Ecology

1.Feely, R. A., Sabine, C. L., Hernandez-Ayon, J. M., Ianson, D. & Hales, B. Evidence for upwelling of corrosive “acidified” water onto the continental shelf. Science 320, 1490–1492 (2008).ADS 
CAS 
PubMed 

Google Scholar 
2.Hofmann, G. E. et al. High-frequency dynamics of ocean ph: A multi-ecosystem comparison. PLoS ONE 6(12), e28983 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Kroeker, K. J. et al. Interacting environmental mosaics drive geographic variation in mussel performance and predation vulnerability. Ecol. Lett. 19, 771–779 (2016).PubMed 

Google Scholar 
4.Gutiérrez, D. et al. Coastal cooling and increased productivity in the main upwelling zone off Peru since the mid-twentieth century. Geophys. Res. Lett. 38, L07603. https://doi.org/10.1029/2010GL046324 (2011).ADS 
Article 

Google Scholar 
5.Aiken, C. M., Navarrete, S. A. & Pelegrí, J. L. Potential changes in larval dispersal and alongshore connectivity on the central Chilean coast due to an altered wind climate. J. Geophys. Res. 116, G04026. https://doi.org/10.1029/2011JG001731 (2011).ADS 
Article 

Google Scholar 
6.Lagos, N. A., Castilla, J. C. & Broitman, B. Spatial Environmental correlates of intertidal recruitment: A test using barnacles in northern Chile. Ecol. Monogr. 78, 245–261 (2008).
Google Scholar 
7.Vargas, C. A. et al. Species-specific responses to ocean acidification should account for local adaptation and adaptive plasticity. Nat. Ecol. Evol. 1, 84. https://doi.org/10.1038/s41559-017-0084 (2017).Article 
PubMed 

Google Scholar 
8.Broitman, B. R. et al. Phenotypic plasticity is not a cline: Thermal physiology of an intertidal barnacle over 20° of latitude. J. Anim. Ecol. 00, 1–12. https://doi.org/10.1111/1365-2656.13514 (2021).Article 

Google Scholar 
9.Ramajo, L. et al. Physiological responses of juvenile Chilean scallops (Argopecten purpuratus) to isolated and combined environmental drivers of coastal upwelling. ICES J. Mar. Sci. 76, 1836e1849 (2019).
Google Scholar 
10.Saavedra, L. M., Saldías, G., Broitman, B. & Vargas, C. Carbonate chemistry dynamics in shellfish farming areas along the Chilean coast: Natural ranges and biological implications. ICES J. Mar. Sci. 78, 323–339 (2021).
Google Scholar 
11.Lardies, M. A. et al. Physiological and histopathological impacts of increased carbon dioxide and temperature on the scallops Argopecten purpuratus cultured under upwelling influences in northern Chile. Aquaculture 479, 455–466 (2017).
Google Scholar 
12.Ramajo, L. et al. Upwelling intensity modulates the fitness and physiological performance of coastal species: Implications for the aquaculture of the scallop Argopecten purpuratus in the Humboldt Current System. Sci. Total Environ. 745, 140949 (2020).ADS 
CAS 
PubMed 

Google Scholar 
13.Bakun, A. Global climate change and intensification of coastal ocean upwelling. Science 247, 198–201 (1990).ADS 
CAS 
PubMed 

Google Scholar 
14.Wang, D. et al. Intensification and spatial homogenization of coastal upwelling under climate change. Nature 518, 390–394 (2015).ADS 
CAS 
PubMed 

Google Scholar 
15.Kim, T. W., Barry, J. P. & Micheli, F. The effects of intermittent exposure to low-pH and low-oxygen conditions on survival and growth of juvenile red abalone. Biogeosciences 10, 7255–7262 (2013).ADS 

Google Scholar 
16.Ramajo, L. et al. Plasticity and trade-offs in physiological traits of intertidal mussels subjected to freshwater-induced environmental variation. Mar. Ecol. Prog. Ser. 553, 93–109 (2016).ADS 

Google Scholar 
17.Leung, J. Y., Connell, S. D., Nagelkerken, I. & Russell, B. D. Impacts of near-future ocean acidification and warming on the shell mechanical and geochemical properties of gastropods from intertidal to subtidal zones. Environ. Sci. Technol. 51, 12097–12103 (2017).ADS 
CAS 
PubMed 

Google Scholar 
18.Findlay, H. et al. Calcification, a physiological process to be considered in the context of the whole organism. Biogeosciences Discuss. 6, 2267–2284 (2009).ADS 

Google Scholar 
19.Waldbusser, G. et al. Saturation-state sensitivity of marine bivalves larvae to ocean acidification. Nat. Clim. Change 5, 273–280 (2015).ADS 
CAS 

Google Scholar 
20.Tunnicliffe, V. et al. Survival of mussels in extremely acidic waters on a submarine volcano. Nat. Geosci. 2, 344–348 (2009).ADS 
CAS 

Google Scholar 
21.Ries, J. B., Cohen, A. L. & McCorkle, D. C. Marine calcifiers exhibit mixed responses to CO2-induced ocean acidification. Geology 37, 1131–1134 (2009).ADS 
CAS 

Google Scholar 
22.Leung, J. Y., Russell, B. D. & Connell, S. D. Mineralogical plasticity acts as a compensatory mechanism to the impacts of ocean acidification. Environ. Sci. Technol. 51, 2652–2659 (2017).ADS 
CAS 
PubMed 

Google Scholar 
23.Duarte, C. et al. The energetic physiology of juvenile mussels, Mytilus chilensis (Hupe): The prevalent role of salinity under current and predicted pCO2 scenarios. Environ. Pollut. 242, 156–163 (2018).CAS 
PubMed 

Google Scholar 
24.Rodolfo-Metalpa, R. et al. Coral and mollusc resistance to ocean acidification adversely affected by warming. Nat. Clim. Change. 1, 308–312 (2011).ADS 
CAS 

Google Scholar 
25.Waldbusser, G. et al. Slow shell building, a possible trait for resistance to the effects of acute ocean acidification. Limnol. Oceanogr. 61, 1969–1983 (2016).ADS 

Google Scholar 
26.Fitzer, S. C. et al. Ocean acidification and temperature increase impact mussel shell shape and thickness: Problematic for protection?. Ecol. Evol. 5, 4875–4884 (2015).PubMed 
PubMed Central 

Google Scholar 
27.Fitzer, S. C., Phoenix, V. R., Cusack, M. & Kamenos, N. A. Ocean acidification impacts mussel control on biomineralization. Sci. Rep. 28, 6218 (2014).
Google Scholar 
28.Fitzer, S. C., Cusack, M., Phoenix, V. R. & Kamenos, N. A. Ocean acidification reduces the crystallographic control in juvenile mussel shells. J. Struct. Biol. 188, 39–45 (2014).CAS 
PubMed 

Google Scholar 
29.Fitzer, S. C. et al. Biomineral shell formation under ocean acidification: A shift from order to chaos. Sci. Rep. 6, 21076 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Lagos, N. A. et al. Effects of temperature and ocean acidification on shell characteristics of Argopecten purpuratus: Implications for scallop aquaculture in an upwelling-influenced area. Aquac. Environ. Interact. 8, 357–370 (2016).
Google Scholar 
31.Ramajo, L. et al. Biomineralization changes with food supply confer juvenile scallops (Argopecten purpuratus) resistance to ocean acidification. Glob. Chang. Biol. 22, 2025–2203 (2016).ADS 
PubMed 

Google Scholar 
32.Osores, S. J. et al. Plasticity and inter-population variability in physiological and life-history traits of the mussel Mytilus chilensis: A reciprocal transplant experiment. J. Exp. Mar. Biol. Ecol. 490, 1–12 (2017).
Google Scholar 
33.Telesca, L. et al. Plasticity and environmental heterogeneity predict geographic resilience patterns of foundation species to future change. Glob. Chang. Biol. https://doi.org/10.1111/gcb.14758 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
34.Grenier, C. et al. The combined effects of salinity and pH on shell biomineralization of the edible mussel Mytilus chilensis. Environ. Pollut. 263, 114555 (2020).CAS 
PubMed 

Google Scholar 
35.Kroeker, K. J. et al. Impacts of ocean acidification on marine organisms: Quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896 (2013).ADS 

Google Scholar 
36.Mackenzie, C. L. et al. Ocean warming, more than acidification, reduces shell strength in a commercial shellfish species during food limitation. PLoS ONE 9(1), e86764 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
37.Rykaczewski, R. R. et al. Poleward displacement of coastal upwelling-favorable winds in the ocean’s eastern boundary currents through the 21st century. Geophys. Res. Lett. 42, 6424–6431 (2015).ADS 

Google Scholar 
38.Rodríguez-Navarro, A. B. Rapid quantification of avian eggshell microstructure and crystallographic-texture using two-dimensional X-ray diffraction. Br. Poult. Sci. 48, 133–144 (2007).PubMed 

Google Scholar 
39.Rodríguez-Navarro, A. B. XRD2DScan: New software for polycrystalline materials characterization using two-dimensional X-ray diffraction. J. Appl. Cryst. 39, 905–909 (2006).
Google Scholar 
40.Li, S. et al. Interactive effects of seawater acidification and elevated temperature on biomineralization and amino acid metabolism in the mussel Mytilus edulis. J. Exp. Biol. 218, 3623–3631 (2015).PubMed 

Google Scholar 
41.Li, S. et al. Interactive effects of seawater acidification and elevated temperature on the transcriptome and biomineralization in the pearl oyster Pinctada fucata. Environ. Sci. Technol. 50, 1157–1165 (2016).ADS 
CAS 
PubMed 

Google Scholar 
42.Gestoso, I., Arenas, F. & Olabarria, C. Ecological interactions modulate responses of two intertidal mussel species to changes in temperature and pH. J. Exp. Mar. Biol. 474, 116–125 (2016).
Google Scholar 
43.Babarro, J. M., Abad, M. J., Gestoso, I., Silva, E. & Olabarria, C. Susceptibility of two co-existing mytilid species to simulated predation under projected climate change conditions. Hydrobiologia 807, 247–261 (2018).
Google Scholar 
44.Barthelat, F., Rim, J. E. & Espinosa, H. D. A review on the structure and mechanical properties of mollusk shells: Perspectives on synthetic biomimetic materials. In Applied Scanning Probe Methods XIII (eds Bhushan, B. & Fuchs, H.) 17–44 (Springer, 2009).
Google Scholar 
45.Leung, J. Y. et al. Calcifiers can adjust shell building at the nanoscale to resist ocean acidification. Small 16, 2003186 (2020).CAS 

Google Scholar 
46.Chatzinikolaou, E., Grigoriou, P., Keklikoglou, K., Faulwetter, S. & Papageorgiou, N. The combined effects of reduced pH and elevated temperature on the shell density of two gastropod species measured using micro-CT imaging. ICES J. Mar. Sci. 74, 1135–1149 (2017).
Google Scholar 
47.Nienhuis, S., Palmer, R. & Harley, C. Elevated CO2 affects shell dissolution rate but not calcification rate in a marine snail. Proc. R. Soc. Lond. B Biol. Sci. 277, 2553–2558 (2010).CAS 

Google Scholar 
48.Bourdeau, P. E. Prioritized phenotypic responses to combined predators in a marine snail. Ecology 90, 1659–1669 (2009).PubMed 

Google Scholar 
49.Weiner, S. & Addadi, L. Crystallization pathways in biomineralization. Annu. Rev. Mater. Sci. 41, 21–40 (2011).ADS 
CAS 

Google Scholar 
50.Nudelman, F. Nacre biomineralisation: A review on the mechanisms of crystal nucleation (In Seminars in cell & developmental biology), 2–10 (Academic Press, 2015).51.Harper, E. M., Checa, A. G. & Rodríguez-Navarro, A. B. Organization and mode of secretion of the granular prismatic microstructure of Entodesma navicular (Bivalvia: Mollusca). Acta Zool. 90, 132e141 (2009).
Google Scholar 
52.Pennington, B. J. & Currey, J. D. A mathematical model for the mechanical properties of scallop shells. J. Zool. 202, 239–263 (1984).
Google Scholar 
53.Yevenes, M. A., Lagos, N. A., Farías, L. & Vargas, C. A. Greenhouse gases, nutrients and the carbonate system in the Reloncaví Fjord (Northern Chilean Patagonia): Implications on aquaculture of the mussel, Mytilus chilensis, during an episodic volcanic eruption. Sci. Total Environ. 669, 49–61 (2019).ADS 
CAS 
PubMed 

Google Scholar 
54.Dickinson, G. H. et al. Interactive effects of salinity and elevated CO2 levels on juvenile eastern oysters, Crassostrea virginica. J. Exp. Biol. 215, 29–43 (2012).CAS 
PubMed 

Google Scholar 
55.Gaylord, B. et al. Functional impacts of ocean acidification in an ecologically critical foundation species. J. Exp. Biol. 214, 2586–2594 (2011).CAS 
PubMed 

Google Scholar 
56.O’Toole-Howes, M. et al. Deconvolution of the elastic properties of bivalve shell nanocomposites from direct measurement and finite element analysis. J. Mater. Res. 34, 2869–2880 (2019).ADS 

Google Scholar 
57.Auzoux-Bordenave, S. et al. Ocean acidification impacts growth and shell mineralization in juvenile abalone (Haliotis tuberculata). Mar. Biol. 167, 11 (2020).CAS 

Google Scholar 
58.Torres, R. et al. Evaluation of a semiautomatic system for long-term seawater carbonate chemistry manipulation. Rev. Chil. Hist. Nat. 86, 443–451 (2013).
Google Scholar 
59.IPCC. Climate Change 2021. The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Eds. Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou). Cambridge University Press. In Press. (2021).60.DOE. Handbook of methods for the analysis of the various parameters of the carbon dioxide system in seawater; version 2 (eds. Dickson, A.G. & Goyet, C.), (ORNL/CDIAC, 74, 1994).61.Meinshausen, M. et al. The RPC greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change. 109, 213–241 (2011).ADS 
CAS 

Google Scholar 
62.Rahn, D. A., Rosenblüth, B. & Rutllant, J. A. Detecting subtle seasonal transitions of upwelling in North-Central Chile. J. Phys. Oceanogr. 45, 854–867 (2015).ADS 

Google Scholar 
63.Meng, Y., Guo, Z., Yao, H., Yeung, K. W. & Thiyagarajan, V. Calcium carbonate unit realignment under acidification: A potential compensatory mechanism in an edible estuarine oyster. Mar. Pollut. Bull. 139, 141–149 (2019).CAS 
PubMed 

Google Scholar 
64.Rasband, W. S. ImageJ U.S. National Institute of Health, Maryland, USA (1997–2020). More

Study populationWe studied wild meerkats at the Kuruman River Reserve (a ~63 km2 area comprising dry riverbeds, herbaceous flats and grassy dunes) in the Kalahari region of South Africa (26°58′S, 21°49′E)28,48. Our study period (Nov 2011–Apr 2015) included an extended drought, during which female reproductive success tracked rainfall22 (Supplementary Fig. 1). The annual mean population size was 270 animals, in 22 established clans of 4–39 animals15,22. Habituated to close observation ( More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

1.Chiang, F., Mazdiyasni, O. & AghaKouchak, A. Evidence of anthropogenic impacts on global drought frequency, duration, and intensity. Nat. Commun. 12, 2754 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Spinoni, J., Naumann, G., Carrao, H., Barbosa, P. & Vogt, J. World drought frequency, duration, and severity for 1951–2010. Int. J. Climatol. 34, 2792–2804 (2014).
Google Scholar 
3.Duane, A., Castellnou, M. & Brotons, L. Towards a comprehensive look at global drivers of novel extreme wildfire events. Clim. Change 165(3), 1–21 (2021).
Google Scholar 
4.Krawchuk, M. A., Moritz, M. A., Parisien, M. A., Van Dorn, J. & Hayhoe, K. Global Pyrogeography: The current and future distribution of wildfire. PLoS ONE 4(4), e5102 (2009).ADS 
PubMed 
PubMed Central 

Google Scholar 
5.Williams, A. P. et al. Observed impacts of anthropogenic climate change on wildfire in California. Earth’s Fut. 7, 892–910 (2019).ADS 

Google Scholar 
6.Garcia, L. C. et al. Record-breaking wildfires in the world’s largest continuous tropical wetland: Integrative Fire Management is urgently needed for both biodiversity and humans. J. Environ. Manag. 293, 112870 (2021).CAS 

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

Google Scholar 
8.Criado, M. G., Myers-Smith, I. H., Bjorkman, A. D., Lehmann, C. E. R. & Stevens, N. Woody plant encroachment intensifies under climate change across tundra and savanna biomes. Glob. Ecol. Biogeogr. 29(5), 925–943 (2020).
Google Scholar 
9.Mancini, L. D., Corona, P. & Salvati, L. Ranking the importance of Wildfires’ human drivers through a multi-model regression approach. Environ. Impact Assess. Rev. 72, 177–186 (2018).
Google Scholar 
10.Moreira, F. et al. Landscape – wildfire interactions in southern Europe: Implications for landscape management. J. Environ. Manag. 92(10), 2389–2402 (2011).
Google Scholar 
11.Clarke, H. et al. The proximal drivers of large fires: A pyrogeographic study. Front. Earth Sci. 8, 90 (2020).ADS 

Google Scholar 
12.Abram, N. J. et al. Connections of climate change and variability to large and extreme forest fires in southeast Australia. Commun. Earth Environ. 2, 1 (2021).ADS 

Google Scholar 
13.Daskin, J. H., Aires, F. & Staver, A. C. Determinants of tree cover in tropical floodplains. Proc. R. Soc. B. 286, 20191755 (2019).PubMed 
PubMed Central 

Google Scholar 
14.Kotze, D. C. The effects of fire on wetland structure and functioning. Afr. J. Aquat. Sci. 38(3), 237–247 (2013).
Google Scholar 
15.Tedim, F. et al. Defining Extreme Wildfire Events: difficulties, challenges, and impacts. Fire 1, 9 (2018).
Google Scholar 
16.Libonati, R. et al. Sistema ALARMES – Alerta de área queimada Pantanal, situação final de 2020 https://www.researchgate.net/publication/350103205_Nota_Tecnica_012021_LASA-UFRJ_Queimadas_Pantanal_2020?channel=doi&linkId=6051109d92851cd8ce483fb1&showFulltext=true (2021).17.Libonati, R., DaCamara, C. C., Peres, F. L., de Carvalho, L. A. S. & Garcia, L. C. Rescue Brazil’s burning Pantanal wetlands. Nature 588, 217–219 (2020).ADS 
CAS 
PubMed 

Google Scholar 
18.Marengo, J. A. et al. Extreme drought in the Brazilian Pantanal in 2019–2020: Characterization, causes and impacts. Front. Water 3, 639204 (2021).
Google Scholar 
19.Marengo, J. A., Alves, L. M. & Torres, R. R. Regional climate change scenarios in the Brazilian Pantanal watershed. Clim. Res. 68(2–3), 201–213 (2016).
Google Scholar 
20.Hardesty, J., Myers, R. & Fulks, W. Fire, ecosystems, and people: A preliminary assessment of fire as a global conservation issue. George Wright Forum 22, 78–87 (2005).
Google Scholar 
21.Bliege Bird, R., Bird, D. W., Codding, B. F., Parker, C. H. & Jones, J. H. The “fire stick farming” hypothesis: Australian Aboriginal foraging strategies, biodiversity, and anthropogenic fire mosaics. Proc. Natl. Acad. Sci. USA 105(39), 14796–14801 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Beerling, D. J. & Osborne, C. P. The origin of the savanna biome. Glob. Chang. Biol. 12, 2023–2031 (2006).ADS 

Google Scholar 
23.Simon, M. F. et al. Recent assembly of the Cerrado, a neotropical plant diversity hotspot, by in situ evolution of adaptations to fire. Proc. Natl. Acad. Sci. USA 106, 20359–20364 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Pott, A. & Pott, V. J. Features and conservation of the Brazilian Pantanal wetland. Wetl. Ecol. Manag. 12, 547–552 (2004).
Google Scholar 
25.Ferraz-Vicentini, K. R. & Salgado-Laboriau, M. L. Palynological analysis of a palm swamp in Central Brasil. J. South Am. Earth Sci. 9(3–4), 207–219 (1996).ADS 

Google Scholar 
26.Engstrom, R. T. First-order fire effects on animals: review and recommendations. Fire Ecol. 6(1), 115–130 (2010).
Google Scholar 
27.Whelan, R. J., Rodgerson, L., Dickman, C. R. & Sutherland, E. F. Critical life processes of plants and animals: Developing a process-based understanding of population changes in fireprone landscapes (Cambridge University Press, 2002).
Google Scholar 
28.van Eeden, L. M. et al. Impacts of the unprecedented 2019–2020 bushfires on Australian animals. https://www.wwf.org.au/ArticleDocuments/353/WWF_Impacts-of-the-unprecedented-2019-2020-bushfires-on-Australian-animals.pdf.aspx (2020).29.Pacheco, L. F., Quispe-Calle, L. C., Suárez-Guzmán, F. A., Ocampo, M. & Claure-Herrera, A. J. Muerte de mamíferos por los incendios de 2019 en la Chiquitania. Ecol. Boliv. 56(1), 4–16 (2021).
Google Scholar 
30.Berlinck, C. B. et al. The Pantanal is on fire and only a sustainable agenda can save the largest wetland in the world. Braz. J. Biol. 82, e244200 (2021).CAS 
PubMed 

Google Scholar 
31.Andersen, A. N., Woinarski, J. C. Z. & Parr, C. L. Savanna burning for biodiversity: Fire management for faunal conservation in Australian tropical savannas. Austral Ecol. 37, 658–667 (2012).
Google Scholar 
32.Komarek, R. Fire and the changing wildlife habitat. Proc. Tall Timbers Fire Ecol. Conf. 2, 35–43 (1963).
Google Scholar 
33.Layme, V. M. G., Lima, A. P. & Magnusson, W. E. Effects of fire, food availability and vegetation on the distribution of the rodent Bolomys lasiurus in an Amazonian savanna. J. Trop. Ecol. 20, 183–187 (2004).
Google Scholar 
34.Roberts, S. L., van Wagtendonk, J. W., Miles, A. K., Kelt, D. A. & Lutz, J. A. Modeling the effects of fire severity and spatial complexity on small mammals in Yosemite National Park, California. Fire Ecol. 4(2), 83–104 (2008).
Google Scholar 
35.Smith, J. K. Wildland Fire in Ecosystems: Effects of Fire on Fauna (Rocky Mountain Research Station, Colorado, 2000).36.Woinarski, J. C. Z. & Legge, S. The impacts of fire on birds in Australia’s tropical savannas. Emu 113(4), 319–352 (2013).
Google Scholar 
37.Pires, A. S., Fernandez, F. A., de Freitas, D. & Feliciano, B. R. Influence of edge and fire-induced changes on spatial distribution of small mammals in Brazilian Atlantic Forest fragments. Stud. Neotrop. Fauna Environ. 40(1), 7–14 (2005).
Google Scholar 
38.Silveira, L. F., Rodrigues, H. G., Jácomo, A. T. A. & Diniz Filho, J. A. F. Impact of wildfires on the megafauna of Emas National Park, Central Brazil. Oryx 33, 108–114 (1999).39.Tomas, W. M. et al. Checklist of mammals from Mato Grosso do Sul, Brazil. Iheringia, Sér. zool. 107(Suppl), e2017155 (2017).40.Tomas, W. M. et al. Mammals in the Pantanal wetland, Brazil (Pensoft Publishers, 2010).
Google Scholar 
41.Burnham, K. P., Anderson, D. R. & Laake, J. L. Estimation of density from line transect sampling of biological populations. Ecol. Monogr. 72, 1–202 (1980).
Google Scholar 
42.Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537 (2015).ADS 
CAS 
PubMed 

Google Scholar 
43.Thielen, D. Quo vadis Pantanal? Expected precipitation extremes and drought dynamics from changing sea surface temperature. PLoS ONE 15(1), e0227437 (2020).44.Ciemer, C. et al. An early-warning indicator for Amazon droughts exclusively based on tropical Atlantic Sea surface temperatures. Environ. Res. Lett. 15, 094087 (2020).45.Boers, N., Marwan, N., Barbosa, H. M. J. & Kurths, J. A deforestation-induced tipping point for the South American monsoon system. Sci. Rep. 7, 41489 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Bergier, I. et al. Amazon rainforest modulation of water security in the Pantanal wetland. Sci. Total Environ. 619–620, 1116–1125 (2018).ADS 
PubMed 

Google Scholar 
47.Hofmann, G. et al. The Brazilian Cerrado is becoming hotter and drier. Glob. Chang. Biol. 00, 1–14 (2021).
Google Scholar 
48.Tomas, W. M. et al. Sustainability Agenda for the Pantanal Wetland: perspectives on a collaborative interface for science, policy, and decision-making. Trop. Conserv. Sci. 12, 1–30 (2019).ADS 

Google Scholar 
49.Schulz, C. Physical, ecological and human dimensions of environmental change in Brazil’s Pantanal wetland: Synthesis and research agenda. Sci. Total Environ. 687, 1011–1027 (2019).ADS 
CAS 
PubMed 

Google Scholar 
50.Harris, M. B. et al. Safeguarding the Pantanal wetlands: Threats and conservation initiatives. Conserv. Biol. 19(3), 714–720 (2005).
Google Scholar 
51.Ely, P., Fantin-Cruz, I., Tritico, H. M., Girard, P. & Kaplan, D. Dam-induced hydrologic alterations in the rivers feeding the Pantanal. Front. Environ. Sci. 8, 256 (2020).
Google Scholar 
52.Roque, F. O. et al. Simulating land use changes, sediment yields, and pesticide use in the Upper Paraguay River Basin: Implications for conservation of the Pantanal wetland. Agric. Ecosyst. Environ. 314, 107405 (2021).53.Guerra, A. et al. Drivers and projections of vegetation loss in the Pantanal and surrounding ecosystems. Land Use Policy 91, 104388 (2020).54.Berlinck, C. N., Lima, L. H. A. & Carvalho Junior, E. A. R. Historical survey of research related to fire management and fauna conservation in the world and in Brazil. Biota Neotropica 21(3), e20201144 (2021).55.Estado de Mato Grosso do Sul. DECRETO Nº 15.654, de 15 de abril de 2021. Institui o Plano Estadual de Manejo Integrado do Fogo, e Dá Outras Providências. (Diário Oficial do Estado, Mato Grosso do Sul nº 10.477, 2021).56.Marino, E. et al. Forest fuel management for wildfire prevention in Spain: A quantitative SWOT analysis. Int. J. Wildland Fire 23, 373–384 (2014).
Google Scholar 
57.Finney, M. A. & Cohen, J. D. Expectation and Evaluation of Fuel Management Objectives (Rocky Mountain Research Station, Colorado, 2003).58.Amiro, B. D., Stocks, B. J., Alexander, M. E., Flannigan, M. D. & Wotton, B. M. Fire, climate change, carbon and fuel management in the Canadian boreal forest. Int. J. Wildland Fire 10(4), 405–413 (2001).
Google Scholar 
59.Rocca, M. E., Brown, P. M., MacDonald, L. H. & Carrico, C. M. Climate change impacts on fire regimes and key ecosystem services in Rocky Mountain forests. Forest Ecol. Manag. 327, 290–305 (2014).
Google Scholar 
60.Pott, V. J., Pott, A., Lima, L. C. P., Moreira, S. N. & Oliveira, A. K. M. Aquatic macrophyte diversity of the Pantanal wetland and upper basin. Braz. J. Biol. 71(1), 255–563 (2011).CAS 
PubMed 

Google Scholar 
61.Britski, H. A., Silimon, K. Z. S. & Lopes, B. S. Peixes do Pantanal: Manual de Identificação (EMPRAPA, Brasília, 2007).62.Sousa, T. P. et al. Cytogenetic and molecular data Support the occurrence of three Gymnotus species (Gymnotiformes: Gymnotidae) used as live bait in Corumbá, Brazil: Implications for conservation and management of professional fishing. Zebrafish 14(2), 177–186 (2017).PubMed 

Google Scholar 
63.Piva, A., Caramaschi, U. & Albuquerque, N. R. A new species of Elachistocleis (Anura: Microhylidae) from the Brazilian Pantanal. Phyllomedusa 16(2), 143–154 (2017).
Google Scholar 
64.Strüssmann, C., Ribeiro, R. A. K., Ferreira, V. L., & Beda, A. D. F. Herpetofauna do Pantanal Brasileiro [Herpetofauna of the Brazilian Pantanal]. (Sociedade Brasileira de Herpetologia, Belo Horizonte, 2007).65.Ferreira, V. L. et al. Répteis do Mato Grosso do Sul [Reptiles from Mato Grosso do Sul]. Brazil. Iheringia Sér. Zool. 107(Suppl), e2017153 (2017).66.Nunes, A. P. Quantas espécies de aves ocorrem no Pantanal? [How many bird species do occur in the Pantanal?]. Atualidades Ornitológicas 160, 45–54 (2011).
Google Scholar 
67.Tubelis, D. P. & Tomas, W. M. Bird species of the Pantanal wetland, Brazil.. Ararajuba 11(1), 5–37 (2003).
Google Scholar 
68.Thomas, L. et al. Distance software: design and analysis of distance sampling surveys for estimating population size. J. Appl. Ecol. 47, 5–14 (2010).PubMed 

Google Scholar  More

To the Editor — In an insightful Comment Bliege Bird and Codding1 highlight a number of important issues to consider in the analysis of cross-cultural anthropological data. However, a casual reader of the Comment could be forgiven for taking away the message that cross-cultural data in anthropology is inherently flawed, and so is of limited use. We want to emphasize that comparative analysis plays an essential role in all non-experimental sciences, including anthropology and archaeology. This is because when systems cannot be manipulated due to scales of time and space, or issues of logistics or ethics, the only way to evaluate alternative outcomes is by analysing the results of natural experiments. More

1.Nettle, D. Linguistic Diversity (Oxford Univ. Press, USA, 1999).2.Campbell, L. & Belew, A. Cataloguing the World’s Endangered Languages (Routledge, 2018).3.Bromham, L. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01604-y (2021).4.Amano, T. et al. Proc. R. Soc. B 281, 20141574 (2014).Article 

Google Scholar 
5.Austin, P. K. & Sallabank, J. The Cambridge Handbook of Endangered Languages (Cambridge Univ. Press, 2011).6.Kandler, A., Unger, R. & Steele, J. Phil. Trans. R. Soc. B 365, 3855–3864 (2010).Article 

Google Scholar 
7.Kik, A. et al. Proc. Natl Acad. Sci. USA 118, e2100096118 (2021).CAS 
Article 

Google Scholar 
8.Lewis, M. P., Simons, G. F. & Fennig, C. D. Ethnologue: Languages of the World 17th edn (SIL International, 2013).9.Fischer, S. D. in The Routledge Handbook of Historical Linguistics (ed. Bowern, C. & Evans, B.) Ch. 20, 443–465 (CRC Press, Routledge, 2015).10.Hou, L. & Kusters, A. in The Routledge Handbook of Linguistic Ethnography (ed. Tusting, K.) Ch. 25 (CRC Press, Routledge, 2019).11.Turner, M. K. & McDonald, B. M. J. Iwenhe Tyerrtye: What it Means to be an Aboriginal Person (IAD Press, 2010).12.Hercus, L. A. & Sutton, P. This is What Happened: Historical Narratives by Aborigines (Australian Institute of Aboriginal Studies, 1986).13.Meek, B. A. Annu. Rev. Anthropol. 48, 95–115 (2019).Article 

Google Scholar  More

1.Cornwell, W. K. et al. Plant traits and wood fates across the globe: Rotted, burned, or consumed?. Glob. Change Biol. 15, 2431–2449 (2009).ADS 
Article 

Google Scholar 
2.Ulyshen, M. D. Wood decomposition as influenced by invertebrates. Biol. Rev. 91, 70–85 (2016).Article 

Google Scholar 
3.Rayner, A. D. M. & Boddy, L. Fungal Decomposition of Wood: Its Biology and Ecology 587 (Wiley, 1988).
Google Scholar 
4.Hyde, J. C., Smith, A. M. S., Ottmar, R. D., Alvarado, E. C. & Morgan, P. The combustion of sound and rotten coarse woody debris: A review. Int. J. Wildland Fire 20, 163–174. https://doi.org/10.1071/WF09113 (2011).Article 

Google Scholar 
5.Griffiths, H. M., Ashton, L. A., Evans, T. A., Parr, C. L. & Eggleton, P. Termites can decompose more than half of deadwood in tropical rainforest. Curr. Biol. 29, R118–R119 (2019).Article 
CAS 

Google Scholar 
6.Wu, C. et al. Stronger effects of termites than microbes on wood decomposition in a subtropical forest. For. Ecol. Manage. 493, 119263. https://doi.org/10.1016/j.foreco.2021.119263 (2021).Article 

Google Scholar 
7.Jacobsen, R. M., Kauserud, H., Sverdrup-Thygeson, A., Bjorbækmo, M. M. & Birkemoe, T. Wood-inhabiting insects can function as targeted vectors for decomposer fungi. Fungal Ecol. 29, 76–84. https://doi.org/10.1016/j.funeco.2017.06.006 (2017).Article 

Google Scholar 
8.Leach, J. G., Orr, L. W. & Christensen, C. Further studies on the interrelationship of insects and fungi in the deterioration of felled Norway pine logs. J. Agric. Res. 55, 129–140 (1937).
Google Scholar 
9.Skelton, J. et al. Fungal symbionts of bark and ambrosia beetles can suppress decomposition of pine sapwood by competing with wood-decay fungi. Fungal Ecol. 45, 100926. https://doi.org/10.1016/j.funeco.2020.100926 (2020).Article 

Google Scholar 
10.Wikars, L.-O. Dependence on fire in wood-living insects: An experiment with burned and unburned spruce and birch logs. J. Insect Conserv. 6, 1–12. https://doi.org/10.1023/a:1015734630309 (2002).Article 

Google Scholar 
11.Holden, S. R., Gutierrez, A. & Treseder, K. K. Changes in soil fungal communities, extracellular enzyme activities, and litter decomposition across a fire chronosequence in Alaskan boreal forests. Ecosystems 16, 34–46. https://doi.org/10.1007/s10021-012-9594-3 (2013).Article 
CAS 

Google Scholar 
12.Ulyshen, M. D., Lucky, A. & Work, T. T. Effects of prescribed fire and social insects on saproxylic beetles in a subtropical forest. Sci. Rep. 10, 9630. https://doi.org/10.1038/s41598-020-66752-w (2020).ADS 
Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
13.Ulyshen, M. D., Horn, S., Barnes, B. & Gandhi, K. J. K. Impacts of prescribed fire on saproxylic beetles in loblolly pine logs. Insect Conserv. Divers. 3, 247–251 (2010).Article 

Google Scholar 
14.Billings, R. F. et al. Bark beetle outbreaks and fire: A devastating combination for Central America’s pine forests. Unasylva 55, 7 (2004).
Google Scholar 
15.Ulyshen, M. D., Wagner, T. L. & Mulrooney, J. E. Contrasting effects of insect exclusion on wood loss in a temperate forest. Ecosphere 5, article 47 (2014).16.Van Lear, D. H., Carroll, W. D., Kapeluck, P. R. & Johnson, R. History and restoration of the longleaf pine-grassland ecosystem: Implications for species at risk. For. Ecol. Manag. 211, 150–165 (2005).Article 

Google Scholar 
17.Noss, R. F. & Scott, J. M. Endangered Ecosystems of the United States: A Preliminary Assessment of Loss and Degradation. Vol. 28. (US Department of the Interior, National Biological Service, 1995).18.Folkerts, G. W., Deyrup, M. A. & Sisson, D. C. Arthropods associated with xeric longleaf pine habitats in the southeastern United States: A brief overview. Proc. Tall Timbers Fire Ecol. Conf. 18, 159–191 (1993).
Google Scholar 
19.Guyette, R. P., Stambaugh, M. C., Dey, D. C. & Muzika, R.-M. Predicting fire frequency with chemistry and climate. Ecosystems 15, 322–335. https://doi.org/10.1007/s10021-011-9512-0 (2012).Article 

Google Scholar 
20.Ulyshen, M. D., Horn, S., Pokswinski, S., McHugh, J. V. & Hiers, J. K. A comparison of coarse woody debris volume and variety between old-growth and secondary longleaf pine forests in the southeastern United States. For. Ecol. Manag. 429, 124–132. https://doi.org/10.1016/j.foreco.2018.07.017 (2018).Article 

Google Scholar 
21.Hanula, J. L., Ulyshen, M. D. & Wade, D. D. Impacts of prescribed fire frequency on coarse woody debris volume, decomposition and termite activity in the longleaf pine flatwoods of Florida. Forests 3, 317–331 (2012).Article 

Google Scholar 
22.Goebel, P. C. et al. Forest Ecosystems of a Lower Gulf Coastal Plain Landscape: Multifactor Classification and Analysis. 47–75. (2001).23.Ulyshen, M. D., Müller, J. & Seibold, S. Bark coverage and insects influence wood decomposition: Direct and indirect effects. Appl. Soil. Ecol. 105, 25–30. https://doi.org/10.1016/j.apsoil.2016.03.017 (2016).Article 

Google Scholar 
24.Kirkman, L. K. et al. Productivity and species richness in longleaf pine woodlands: Resource-disturbance influences across an edaphic gradient. Ecology 97, 2259–2271. https://doi.org/10.1002/ecy.1456 (2016).Article 
PubMed 
CAS 

Google Scholar 
25.Ulyshen, M. D. & Wagner, T. L. Quantifying arthropod contributions to wood decay. Methods Ecol. Evol. 4, 345–352 (2013).Article 

Google Scholar 
26.R Core Team. R: A Language and Environment for Statistical Computing (Version 3.6.1). http://www.R-project.org. (R Foundation for Statistical Computing, 2019).27.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
28.Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. Emmeans: Estimated marginal means, aka least-squares means. R Package Version 1, 3 (2018).
Google Scholar 
29.Graves, S., Piepho, H.-P. & Selzer, L. multcompView: Visualizations of paired comparisons. R Package Version 0.1-7. (2015).30.Ulyshen, M. D. Interacting effects of insects and flooding on wood decomposition. PLoS ONE 9, e101867 (2014).31.Stoklosa, A. M. et al. Effects of mesh bag enclosure and termites on fine woody debris decomposition in a subtropical forest. Basic Appl. Ecol. 17, 463–470. https://doi.org/10.1016/j.baae.2016.03.001 (2016).Article 

Google Scholar 
32.Kampichler, C. & Bruckner, A. The role of microarthropods in terrestrial decomposition: A meta-analysis of 40 years of litterbag studies. Biol. Rev. 84, 375–389 (2009).Article 

Google Scholar 
33.Mackensen, J., Bauhus, J. & Webber, E. Decomposition rates of coarse woody debris—A review with particular emphasis on Australian tree species. Aust. J. Bot. 51, 27–37 (2003).Article 

Google Scholar  More

Dissolved oxygen modelThe dissolved oxygen in this model had a number of interactions to consider. Oxygen consumption through the processes of both respiration and nitrification. On the other hand, the water receives oxygen through water agitation as it is pumped through the system and from the oxygen generator. Oxygen is added to the water by oxygen generator and flow aeration (Fig. 1).Figure 1Dissolved oxygen model.Full size imageThe required oxygen supplementation is a sum of the pervious components as follows:$$ DO_{FR} + DO_{B} + DO_{N} = DO_{sup } + DO_{PF} $$
(1)
where DOFR is the dissolved oxygen consumption through fish respiration, g O2 m−3 h−1. DOB is the dissolved oxygen consumption through the biofilter, g O2 m−3 h−1. DON is the dissolved oxygen consumption through nitrification, g O2 m−3 h−1. DOPF is the dissolved oxygen addition through pipe flow, g O2 m−3 h−1. DOsup is the required oxygen supplementation (oxygen generator), g O2 m−3 h−1.The rate of change in DO concentration in fish tank:$$ frac{dDO}{{dt}} = DO_{FR} + DO_{B} + DO_{N} – DO_{PF} $$
(2)
where (frac{dDO}{{dt}}) is the rate of change in DO concentration during the time interval, g O2 m−3 h−1. dt is the rate of change in the time interval, hAfter calculating oxygen concentration for each element at each time step, the net oxygen change is then added to or subtracted from the previous time step`s oxygen concentration. DO concentrations can be calculated at any time (t) as:$$ DO_{t} = DO_{t – 1} + left( {frac{dDO}{{dt}} cdot dt} right) $$
(3)
where DOt is the DO concentration (g m−3) at time t. DOt−1 is the DO concentration (g m−3) at time t−1.The rate of oxygen consumption through fish respiration can be calculated on water temperature and average fish weight. This calculation is shown in the following equation10:$$ FR = 2014.45 + 2.75W – 165.2T + 0.007W^{2} + 3.93T^{2} – 0.21WT $$
(4)
$$ DO_{FR} = frac{FR times SD}{{1000}} $$
(5)
where FR is rate of oxygen consumption through fish respiration, mg O2 kg−1 fish. h−1. W is average of individual fish mass, g. T is water temperature, °C. SD is the stocking density of fish, kg m−3.The correlation coefficient for the equation was 0.99. Data used in preparing the equation ranged from 20 to 200 g for fish weight and from 24 to 32 °C.The rate of oxygen consumption through nitrification is calculated in terms of Total Ammonia Nitrogen (TAN) that is converted from ammonia to nitrate. The rate found in the literature is 4.57 g O2 g−1 TAN6.The oxygen consumption in nitrification process can be calculated as11:$$ DO_{N} = 4.57 times K_{NR} times {{{text{Nr}}} mathord{left/ {vphantom {{{text{Nr}}} {text{V}}}} right. kern-nulldelimiterspace} {text{V}}} $$
(6)
$$ K_{NR} = 0.1left( {1.08} right)^{{left( {T – 20} right)}} $$
(7)
$$ Nr = frac{{0.03 times F_{r} times W times N_{F} }}{24 times 1000} $$
(8)
where KNR is the coefficient of nitrification. Nr is the nitrification rate, g TAN h−1. Fr is the feeding ratio, % of body fish day−1. NF is the number of fish. V is the water volume, m3.The feeding ratio can be calculated as the following equation:$$ F_{r} = 17.02 times e^{{left[ {{raise0.7exhbox{${left( {ln W + 1.14} right)^{2} }$} !mathord{left/ {vphantom {{left( {ln W + 1.14} right)^{2} } { – 19.52}}}right.kern-nulldelimiterspace} !lower0.7exhbox{${ – 19.52}$}}} right]}} $$
(9)
The bacteria in the biofilter are a second source of oxygen consumption. Lawson explains that the biofilter oxygen demand is approximated 2.3 times the BOD5 production rate of fish6. The oxygen consumption of the biofilter is calculated using following equation:$$ DO_{B} = frac{{(2.3)left( {BOD_{5} } right)left( {W_{n} } right)}}{{left( V right)left( {24} right)left( {1000} right)}} $$
(10)
where BOD5 is average unfiltered BOD5 excretion rate, 2160 mg O2 kg−1 fish day−1. Wn is biomass, kg fish.The water pumping cycle was a source of oxygen addition to the system. The amount of oxygen addition through the water pumping cycle was calculated on an hourly basis. The method of calculating aeration from a pipe is detailed by12:$$ DO_{PF} = frac{PC times f times E times OTR}{V} $$
(11)
where PC is pump cycle length, h. f is pumping frequency, h−1. E is efficiency, %. OTR is oxygen transfer rate, g O2 h−1.This model sums the DOFR, DOB, DON, and DOPF to determine the supplemental DO demand in kg h−1. This number can be used to estimate the oxygen consumption if pure oxygen transfers system is used.Fish growth modelFish growth is affected by environmental and physical factors, such as water temperature, dissolved oxygen, unionized ammonia, photoperiod, fish stocking density, food availability, and food quality.In order to calculate the fish growth rate (g day−1) for individual fish, the following model was used13 as it includes the main environmental factors influencing fish growth. These factors are temperature, dissolved oxygen and unionized ammonia.$$ FGR = left( {0.2919 , tau , kappa , delta , varphi , h , f , W^{m} } right) – K.W^{n} $$
(12)
Where FGR is the fish growth rate, g day−1. τ is the temperature factor (0  > τ  к  δ  φ  ƒ  More