in Tropical Peatland Ecosystems (eds Osaki, M. & Tsuji, N.) 33–48 (Springer, 2016).Anderson, J. A. R. in Mires: Swamp, Bog, Fen and Moor: Regional Studies (ed. Gore, A. J. P.) 191–199 (Elsevier, 1983).Draper, F. C. et al. Peatland forests are the least diverse tree communities documented in Amazonia, but contribute to high regional beta-diversity. Ecography 41, 1256–1269 (2018).Article 

    Google Scholar 
    Anderson, J. A. R. Ecology and Forest Types of The Peat Swamp Forests of Sarawak and Brunei in Relation to their Silviculture. Thesis, Univ. Edinburgh (1961).Freund, C. A., Harsanto, F. A., Purwanto, A., Takahashi, H. & Harrison, M. E. Microtopographic specialization and flexibility in tropical peat swamp forest tree species. Biotropica 50, 208–214 (2018).Article 

    Google Scholar 
    Lampela, M. et al. Ground surface microtopography and vegetation patterns in a tropical peat swamp forest. CATENA 139, 127–136 (2016).Article 

    Google Scholar 
    Miyamoto, K. et al. Habitat differentiation among tree species with small-scale variation of humus depth and topography in a tropical heath forest of Central Kalimantan, Indonesia. J. Trop. Ecol. 19, 43–54 (2003).Article 

    Google Scholar 
    Miettinen, J., Shi, C. & Liew, S. C. Land cover distribution in the peatlands of peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Glob. Ecol. Conserv. 6, 67–78 (2016).Article 

    Google Scholar 
    Wijedasa, L. S. et al. Carbon emissions from South-East Asian peatlands will increase despite emission-reduction schemes. Glob. Change Biol. 24, 4598–4613 (2018).Article 

    Google Scholar 
    Page, S. E. & Hooijer, A. In the line of fire: the peatlands of Southeast Asia. Phil. Trans. R. Soc. B 371, 20150176.(2016).Article 

    Google Scholar 
    Hergoualc’h, K., Gutiérrez-Vélez, V. H., Menton, M. & Verchot, L. V. Characterizing degradation of palm swamp peatlands from space and on the ground: an exploratory study in the Peruvian Amazon. For. Ecol. Manag. 393, 63–73 (2017).Article 

    Google Scholar 
    Horn, C. M., Vargas Paredes, V. H., Gilmore, M. P. & Endress, B. A. Spatio-temporal patterns of Mauritia flexuosa fruit extraction in the Peruvian Amazon: implications for conservation and sustainability. Appl. Geogr. 97, 98–108 (2018).Article 

    Google Scholar 
    Dargie, G. C. et al. Congo Basin peatlands: threats and conservation priorities. Mitig. Adapt. Strateg. Glob. Change 24, 669–686 (2019).Article 

    Google Scholar 
    Grundling, P.-L. & Grootjans, A. P. in The Wetland Book. II: Distribution, Description, and Conservation (eds Finlayson, M., Milton, G., Prentice, R. & Davidson, N.) (Springer, 2018).Roucoux, K. H. et al. Threats to intact tropical peatlands and opportunities for their conservation. Conserv. Biol. 31, 1283–1292 (2017).Article 

    Google Scholar 
    Baird, A. J. et al. High permeability explains the vulnerability of the carbon store in drained tropical peatlands. Geophys. Res. Lett. 44, 1333–1339 (2017). This study finds that the permeability of ombrotrophic tropical peat is higher than expected, resulting in deep water tables in ditched tropical peatlands and associated high rates of peat oxidation.Article 

    Google Scholar 
    Kelly, T. J. et al. The high hydraulic conductivity of three wooded tropical peat swamps in northeast Peru: measurements and implications for hydrological function. Hydrol. Process. 28, 3373–3387 (2014).Article 

    Google Scholar 
    Tonks, A. J. et al. Impacts of conversion of tropical peat swamp forest to oil palm plantation on peat organic chemistry, physical properties and carbon stocks. Geoderma 289, 36–45 (2017).Article 

    Google Scholar 
    Mezbahuddin, M., Grant, R. F. & Hirano, T. How hydrology determines seasonal and interannual variations in water table depth, surface energy exchange, and water stress in a tropical peatland: modeling versus measurements. J. Geophys. Res. Biogeosci. 120, 2132–2157 (2015).Article 

    Google Scholar 
    Laurén, A. et al. Nutrient balance as a tool for maintaining yield and mitigating environmental impacts of Acacia plantation in drained tropical peatland — description of plantation simulator. Forests 12, 312 (2021).Article 

    Google Scholar 
    Hooijer, A. et al. Subsidence and carbon loss in drained tropical peatlands. Biogeosciences 9, 1053–1071 (2012).Article 

    Google Scholar 
    Anshari, G. Z., Gusmayanti, E. & Novita, N. The use of subsidence to estimate carbon loss from deforested and drained tropical peatlands in Indonesia. Forests 12, 732 (2021).Article 

    Google Scholar 
    Evans, C. D. et al. A novel low-cost, high-resolution camera system for measuring peat subsidence and water table dynamics. Front. Environ. Sci. 9, 33 (2021).
    Google Scholar 
    Evans, C. D. et al. Rates and spatial variability of peat subsidence in Acacia plantation and forest landscapes in Sumatra, Indonesia. Geoderma 338, 410–421 (2019).Article 

    Google Scholar 
    Hoyt, A. M., Chaussard, E., Seppalainen, S. S. & Harvey, C. F. Widespread subsidence and carbon emissions across Southeast Asian peatlands. Nat. Geosci. 13, 435–440 (2020). Using remote sensing, this study quantifies the rate of peat subsidence and carbon loss across peatlands in Southeast Asia.Article 

    Google Scholar 
    Cobb, A. R., Dommain, R., Tan, F., Heng, N. H. E. & Harvey, C. F. Carbon storage capacity of tropical peatlands in natural and artificial drainage networks. Environ. Res. Lett. 15, 114009 (2020).Article 

    Google Scholar 
    Ritzema, H., Limin, S., Kusin, K., Jauhiainen, J. & Wösten, H. Canal blocking strategies for hydrological restoration of degraded tropical peatlands in central Kalimantan, Indonesia. CATENA 114, 11–20 (2014).Article 

    Google Scholar 
    Hooijer, A., Vernimmen, R., Visser, M. & Mawdsley, N. Flooding projections from elevation and subsidence models for oil palm plantations in the Rajang Delta peatlands, Sarawak, Malaysia (Deltares, 2015).Sumarga, E., Hein, L., Hooijer, A. & Vernimmen, R. Hydrological and economic effects of oil palm cultivation in Indonesian peatlands. Ecol. Soc. 21, 52 (2016).Article 

    Google Scholar 
    Evers, S., Yule, C. M., Padfield, R., O’Reilly, P. & Varkkey, H. Keep wetlands wet: the myth of sustainable development of tropical peatlands — implications for policies and management. Glob. Change Biol. 23, 534–549 (2017). This study reviews the ecosystem services provided by Southeast Asian peatlands and discusses key policy challenges for peatland management.Article 

    Google Scholar 
    Tan, Z. D., Lupascu, M. & Wijedasa, L. S. Paludiculture as a sustainable land use alternative for tropical peatlands: a review. Sci. Total Environ. 753, 142111 (2021). This study evaluates the current understanding of and opportunities for paludiculture in the context of tropical peatlands, emphasizing that tropical paludiculture will be heavily influenced by socioeconomic considerations.Article 

    Google Scholar 
    Haraguchi, A. in Tropical Peatland Ecosystems (Osaki, M. & Tsuji, N.) 297–311 (Springer, 2016).Wösten, J. H. M., Ismail, A. B. & van Wijk, A. L. M. Peat subsidence and its practical implications: a case study in Malaysia. Geoderma 78, 25–36 (1997).Article 

    Google Scholar 
    Grealish, G. J. & Fitzpatrick, R. W. Acid sulphate soil characterization in Negara Brunei Darussalam: a case study to inform management decisions. Soil. Use Manag. 29, 432–444 (2013).Article 

    Google Scholar 
    Klepper, O., Chairuddin, G. T., Iriansyah & Rijksen, H. D. Water quality and the distribution of some fishes in an area of acid sulphate soils, Kalimantan, Indonesia. Hydrobiol. Bull. 25, 217–224 (1992).Article 

    Google Scholar 
    Shamshuddin, J. & Muhrizal, S. Chemical pollution in acid sulfate soils. Proc. Geol. Soc. Malaysia Annu. Geol.Conf. 2000, 231–234 (2000).
    Google Scholar 
    Suwardi. Utilization and improvement of marginal soils for agricultural development in Indonesia. IOP Conf. Ser. Earth Environ. Sci. 383, 012047 (2019).Article 

    Google Scholar 
    Hirano, T., Jauhiainen, J., Inoue, T. & Takahashi, H. Controls on the carbon balance of tropical peatlands. Ecosystems 12, 873–887 (2009).Article 

    Google Scholar 
    Stumm, W. & Morgan, J. J. Aquatic Chemistry: Chemical Equilibria and Rates in Natural Waters (Wiley, 1996).Billett, M. F., Garnett, M. H. & Dinsmore, K. J. Should aquatic CO evasion be included in contemporary carbon budgets for peatland ecosystems? Ecosystems 18, 471–480 (2015).Article 

    Google Scholar 
    Chimner, R. A. & Ewel, K. C. A tropical freshwater wetland: II. Production, decomposition, and peat formation. Wetl. Ecol. Manag. 13, 671–684 (2005).Article 

    Google Scholar 
    Hoyos-Santillan, J. et al. Getting to the root of the problem: litter decomposition and peat formation in lowland neotropical peatlands. Biogeochemistry 126, 115–129 (2015).Article 

    Google Scholar 
    Könönen, M. et al. Land use increases the recalcitrance of tropical peat. Wetl. Ecol. Manag. 24, 717–731 (2016).Article 

    Google Scholar 
    Sangok, F. E., Maie, N., Melling, L. & Watanabe, A. Evaluation on the decomposability of tropical forest peat soils after conversion to an oil palm plantation. Sci. Total Environ. 587–588, 381–388 (2017).Article 

    Google Scholar 
    Yule, C. M., Lim, Y. Y. & Lim, T. Y. Degradation of tropical Malaysian peatlands decreases levels of phenolics in soil and in leaves of Macaranga pruinosa. Front. Earth Sci. 4, 1–9 (2016).Article 

    Google Scholar 
    Yu, Z. et al. Peatlands and their role in the global carbon cycle. Eos 92, 97–98 (2011).Article 

    Google Scholar 
    Lähteenoja, O., Ruokolainen, K., Schulman, L. & Oinonen, M. Amazonian peatlands: an ignored C sink and potential source. Glob. Change Biol. 15, 2311–2320 (2009).Article 

    Google Scholar 
    Garneau, M. et al. Holocene carbon dynamics of boreal and subarctic peatlands from Québec, Canada. Holocene 24, 1043–1053 (2014).Article 

    Google Scholar 
    Gorham, E. Northern peatlands: role in the carbon cycle and probable responses to climatic warming. Ecol. Appl. 1, 182–195 (1991).Article 

    Google Scholar 
    Turunen, J., Roulet, N. T., Moore, T. R. & Richard, P. J. H. Nitrogen deposition and increased carbon accumulation in ombrotrophic peatlands in eastern Canada. Glob. Biogeochem. Cycles 18, GB3002 (2004).Article 

    Google Scholar 
    Yu, Z. C. Northern peatland carbon stocks and dynamics: a review. Biogeosciences 9, 4071–4085 (2012).Article 

    Google Scholar 
    Poulter, B. et al. in Wetland Carbon And Environmental Management (eds Krauss, K. W., Zhu, Z. & Stagg, C. L.) 1–20 (American Geophysical Union, 2021).Honorio Coronado, E. et al. Intensive field sampling increases the known extent of carbon-rich Amazonian peatland pole forests. Environ. Res. Lett. 16, 074048 (2021).Article 

    Google Scholar 
    Sjögersten, S. et al. Tropical wetlands: a missing link in the global carbon cycle? Carbon cycling in tropical wetlands. Glob. Biogeochem. Cycles 28, 1371–1386 (2014).Article 

    Google Scholar 
    Griffis, T. J. et al. Hydrometeorological sensitivities of net ecosystem carbon dioxide and methane exchange of an Amazonian palm swamp peatland. Agric. For. Meteorol. 295, 108167 (2020).Article 

    Google Scholar 
    Kiew, F. et al. CO2 balance of a secondary tropical peat swamp forest in Sarawak, Malaysia. Agric. For. Meteorol. 248, 494–501 (2018).Article 

    Google Scholar 
    Hirano, T. et al. Effects of disturbances on the carbon balance of tropical peat swamp forests. Glob. Change Biol. 18, 3410–3422 (2012).Article 

    Google Scholar 
    Tang, A. C. I. et al. A Bornean peat swamp forest is a net source of carbon dioxide to the atmosphere. Glob. Change Biol. 26, 6931–6944 (2020).Article 

    Google Scholar 
    Deshmukh, C. S. et al. Conservation slows down emission increase from a tropical peatland in Indonesia. Nat. Geosci. 14, 484–490 (2021). This study presented measurements of CO2 and CH4 fluxes obtained using the eddy covariance method from both intact and degraded peat swamp forest in Sumatra, Indonesia, during the 2019 ENSO drought.Article 

    Google Scholar 
    Kiew, F. et al. Carbon dioxide balance of an oil palm plantation established on tropical peat. Agric. For. Meteorol. 295, 108189 (2020).Article 

    Google Scholar 
    McCalmont, J. et al. Short- and long-term carbon emissions from oil palm plantations converted from logged tropical peat swamp forest. Glob. Change Biol. 27, 2361–2376 (2021).Article 

    Google Scholar 
    Germer, J. & Sauerborn, J. Estimation of the impact of oil palm plantation establishment on greenhouse gas balance. Environ. Dev. Sustain. 10, 697–716 (2008).Article 

    Google Scholar 
    Lewis, K. et al. An assessment of oil palm plantation aboveground biomass stocks on tropical peat using destructive and non-destructive methods. Sci. Rep. 10, 2230 (2020).Article 

    Google Scholar 
    Wijedasa, L. S. Peat Swamp Forest Conservation in Southeast Asia. Thesis, National Univ. Singapore (2019).Moore, S. et al. Deep instability of deforested tropical peatlands revealed by fluvial organic carbon fluxes. Nature 493, 660–663 (2013).Article 

    Google Scholar 
    Cook, S. et al. Fluvial organic carbon fluxes from oil palm plantations on tropical peatland. Biogeosciences 15, 7435–7450 (2018).Article 

    Google Scholar 
    Waldron, S. et al. C mobilisation in disturbed tropical peat swamps: old DOC can fuel the fluvial efflux of old carbon dioxide, but site recovery can occur. Sci. Rep. 9, 11429 (2019).Article 

    Google Scholar 
    Brady, M. A. Organic Matter Dynamics of Coastal Peat Deposits in Sumatra, Indonesia. Thesis, Univ. British Columbia (1997).Jauhiainen, J., Limin, S., Silvennoinen, H. & Vasander, H. Carbon dioxide and methane fluxes in drained tropical peat before and after hhydrological restoration. Ecology 89, 3503–3514 (2008).Article 

    Google Scholar 
    Jauhiainen, J., Takahashi, H., Heikkinen, J. E. P., Martikainen, P. J. & Vasander, H. Carbon fluxes from a tropical peat swamp forest floor. Glob. Change Biol. 11, 1788–1797 (2005).Article 

    Google Scholar 
    Yule, C. M. & Gomez, L. N. Leaf litter decomposition in a tropical peat swamp forest in peninsular Malaysia. Wetl. Ecol. Manag. 17, 231–241 (2009).Article 

    Google Scholar 
    Swails, E., Hertanti, D., Hergoualc’h, K., Verchot, L. & Lawrence, D. The response of soil respiration to climatic drivers in undrained forest and drained oil palm plantations in an Indonesian peatland. Biogeochemistry 142, 37–51 (2019).Article 

    Google Scholar 
    Ishikura, K. et al. Carbon dioxide and methane emissions from peat soil in an undrained tropical peat swamp forest. Ecosystems 22, 1852–1868 (2019).Article 

    Google Scholar 
    Melling, L., Tan, C. Y., Goh, K. J. & Hatano, R. Soil microbial and root respirations from three ecosystems in tropical peatland of Sarawak, Malaysia. J. Oil Palm. Res. 25, 44–57 (2013).
    Google Scholar 
    Cooper, H. V. et al. Greenhouse gas emissions resulting from conversion of peat swamp forest to oil palm plantation. Nat. Commun. 11, 407 (2020).Article 

    Google Scholar 
    Girkin, N. T., Turner, B. L., Ostle, N. & Sjögersten, S. Root-derived CO2 flux from a tropical peatland. Wetl. Ecol. Manag. 26, 985–991 (2018).Article 

    Google Scholar 
    Dhandapani, S., Ritz, K., Evers, S., Yule, C. M. & Sjögersten, S. Are secondary forests second-rate? Comparing peatland greenhouse gas emissions, chemical and microbial community properties between primary and secondary forests in peninsular Malaysia. Sci. Total Environ. 655, 220–231 (2019).Article 

    Google Scholar 
    Dhandapani, S. et al. Land-use changes associated with oil palm plantations impact PLFA microbial phenotypic community structure throughout the depth of tropical peats. Wetlands 40, 2351–2366 (2020).Article 

    Google Scholar 
    Mishra, S. et al. Microbial and metabolic profiling reveal strong influence of water table and land-use patterns on classification of degraded tropical peatlands. Biogeosciences 11, 1727–1741 (2014).Article 

    Google Scholar 
    Mishra, S. et al. Degradation of Southeast Asian tropical peatlands and integrated strategies for their better management and restoration. J. Appl. Ecol. 58, 1370–1387 (2021). This paper reviews current understanding of intact and degraded peatlands in Southeast Asia and proposes an approach for peatland management and restoration involving explicit consideration of interacting ecological factors and the involvement of local communities.Article 

    Google Scholar 
    Carlson, K. M., Goodman, L. K. & May-Tobin, C. C. Modeling relationships between water table depth and peat soil carbon loss in Southeast Asian plantations. Environ. Res. Lett. 10, 074006 (2015).Article 

    Google Scholar 
    Carlson, K. M. et al. Committed carbon emissions, deforestation, and community land conversion from oil palm plantation expansion in West Kalimantan, Indonesia. Proc. Natl Acad. Sci. USA 109, 7559–7564 (2012).Article 

    Google Scholar 
    Couwenberg, J., Dommain, R. & Joosten, H. Greenhouse gas fluxes from tropical peatlands in south-east Asia. Glob. Change Biol. 16, 1715–1732 (2010).Article 

    Google Scholar 
    Evans, C. D. et al. Overriding water table control on managed peatland greenhouse gas emissions. Nature 593, 548–552 (2021). Using data for CO2 and CH4 fluxes from all major peatland biomes, this paper demonstrates that greenhouse gas emissions from drained agricultural peatlands could be greatly reduced by raising water levels closer to the peat surface while maintaining productive agricultural use.
    Google Scholar 
    Hooijer, A. et al. Current and future CO2 emissions from drained peatlands in Southeast Asia. Biogeosciences 7, 1505–1514 (2010).Article 

    Google Scholar 
    Hiraishi, T. et al. (eds) 2013 Supplement to the 2006 IPCC guidelines for national greenhouse gas inventories: wetlands (IPCC, 2014).Jauhiainen, J., Kerojoki, O., Silvennoinen, H., Limin, S. & Vasander, H. Heterotrophic respiration in drained tropical peat is greatly affected by temperature — a passive ecosystem cooling experiment. Environ. Res. Lett. 9, 105013 (2014).Article 

    Google Scholar 
    Manning, F. C., Kho, L. K., Hill, T. C., Cornulier, T. & Teh, Y. A. Carbon emissions from oil palm plantations on peat soil. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2019.00037 (2019).Article 

    Google Scholar 
    Deshmukh, C. S. et al. Impact of forest plantation on methane emissions from tropical peatland. Glob. Change Biol. 26, 2477–2495 (2020).Article 

    Google Scholar 
    Wong, G. X. et al. How do land use practices affect methane emissions from tropical peat ecosystems? Agric. For. Meteorol. 282–283, 107869 (2020).Article 

    Google Scholar 
    Pangala, S. R. et al. Large emissions from floodplain trees close the Amazon methane budget. Nature 552, 230–234 (2017).Article 

    Google Scholar 
    Pangala, S. R., Moore, S., Hornibrook, E. R. C. & Gauci, V. Trees are major conduits for methane egress from tropical forested wetlands. N. Phytol. 197, 524–531 (2013).Article 

    Google Scholar 
    Hergoualc’h, K. et al. Spatial and temporal variability of soil N2O and CH4 fluxes along a degradation gradient in a palm swamp peat forest in the Peruvian Amazon. Glob. Change Biol. 26, 7198–7216 (2020).Article 

    Google Scholar 
    Teh, Y. A., Murphy, W. A., Berrio, J.-C., Boom, A. & Page, S. E. Seasonal variability in methane and nitrous oxide fluxes from tropical peatlands in the western Amazon basin. Biogeosciences 14, 3669–3683 (2017).Article 

    Google Scholar 
    Hoyos-Santillan, J. et al. Evaluation of vegetation communities, water table, and peat composition as drivers of greenhouse gas emissions in lowland tropical peatlands. Sci. Total Environ. 688, 1193–1204 (2019).Article 

    Google Scholar 
    van Haren, J. et al. A versatile gas flux chamber reveals high tree stem CH4 emissions in Amazonian peatland. Agric. For. Meteorol. 307, 108504 (2021).Article 

    Google Scholar 
    Sjögersten, S. et al. Temperature response of ex-situ greenhouse gas emissions from tropical peatlands: interactions between forest type and peat moisture conditions. Geoderma 324, 47–55 (2018).Article 

    Google Scholar 
    Girkin, N. T. et al. Spatial variability of organic matter properties determines methane fluxes in a tropical forested peatland. Biogeochemistry 142, 231–245 (2019).Article 

    Google Scholar 
    Girkin, N. T., Turner, B. L., Ostle, N. & Sjögersten, S. Composition and concentration of root exudate analogues regulate greenhouse gas fluxes from tropical peat. Soil. Biol. Biochem. 127, 280–285 (2018).Article 

    Google Scholar 
    Girkin, N. T., Vane, C. H., Turner, B. L., Ostle, N. J. & Sjögersten, S. Root oxygen mitigates methane fluxes in tropical peatlands. Environ. Res. Lett. 15, 064013 (2020).Article 

    Google Scholar 
    Jauhiainen, J., Silvennoinen, H., Könönen, M., Limin, S. & Vasander, H. Management driven changes in carbon mineralization dynamics of tropical peat. Biogeochemistry 129, 115–132 (2016).Article 

    Google Scholar 
    Wright, E. L. et al. Contribution of subsurface peat to CO2 and CH fluxes in a neotropical peatland. Glob. Change Biol. 17, 2867–2881 (2011).Article 

    Google Scholar 
    Prananto, J. A., Minasny, B., Comeau, L., Rudiyanto, R. & Grace, P. Drainage increases CO2 and N2O emissions from tropical peat soils. Glob. Change Biol. 26, 4583–4600 (2020).Article 

    Google Scholar 
    Peacock, M. et al. Global importance of methane emissions from drainage ditches and canals. Environ. Res. Lett. 16, 044010 (2021).Article 

    Google Scholar 
    Chuang, P.-C. et al. Methane fluxes from tropical coastal lagoons surrounded by mangroves, Yucatán, Mexico. J. Geophys. Res. Biogeosci. 122, 1156–1174 (2017).Article 

    Google Scholar 
    Jauhiainen, J. & Silvennoinen, H. Diffusion GHG fluxes at tropical peatland drainage canal water surfaces. Suoseura 63, 93–105 (2012).
    Google Scholar 
    Yupi, H. M., Inoue, T. & Bathgate, J. Concentrations, loads and yields of organic carbon from two tropical peat swamp forest streams in Riau Province, Sumatra, Indonesia. Mires Peat 18, 1–15 (2016).
    Google Scholar 
    Zhou, Y., Evans, C. D., Chen, Y., Chang, K. Y. W. & Martin, P. Extensive remineralization of peatland-derived dissolved organic carbon and ocean acidification in the Sunda Shelf Sea, Southeast Asia. J. Geophys. Res. Ocean. 126, e2021JC017292 (2021).
    Google Scholar 
    Alkhatib, M., Jennerjahn, T. C. & Samiaji, J. Biogeochemistry of the Dumai River estuary, Sumatra, Indonesia, a tropical black-water river. Limnol. Oceanogr. 52, 2410–2417 (2007).Article 

    Google Scholar 
    Gandois, L. et al. From canals to the coast: dissolved organic matter and trace metal composition in rivers draining degraded tropical peatlands in Indonesia. Biogeosciences 17, 1897–1909 (2020).Article 

    Google Scholar 
    Rixen, T. et al. The Siak, a tropical black water river in central Sumatra on the verge of anoxia. Biogeochemistry 90, 129–140 (2008).Article 

    Google Scholar 
    Miettinen, J., Hooijer, A., Vernimmen, R., Liew, S. C. & Page, S. E. From carbon sink to carbon source: extensive peat oxidation in insular Southeast Asia since 1990. Environ. Res. Lett. 12, 024014 (2017).Article 

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

    Google Scholar 
    Boysen, L. R. et al. Global and regional effects of land-use change on climate in 21st century simulations with interactive carbon cycle. Earth Syst. Dyn. 5, 309–319 (2014).Article 

    Google Scholar 
    Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).Article 

    Google Scholar 
    Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).Article 

    Google Scholar 
    Naidu, D. G. T. & Bagchi, S. Greening of the Earth does not compensate for rising soil heterotrophic respiration under climate change. Glob. Change Biol. 27, 2029–2038 (2021).Article 

    Google Scholar 
    Li, W. et al. Future precipitation changes and their implications for tropical peatlands. Geophys. Res. Lett. 34, 01403 (2007).Article 

    Google Scholar 
    Barichivich, J. et al. Recent intensification of Amazon flooding extremes driven by strengthened Walker circulation. Sci. Adv. 4, eaat8785 (2018).Article 

    Google Scholar 
    Marengo, J. A. et al. Changes in climate and land use over the Amazon region: current and future variability and trends. Front. Earth Sci. 6, 228 (2018).Article 

    Google Scholar 
    Cobb, A. R. et al. How temporal patterns in rainfall determine the geomorphology and carbon fluxes of tropical peatlands. Proc. Natl. Acad. Sci. USA 114, E5187–E5196 (2017).Article 

    Google Scholar 
    Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018).Article 

    Google Scholar 
    Rifai, S. W., Li, S. & Malhi, Y. Coupling of El Niño events and long-term warming leads to pervasive climate extremes in the terrestrial tropics. Environ. Res. Lett. 14, 105002 (2019).Article 

    Google Scholar 
    Girkin, N. T. et al. Interactions between labile carbon, temperature and land use regulate carbon dioxide and methane production in tropical peat. Biogeochemistry 147, 87–97 (2020).Article 

    Google Scholar 
    Cole, L. E. S., Bhagwat, S. A. & Willis, K. J. Long-term disturbance dynamics and resilience of tropical peat swamp forests. J. Ecol. 103, 16–30 (2015).Article 

    Google Scholar 
    Weiss, D. et al. The geochemistry of major and selected trace elements in a forested peat bog, Kalimantan, SE Asia, and its implications for past atmospheric dust deposition. Geochim. Cosmochim. Acta 66, 2307–2323 (2002).Article 

    Google Scholar 
    Lähteenoja, O. & Page, S. High diversity of tropical peatland ecosystem types in the Pastaza-Marañón basin, Peruvian Amazonia. J. Geophys. Res. 116, G02025 (2011).
    Google Scholar 
    Roucoux, K. H. et al. Vegetation development in an Amazonian peatland. Palaeogeogr. Palaeoclimatol. Palaeoecol. 374, 242–255 (2013).Article 

    Google Scholar 
    Lampela, M., Jauhiainen, J. & Vasander, H. Surface peat structure and chemistry in a tropical peat swamp forest. Plant. Soil. 382, 329–347 (2014).Article 

    Google Scholar 
    Page, S. E., Rieley, J. O., Shotyk, Ø. W. & Weiss, D. Interdependence of peat and vegetation in a tropical peat swamp forest. Phil. Trans. R. Soc. Lond. B 354, 1885–1897 (1999).Article 

    Google Scholar 
    Sjögersten, S., Cheesman, A. W., Lopez, O. & Turner, B. L. Biogeochemical processes along a nutrient gradient in a tropical ombrotrophic peatland. Biogeochemistry 104, 147–163 (2011).Article 

    Google Scholar 
    Yule, C. M. Loss of biodiversity and ecosystem functioning in Indo-Malayan peat swamp forests. Biodivers. Conserv. 19, 393–409 (2010).Article 

    Google Scholar 
    Basilier, K. Moss-associated nitrogen fixation in some mire and coniferous forest environments around Uppsala, Sweden. Lindbergia 5, 84–88 (1979).
    Google Scholar 
    Ong, C. S. P., Juan, J. C. & Yule, C. M. Litterfall production and chemistry of Koompassia malaccensis and Shorea uliginosa in a tropical peat swamp forest: plant nutrient regulation and climate relationships. Trees 29, 527–537 (2015).Article 

    Google Scholar 
    Wüst, R. A. J. & Bustin, R. M. Opaline and Al–Si phytoliths from a tropical mire system of West Malaysia: abundance, habit, elemental composition, preservation and significance. Chem. Geol. 200, 267–292 (2003).Article 

    Google Scholar 
    Neuzil, S. G., Cecil, C. B., Kane, J. S. & Soedjono, K. in Modern and Ancient Coal-Forming Environments Vol. 286 (Geological Society of America, 1993).Too, C. C., Keller, A., Sickel, W., Lee, S. M. & Yule, C. M. Microbial community structure in a Malaysian tropical peat swamp forest: the influence of tree species and depth. Front. Microbiol. 9, 2859 (2018).Article 

    Google Scholar 
    Sulistiyanto, Y. Nutrient Dynamics in Different Sub-types of Peat Swamp Forest in Central Kalimantan, Indonesia. Thesis, Univ. Nottingham (2005).Hoyos Santillán, J. Controls of Carbon Turnover in Tropical Peatlands. Thesis, Univ. Nottingham (2014).Damman, A. W. H. Distribution and movement of elements in ombrotrophic peat bogs. Oikos 30, 480–495 (1978).Article 

    Google Scholar 
    Laiho, R. & Laine, J. Nitrogen and phosphorus stores in peatlands drained for forestry in Finland. Scand. J. For. Res. 9, 251–260 (1994).Article 

    Google Scholar 
    Wang, M., Moore, T. R., Talbot, J. & Riley, J. L. The stoichiometry of carbon and nutrients in peat formation. Glob. Biogeochem. Cycles 29, 113–121 (2015).Article 

    Google Scholar 
    Hodgkins, S. B. et al. Tropical peatland carbon storage linked to global latitudinal trends in peat recalcitrance. Nat. Commun. 9, 3640 (2018).Article 

    Google Scholar 
    Jackson, C. R., Liew, K. C. & Yule, C. M. Structural and functional changes with depth in microbial communities in a tropical Malaysian peat swamp forest. Microb. Ecol. 57, 402–412 (2009).Article 

    Google Scholar 
    Kolb, S. & Horn, M. A. Microbial CH4 and NO consumption in acidic wetlands. Front. Microbiol. 3, 78 (2012).Article 

    Google Scholar 
    Golovchenko, A. V., Tikhonova, E. Y. & Zvyagintsev, D. G. Abundance, biomass, structure, and activity of the microbial complexes of minerotrophic and ombrotrophic peatlands. Microbiology 76, 630–637 (2007).Article 

    Google Scholar 
    Martikainen, P. J., Nykänen, H., Crill, P. & Silvola, J. Effect of a lowered water table on nitrous oxide fluxes from northern peatlands. Nature 366, 51–53 (1993).Article 

    Google Scholar 
    Davidson, E. A., Keller, M., Erickson, H. E., Verchot, L. V. & Veldkamp, E. Testing a conceptual model of soil emissions of nitrous and nitric oxides. Bioscience 50, 667 (2000).Article 

    Google Scholar 
    Rubol, S., Silver, W. L. & Bellin, A. Hydrologic control on redox and nitrogen dynamics in a peatland soil. Sci. Total Environ. 432, 37–46 (2012).Article 

    Google Scholar 
    Jauhiainen, J. et al. Nitrous oxide fluxes from tropical peat with different disturbance history and management. Biogeosciences 9, 1337–1350 (2012).Article 

    Google Scholar 
    Könönen, M., Jauhiainen, J., Laiho, R., Kusin, K. & Vasander, H. Physical and chemical properties of tropical peat under stabilised land uses. Mires Peat 16, 1–13 (2015).
    Google Scholar 
    Chotimah, H., Jaya, A., Suparto, H., Saraswati, D. & Nawansyah, W. Utilizing organic fertilizers on two types of soil to improve growth and yield of Bawang Dayak (Eleutherine americana Merr). Agrivita J. Agric. Sci. 43, 164–173 (2021).
    Google Scholar 
    Mohidin, H. et al. Optimum levels of N, P, and K nutrition for oil palm seedlings grown in tropical peat soil. J. Plant. Nutr. 42, 1461–1471 (2019).Article 

    Google Scholar 
    Mutert, E., Fairhurst, T. H. & Von Uexküll, H. R. Agronomic management of oil palms on deep peat. Better. Crop. Int. 13, 22–27 (1999).
    Google Scholar 
    Hashim, S. A., Teh, C. B. S. & Ahmed, O. H. Influence of water table depths, nutrients leaching losses, subsidence of tropical peat soil and oil palm (Elaeis guineensis Jacq.) seedling growth. Malays. J. Soil. Sci. 23, 13–30 (2019).
    Google Scholar 
    Oktarita, S., Hergoualc’h, K., Anwar, S. & Verchot, L. V. Substantial N2O emissions from peat decomposition and N fertilization in an oil palm plantation exacerbated by hotspots. Environ. Res. Lett. 12, 104007 (2017).Article 

    Google Scholar 
    Hoyos-Santillan, J. et al. Root oxygen loss from Raphia taedigera palms mediates greenhouse gas emissions in lowland neotropical peatlands. Plant. Soil. 404, 47–60 (2016).Article 

    Google Scholar 
    Hatano, R. Impact of land use change on greenhouse gases emissions in peatland: a review. Int. Agrophys. 33, 167–173 (2019). This study reviews the impacts of changes in water-table level and nitrogen inputs on greenhouse gas emissions in tropical and northern peatlands and evaluates the optimal water-table level for minimizing emissions.Article 

    Google Scholar 
    Zawawi, N. Z. et al. The effect of nitrogen fertiliser on nitrous oxide emission in oil palm plantation. Proc. 15th Int. Peat Congress 355, 515–518 (2016).
    Google Scholar 
    Turetsky, M. R. et al. Global vulnerability of peatlands to fire and carbon loss. Nat. Geosci. 8, 11–14 (2015). This paper reviews peatland vulnerability to burning, fire-driven carbon emissions and current and future risks of peatland fires.Article 

    Google Scholar 
    Hu, Y. et al. Review of emissions from smouldering peat fires and their contribution to regional haze episodes. Int. J. Wildland Fire 27, 293–312 (2018).Article 

    Google Scholar 
    Huijnen, V. et al. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Sci. Rep. 6, 26886 (2016).Article 

    Google Scholar 
    Smith, T. E. L., Evers, S., Yule, C. M. & Gan, J. Y. In situ tropical peatland fire emission factors and their variability, as determined by field measurements in peninsula Malaysia. Glob. Biogeochem. Cycles 32, 18–31 (2018).Article 

    Google Scholar 
    Stockwell, C. E. et al. Field measurements of trace gases and aerosols emitted by peat fires in Central Kalimantan, Indonesia, during the 2015 El Niño. Atmos. Chem. Phys. 16, 11711–11732 (2016).Article 

    Google Scholar 
    Betha, R. et al. Chemical speciation of trace metals emitted from Indonesian peat fires for health risk assessment. Atmos. Res. 122, 571–578 (2013).Article 

    Google Scholar 
    Breulmann, G. et al. Heavy metals in emergent trees and pioneers from tropical forest with special reference to forest fires and local pollution sources in Sarawak, Malaysia. Sci. Total Environ. 285, 107–115 (2002).Article 

    Google Scholar 
    Othman, M. & Latif, M. T. Dust and gas emissions from small-scale peat combustion. Aerosol Air Qual. Res. 13, 1045–1059 (2013).Article 

    Google Scholar 
    See, S. W., Balasubramanian, R. & Wang, W. A study of the physical, chemical, and optical properties of ambient aerosol particles in Southeast Asia during hazy and nonhazy days. J. Geophys. Res. 111, D10S08 (2006).
    Google Scholar 
    Nikonovas, T., Spessa, A., Doerr, S. H., Clay, G. D. & Mezbahuddin, S. Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan. Commun. Earth Env. 1, 65 (2020).Article 

    Google Scholar 
    Field, R. D., van der Werf, G. R. & Shen, S. S. P. Human amplification of drought-induced biomass burning in Indonesia since 1960. Nat. Geosci. 2, 185–188 (2009).Article 

    Google Scholar 
    Astiani, D., Taherzadeh, M. J., Gusmayanti, E., Widiastuti, T. & Burhanuddin, B. Local knowledge on landscape sustainable-hydrological management reduces soil CO2 emission, fire risk and biomass loss in west Kalimantan peatland, Indonesia. Biodiversiitas J. Biol. Divers. 20, 725–731 (2019).Article 

    Google Scholar 
    Cattau, M. E. et al. Sources of anthropogenic fire ignitions on the peat-swamp landscape in Kalimantan, Indonesia. Glob. Environ. Change 39, 205–219 (2016).Article 

    Google Scholar 
    Edwards, R. B., Naylor, R. L., Higgins, M. M. & Falcon, W. P. Causes of Indonesia’s forest fires. World Dev. 127, 104717 (2020).Article 

    Google Scholar 
    Field, R. D. & Shen, S. S. P. Predictability of carbon emissions from biomass burning in Indonesia from 1997 to 2006. J. Geophys. Res. Biogeosci. 113, G04024 (2008).Article 

    Google Scholar 
    Sloan, S., Locatelli, B., Wooster, M. J. & Gaveau, D. L. A. Fire activity in Borneo driven by industrial land conversion and drought during El Niño periods, 1982–2010. Glob. Environ. Change 47, 95–109 (2017).Article 

    Google Scholar 
    Page, S. E. et al. The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature 420, 61–65 (2002).Article 

    Google Scholar 
    World Bank. The cost of fire: an economic analysis of Indonesia’s 2015 fire crisis (World Bank, 2016).Tacconi, L. Preventing fires and haze in Southeast Asia. Nat. Clim. Chang. 6, 640–643 (2016).Article 

    Google Scholar 
    Lupascu, M., Akhtar, H., Smith, T. E. L. & Sukri, R. S. Post-fire carbon dynamics in the tropical peat swamp forests of Brunei reveal long-term elevated CH4 flux. Glob. Change Biol. 26, 5125–5145 (2020).Article 

    Google Scholar 
    Milner, L. E. Influence of Fire on Peat Organic Matter from Indonesian Tropical Peatlands. Thesis, Univ. Leicester (2013).Saharjo, B. H. & Nurhayati, A. D. Changes in chemical and physical properties of hemic peat under fire-based shifting cultivation. Tropics 14, 263–269 (2005).Article 

    Google Scholar 
    Dhandapani, S. & Evers, S. Oil palm ‘slash-and-burn’ practice increases post-fire greenhouse gas emissions and nutrient concentrations in burnt regions of an agricultural tropical peatland. Sci. Total Environ. 742, 140648 (2020).Article 

    Google Scholar 
    Konecny, K. et al. Variable carbon losses from recurrent fires in drained tropical peatlands. Glob. Change Biol. 22, 1469–1480 (2016).Article 

    Google Scholar 
    Akhtar, H. et al. Significant sedge-mediated methane emissions from degraded tropical peatlands. Environ. Res. Lett. 16, 014002 (2020).
    Google Scholar 
    Rein, G. in Fire Phenomena and the Earth System (ed. Belcher, C. M.) 15–33 (Wiley, 2013).Graham, L. L. B. & Page, S. E. A limited seed bank in both natural and degraded tropical peat swamp forest: the implications for restoration. Mires Peat 22, 02 (2018).
    Google Scholar 
    Graham, E. B. et al. Microbes as engines of ecosystem function: when does community structure enhance predictions of ecosystem processes? Front. Microbiol. 7, 214 (2016).
    Google Scholar 
    Page, S. et al. Restoration ecology of lowland tropical peatlands in Southeast Asia: current knowledge and future research directions. Ecosystems 12, 888–905 (2009).Article 

    Google Scholar 
    Sazawa, K. et al. Impact of peat fire on the soil and export of dissolved organic carbon in tropical peat soil, Central Kalimantan, Indonesia. ACS Earth Space Chem. 2, 692–701 (2018).Article 

    Google Scholar 
    Dove, N. C. & Hart, S. C. Fire reduces fungal species richness and in situ mycorrhizal colonization: a meta-analysis. Fire Ecol. 13, 37–65 (2017).Article 

    Google Scholar 
    Veldkamp, E., Schmidt, M., Powers, J. S. & Corre, M. D. Deforestation and reforestation impacts on soils in the tropics. Nat. Rev. Earth Env. 1, 590–605 (2020).Article 

    Google Scholar 
    Qie, L. et al. Long-term carbon sink in Borneo’s forests halted by drought and vulnerable to edge effects. Nat. Commun. 8, 1966 (2017).Article 

    Google Scholar 
    Giesen, W. & Sari, E. N. N. Tropical peatland restoration report: the Indonesian case. MCA Indonesia https://doi.org/10.13140/RG.2.2.30049.40808 (2018).Article 

    Google Scholar 
    Dohong, A., Abdul Aziz, A. & Dargusch, P. A review of techniques for effective tropical peatland restoration. Wetlands 38, 275–292 (2018).Article 

    Google Scholar 
    Shell. Redd+ Katingan Mentaya, Indonesia. Shell https://www.shell.co.uk/motorist/make-the-change-drive-carbon-neutral/redd-plus-katingan-mentaya-indonesia.html (2021).Uda, S. K., Hein, L. & Sumarga, E. Towards sustainable management of Indonesian tropical peatlands. Wetl. Ecol. Manag. 25, 683–701 (2017).Article 

    Google Scholar 
    Wichtmann, W., Tanneberger, F., Wichmann, S. & Joosten, H. Paludiculture is paludifuture: climate, biodiversity and economic benefits from agriculture and forestry on rewetted peatland. Peatl. Int. 1, 48–51 (2010).
    Google Scholar 
    Giesen, W. in Tropical Peatland Eco-Management (eds Osaki, M., Tsuji, N., Foead, N. & Rieley, J.) 411–441 (Springer, 2021).Shurpali, N. J. et al. Atmospheric impact of bioenergy based on perennial crop (reed canary grass, Phalaris arundinaceae, L.) cultivation on a drained boreal organic soil. GCB Bioenergy 2, 130–138 (2010).
    Google Scholar 
    Lawson, I. T. et al. Improving estimates of tropical peatland area, carbon storage, and greenhouse gas fluxes. Wetl. Ecol. Manag. 23, 327–346 (2015).Article 

    Google Scholar 
    Anda, M. et al. Revisiting tropical peatlands in Indonesia: semi-detailed mapping, extent and depth distribution assessment. Geoderma 402, 115235 (2021).Article 

    Google Scholar 
    Saxon, E. C., Neuzil, S. G., Biladi, D. B. C., Kinser, J. & Sheppard, S. M. 3D mapping of lowland coastal peat domes in Indonesia. Mires Peat 27, 1–18 (2021).
    Google Scholar 
    Silvestri, S. et al. Quantification of peat thickness and stored carbon at the landscape scale in tropical peatlands: a comparison of airborne geophysics and an empirical topographic method. J. Geophys. Res. Earth Surf. 124, 3107–3123 (2019).Article 

    Google Scholar 
    Vernimmen, R. et al. Mapping deep peat carbon stock from a LiDAR based DTM and field measurements, with application to eastern Sumatra. Carbon Balance Manag. 15, 4 (2020).Article 

    Google Scholar 
    Andersen, R., Chapman, S. J. & Artz, R. R. E. Microbial communities in natural and disturbed peatlands: a review. Soil. Biol. Biochem. 57, 979–994 (2013).Article 

    Google Scholar 
    Morrison, E. S. et al. Characterization of bacterial and fungal communities reveals novel consortia in tropical oligotrophic peatlands. Microb. Ecol. 82, 188–201 (2020).Article 

    Google Scholar 
    Finn, D. R. et al. Methanogens and methanotrophs show nutrient-dependent community assemblage patterns across tropical peatlands of the Pastaza–Marañón Basin, Peruvian Amazonia. Front. Microbiol. 11, 746 (2020).Article 

    Google Scholar 
    Troxler, T. G. et al. Patterns of soil bacteria and canopy community structure related to tropical peatland development. Wetlands 32, 769–782 (2012).Article 

    Google Scholar 
    Tripathi, B. M. et al. Distinctive tropical forest variants have unique soil microbial communities, but not always low microbial diversity. Front. Microbiol. 7, 376 (2016).Article 

    Google Scholar 
    Kwon, M. J., Haraguchi, A. & Kang, H. Long-term water regime differentiates changes in decomposition and microbial properties in tropical peat soils exposed to the short-term drought. Soil. Biol. Biochem. 60, 33–44 (2013).Article 

    Google Scholar 
    Hadi, A. et al. Effects of land-use change in tropical peat soil on the microbial population and emission of greenhouse gases. Microbes Env. 16, 79–86 (2001).Article 

    Google Scholar 
    Kusai, N. A., Ayob, Z., Maidin, M. S. T., Safari, S. & Ahmad Ali, S. R. Characterization of fungi from different ecosystems of tropical peat in Sarawak, Malaysia. Rendiconti Lincei Sci. Fis. E 29, 469–482 (2018).Article 

    Google Scholar 
    Shuhada, S. N., Salim, S., Nobilly, F., Zubaid, A. & Azhar, B. Logged peat swamp forest supports greater macrofungal biodiversity than large-scale oil palm plantations and smallholdings. Ecol. Evol. 7, 7187–7200 (2017).Article 

    Google Scholar 
    Liu, B. et al. The microbial diversity and structure in peatland forest in Indonesia. Soil. Use Manag. 36, 123–138 (2020).Article 

    Google Scholar 
    Moyersoen, B., Becker, P. & Alexander, I. J. Are ectomycorrhizas more abundant than arbuscular mycorrhizas in tropical heath forests? N. Phytol. 150, 591–599 (2001).Article 

    Google Scholar 
    Muliyani, R. B., Sastrahidayat, I. R., Abdai, A. L. & Djauhari, S. Exploring ectomycorrhiza in peat swamp forest of Nyaru Menteng Palangka Raya Central Borneo. J. Biodivers. Environ. Sci. 5, 133–145 (2014).
    Google Scholar 
    Turjaman, M. et al. Improvement of early growth of two tropical peat-swamp forest tree species Ploiarium alternifolium and Calophyllum hosei by two arbuscular mycorrhizal fungi under greenhouse conditions. New Forests 36, 1–12 (2008).Article 

    Google Scholar 
    Tawaraya, K. et al. Arbuscular mycorrhizal colonization of tree species grown in peat swamp forests of Central Kalimantan, Indonesia. For. Ecol. Manag. 182, 381–386 (2003).Article 

    Google Scholar 
    Fenner, N. & Freeman, C. Drought-induced carbon loss in peatlands. Nat. Geosci. 4, 895–900 (2011).Article 

    Google Scholar 
    Yuwati, T. W. & Putri, W. S. Diversity of arbuscular mycorrhiza spores under Shorea balangeran (Korth.) Burck. plantation as bioindicator for the revegetation success. J. Galam 1, 15–26 (2020).Article 

    Google Scholar 
    Graham, L. L. B., Turjaman, M. & Page, S. E. Shorea balangeran and Dyera polyphylla (syn. Dyera lowii) as tropical peat swamp forest restoration transplant species: effects of mycorrhizae and level of disturbance. Wetl. Ecol. Manag. 21, 307–321 (2013).Article 

    Google Scholar  More

  • in

    Changing surface ocean circulation caused the local demise of echinoid Scaphechinus mirabilis in Taiwan during the Pleistocene–Holocene transition

    Hu, C.-H. in Introduction to Roadside Geology of Ten Field Geology Excursion Routes in Northern Taiwan (ed Taiwan Normal University Department of Earth Science) 63–100 (Taiwan Normal University, 1987).Hu, C.-H. Fossil molluscs of Tongxiao Formation (Pleistocene), Longgang area, Miaoli County. Atlas Fossil Mollusca Taiwan 2, 689–754 (1992).
    Google Scholar 
    Hu, C.-H. Fossil molluscs of Tongxiao Formation (Pleistocene) in Baishatun and Touwo, Tongxiao village, Miaoli County. Atlas Fossil Mollusca Taiwan 1, 175–314 (1991).
    Google Scholar 
    Hayasaka, I. & Morishita, A. Notes on some fossil echinoids of Taiwan, II. Acta Geol. Taiwan. 1, 93–110 (1947).
    Google Scholar 
    Lin, Y.-J., Fang, J.-N., Chang, C.-C., Cheng, C.-C. & Lin, J. P. Stereomic microstructure of Clypeasteroida in thin section based on new material from Pleistocene strata in Taiwan. Terr. Atmos. Ocean. Sci. J. https://doi.org/10.3319/TAO.2021.07.28.01 (2021).Article 

    Google Scholar 
    Morishita, A. in Contributions to Celebrate Prof. Ichiro Hayasaka’s 76th Birthday 109–116 (1967).Wang, C.-C., Lin, C.-F. & Li, L.-C. Measurements on Late Pleistocene sand dollar Scaphechinus mirabilis from northern Taiwan. Annu. Rep. Central Geol. Surv. 72, 49–56 (1984).
    Google Scholar 
    Nisiyama, S. The echinoid fauna from Japan and adjacent regions. Part 2. Palaeontol. Soc. Jpn. Spec. Pap. 13, 1–491 (1968).
    Google Scholar 
    Kashenko, S. D. Effects of extreme changes of sea water temperature and salinity on the development of the sand dollar Scaphechinus mirabilis. Russ. J. Mar. Biol. 35, 422–430. https://doi.org/10.1134/s1063074009050083 (2009).Article 

    Google Scholar 
    Davies, A. J. & John, C. M. The clumped (13C–18O) isotope composition of echinoid calcite: Further evidence for “vital effects” in the clumped isotope proxy. Geochim. Cosmochim. Acta 245, 172–189. https://doi.org/10.1016/j.gca.2018.07.038 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Chen, W.-S., Yeh, J.-J. & Syu, S.-J. Late Cenozoic exhumation and erosion of the Taiwan orogenic belt: New insights from petrographic analysis of foreland basin sediments and thermochronological dating on the metamorphic orogenic wedge. Tectonophysics 750, 56–69. https://doi.org/10.1016/j.tecto.2018.09.003 (2019).ADS 
    Article 

    Google Scholar 
    Peng, T.-R., Wang, C.-H. & Chen, C. T. A. Oxygen and carbon isotopic studies of fossil Mollusca in the Kuokang Shell Bed, Paishatung, Miaoli. Spec. Publ. Central Geol. Surv. 4, 307–322 (1990).
    Google Scholar 
    Lee, C.-L. Biostratigraphy and sedimentary environments of Toukoshan Formation in Baishatun area, Miaoli MS thesis, National Central University (2000).Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. 1–52 (NOAA, 2019).Liew, P.-M. Quaternary stratigraphy in western Taiwan: Palynological correlation. Proc. Geol. Soc. China 31, 169–180 (1988).
    Google Scholar 
    Siddall, M., Rohling, E. J., Thompson, W. G. & Waelbroeck, C. Marine isotope stage 3 sea level fluctuations: Data synthesis and new outlook. Rev. Geophys. https://doi.org/10.1029/2007rg000226 (2008).Article 

    Google Scholar 
    LeGrande, A. N. & Schmidt, G. A. Global gridded data set of the oxygen isotopic composition in seawater. Geophys. Res. Lett. https://doi.org/10.1029/2006gl026011 (2006).Article 

    Google Scholar 
    Waelbroeck, C. et al. Sea-level and deep water temperature changes derived from benthic formainifera isotopic records. Quatern. Sci. Rev. 21, 295–305 (2002).ADS 
    Article 

    Google Scholar 
    Epstein, S., Buchsbaum, R., Lowenstam, H. A. & Urey, H. C. Revised carbonate-water isotopic temperature scale. Bull. Geol. Soc. Am. 64, 1315–1326 (1963).Article 

    Google Scholar 
    Weber, J. N. & Raup, D. M. Fractionation of the stable isotopes of carbon and oxygen in marine calcareous organisms—the Echinoidea. Part II. Environmental and genetic factors. Geochim. Cosmochim. Acta 30, 705–736 (1966).ADS 
    CAS 
    Article 

    Google Scholar 
    Eiler, J. M. Paleoclimate reconstruction using carbonate clumped isotope thermometry. Quatern. Sci. Rev. 30, 3575–3588. https://doi.org/10.1016/j.quascirev.2011.09.001 (2011).ADS 
    Article 

    Google Scholar 
    Takeda, S. Mechanism maintaining dense beds of the sand dollar Scaphechinus mirabilis in northern Japan. J. Exp. Mar. Biol. Ecol. 363, 21–27. https://doi.org/10.1016/j.jembe.2008.06.010 (2008).Article 

    Google Scholar 
    Takatsu, T., Nakatani, T., Miyamoto, T., Kooka, K. & Takahashi, T. Spatial distribution and feeding habits of Pacific cod (Gadus macrocephalus) larvae in Mutsu Bay, Japan. Fish. Oceanogr. 11, 90–101 (2002).Article 

    Google Scholar 
    Zhao, M., Huang, C.-Y. & Wei, K.-Y. A 28,000 year U37 K’ sea-surface temperature record of ODP Site 1202B, the southern Okinawa Trough. TAO 16, 45–56 (2005).ADS 
    Article 

    Google Scholar 
    Jan, S., Tseng, Y.-H. & Dietrich, D. E. Sources of water in the Taiwan Strait. J. Oceanogr. 66, 211–221 (2010).Article 

    Google Scholar 
    Liao, E., Oey, L. Y., Yan, X.-H., Li, L. & Jiang, Y. The deflection of the China Coastal Current over the Taiwan Bank in winter. J. Phys. Oceanogr. 48, 1433–1450. https://doi.org/10.1175/jpo-d-17-0037.1 (2018).ADS 
    Article 

    Google Scholar 
    Hu, J., Kawamura, H., Li, C., Hong, H. & Jiang, Y. Review on current and seawater volume transport through the Taiwan Strait. J. Oceanogr. 66, 591–610 (2010).Article 

    Google Scholar 
    Pico, T., Mitrovica, J. X., Ferrier, K. L. & Braun, J. Global ice volume during MIS 3 inferred from a sea-level analysis of sedimentary core records in the Yellow River Delta. Quatern. Sci. Rev. 152, 72–79. https://doi.org/10.1016/j.quascirev.2016.09.012 (2016).ADS 
    Article 

    Google Scholar 
    Klein, R. T., Lohmann, K. C. & Kennedy, G. L. Elemental and isotopic proxies of paleotemperature and paleosalinity: Climate reconstruction of the marginal northeast Pacific ca. 80 ka. Geology 25, 363–366 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    Jarvis, I., Trabucho-Alexandre, J., Gröcke, D. R., Uličný, D. & Laurin, J. Intercontinental correlation of organic carbon and carbonate stable isotope records: Evidence of climate and sea-level change during the Turonian (Cretaceous). Depos. Rec. 1, 53–90. https://doi.org/10.1002/dep2.6 (2016).Article 

    Google Scholar 
    Chen, P. S. M. A study of the stratigraphy and molluscan fossils of the Tunghsiao area, Miaoli, Taiwan, R.O.C.. Bull. Malacol. Republic of China 4, 63–78 (1977).
    Google Scholar 
    Chen, W.-S. & Hsu, W.-J. The Pleistocene paleoenvironmental significance of the unearthed megafauna strata in Taiwan. Bull. Central Geol. Surv. 23, 137–163 (2010).
    Google Scholar 
    Chang, C. H. et al. The first archaic Homo from Taiwan. Nat. Commun. 6, 6037. https://doi.org/10.1038/ncomms7037 (2015).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Cai, B.-Q. Fossil human humerus of Late Pleistocene from the Taiwan Straits. Acta Antrhopologica Sinica 20, 178–185 (2001).
    Google Scholar 
    Tong, H. & Patou-Mathis, M. Mammoth and other proboscideans in China during the Late Pleistocene. Deinsea 9, 421–428 (2003).
    Google Scholar 
    Koch, P. L. & Barnosky, A. D. Late quaternary extinctions: State of the debate. Annu. Rev. Ecol. Evol. Syst. 37, 215–250. https://doi.org/10.1146/annurev.ecolsys.34.011802.132415 (2006).Article 

    Google Scholar 
    Brook, B. W. & Bowman, D. M. J. S. Explaining the Pleistocene megafaunal extinctions: Models, chronologies, and assumptions. PNAS 99, 14624–14627 (2002).ADS 
    CAS 
    Article 

    Google Scholar 
    Barnosky, A. D., Koch, P. L., Feranec, R. S., Wing, S. L. & Shabel, A. B. Assessing the causes of Late Pleistocene extinctions on the continents. Science 306, 70–75 (2004).ADS 
    CAS 
    Article 

    Google Scholar 
    Ugan, A. & Byers, D. A global perspective on the spatiotemporal pattern of the Late Pleistocene human and woolly mammoth radiocarbon record. Quatern. Int. 191, 69–81. https://doi.org/10.1016/j.quaint.2007.09.035 (2008).Article 

    Google Scholar 
    Adlan, Q., Davies, A. J. & John, C. M. Effects of oxygen plasma ashing treatment on carbonate clumped isotopes. Rapid Commun. Mass Spectrom. 34, e8802. https://doi.org/10.1002/rcm.8802 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    John, C. M. & Bowen, D. Community software for challenging isotope analysis: First applications of “Easotope” to clumped isotopes. Rapid Commun. Mass Spectrom. 30, 2285–2300 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Bernasconi, S. M. et al. Background effects on Faraday collectors in gas-source mass spectrometry and implications for clumped isotope measurements. Rapid Commun. Mass Spectrom. 27, 603–612. https://doi.org/10.1002/rcm.6490 (2013).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Bernasconi, S. M. et al. InterCarb: A community effort to improve interlaboratory standardization of the carbonate clumped isotope thermometer using carbonate standards. Geochem. Geophys. Geosyst. 22, e2020GC009588. https://doi.org/10.1029/2020GC009588 (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Anderson, N. T. et al. Unified clumped isotope thermometer calibration (0.5–1,100°C) using carbonate-based standardization. Geophys. Res. Lett. 48, e2020GL092069 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    Lee, H. et al. Young colonization history of a widespread sand dollar (Echinodermata; Clypeasteroida) in western Taiwan. Quatern. Int. 528, 120–129 (2019).Article 

    Google Scholar 
    Reimer, P. J. et al. IntCal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon 55, 1869–1887 (2013).CAS 
    Article 

    Google Scholar  More

  • in

    Infected food web and ecological stability

    Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F. & Jetz, W. Homage to Linnaeus: How many parasites? How many hosts?. Proc. Natl. Acad. Sci. 105, 11482–11489 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    Kuris, A. M. et al. Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454, 515–518 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    Seabloom, E. W. et al. The community ecology of pathogens: Coinfection, coexistence and community composition. Ecol. Lett. 18, 401–415 (2015).Article 

    Google Scholar 
    French, R. K. & Holmes, E. C. An ecosystems perspective on virus evolution and emergence. Trends Microbiol. 28, 165–175 (2020).CAS 
    Article 

    Google Scholar 
    Hudson, P. J., Dobson, A. P. & Lafferty, K. D. Is a healthy ecosystem one that is rich in parasites?. Trends Ecol. Evol. 21, 381–385 (2006).Article 

    Google Scholar 
    Raffel, T. R., Martin, L. B. & Rohr, J. R. Parasites as predators: Unifying natural enemy ecology. Trends Ecol. Evol. 23, 610–618 (2008).Article 

    Google Scholar 
    Johnson, P. T. J. et al. When parasites become prey: Ecological and epidemiological significance of eating parasites. Trends Ecol. Evol. 25, 362–371 (2010).Article 

    Google Scholar 
    Frainer, A., McKie, B. G., Amundsen, P. A., Knudsen, R. & Lafferty, K. D. parasitism and the biodiversity-functioning relationship. Trends Ecol. Evol. 33, 260–268 (2018).Article 

    Google Scholar 
    Jephcott, T. G., Sime-Ngando, T., Gleason, F. H. & Macarthur, D. J. Host-parasite interactions in food webs: Diversity, stability, and coevolution. Food Webs 6, 1–8 (2016).Article 

    Google Scholar 
    Rohr, J. R. et al. Towards common ground in the biodiversity–disease debate. Nat. Ecol. Evol. 4, 24–33 (2020).Article 

    Google Scholar 
    Johnson, P. T. J., De Roode, J. C. & Fenton, A. Why infectious disease research needs community ecology. Science 349, 1259504 (2015).Article 

    Google Scholar 
    Marcogliese, D. J. & Cone, D. K. Food webs: A plea for parasites. Trends Ecol. Evol. 12, 320–325 (1997).CAS 
    Article 

    Google Scholar 
    Chen, H.-W. et al. Network position of hosts in food webs and their parasite diversity. Oikos 117, 1847–1855 (2008).Article 

    Google Scholar 
    Lafferty, K. D., Dobson, A. P. & Kuris, A. M. Parasites dominate food web links. Proc. Natl. Acad. Sci. USA 103, 11211–11216 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    Lafferty, K. D. et al. Parasites in food webs: The ultimate missing links. Ecol. Lett. 11, 533–546 (2008).Article 

    Google Scholar 
    Dunne, J. A. The network structure of food webs. In Ecological Networks: Linking Structure to Dynamics (eds Pascual, M. & Dunne, J. A.) 27–28 (Oxford University Press, 2005).
    Google Scholar 
    Dunne, J. A., Williams, R. J. & Martinez, N. D. Network structure and biodiversity loss in food webs: Robustness increases with connectance. Ecol. Lett. 5, 558–567 (2002).Article 

    Google Scholar 
    Hudson, P. J., Rizzoli, A., Grenfell, B. T., Heesterbeek, H. & Dobson, A. P. The Ecology of Wildlife Diseases. (Oxford University Press, Oxford, 2002).
    Google Scholar 
    Anderson, R. M. & May, R. M. Infectious Diseases of Humans: Dynamics and Control (Oxford University Press, 1992).
    Google Scholar 
    McCallum, H. & Dobson, A. Detecting disease and parasite threats to endangered species and ecosystems. Trends Ecol. Evol. 10, 190–194 (1995).CAS 
    Article 

    Google Scholar 
    De Castro, F. & Bolker, B. M. Parasite establishment and host extinction in model communities. Oikos 111, 501–513 (2005).Article 

    Google Scholar 
    McQuaid, C. F. & Britton, N. F. Parasite species richness and its effect on persistence in food webs. J. Theor. Biol. 364, 377–382 (2015).ADS 
    Article 

    Google Scholar 
    Holt, R. D., Dobson, A. P., Begon, M., Bowers, R. G. & Schauber, E. M. Parasite establishment in host communities. Ecol. Lett. 6, 837–842 (2003).
    Article 

    Google Scholar 
    Hatcher, M. J. & Dunn, A. M. Parasites in Ecological Communities: From Interactions to Ecosystems (Cambridge University Press, 2011).Book 

    Google Scholar 
    Dobson, A. Population dynamics of pathogens with multiple host species. Am. Nat. 164, S64–S78 (2004).Article 

    Google Scholar 
    McCann, K., Hastings, A. & Huxel, G. R. Weak trophic interactions and the balance of nature. Nature 395, 794–798 (1998).ADS 
    CAS 
    Article 

    Google Scholar 
    Neutel, A. M., Heesterbeek, J. A. P. & de Ruiter, P. C. Stability in real food webs: Weak links in long loops. Science 296, 1120–1123 (2002).ADS 
    CAS 
    Article 

    Google Scholar 
    Chen, X. & Cohen, J. E. Transient dynamics and food–web complexity in the Lotka–Volterra cascade model. Proc. R. Soc. Lond. Ser. B Biol. Sci. 268, 869–877 (2001).CAS 
    Article 

    Google Scholar 
    May, R. M. Stability in multispecies community models. Math. Biosci. 12, 59–79 (1971).MathSciNet 
    Article 

    Google Scholar 
    May, R. M. Will a large complex system be stable?. Nature 238, 413–414 (1972).ADS 
    CAS 
    Article 

    Google Scholar 
    Hilker, F. M. & Schmitz, K. Disease-induced stabilization of predator-prey oscillations. J. Theor. Biol. 255, 299–306 (2008).ADS 
    MathSciNet 
    Article 

    Google Scholar 
    Hethcote, H. W., Wang, W., Han, L. & Ma, Z. A predator–prey model with infected prey. Theor. Popul. Biol. 66, 259–268 (2004).Article 

    Google Scholar 
    Kooi, B. W., van Voorn, G. A. K. & Das, K. P. Stabilization and complex dynamics in a predator-prey model with predator suffering from an infectious disease. Ecol. Complex. 8, 113–122 (2011).Article 

    Google Scholar 
    Winemiller, K. O. Spatial and temporal variation in tropical fish trophic networks. Ecol. Monogr. 60, 331–367 (1990).Article 

    Google Scholar 
    Paine, R. T. Food-web analysis through field measurement of per capita interaction strength. Nature 355, 73–75 (1992).ADS 
    Article 

    Google Scholar 
    Wootton, J. T. Estimates and tests of per capita interaction strength: Diet, abundance, and impact of intertidally foraging birds. Ecol. Monogr. 67, 45–64 (1997).Article 

    Google Scholar 
    Cohen, J. E., Briand, F. & Newman, C. M. Community Food Webs: Data and Theory (Springer, 1990).Book 

    Google Scholar 
    Mougi, A. Diversity of biological rhythm and food web stability. Biol. Lett. 17, 20200673 (2021).Article 

    Google Scholar  More

  • in

    Endocranial volume increases across captive generations in the endangered Mexican wolf

    Sol, D., Bacher, S., Reader, S. M. & Lefebvre, L. Brain size predicts the success of mammal species introduced into novel environments. Am. Nat. 172(Suppl. 1), S63–S71 (2008).PubMed 
    Article 

    Google Scholar 
    González-Lagos, C., Sol, D. & Reader, S. M. Large-brained mammals live longer. J. Evol. Biol. 23, 1064–1074 (2010).PubMed 
    Article 

    Google Scholar 
    Gonda, A., Herczeg, G. & Merilä, J. Evolutionary ecology of intraspecific brain size variation: A review. Ecol. Evol. 3(8), 2751–2764 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Benson-Amram, S., Dantzer, B., Stricker, G., Swanson, E. M. & Holekamp, K. E. Brain size predicts problem-solving ability in mammalian carnivores. PNAS 113(9), 2532–2537 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Näslund, J., Aarestrup, K., Thomassen, S. T. & Johnsson, J. I. Early enrichment effects on brain development in hatchery-reared Atlantic salmon (Salmo salar): No evidence for a critical period. Can. J. Fish. Aquat. Sci. 69(9), 1481–1490 (2012).Article 

    Google Scholar 
    Logan, C. J., Kruuk, L. E. B., Stanley, R., Thompson, A. M. & Clutton-Brock, T. H. Endocranial volume is heritable and is associated with longevity and fitness in a wild mammal. R. Soc. Open Sci. 3(12), 160622 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yamaguchi, N., Kitchener, A. C., Gilissen, E. & MacDonald, D. W. Brain size of the lion (Panthera leo) and the tiger (P. tigris): Implications for intrageneric phylogeny, intraspecific differences and the effects of captivity. Biol. J. Linn. Soc. 98, 85–93 (2009).Article 

    Google Scholar 
    Turschwell, M. P. & White, C. R. The effects of laboratory housing and spatial enrichment on brain size and metabolic rate in the eastern mosquitofish Gambusia holbrooki. Biol. Open. 5(3), 205–210 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Welniak-Kaminska, M. et al. Volumes of brain structures in captive wild-type and laboratory rats: 7T magnetic resonance in vivo automatic atlas-based study. PLoS ONE 14(4), e0215348 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Guay, P. J., Parrott, M. & Selwood, L. Captive breeding does not alter brain volume in a marsupial over a few generations. Zoo Biol. 31, 82–86 (2012).PubMed 
    Article 

    Google Scholar 
    Isler, K. et al. Endocranial volumes of primate species: Scaling analyses using a comprehensive and reliable data set. J. Hum. Evol. 55(6), 967–978 (2008).PubMed 
    Article 

    Google Scholar 
    Burns, J. G., Saravanan, A. & Rodd, F. H. Rearing environment affects the brain size of guppies: Lab-reared guppies have smaller brains than wild-caught guppies. Ethol. 115(2), 122–133 (2009).Article 

    Google Scholar 
    Kruska, D. On the evolutionary significance of encephalization in some eutherian mammals: Effects of adaptive radiation, domestication, and feralization. Brain Behav. Evol. 65(2), 73–108 (2005).PubMed 
    Article 

    Google Scholar 
    Logan, C. J. & Clutton-Brock, T. H. Validating methods for estimating endocranial volume in individual red deer (Cervus elaphus). Behav. Processes. 92, 143–146 (2013).PubMed 
    Article 

    Google Scholar 
    Colby, A. E., Kimock, C. M. & Higham, J. P. Endocranial volume is variable and heritable, but not related to fitness, in a free-ranging primate. Sci. Rep. 11, 1–11 (2021).Article 
    CAS 

    Google Scholar 
    Stuermer, I. W. & Wetzel, W. Early experience and domestication affect auditory discrimination learning, open field behaviour and brain size in wild Mongolian gerbils and domesticated Laboratory gerbils (Meriones unguiculatus forma domestica). Behav. Brain Res. 173, 11–21 (2006).PubMed 
    Article 

    Google Scholar 
    Agnvall, B., Bélteky, J. & Jensen, P. Brain size is reduced by selection for tameness in red junglefowl-correlated effects in vital organs. Sci. Rep. 7(3306), 1–7 (2017).CAS 

    Google Scholar 
    Röhrs, M. & Ebinger, P. Wild is not really wild: Brain weight of wild and domestic mammals. Berl. Munch. Tierarztliche Wochenschrift. 112(6–7), 234–238 (1999).
    Google Scholar 
    Smith, B. P., Lucas, T. A., Norris, R. M. & Henneberg, M. Brain size/body weight in the dingo (Canis dingo): Comparisons with domestic and wild canids. Aust. J. Zool. 65(5), 292–301 (2017).Article 

    Google Scholar 
    Roberts, T., McGreevy, P. & Valenzuela, M. Human induced rotation and reorganization of the brain of domestic dogs. PLoS ONE 5(7), e11946 (2010).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Pollen, A. A. et al. Environmental complexity and social organization sculpt the brain in Lake Tanganyikan cichlid fish. Brain Behav. Evol. 70, 21–39 (2007).PubMed 
    Article 

    Google Scholar 
    Kihslinger, R. L., Lema, S. C. & Nevitt, G. A. Environmental rearing conditions produce forebrain differences in wild Chinook salmon Oncorhynchus tshawytscha. Comp. Biochem. Physiol. 145(2), 145–151 (2006).CAS 
    Article 

    Google Scholar 
    Guay, P. J. & Iwaniuk, A. N. Captive breeding reduces brain volume in waterfowl (Anseriformes). Condor 110(2), 276–284 (2008).Article 

    Google Scholar 
    Diamond, M. C., Ingham, C. A., Johnson, R. E., Bennett, E. L. & Rosenzweig, M. R. Effects of environment on morphology of rat cerebral cortex and hippocampus. J. Neurobiol. 7, 75–85 (1976).CAS 
    PubMed 
    Article 

    Google Scholar 
    Courtney Jones, S. K., Munn, A. J. & Byrne, P. G. Effect of captivity on morphology: Negligible changes in external morphology mask significant changes in internal morphology. R. Soc. Open Sci. 5(5), 1–13 (2018).Article 

    Google Scholar 
    Kruska, D. & Röhrs, M. Comparative-quantitative investigations on brains of feral pigs from the Galapagos Islands and of European domestic pigs. Z. Anat. Entwicklungsgesch. 144(1), 61–73 (1974).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kruska, D. Changes of brain size in Tylopoda during phylogeny and caused by domestication. Verh. Dtsch. Zool. Ges. 75, 173–183 (1982).
    Google Scholar 
    Groves, C. P. Skull-changes due to captivity in certain Equidae. Z. Säugetierkd. 31, 44–46 (1966).
    Google Scholar 
    Groves, C. P. The skulls of Asian rhinoceroses: Wild and captive. Zoo Biol. 1, 251–261 (1982).Article 

    Google Scholar 
    Hollister, N. Some effects of environment and habit on captive lions. Proc. US. Natl. Mus. 53, 177–193 (1917).Article 

    Google Scholar 
    Price, E. O. Behavioral development in animals undergoing domestication. Appl. Anim. Behav. Sci. 65(3), 245–271 (1999).Article 

    Google Scholar 
    Wolff, J. Das Gesetz der Transformation der Knochen (A. Hirchwild, 1892).
    Google Scholar 
    Herring, S. W. Formation of the vertebrate face: Epigenetic and functional influences. Am. Zool. 33, 472–483 (1993).Article 

    Google Scholar 
    Wroe, S. & Milne, N. Convergence and remarkably consistent constraint in the evolution of carnivore skull shape. Evol. 61(5), 1251–1260 (2007).Article 

    Google Scholar 
    Damasceno, E. M., Hingst-Zaher, E. & Astúa, D. Bite force and encephalization in the Canidae (Mammalia: Carnivora). J. Zool. 290(4), 246–254 (2013).Article 

    Google Scholar 
    Van Valkenburgh, B. Deja vu: the evolution of feeding morphologies in the Carnivora. Integr. Comp. Biol. 47, 147–163 (2007).PubMed 
    Article 

    Google Scholar 
    Van Valkenburgh, B. Carnivore dental adaptations and diet: A study of trophic diversity within guilds in Carnivore behavior, ecology, and evolution (ed. Gittleman, J. L.) 410–436 (Springer Science & Business Media, 1989).Slater, G. J., Dumont, E. R. & Van Valkenburgh, B. Implications of predatory specialization for cranial form and function in canids. J. Zool. 278(3), 181–188 (2009).Article 

    Google Scholar 
    Michaud, M., Veron, G. & Fabre, A. C. Phenotypic integration in feliform carnivores: Covariation patterns and disparity in hypercarnivores versus generalists. Evol. 74(12), 2681–2702 (2020).Article 

    Google Scholar 
    O’Regan, H. J. & Kitchener, A. C. The effects of captivity on the morphology of captive, domesticated and feral mammals. Mamm. Rev. 35, 215–230 (2005).Article 

    Google Scholar 
    Kapoor, V., Antonelli, T., Parkinson, J. A. & Hartstone-Rose, A. Oral health correlates of captivity. Res. Vet. Sci. 107, 213–219 (2016).PubMed 
    Article 

    Google Scholar 
    Mitchell, D. R., Wroe, S., Ravosa, M. J. & Menegaz, R. A. More challenging diets sustain feeding performance: Applications toward the captive rearing of wildlife. Integr. Org. Biol. 3, 1–13 (2021).
    Google Scholar 
    Curtis, A. A., Orke, M., Tetradis, S. & Van Valkenburgh, B. Diet-related differences in craniodental morphology between captive-reared and wild coyotes, Canis latrans (Carnivora: Canidae). Biol. J. Linn. Soc. 123(3), 677–693 (2018).Article 

    Google Scholar 
    Siciliano-Martina, L., Light, J. E. & Lawing, A. M. Cranial morphology of captive mammals: A meta-analysis. Front. Zool. 18(4), 1–13 (2021).
    Google Scholar 
    Corruccini, R. S. & Beecher, R. M. Occlusal variation related to soft diet in a nonhuman primate. Science 218, 74–75 (1982).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Ramirez Rozzi, F. V., González-José, R. & Pucciarelli, H. M. Cranial growth in normal and low-protein-fed Saimiri An environmental heterochrony. J. Hum. Evol. 49(4), 515–535 (2005).PubMed 
    Article 

    Google Scholar 
    Taylor, A. B. & van Schaik, C. P. Variation in brain size and ecology in Pongo. J. Hum. Evol. 52, 59–71 (2007).PubMed 
    Article 

    Google Scholar 
    AZA Canid TAG. Large Canid (Canidae) Care Manual. (Association of Zoos and Aquariums, 2012).Mexican Wolf Species Survival Plan. Mexican Gray Wolf Husbandry Manual: Guidelines for Captive Management (2009 edition). (Mexican Wolf Species Survival Plan and U.S. Fish and Wildlife Service, 2009).Carrera, R. et al. Comparison of Mexican wolf and coyote diets in Arizona and New Mexico. The J. Wildl. Manag. 72(2), 376–381 (2008).Article 

    Google Scholar 
    Reed, J. E. et al. Diets of free-ranging Mexican gray wolves in Arizona and New Mexico. Wildl. Soc. Bull. 34(4), 1127–1133 (2006).Article 

    Google Scholar 
    Kazimierska, K., Biel, W. & Witkowicz, R. Mineral composition of cereal and cereal-free dry dog foods versus nutritional guidelines. Molecules 25(21), 1–24 (2020).Article 
    CAS 

    Google Scholar 
    Pezzali, J. G. & Aldrich, C. G. Effect of ancient grains and grain-free carbohydrate sources on extrusion parameters and nutrient utilization by dogs. J. Anim. Sci. 98(2), 3758–3767 (2019).Article 

    Google Scholar 
    Hartstone-Rose, A., Selvey, H., Villari, J. R., Atwell, M. & Schmidt, T. The three-dimensional morphological effects of captivity. PLoS ONE 9(11), 1–15 (2014).Article 
    CAS 

    Google Scholar 
    Siciliano-Martina, L., Light, J. E. & Lawing, A. M. Changes in canid cranial morphology induced by captivity and conservation implications. Biol. Conserv. 257, 109143 (2021).Article 

    Google Scholar 
    Hedrick, P. W. & Fredrickson, R. Genetic rescue guidelines with examples from Mexican wolves and Florida panthers. Conserv. Genet. 11(2), 615–626 (2010).Article 

    Google Scholar 
    Greely, S. E. Mexican Wolf, Canis lupus baileyi, International Studbook 2018. Palm Desert, California. (2018).Kalinowski, S. T., Hedrick, P. W. & Miller, P. S. No inbreeding depression observed in Mexican and red wolf captive breeding programs. Conserv. Biol. 13(6), 1371–1377 (1999).Article 

    Google Scholar 
    Sakai, S. T., Whitt, B., Arsznov, B. M. & Lundrigan, B. L. Endocranial development in the coyote (Canis latrans) and gray wolf (Canis lupus): A computed tomographic study. Brain Behav. Evol. 91(2), 1–18 (2018).Article 

    Google Scholar 
    Van Valkenburgh, B. Skeletal and dental predictors of body mass in carnivores in Body size in mammalian paleobiology: estimation and biological implications (eds. Damuth, J. & MacFadden, B. J.) (Cambridge University Press, 1990).Rohlf, F. J. TPSDig2: a program for landmark development and analysis (2001).Siciliano-Martina, L., Light, J. E., Riley, D. G. & Lawing, A. M. One of these wolves is not like the other: morphological effects and conservation implications of captivity in Mexican wolves. Anim. Conserv. 25, 77–90 (2021).Article 

    Google Scholar 
    Zelditch, M. L., Donald, L., Swiderski, H., Sheets, D. & Fink, W. L. Geometric morphometrics for biologists: a primer. (Elsevier Academic Press, 2004).Coster, A. pedigree: Pedigree functions. R package version 1.4 (2013).Traylor-Holzer, K. (ed.). PMx user’s manual. Version 1.0. Apple Valley, MN: IUCN SSC Conservation Breeding Specialist Group. (2011).Thomason, J. J. Cranial strength in relation to estimated biting forces in some Mammals. Can. J. Zool. 69, 2326–2333 (1991).Article 

    Google Scholar 
    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods. 9(7), 676–682 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2020).Cofran, Z. Brain size growth in wild and captive chimpanzees (Pan troglodytes). Am. J. Primat. 80(7), 1–8 (2018).Article 

    Google Scholar 
    Witzenberger, K. A. & Hochkirch, A. Ex situ conservation genetics: A review of molecular studies on the genetic consequences of captive breeding programmes for endangered animal species. Biodivers. Conserv. 20(9), 1843–1861 (2011).Article 

    Google Scholar 
    Gómez-Sánchez, D. et al. On the path to extinction: Inbreeding and admixture in a declining grey wolf population. Mole. Ecol. 27(18), 3599–3612 (2018).Article 

    Google Scholar 
    Elbroch, M. Animal skulls: a guide to North American species. (Stackpole Books, 2006).Conde, D. A., Flesness, N., Colchero, F., Jones, O. R. & Scheuerlein, A. An emerging role of zoos to conserve biodiversity. Science 331, 1390–1391 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Prado, E. L. & Dewey, K. G. Nutrition and brain development in early life. Nutr. Rev. 72(4), 267–284 (2014).PubMed 
    Article 

    Google Scholar 
    Hecht, E. E. et al. Neuromorphological changes following selection for tameness and aggression in the Russian farm-fox experiment. J. Neurosci. 41(28), 6144–6156 (2021).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Bennett, E. L., Rosenzweig, M. R. & Diamond, M. C. Rat brain: Effects of environmental enrichment on wet and dry weights. Science 163(3869), 825–826 (1969).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Cummins, R. A., Walsh, R. N., Budtz-Olsen, O. E., Konstantinos, T. & Horsfall, C. R. Environmentally-induced changes in the brains of elderly rats. Nature 243(5409), 516–518 (1973).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Welch, B. L., Brown, D. G., Welch, A. S. & Lin, D. C. Isolation, restrictive confinement or crowding of rats for one year. I. Weight, nucleic acids and protein of brain regions. Brain Res. 75, 71–84 (1974).CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Human-ignited fires result in more extreme fire behavior and ecosystem impacts

    Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).ADS 
    Article 

    Google Scholar 
    Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    United Nations Environment Programme. Spreading like Wildfire–The Rising Threat of Extraordinary Landscape Fires. A UNEP Rapid Response Assessment. (United Nations Environment Programme, Nairobi, 2022).Williams, A. P. et al. Observed impacts of anthropogenic climate change on wildfire in California. Earth’s Future 7, 892–910 (2019).ADS 
    Article 

    Google Scholar 
    Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Dennison, P. E., Brewer, S. C., Arnold, J. D. & Moritz, M. A. Large wildfire trends in the western United States, 1984–2011. Geophys. Res. Lett. 41, 2928–2933 (2014).ADS 
    Article 

    Google Scholar 
    Westerling, A. L. Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring. Philos. Trans. R. Soc. B: Biol. Sci. 371, 20150178 (2016).Article 

    Google Scholar 
    Pyne, S. J. Fire in America: A Cultural History of Wildland and Rural Fire. (University of Washington Press, 2017).Fire and Resource Assessment Program. Fire Perimeters. Available: https://frap.fire.ca.gov/frap-projects/fire-perimeters/. (California Department of Forestry & Fire Protection, 2018).Westerling, A. L., Hidalgo, H. G., Cayan, D. R. & Swetnam, T. W. Warming and earlier spring increase Western U.S. forest wildfire activity. Science 313, 940–943 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    Starrs, C. F., Butsic, V., Stephens, C. & Stewart, W. The impact of land ownership, firefighting, and reserve status on fire probability in California. Environ. Res. Lett. 13, 034025 (2018).ADS 
    Article 

    Google Scholar 
    Lydersen, J. M. et al. Evidence of fuels management and fire weather influencing fire severity in an extreme fire event. Ecol. Appl. 27, 2013–2030 (2017).Article 

    Google Scholar 
    Parsons, D. J. & DeBenedetti, S. H. Impact of fire suppression on a mixed-conifer forest. For. Ecol. Manag. 2, 21–33 (1979).Article 

    Google Scholar 
    Vose, R., Easterling, D. R., Kunkel, K. & Wehner, M. Temperature Changes in the United States. (NASA, 2017).Balch, J. K. et al. Human-started wildfires expand the fire niche across the United States. Proc. Natl Acad. Sci. USA 114, 2946–2951 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    Stephens, S. L., Martin, R. E. & Clinton, N. E. Prehistoric fire area and emissions from California’s forests, woodlands, shrublands, and grasslands. For. Ecol. Manag. 251, 205–216 (2007).Article 

    Google Scholar 
    Sugihara, N. G., Van Wagtendonk, J. W., Fites-Kaufman, J., Shaffer, K. E. & Thode, A. E. Fire in California’s Ecosystems. (University of California Press, 2006).Jin, Y. et al. Identification of two distinct fire regimes in Southern California: implications for economic impact and future change. Environ. Res. Lett. 10, 094005 (2015).ADS 
    Article 

    Google Scholar 
    Trollope, W. in Ecological Effects of Fire In South African Ecosystems. 199–217 (Springer, 1984).Byram, G. M. in Forest Fire: Control and Use (ed. Davis, K. P.) 155–182 (McGraw-Hill, 1959).McLauchlan, K. K. et al. Fire as a fundamental ecological process: Research advances and frontiers. J. Ecol. https://doi.org/10.1111/1365-2745.13403 (2020).Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought–fire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    Schroeder, W., Oliva, P., Giglio, L. & Csiszar, I. A. The New VIIRS 375m active fire detection data product: Algorithm description and initial assessment. Remote Sens. Environ. 143, 85–96 (2014).ADS 
    Article 

    Google Scholar 
    Rothermel, R. C. A Mathematical Model for Predicting Fire Spread in Wildland Fuels (USFS, 1972).Hood, S. M., Varner, J. M., van Mantgem, P. & Cansler, C. A. Fire and tree death: understanding and improving modeling of fire-induced tree mortality. Environ. Res. Lett. 13, 113004 (2018).ADS 
    Article 

    Google Scholar 
    Cattau, M. E., Wessman, C., Mahood, A., Balch, J. K. & Poulter, B. Anthropogenic and lightning‐started fires are becoming larger and more frequent over a longer season length in the USA. Glob. Ecol. Biogeogr. 29, 668–681 (2020).Article 

    Google Scholar 
    Abatzoglou, J. T., Balch, J. K., Bradley, B. A. & Kolden, C. A. Human-related ignitions concurrent with high winds promote large wildfires across the USA. Int. J. Wildland Fire 27, 377–386 (2018).Article 

    Google Scholar 
    Fried, J. S. et al. Predicting the effect of climate change on wildfire behavior and initial attack success. Clim. Change 87, 251–264 (2008).Article 

    Google Scholar 
    van Wagtendonk, J. W. The history and evolution of wildland fire use. Fire Ecol. 3, 3–17 (2007).Article 

    Google Scholar 
    Sullivan, A. L. Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models. Int. J. Wildland Fire 18, 369–386 (2009).Article 

    Google Scholar 
    Wang, X. et al. Projected changes in fire size from daily spread potential in Canada over the 21st century. Environ. Res. Lett. 15, 104048 (2020).ADS 
    Article 

    Google Scholar 
    Parks, S. A. et al. High-severity fire: evaluating its key drivers and mapping its probability across western US forests. Environ. Res. Lett. 13, 044037 (2018).ADS 
    Article 

    Google Scholar 
    Hantson, S. et al. The status and challenge of global fire modelling. Biogeosciences 13, 3359–3375 (2016).ADS 
    Article 

    Google Scholar 
    Reinhardt, E. D. First Order Fire Effects Model: FOFEM 4.0, User’s Guide. (Intermountain Forest and Range Experiment Station, Forest Service, US …, 1997).Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 1–11 (2015).CAS 
    Article 

    Google Scholar 
    Pateiro-Lopez, B. & Rodriguez-Casal, A. alphahull: Generalization of the Convex Hull of a Sample of Points in the Plane v. R package version 2.2 (2019).Edelsbrunner, H., Kirkpatrick, D. & Seidel, R. On the shape of a set of points in the plane. IEEE Trans. Inf. theory 29, 551–559 (1983).MathSciNet 
    Article 

    Google Scholar 
    Rodríguez Casal, A. & Pateiro López, B. Generalizing the Convex Hull of A Sample: the R Package alphahull. (2010).Bell, D. M. et al. Multiscale divergence between Landsat-and lidar-based biomass mapping is related to regional variation in canopy cover and composition. Carbon Balance Manag. 13, 15 (2018).Article 

    Google Scholar 
    Abatzoglou, J. T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 33, 121–131 (2013).Article 

    Google Scholar 
    MTBS. Monitoring Trends in Burn Severity Data Access: Fire Level Geospatial Data. (MTBS). (2018).Miller, J. D. et al. Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sens. Environ. 113, 645–656 (2009).ADS 
    Article 

    Google Scholar 
    Homer, C. et al. Completion of the 2011 National Land Cover Database for the conterminous United States–representing a decade of land cover change information. Photogrammetric Eng. Remote Sens. 81, 345–354 (2015).
    Google Scholar  More

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    A species diversity dataset of beetles by three passive acquisition methods in Tei Tong Tsai (Hong Kong)

    Study sitesThe sample site Tei Tong Tsai is located within the Island District (112°5’ E, 22°5’ N Hong Kong, China) and connected to Lantau Country Park. The rich woods in Tei Tong Tsai provide a suitable environment for insects to survive, with rich biodiversity. Weather records (Supplement 1) for May 2019 show that the highefst temperature was 27.2 °C, the lowest was 15.7 °C, the average was 21.7 °C; and the annual average rainfall was 297.8 mm. The suitable temperature and rainfall have created a suitable ecological environment and high biodiversity, establishing Tei Tong Tsai as a prime location for studying beetle diversity. In May 2019, a 13 sample sites were selected for beetle collection (Fig. 1). All latitude and longitude formats were converted to degrees, minutes, and seconds.Fig. 1Sampling points for the three passive acquisition methods used in the Tei Tong Tsai sampling site (indicated by red dots).Full size imageExperimental protocolIn this study, three passive collection methods were used for beetle collection. FIT is an efficient collecting method for insects with strong flying abilities and was first developed and used abroad14. MT and PT collect insects that are not strong flyers and live on the surface. A flight interception trap, a malaise trap, and 10 pitfall traps were set up to collect beetles in each sample site. Samples were selected to cover ecological environments at different longitudes, latitudes, altitudes, and distances from water sources. Reasonable sampling distances (depending on the terrain, with an interval between 100 and 200 m) were set up between sample sites to fully cover Tei Tong Tsai’s habitats. Due to the topography, the distance between the 10th and 11th sample points was about 350 m. The distance between two other close sample points were in the range of 100–200 m. All three traps were based on the original device to maximize the advantages and achieve better collection results.Collection devices. The flight interception trap (Fig. 2a) mainly comprises an interceptor screen (plastic net, PVC plastic glass, or plexiglas) and an insect specimen receiver (PVC), which is an efficient collection device for intercepting and collecting insects with strong flight ability. The detailed installation steps include the following: Firstly, punch two holes on the long side of the PVC screen with a hole puncher spaced about 30 cm apart; then, fix the screen to a bamboo pole with silk, install the specimen receiver, fix all three, bolt the rope, and fix it in the air with a thick rope (the sink is about 0.5–1 m from the ground). After installation, relevant drugs were placed inside the specimen receiver to poison the insects. The drugs used depend on the purpose of the study. For morphological studies, saline (5 mmol/L NaCl solution) or water with detergent is used. By contrast, DNA molecular studies use a mixture of 2% SDS (sodium dodecyl sulfate) and EDTA (ethylene diamine tetraacetic acid, 0.1 mol/L, PH = 8) or highly concentrated alcohol, which effectively controls the degradation of DNA. Currently, high-concentration alcohol, SDS and EDTA mixtures are commonly used. The device is widely applicable and can be installed in almost any habitat; however, it is best installed along the insects’ flight paths, including roads, rivers, or creeks between valleys. In this experiment, we improved this device by increasing the size of the water trough considering the actual situation of the sample site. Also, to properly conduct the molecular experiments, the reagents we used were a mixture of SDS and EDTA. Therefore, the improved device was more suitable for diverse habitats, and the insect species collected were abundant, reflecting good collection practices14.Fig. 2Three passive acquisition methods: (a) flight interception trap; (b) malaise trap; (c) pitfall trap.Full size imageMalaise traps (Fig. 2b) are large tent-like structures constructed from thin mesh. They are among the most commonly used static non-attractant insect traps and insect collection devices. Invented by Malaise (1937) and later improved upon by Townes and Sharkey, these traps are important tools for insect collection and monitoring worldwide15. The malaise trap used at the Tei Tong Tsai Country Park was the Townes type, which is generally set up in forest areas with rich habitats and relatively stable ground. The material is usually meshed mosquito netting fabricated into a tent-shaped insect interception field. The insects hit the net vertically, continue to fly upward, and are gradually led into the trap by the tilted top. The drug in the trap is usually anhydrous ethanol, which intercepts beetles with weak flying abilities16,17.The pitfall trap (Fig. 2c) is an effective method for capturing surface beetles; it is simple to use, easy to carry, and a common device for collection in the wild. The PT is created by digging a pit into the ground with the same depth as a wide-mouth plastic cup (20 cm high, 10 cm in diameter); The upper edge of the cup must be flushed with the soil surface, and a mixture of absolute ethanol is poured inside to collect flightless beetles14. About one-quarter of the way from the top, small holes are punched above the wide-mouth cup to prevent the loss of specimens from rainwater filling the cups. The 10 sets of traps in this experiment were not evenly distributed, but they were all in suitable habitats.Specimen samplingThe sampling site for this study was Tei Tong Tsai, and the sampling period was from 1st May to 28th May (2019). FIT, and PTs were collected once every two days. Due to the small number of beetles collected by MT, mt was collected only once. All beetles were picked out and arranged separately after collection, added to anhydrous ethanol, preserved, and labeled. The beetles collected by the three passive acquisition methods were picked according to morphological species.Specimen identificationThe taxonomic status for the family level of all samples was determined based on the relevant literature18,19,20,21. Relevant experts completed further identification (Supplement 2).All the specimens collected in this study are currently in the zoological museum of the Institute of Zoology, Chinese Academy of Sciences (Beijing, China).Specimen photographyBeetles were poured from the bottle and arranged separately according to the general species. Firstly, we used tweezers or a brush to place the beetles on unbreakable and unwrinkled paper (as far as possible with the backside upwards to keep them tight and neat, reducing the space left, and considering the label in the photograph). Simultaneously, we captured multiple photos according to the size and species of insect for the large specimens in the tube, adjusted the light near them to brighten the background, placed graph paper next to the beetles as a reference scale, then adjusted our Olympus camera settings to the appropriate photographing parameters. Finally, we inserted the photographed beetles and matching labels back into the tube and added anhydrous ethanol for preservation (Fig. 3). The labels were set in the photos as 2019 DTZ-FIT/MT/PTX-5XX-5XX (-N), in which 2019 represents the collection time, DTZ represents Tei Tong Tsai, FIT/MT/PT signifies the collection method, X represents the number of sampling points, 5XX-5XX represents sampling time, and N represents the photo number. If a sample site had many insects on the same date and required more than one photo, n was used to represent the number of photos. See the Supplement 3 for the complete document.Fig. 3Examples of beetles collected from three passive acquisition methods: overall photos of beetles collected by (a) FIT, (b) PT, and (c) MT. On the bottom right corner shows scale in each photo.Full size imageAfter the morphological data of the samples were collected, their Latin name and collection information were recorded in a table. Each passive acquisition method corresponded to a table, and each table was divided into 13 sheets according to 13 sampling points. The collection time was listed horizontally on each sheet, and the beetles’ species names were listed vertically (were named in the morphological species order as 1, 2, 3, …, N). The number of beetles was recorded in the corresponding position and the Supplement 4 file.Finally, data show the beetles’ biodiversity collected from each sampling site. Firstly, we summarized the data from each sampling point after completing the data statistics. Afterward, we counted the number of beetle individuals collected under the different passive acquisition methods at different points (Fig. 4). In Fig. 4, red, blue, and green represent the number of beetle individuals collected by MT, PT, and FIT, respectively. Fig. 4 shows that MT collected fewer beetles than FIT and PT. Secondly, the data of 13 sampling points in each collecting method were summarized to obtain the total number of families and species collected by each method (Fig. 5). A graph created in Excel 2016 displays the collection method as the horizontal coordinate and the number as the vertical coordinate. In the graph, red represents the number of families, and blue represents the number of species. Fig. 5 shows that FIT collected more beetle species and individuals than PT and MT, and MT collected the least. Thirdly, all data from the 13 sampling points and the three collection methods were summarized. The number of species collected in all families was counted. Families with more than ten species were selected (a total of 11 families) for data presentation (Fig. 6). Finally, a graphic was drawn in Excel 2016. Fig. 6 shows that the number of species in Staphylinidae, Curculionidae, and Chrysomelidae accounted for a large number, and the diversity was relatively high.Fig. 4Data table of numbers of individual beetles collected by different methods at 13 sampling points. The red, blue, and green columns represent the number of beetles collected by MT, PT, and FIT, respectively.Full size imageFig. 5The number of beetles collected by different passive acquisition methods. Horizontal coordinates represent collection methods. The red column and blue column represent the number of beetles collected on the family level and species level, respectively.Full size imageFig. 6Families with more than ten species (a total of 11 families) were selected for presentation. The sample sizes of each groups were also shown.Full size image More

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    Ranking threats to biodiversity and why it doesn’t matter

    The difficulties inherent in ranking global threats are due to them being context-dependent, which result from conditions and the nature of the threats themselves differing among locations, habitats, and taxa (Fig. 1). Current high-risk hotspots from habitat loss and overexploitation are primarily located in the tropics, whereas Europe is documented as a threat hotspot for pollution6. On islands, biological invasions mainly threaten biodiversity in the Pacific and Atlantic Oceans, while islands in the Indian Ocean and near the coasts of Asia are mostly threatened by overexploitation and agriculture3. Climate change affects species more at higher latitudes and altitudes because species are constrained by the physical environment (geographic barriers and mountain tops) to follow their optimal isotherms.Fig. 1: Divergence of global threat rankings across different references and international agencies.IPBES, WWF, and IUCN established global rankings of the five threats responsible for the current biodiversity crisis (B: central, yellow panel). However, the relative importance of each threat depends on the taxon, system, species’ characteristics, time, and/or the metric considered, resulting in divergences. Global biodiversity threats are represented by colors and symbols, given in the top panel. This figure encapsulates results combined from different studies detailed in Supplementary Table 1 with their associated references.Full size imageThe relative importance of threats also depends on the taxon considered. At the global scale, vertebrates are primarily threatened by habitat loss, overexploitation, and then biological invasions. But even within the vertebrates rankings differ — birds and mammals are mainly affected by overexploitation, while amphibians have a higher probability of succumbing to habitat loss6. Because of species-specific traits and adaptations, some species are likely to respond differently to global threats even within a clade. Large-bodied vertebrates are more likely to be threatened by overexploitation, whereas small-bodied vertebrates are more prone to habitat loss or pollution (Fig. 1). Threat ranking also depends on the habitat under consideration. Marine mammals are more threatened by overexploitation and pollution than terrestrial mammals for which habitat loss is the primary threat (Fig. 1). On islands, habitat loss is secondary to the pressures of biological invasions in freshwater systems, but the former is more important for terrestrial vertebrates and plants3. Another source of uncertainty is that most studies examining threats are based on well-studied taxa such as terrestrial vertebrates, which only represent a small subset of the tree of life. For instance, only 0.2% of fungi, 1.7% of invertebrates, and 10% of described plants are assessed in the IUCN update of 20197, potentially underestimating the intensity of some threats and biasing conservation priorities for these groups. Similarly, there is a bias of research effort towards regions with high-income countries, while research from low or middle-income countries is generally underrepresented8. This may give the false impression of absence of threats in some regions of the world.Likewise, period-specific global threat ranks are subject to the vagaries of temporal dynamics (Fig. 1). However, distinguishing past, current, and future threats is essential for current or future conservation interventions. Historically, overexploitation caused most of the Pleistocene megafauna extinctions, likely exacerbated by climate change. As agricultural practices intensified, habitat loss played a major role in extinctions. As humans later colonized islands, biological invasions caused the extinction of hundreds of species worldwide3. In contrast, climate change is only predicted to become major in the near future9. In fact, the effects of recent threats might be masked by delayed species’ responses, especially in under-studied regions, resulting in a large extinction debt. For instance, the severity of biological invasions often causes native species to decline rapidly to local extinction, while other threats such as habitat loss might affect species more slowly. In both cases, the eventual extinctions are ultimately if similar magnitude. More

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    A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies from 1992 to 2020

    Cox, P. & Jones, C. Climate change – Illuminating the modern dance of climate and CO2. Science 321, 1642–1644 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gilmanov, T. G. et al. Gross primary production and light response parameters of four Southern Plains ecosystems estimated using long-term CO2-flux tower measurements. Glob. Biogeochem. Cycle 17, 1071 (2003).ADS 
    Article 
    CAS 

    Google Scholar 
    Running, S. W. Climate change – Ecosystem disturbance, carbon, and climate. Science 321, 652–653 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sun, Z. et al. Spatial pattern of GPP variations in terrestrial ecosystems and its drivers: Climatic factors, CO2 concentration and land-cover change, 1982–2015. Ecol. Inform. 46, 156–165 (2018).CAS 
    Article 

    Google Scholar 
    Running, S. W. et al. A global terrestrial monitoring network integrating tower fluxes, flask sampling, ecosystem modeling and EOS satellite data. Remote Sens. Environ. 70, 108–127 (1999).ADS 
    Article 

    Google Scholar 
    Madani, N. et al. The Impacts of Climate and Wildfire on Ecosystem Gross Primary Productivity in Alaska. J. Geophys. Res.-Biogeosci. 126, e2020JG006078 (2021).ADS 
    Article 

    Google Scholar 
    Morales, P. et al. Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes in major European forest biomes. Glob. Change Biol. 11, 2211–2233 (2005).ADS 
    Article 

    Google Scholar 
    Tramontana, G., Ichii, K., Camps-Valls, G., Tomelleri, E. & Papale, D. Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data. Remote Sens. Environ. 168, 360–373 (2015).ADS 
    Article 

    Google Scholar 
    Canadell, J. G. et al. Carbon metabolism of the terrestrial biosphere: A multitechnique approach for improved understanding. Ecosystems 3, 115–130 (2000).CAS 
    Article 

    Google Scholar 
    Fletcher, B. J. et al. Photosynthesis and productivity in heterogeneous arctic tundra: consequences for ecosystem function of mixing vegetation types at stand edges. J. Ecol. 100, 441–451 (2012).CAS 
    Article 

    Google Scholar 
    Liu, L., Guan, L. & Liu, X. Directly estimating diurnal changes in GPP for C3 and C4 crops using far-red sun-induced chlorophyll fluorescence. Agr. Forest Meteorol. 232, 1–9 (2017).ADS 
    Article 

    Google Scholar 
    Xu, X. et al. Long-term trend in vegetation gross primary production, phenology and their relationships inferred from the FLUXNET data. J. Environ. Manage. 246, 605–616 (2019).PubMed 
    Article 

    Google Scholar 
    Baldocchi, D. D. How eddy covariance flux measurements have contributed to our understanding of Global Change Biology. Glob. Change Biol. 26, 242–260 (2020).ADS 
    Article 

    Google Scholar 
    He, L., Chen, J. M., Liu, J., Belair, S. & Luo, X. Assessment of SMAP soil moisture for global simulation of gross primary production. J. Geophys. Res.-Biogeosci. 122, 1549–1563 (2017).Article 

    Google Scholar 
    Wang, S., Ibrom, A., Bauer-Gottwein, P. & Garcia, M. Incorporating diffuse radiation into a light use efficiency and evapotranspiration model: An 11-year study in a high latitude deciduous forest. Agr. Forest Meteorol. 248, 479–493 (2018).ADS 
    Article 

    Google Scholar 
    Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Yu, G., Fu, Y., Sun, X., Wen, X. & Zhang, L. Recent progress and future directions of ChinaFLUX. Sci. China Ser. D-Earth Sci. 49, 1–23 (2006).ADS 
    Article 

    Google Scholar 
    McCallum, I. et al. Improved light and temperature responses for light-use-efficiency-based GPP models. Biogeosciences 10, 6577–6590 (2013).ADS 
    Article 

    Google Scholar 
    Stocker, B. D. et al. Drought impacts on terrestrial primary production underestimated by satellite monitoring. Nature Geoscience 12, 264‐+ (2019).ADS 
    Article 
    CAS 

    Google Scholar 
    Cheng, S. J. et al. Variations in the influence of diffuse light on gross primary productivity in temperate ecosystems. Agr. Forest Meteorol. 201, 98–110 (2015).ADS 
    Article 

    Google Scholar 
    Zhang, M. et al. Effects of cloudiness change on net ecosystem exchange, light use efficiency, and water use efficiency in typical ecosystems of China. Agr. Forest Meteorol. 151, 803–816 (2011).ADS 
    Article 

    Google Scholar 
    Oliphant, A. J. et al. The role of sky conditions on gross primary production in a mixed deciduous forest. Agr. Forest Meteorol. 151, 781–791 (2011).ADS 
    Article 

    Google Scholar 
    Urban, O. et al. Ecophysiological controls over the net ecosystem exchange of mountain spruce stand. Comparison of the response in direct vs. diffuse solar radiation. Glob. Change Biol. 13, 157–168 (2007).ADS 
    Article 

    Google Scholar 
    Zhou, H. et al. Large contributions of diffuse radiation to global gross primary productivity during 1981–2015. Glob. Biogeochem. Cycle 35, e2021GB006957 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    Guanter, L. et al. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sens. Environ. 121, 236–251 (2012).ADS 
    Article 

    Google Scholar 
    Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl. Acad. Sci. USA 111, E1327–E1333 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Liu, L. & Cheng, Z. Detection of vegetation light-use efficiency based on solar-induced chlorophyll fluorescence separated from canopy radiance spectrum. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 3, 306–312 (2010).ADS 
    Article 

    Google Scholar 
    MacBean, N. et al. Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data (vol 8, 1973, 2018). Sci. Rep. 8, 10420 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Meroni, M. et al. Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications. Remote Sens. Environ. 113, 2037–2051 (2009).ADS 
    Article 

    Google Scholar 
    Zheng, T. & Chen, J. M. Photochemical reflectance ratio for tracking light use efficiency for sunlit leaves in two forest types. ISPRS-J. Photogramm. Remote Sens. 123, 47–61 (2017).ADS 
    Article 

    Google Scholar 
    Damm, A. et al. Remote sensing of sun-induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP). Glob. Change Biol. 16, 171–186 (2010).ADS 
    Article 

    Google Scholar 
    Lee, J. E. et al. Simulations of chlorophyll fluorescence incorporated into the Community Land Model version 4. Glob. Change Biol. 21, 3469–3477 (2015).ADS 
    Article 

    Google Scholar 
    Pinto, F. et al. Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to quantify spatio-temporal patterns of photosynthetic function in crop canopies. Plant Cell Environ. 39, 1500–1512 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Porcar-Castell, A. et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J. Exp. Bot. 65, 4065–4095 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Xie, X., Li, A., Jin, H., Yin, G. & Nan, X. Derivation of temporally continuous leaf maximum carboxylation rate (V-cmax) from the sunlit leaf gross photosynthesis productivity through combining BEPS model with light response curve at tower flux sites. Agr. Forest Meteorol. 259, 82–94 (2018).ADS 
    Article 

    Google Scholar 
    Chen, J. M., Liu, J., Leblanc, S. G., Lacaze, R. & Roujean, J. L. Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption. Remote Sens. Environ. 84, 516–525 (2003).ADS 
    Article 

    Google Scholar 
    Chen, J. M. et al. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Glob. Biogeochem. Cycle 26, GB1019 (2012).ADS 
    Article 
    CAS 

    Google Scholar 
    Running, S. W., Thornton, P. E., Nemani, R. & Glassy, J. M. in Methods in Ecosystem Science. Ch.3 (Springer, New York, NY. Press, 2000).Wu, C., Munger, J. W., Niu, Z. & Kuang, D. Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest. Remote Sens. Environ. 114, 2925–2939 (2010).ADS 
    Article 

    Google Scholar 
    Makela, A. et al. Developing an empirical model of stand GPP with the LUE approach: analysis of eddy covariance data at five contrasting conifer sites in Europe. Glob. Change Biol. 14, 92–108 (2008).ADS 
    Article 

    Google Scholar 
    McCallum, I. et al. Satellite-based terrestrial production efficiency modeling. Carbon Balanc. Manag. 4, 8–8 (2009).Article 

    Google Scholar 
    Wang, H. et al. Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling. Remote Sens. Environ 114, 2248–2258 (2010).ADS 
    Article 

    Google Scholar 
    Yu, R. An improved estimation of net primary productivity of grassland in the Qinghai-Tibet region using light use efficiency with vegetation photosynthesis model. Ecol. Model. 431, 109121 (2020).Article 

    Google Scholar 
    Yuan, W. et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agr. Forest Meteorol. 143, 189–207 (2007).ADS 
    Article 

    Google Scholar 
    Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).Article 

    Google Scholar 
    Zhang, Y. et al. Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data. Agr. Forest Meteorol. 223, 116–131 (2016).ADS 
    Article 

    Google Scholar 
    He, M. et al. Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity. Agr. Forest Meteorol. 173, 28–39 (2013).ADS 
    Article 

    Google Scholar 
    Zhou, Y. et al. Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites. J. Geophys. Res.-Biogeosci. 121, 1045–1072 (2016).Article 

    Google Scholar 
    Friedlingstein, P. et al. Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks. J. Clim. 27, 511–526 (2014).ADS 
    Article 

    Google Scholar 
    Raich, J. W. et al. Potential net primary productivity in South-America – application of a global-model. Ecol. Appl. 1, 399–429 (1991).CAS 
    PubMed 
    Article 

    Google Scholar 
    Li, J. et al. An algorithm differentiating sunlit and shaded leaves for improving canopy conductance and vapotranspiration estimates. J. Geophys. Res.-Biogeosci. 124, 807–824 (2019).ADS 
    Article 

    Google Scholar 
    Chen, J. M., Liu, J., Cihlar, J. & Goulden, M. L. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 124, 99–119 (1999).CAS 
    Article 

    Google Scholar 
    Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).PubMed 
    Article 

    Google Scholar 
    Korson, L., Drosthan, W. & Millero, F. J. Viscosity of water at various temperatures. J. Phys. Chem. 73, 34–39 (1969).CAS 
    Article 

    Google Scholar 
    Olofsson, P., Van Laake, P. E. & Eklundh, L. Estimation of absorbed PAR across Scandinavia from satellite measurements Part I: Incident PAR. Remote Sens. Environ. 110, 252–261 (2007).ADS 
    Article 

    Google Scholar 
    González, J. A. & Calbó, J. Modelled and measured ratio of PAR to global radiation under cloudless skies. Agr. Forest Meteorol. 110, 319–325 (2002).ADS 
    Article 

    Google Scholar 
    Zhang, X., Zhang, Y. & Zhoub, Y. Measuring and modelling photosynthetically active radiation in Tibet Plateau during April–October. Agr. Forest Meteorol. 102, 207–212 (2000).ADS 
    Article 

    Google Scholar 
    Yang, Y., Xiao, P., Feng, X. & Li, H. Accuracy assessment of seven global land cover datasets over China. ISPRS-J. Photogramm. Remote Sens. 125, 156–173 (2017).ADS 
    Article 

    Google Scholar 
    Liu, Y., Liu, R. & Chen, J. M. GLOBMAP global Leaf Area Index since 1981. Zenodo https://doi.org/10.5281/zenodo.4700264 (2019).Vermote, E. MOD09A1 MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MOD09A1.006 (2015).Deng, F., Chen, J. M., Plummer, S., Chen, M. & Pisek, J. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Trans. Geosci. Remote Sens. 44, 2219–2229 (2006).ADS 
    Article 

    Google Scholar 
    Vermote, E. NOAA CDR Program. NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5. LAI. NOAA National Centers for Environmental Information https://doi.org/10.7289/V5TT4P69 (2019).He, L., Chen, J. M., Pisek, J., Schaaf, C. & Strahler, A. Global clumping index map derived from the MODIS BRDF product. Remote Sens. Environ. 119, 118–130 (2012).ADS 
    Article 

    Google Scholar 
    Liu, R. G. & Liu, Y. Generation of new cloud masks from MODIS land surface reflectance products. Remote Sens. Environ. 133, 21–37 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    Chen, J. M., Deng, F. & Chen, M. Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter. IEEE Trans. Geosci. Remote Sens. 44, 2230–2238 (2006).ADS 
    Article 

    Google Scholar 
    Harris, I.C. CRU JRA: Collection of CRU JRA forcing datasets of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data. Centre for Environmental Data Analysis http://catalogue.ceda.ac.uk/uuid/863a47a6d8414b6982e1396c69a9efe8 (2019).Li, X., Liang, H. & Cheng, W. Evaluation and comparison of light use efficiency models for their sensitivity to the diffuse PAR fraction and aerosol loading in China. Int. J. Appl. Earth Obs. Geoinf. 95, 102269 (2021).
    Google Scholar 
    Duan, Q. Y., Sorooshian, S. & Gupta, V. Effective and efficient global optimization for conceptual rain full-runoff models. Water Resour. Res. 28, 1015–1031 (1992).ADS 
    Article 

    Google Scholar 
    Gu, L. H. et al. Advantages of diffuse radiation for terrestrial ecosystem productivity. J. Geophys. Res.-Atmos. 107, 4050 (2002).ADS 

    Google Scholar 
    Bi, W. & Zhou, Y. A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies (1992–2020). Dryad https://doi.org/10.5061/dryad.dfn2z352k (2022).Ogutu, B. O. & Dash, J. Assessing the capacity of three production efficiency models in simulating gross carbon uptake across multiple biomes in conterminous USA. Agr. Forest Meteorol. 174, 158–169 (2013).ADS 
    Article 

    Google Scholar 
    Cai, W. et al. Large differences in terrestrial vegetation production derived from satellite-based light use efficiency models. Remote Sens. 6, 8945–8965 (2014).ADS 
    Article 

    Google Scholar 
    Anav, A. et al. Spatiotemporal patterns of terrestrial gross primary production: a review. Rev. Geophys. 53, 785–818 (2015).ADS 
    Article 

    Google Scholar 
    Li, X. & Xiao, J. Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: A global, fine-resolution dataset of gross primary production derived from OCO-2. Remote Sens. 11, 2563 (2019).ADS 
    Article 

    Google Scholar 
    Alemohammad, S. H. et al. Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence. Biogeosciences 14, 4101–4124 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Joiner, J. et al. Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sens. 10, 1346 (2018).ADS 
    Article 

    Google Scholar 
    Wang, S., Zhang, Y., Ju, W., Qiu, B. & Zhang, Z. Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data. Sci. Total Environ. 755, 142569 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Zheng, Y. et al. Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017. Earth Syst. Sci. Data 12, 2725–2746 (2020).ADS 
    Article 

    Google Scholar 
    Running, S., Mu, Q. & Zhao, M. MOD17A2H MODIS/Terra Gross Primary Productivity 8-Day L4 Global 500m SIN Grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MOD17A2H.006 (2015).Ciais, P. et al. A three-dimensional synthesis study of delta O-18 in atmospheric CO2 .1. Surface fluxes. J. Geophys. Res.-Atmos. 102, 5857–5872 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    Zhang, Y., Joiner, J., Gentine, P. & Zhou, S. Reduced solar-induced chlorophyll fluorescence from GOME-2 during Amazon drought caused by dataset artifacts. Glob. Change Biol. 24, 2229–2230 (2018).ADS 
    Article 

    Google Scholar 
    Xie, X. et al. Assessment of five satellite-derived LAI datasets for GPP estimations through ecosystem models. Sci. Total Environ. 690, 1120–1130 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Fang, H., Wei, S., Jiang, C. & Scipal, K. Theoretical uncertainty analysis of global MODIS, CYCLOPES, and GLOBCARBON LAI products using a triple collocation method. Remote Sens. Environ. 124, 610–621 (2012).ADS 
    Article 

    Google Scholar 
    Camacho, F., Cemicharo, J., Lacaze, R., Baret, F. & Weiss, M. GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products. Remote Sens. Environ. 137, 310–329 (2013).ADS 
    Article 

    Google Scholar 
    Prince, S. D. & Goward, S. N. Global primary production: A remote sensing approach. J. Biogeogr. 22, 815–835 (1995).Article 

    Google Scholar 
    Verma, S. B. et al. Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems. Agr. Forest Meteorol. 131, 77–96 (2005).ADS 
    Article 

    Google Scholar 
    Yan, H. et al. Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants. Ecol. Model. 297, 42–59 (2015).CAS 
    Article 

    Google Scholar 
    Jiang, S. et al. Comparison of satellite-based models for estimating gross primary productivity in agroecosystems. Agr. Forest Meteorol. 297, 108253 (2021).ADS 
    Article 

    Google Scholar 
    Yang, X. et al. Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophys. Res. Lett. 42, 2977–2987 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    Zhou, H. et al. Responses of gross primary productivity to diffuse radiation at global FLUXNET sites. Atmos. Environ. 244, 117905 (2021).CAS 
    Article 

    Google Scholar 
    Han, J. et al. Effects of diffuse photosynthetically active radiation on gross primary productivity in a subtropical coniferous plantation vary in different timescales. Ecol. Indic. 115, 106403 (2020).Article 

    Google Scholar 
    Grant, I. F., Prata, A. J. & Cechet, R. P. The impact of the diurnal variation of albedo on the remote sensing of the daily mean albedo of grassland. J. Appl. Meteorol. 39, 231–244 (2000).ADS 
    Article 

    Google Scholar 
    Singarayer, J. S., Ridgwell, A. & Irvine, P. Assessing the benefits of crop albedo bio-geoengineering. Environ. Res. Lett. 4, 045110 (2009).ADS 
    Article 

    Google Scholar 
    Tang, S. et al. LAI inversion algorithm based on directional reflectance kernels. J. Environ. Manage. 85, 638–648 (2007).CAS 
    PubMed 
    Article 

    Google Scholar  More