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    A new hypothesis for the origin of Amazonian Dark Earths

    1.
    Sombroek, W. G. Amazon Soils. A Reconnaissance of the Soils of the Brazilian Amazon Region 292 (Wageningen, Netherlands, 1966).
    2.
    Palace, M. W. et al. Ancient Amazonian populations left lasting impacts on forest structure. Ecosphere 8, e02035 (2017).
    Article  Google Scholar 

    3.
    Lehmann, J. Amazonian Dark Earths: Origin Properties Management (Kluwer Academic Publishers, Netherlands, 2003).

    4.
    Glaser, B. & Birk, J. J. State of the scientific knowledge on properties and genesis of Anthropogenic Dark Earths in Central Amazonia (terra preta de Índio). Geochim. Cosmochim. Acta 82, 39–51 (2012).
    ADS  CAS  Article  Google Scholar 

    5.
    Macedo, R. S., Teixeira, W. G., Corrêa, M. M., Martins, G. C. & Vidal-Torrado, P. Pedogenetic processes in anthrosols with pretic horizon (Amazonian Dark Earth) in Central Amazon, Brazil. PLoS ONE 12, e0178038 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    6.
    Barbosa, J. Z. et al. Elemental signatures of an Amazonian Dark Earth as result of its formation process. Geoderma 361, 114085 (2020).
    ADS  CAS  Article  Google Scholar 

    7.
    Quesada, C. A. et al. Variations in soil chemical and physical properties explain basin-wide Amazon forest soil carbon concentrations. Soil 6, 53–88 (2020).
    CAS  Article  Google Scholar 

    8.
    Grau, O. et al. Nutrient-cycling mechanisms other than the direct absorption from soil may control forest structure and dynamics in poor Amazonian soils. Sci. Rep. 7, 45017 (2017).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    9.
    Silva, L. C. R. & Lambers, H. Soil-plant-atmosphere interactions: structure, function, and predictive scaling for climate change mitigation. Plant Soil 1–23 https://doi.org/10.1007/s11104-020-04427-1 (2020).

    10.
    Haridasan, M. Nutritional adaptations of native plants of the cerrado biome in acid soils. Braz. J. Plant Physiol. 20, 183–195 (2008).
    Article  Google Scholar 

    11.
    Morello, T. F. et al. Fertilizer adoption by smallholders in the Brazilian Amazon: Farm-level evidence. Ecol. Econ. 144, 278–291 (2018).
    Article  Google Scholar 

    12.
    Lombardo, U. et al. Early Holocene crop cultivation and landscape modification in Amazonia. Nature 581, 190–193 (2020).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    13.
    Capriles, J. M. et al. Persistent early to middle Holocene tropical foraging in southwestern Amazonia. Sci. Adv. 5, eaav5449 (2019).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    14.
    Bush, M. B. et al. A 6900-year history of landscape modification by humans in lowland Amazonia. Quat. Sci. Rev. 141, 52–64 (2016).
    ADS  Article  Google Scholar 

    15.
    Maezumi, S. Y. et al. The legacy of 4,500 years of polyculture agroforestry in the eastern Amazon. Nat. Plants 4, 540–547 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    16.
    Kern, D. C. et al. Terras pretas: approaches to formation processes in a new paradigm. Geoarchaeology 32, 694–706 (2017).
    Article  Google Scholar 

    17.
    McMichael, C. H. et al. Predicting pre-Columbian anthropogenic soils in Amazonia. Proc. R. Soc. B Biol. Sci. 281, 20132475 (2014).
    CAS  Article  Google Scholar 

    18.
    Schmidt, M. J. et al. Dark earths and the human built landscape in Amazonia: a widespread pattern of anthrosol formation. J. Archaeol. Sci. 42, 152–165 (2014).
    Article  Google Scholar 

    19.
    Birk, J. J., Teixeira, W. G., Neves, E. G. & Glaser, B. Faeces deposition on Amazonian Anthrosols as assessed from 5β-stanols. J. Archaeol. Sci. 38, 1209–1220 (2011).
    Article  Google Scholar 

    20.
    Glaser, B. Prehistorically modified soils of central Amazonia: a model for sustainable agriculture in the twenty-first century. Philos. Trans. R. Soc. Lond. B Biol. Sci. 362, 187–196 (2007).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    21.
    El-Naggar, A. et al. Biochar application to low fertility soils: a review of current status, and future prospects. Geoderma 337, 536–554 (2019).
    ADS  CAS  Article  Google Scholar 

    22.
    Cunha, T. J. F. et al. Soil organic matter and fertility of anthropogenic dark earths (Terra Preta de Índio) in the Brazilian Amazon basin. Rev. Bras. Cienc. do Solo 33, 85–93 (2009).
    CAS  Article  Google Scholar 

    23.
    Lutfalla, S. et al. Pyrogenic carbon lacks long-term persistence in temperate arable soils. Front. Earth Sci. 5, 96 (2017).
    ADS  Article  Google Scholar 

    24.
    Chadwick, K. D. & Asner, G. P. Landscape evolution and nutrient rejuvenation reflected in Amazon forest canopy chemistry. Ecol. Lett. 21, 978–988 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    25.
    Chadwick, O. A., Derry, L. A., Vitousek, P. M., Huebert, B. J. & Hedin, L. O. Changing sources of nutrients during four million years of ecosystem development. Nature 397, 491–497 (1999).
    ADS  CAS  Article  Google Scholar 

    26.
    Vitousek, P. M. Nutrient Cycling and Limitation: Hawai’i as a Model System. Ecology Vol. 30 (Princeton University Press, 2004).

    27.
    Silva, L. C. R. et al. Can savannas become forests? A coupled analysis of nutrient stocks and fire thresholds in central Brazil. Plant Soil 373, 829–842 (2013).
    CAS  Article  Google Scholar 

    28.
    Alho, C. F. B. V. et al. Spatial variation of carbon and nutrients stocks in Amazonian Dark Earth. Geoderma 337, 322–332 (2019).
    ADS  CAS  Article  Google Scholar 

    29.
    Bomfim, B., Silva, L. C. R., Doane, T. A. & Horwath, W. R. Interactive effects of land-use change and topography on asymbiotic nitrogen fixation in the Brazilian Atlantic Forest. Biogeochemistry 142, 137–153 (2019).
    CAS  Article  Google Scholar 

    30.
    Hendrixson, H. A., Sterner, R. W. & Kay, A. D. Elemental stoichiometry of freshwater fishes in relation to phylogeny, allometry and ecology. J. Fish. Biol. 70, 121–140 (2007).
    Article  Google Scholar 

    31.
    Nishimuta, M. et al. Moisture and mineral content of human feces–high fecal moisture is associated with increased sodium and decreased potassium content. J. Nutr. Sci. Vitaminol. 52, 121–126 (2006).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    32.
    Rossetti, D. F. et al. Unfolding long-term late Pleistocene-Holocene disturbances of forest communities in the southwestern Amazonian lowlands. Ecosphere 9, e02457 (2018).
    Article  Google Scholar 

    33.
    Carson, J. F. et al. Pre-Columbian land use in the ring-ditch region of the Bolivian Amazon. Holocene 25, 1285–1300 (2015).
    ADS  Article  Google Scholar 

    34.
    Shepard, G. H. et al. in Oxford Research Encyclopedia of Environmental Science (Hazlitt, R. ed.) (Oxford, 2020).

    35.
    Arroyo-Kalin, M. Slash-burn-and-churn: Landscape history and crop cultivation in pre-Columbian Amazonia. Quat. Int. 249, 4–18 (2012).
    Article  Google Scholar 

    36.
    Brugger, S. O. et al. Long-term man-environment interactions in the Bolivian Amazon: 8000 years of vegetation dynamics. Quat. Sci. Rev. 132, 114–128 (2016).
    ADS  Article  Google Scholar 

    37.
    Maezumi, S. Y. et al. New insights from pre-Columbian land use and fire management in Amazonian dark earth forests. Front. Ecol. Evol. 6, 111 (2018).
    Article  Google Scholar 

    38.
    Zani, H., Rossetti, D. F., Cohen, M. L. C., Pessenda, L. C. R. & Cremon, E. H. Influence of landscape evolution on the distribution of floristic patterns in northern Amazonia revealed by δ13C data. J. Quat. Sci. 27, 854–864 (2012).
    Article  Google Scholar 

    39.
    Lombardo, U. et al. Holocene land cover change in south-western Amazonia inferred from paleoflood archives. Glob. Planet. Change 174, 105–114 (2019).
    ADS  Article  Google Scholar 

    40.
    Ward, B. M. et al. Reconstruction of Holocene coupling between the South America Monsoon System and local moisture variability from speleothem δ18O and 87Sr/86Sr records. Quat. Sci. Rev. 210, 51–63 (2019).
    ADS  Article  Google Scholar 

    41.
    Wortham, B. E. et al. Assessing response of local moisture conditions in central Brazil to variability in regional monsoon intensity using speleothem 87Sr/ 86Sr values. Earth Planet. Sci. Lett. 463, 310–322 (2017).
    ADS  CAS  Article  Google Scholar 

    42.
    Silva, L. C. R. Importance of climate-driven forest–savanna biome shifts in anthropological and ecological research. Proc. Natl Acad. Sci. USA 111, E3831–E3832 (2014).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    43.
    Wright, J. et al. Sixteen hundred years of increasing tree cover prior to modern deforestation in Southern Amazon and Central Brazilian savannas. Glob. Chang. Biol. https://doi.org/10.1111/gcb.15382 (2020).

    44.
    Bomfim, B. et al. Fire affects asymbiotic nitrogen fixation in Southern Amazon Forests. J. Geophys. Res. Biogeosci. 125, (2020).

    45.
    Rossetti, D. F., Bertani, T. C., Zani, H., Cremon, E. H. & Hayakawa, E. H. Late Quaternary sedimentary dynamics in Western Amazonia: Implications for the origin of open vegetation/forest contrasts. Geomorphology 177–178, 74–92 (2012).
    ADS  Article  Google Scholar 

    46.
    Hoffmann, W. A. et al. Ecological thresholds at the savanna-forest boundary: How plant traits, resources and fire govern the distribution of tropical biomes. Ecol. Lett. 15, 759–768 (2012).
    PubMed  Article  PubMed Central  Google Scholar 

    47.
    Roddaz, M. et al. Evidence for the control of the geochemistry of Amazonian floodplain sediments by stratification of suspended sediments in the Amazon. Chem. Geol. 387, 101–110 (2014).
    ADS  CAS  Article  Google Scholar 

    48.
    Santos, R. V. et al. Source area and seasonal 87Sr/86Sr variations in rivers of the Amazon basin. Hydrol. Process. 29, 187–197 (2015).
    ADS  CAS  Article  Google Scholar 

    49.
    Passos, M. S. et al. Pleistocene-Holocene sedimentary deposits of the Solimões-Amazonas fluvial system, Western Amazonia. J. South Am. Earth Sci. 98, 102455 (2020).
    Article  Google Scholar 

    50.
    Bayon, G. et al. Rare earth elements and neodymium isotopes in world river sediments revisited. Geochim. Cosmochim. Acta 170, 17–38 (2015).
    ADS  CAS  Article  Google Scholar 

    51.
    Quintana-Cobo, I. et al. Dynamics of floodplain lakes in the Upper Amazon Basin during the late Holocene. Comptes Rendus Geosci. 350, 55–64 (2018).
    ADS  Article  Google Scholar 

    52.
    Hayakawa, E. H., Rossetti, D. F., Hayakawa, E. H. & Rossetti, D. F. Late quaternary dynamics in the Madeira river basin, southern Amazonia (Brazil), as revealed by paleomorphological analysis. Acad. Bras. Cienc. 87, 29–49 (2015).
    Article  Google Scholar 

    53.
    Gonçalves, E. S., Soares, E. A. A., Tatumi, S. H., Yee, M. & Mittani, J. C. R. Pleistocene-Holocene sedimentation of Solimões-Amazon fluvial system between the tributaries Negro and Madeira, Central Amazon. Braz. J. Geol. 46, 167–180 (2016).
    Article  Google Scholar 

    54.
    Viers, J. et al. Seasonal and provenance controls on Nd–Sr isotopic compositions of Amazon rivers suspended sediments and implications for Nd and Sr fluxes exported to the Atlantic Ocean. Earth Planet. Sci. Lett. 274, 511–523 (2008).
    ADS  CAS  Article  Google Scholar 

    55.
    Sant’Anna, L. G. et al. Age of depositional and weathering events in Central Amazonia. Quat. Sci. Rev. 170, 82–97 (2017).
    ADS  Article  Google Scholar 

    56.
    Guyot, J. L. et al. Clay mineral composition of river sediments in the Amazon Basin. CATENA 71, 340–356 (2007).
    Article  Google Scholar 

    57.
    Macedo, R. S. et al. Amazonian dark earths in the fertile floodplains of the Amazon River, Brazil: An example of non-intentional formation of anthropic soils in the Central Amazon region. Bol. do Mus. Para. Emilio Goeldi Cienc. Humanas 14, 207–227 (2019).
    Article  Google Scholar 

    58.
    Gross, D. R. Protein capture and cultural development in the Amazon basin. Am. Anthropol. 77, 526–549 (1975).
    Article  Google Scholar 

    59.
    Bomfim, B. et al. Litter and soil biogeochemical parameters as indicators of sustainable logging in Central Amazonia. Sci. Total Environ. 714, 136780 (2020).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    60.
    Lehmann, J. et al. Nutrient availability and leaching in an archaeological Anthrosol and a Ferralsol of the Central Amazon basin: fertilizer, manure and charcoal amendments. Plant Soil 249, 343–357 (2003).
    CAS  Article  Google Scholar 

    61.
    Gay‐des‐Combes, J. M. et al. Tropical soils degraded by slash‐and‐burn cultivation can be recultivated when amended with ashes and compost. Ecol. Evol. 7, 5378–5388 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    62.
    Isendahl, C. & Smith, M. E. Sustainable agrarian urbanism: The low-density cities of the Mayas and Aztecs. Cities 31, 132–143 (2013).
    Article  Google Scholar 

    63.
    Clement, C. R. et al. The domestication of Amazonia before European conquest. Proc. R. Soc. B Biol. Sci. 282, 20150813 (2015).
    Article  Google Scholar 

    64.
    de Souza, J. G. et al. Climate change and cultural resilience in late pre-Columbian Amazonia. Nat. Ecol. Evol. 3, 1007–1017 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    65.
    Mongeló, G. Early and Middle Holocene human occupations in Southwest Amazon. Bol. Mus. Para. Emílio Goeldi. Cienc. Hum. https://doi.org/10.1590/2178-2547-bgoeldi-2019-0079 (2020).

    66.
    Kistler, L. et al. Multiproxy evidence highlights a complex evolutionary legacy of maize in South America. Science 362, 1309–1313 (2018).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    67.
    Denevan, W. M. A Bluff model of riverine settlement in prehistoric Amazonia. Ann. Assoc. Am. Geogr. 86, 654–681 (1996).
    Article  Google Scholar 

    68.
    Silva, L. C. R., Corrêa, R. S., Doane, T. A., Pereira, E. I. P. & Horwath, W. R. Unprecedented carbon accumulation in mined soils: the synergistic effect of resource input and plant species invasion. Ecol. Appl. 23, 1345–1356 (2000).
    Article  Google Scholar 

    69.
    Kurth, V. J., MacKenzie, M. D. & DeLuca, T. H. Estimating charcoal content in forest mineral soils. Geoderma 137, 135–139 (2006).
    ADS  CAS  Article  Google Scholar 

    70.
    Silva, L. C. R. et al. Iron-mediated stabilization of soil carbon amplifies the benefits of ecological restoration in degraded lands. Ecol. Appl. 25, 1226–1234 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    71.
    Hare, V. J., Loftus, E., Jeffrey, A. & Ramsey, C. B. Atmospheric CO2 effect on stable carbon isotope composition of terrestrial fossil archives. Nat. Commun. 9, 252 (2018).

    72.
    Krull, E. S., Bestland, E. A. & Gates, W. P. Soil organic matter decomposition and turnover in a tropical Ultisol: evidence from δ13C, δ15N and geochemistry. Radiocarbon 44, 93–112 (2002).

    73.
    Gioia, S. M. C. L. & Pimentel, M. M. The Sm-Nd isotopic method in the Geochronology Laboratory of the University of Brasília. Acad. Bras. Cienc. 72, 218–245 (2000).
    Google Scholar  More

  • in

    Bacterial seed endophyte shapes disease resistance in rice

    1.
    Boyd, L. A., Ridout, C., O’Sullivan, D. M., Leach, J. E. & Leung, H. Plant–pathogen interactions: disease resistance in modern agriculture. Trends Genet. 29, 233–240 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 
    2.
    Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl Acad. Sci. USA 112, E911–E920 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    3.
    Bebber, D. P., Ramotowski, M. A. T. & Gurr, S. J. Crop pests and pathogens move polewards in a warming world. Nat. Clim. Change 3, 985–988 (2013).
    Article  Google Scholar 

    4.
    Ham, J. H., Melanson, R. A. & Rush, M. C. Burkholderia glumae: next major pathogen of rice? Mol. Plant Pathol. 12, 329–339 (2011).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    5.
    Naughton, L. M. et al. Functional and genomic insights into the pathogenesis of Burkholderia species to rice. Environ. Microbiol. 18, 780–790 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    6.
    Liu, X. et al. Biotoxin tropolone contamination associated with nationwide occurrence of pathogen Burkholderia plantarii in agricultural environments in China. Environ. Sci. Technol. 52, 5105–5114 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    7.
    Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348, 336–340 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Jung, B. et al. Cooperative interactions between seed-borne bacterial and air-borne fungal pathogens on rice. Nat. Commun. 9, 31 (2018).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    9.
    Miyagawa, H., Ozaki, K. & Kimura, T. Pathogenicity of Pseudomonas glumae and P. plantarii to the ears and leaves of graminaceous plants. Bull. Chugoku Natl Agric. Exp. Stn 3, 31–43 (1988).
    Google Scholar 

    10.
    Wang, M., Hashimoto, M. & Hashidoko, Y. Carot-4-en-9,10-diol, a conidiation-inducing sesquiterpene diol produced by Trichoderma virens PS1-7 upon exposure to chemical stress from highly active iron chelators. Appl. Environ. Microbiol. 79, 1906–1914 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    11.
    Wang, M., Hashimoto, M. & Hashidoko, Y. Repression of tropolone production and induction of a Burkholderia plantarii pseudo-biofilm by carot-4-en-9,10-diol, a cell-to-cell signaling disrupter produced by Trichoderma virens. PLoS ONE 8, e78024 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    12.
    Leach, J. E., Triplett, L. R., Argueso, C. T. & Trivedi, P. Communication in the phytobiome. Cell 169, 587–596 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    13.
    Wu, Y. et al. Policy distortions, farm size, and the overuse of agricultural chemicals in China. Proc. Natl Acad. Sci. USA 115, 7010–7015 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    14.
    Chaparro, J. M., Badri, D. V. & Vivanco, J. M. Rhizosphere microbiome assemblage is affected by plant development. ISME J. 8, 790–803 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    15.
    Derksen, H., Rampitsch, C. & Daayf, F. Signaling cross-talk in plant disease resistance. Plant Sci. 207, 79–87 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    16.
    Toju, H. et al. Core microbiomes for sustainable agroecosystems. Nat. Plants 4, 247–257 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    17.
    Wang, M. & Cernava, T. Overhauling the assessment of agrochemical-driven interferences with microbial communities for improved global ecosystem integrity. Environ. Sci. Ecotechnol. 4, 100061 (2020).
    Article  Google Scholar 

    18.
    Cheng, Y. T., Zhang, L. & He, S. Y. Plant–microbe interactions facing environmental challenge. Cell Host Microbe 26, 183–192 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    19.
    Berg, G., Grube, M., Schloter, M. & Smalla, K. Unraveling the plant microbiome: looking back and future perspectives. Front. Microbiol. 5, 148 (2014).
    PubMed  PubMed Central  Google Scholar 

    20.
    Duran, P. et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell 175, 973–983 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    21.
    Turner, T. R., James, E. K. & Poole, P. S. The plant microbiome. Genome Biol. 14, 209 (2013).

    22.
    Niu, B., Paulson, J. N., Zheng, X. & Kolter, R. Simplified and representative bacterial community of maize roots. Proc. Natl Acad. Sci. USA 114, E2450–E2459 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    23.
    Kwak, M.-J. et al. Rhizosphere microbiome structure alters to enable wilt resistance in tomato. Nat. Biotechnol. 36, 1100 (2018).
    CAS  Article  Google Scholar 

    24.
    Zhang, J. et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat. Biotechnol. 37, 676–684 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    25.
    Haney, C. H., Samuel, B. S., Bush, J. & Ausubel, F. M. Associations with rhizosphere bacteria can confer an adaptive advantage to plants. Nat. Plants 1, 15051 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    26.
    Liu, H., Brettell, L. E. & Singh, B. Linking the phyllosphere microbiome to plant health. Trends Plant Sci. 25, 841–844 (2020).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    27.
    Fan, X. et al. Microenvironmental interplay predominated by beneficial Aspergillus abates fungal pathogen incidence in paddy environment. Environ. Sci. Technol. 53, 13042–13052 (2019).

    28.
    Shade, A., Jacques, M. A. & Barret, M. Ecological patterns of seed microbiome diversity, transmission, and assembly. Curr. Opin. Microbiol. 37, 15–22 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    29.
    Nelson, E. B. The seed microbiome: origins, interactions, and impacts. Plant Soil 422, 7–34 (2017).
    Article  CAS  Google Scholar 

    30.
    Sultan, S. E. Phenotypic plasticity for plant development, function and life history. Trends Plant Sci. 5, 537–542 (2000).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    31.
    Wang, M. et al. Indole-3-acetic acid produced by Burkholderia heleia acts as a phenylacetic acid antagonist to disrupt tropolone biosynthesis in Burkholderia plantarii. Sci. Rep. 6, 22596 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    32.
    Miwa, S. et al. Identification of the three genes involved in controlling production of a phytotoxin tropolone in Burkholderia plantarii. J. Bacteriol. 198, 1604–1609 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    33.
    Solis, R., Bertani, I., Degrassi, G., Devescovi, G. & Venturi, V. Involvement of quorum sensing and RpoS in rice seedling blight caused by Burkholderia plantarii. FEMS Microbiol. Lett. 259, 106–112 (2006).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    34.
    Truyens, S., Weyens, N., Cuypers, A. & Vangronsveld, J. Bacterial seed endophytes: genera, vertical transmission and interaction with plants. Environ. Microbiol. Rep. 7, 40–50 (2015).
    Article  Google Scholar 

    35.
    Rybakova, D. et al. The structure of the Brassica napus seed microbiome is cultivar-dependent and affects the interactions of symbionts and pathogens. Microbiome 5, 104 (2017).

    36.
    Bergna, A. et al. Tomato seeds preferably transmit plant beneficial endophytes. Phytobiomes J. 2, 183–193 (2018).
    Article  Google Scholar 

    37.
    Wassermann, B., Cernava, T., Muller, H., Berg, C. & Berg, G. Seeds of native alpine plants host unique microbial communities embedded in cross-kingdom networks. Microbiome 7, 108 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    38.
    Berg, G. & Raaijmakers, J. M. Saving seed microbiomes. ISME J. 12, 1167–1170 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    39.
    Kim, H., Nishiyama, M., Kunito, T. & Oyaizu, H. High population of Sphingomonas species on plant surface. J. Appl. Microbiol. 85, 731–736 (1998).
    Article  Google Scholar 

    40.
    Carlström, C. I. et al. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat. Ecol. Evol. 3, 1445–1454 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    41.
    Rochefort, A. et al. Influence of environment and host plant genotype on the structure and diversity of the Brassica napus seed microbiota. Phytobiomes J. 3, 326–336 (2019).
    Article  Google Scholar 

    42.
    Berg, G. et al. Microbiome definition re-visited: old concepts and new challenges. Microbiome 8, 103 (2020).

    43.
    Kim, H., Lee, K. K., Jeon, J., Harris, W. A. & Lee, Y. H. Domestication of Oryza species eco-evolutionarily shapes bacterial and fungal communities in rice seed. Microbiome 8, 20 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    44.
    Cordovez, V., Dini-Andreote, F., Carrion, V. J. & Raaijmakers, J. M. Ecology and evolution of plant microbiomes. Annu. Rev. Microbiol. 73, 69–88 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    45.
    Banerjee, S., Schlaeppi, K. & van der Heijden, M. G. A. Keystone taxa as drivers of microbiome structure and functioning. Nat. Rev. Microbiol. 16, 567–576 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    46.
    Thomas, F., Corre, E. & Cebron, A. Stable isotope probing and metagenomics highlight the effect of plants on uncultured phenanthrene-degrading bacterial consortium in polluted soil. ISME J. 13, 1814–1830 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    47.
    Wang, H., Zhi, X. Y., Qiu, J., Shi, L. & Lu, Z. Characterization of a novel nicotine degradation gene cluster ndp in Sphingomonas melonis TY and its evolutionary analysis. Front. Microbiol. 8, 337 (2017).
    PubMed  PubMed Central  Google Scholar 

    48.
    Maeda, H. et al. A rice gene that confers broad-spectrum resistance to β-triketone herbicides. Science 365, 393 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    49.
    Bakker, P., Pieterse, C. M. J., de Jonge, R. & Berendsen, R. L. The soil-borne legacy. Cell 172, 1178–1180 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    50.
    Scholthof, K. B. The disease triangle: pathogens, the environment and society. Nat. Rev. Microbiol. 5, 152–156 (2007).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    51.
    Barillot, C. D. C., Sarde, C. O., Bert, V., Tarnaud, E. & Cochet, N. A standardized method for the sampling of rhizosphere and rhizoplan soil bacteria associated to a herbaceous root system. Ann. Microbiol. 63, 471–476 (2013).
    CAS  Article  Google Scholar 

    52.
    Maeda, Y. et al. Phylogenetic study and multiplex PCR-based detection of Burkholderia plantarii, Burkholderia glumae and Burkholderia gladioli using gyrB and rpoD sequences. Int. J. Syst. Evol. Microbiol. 56, 1031–1038 (2006).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    53.
    Takeuchi, T., Sawada, H., Suzuki, F. & Matsuda, I. Specific detection of Burkolderia plantarii and B. glumae by PCR using primers selected from the 16S–23S rDNA spacer regions. Ann. Phytopath. Soc. Japan 63, 455–462 (1997).
    CAS  Article  Google Scholar 

    54.
    Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86–90 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    55.
    Kusstatscher, P. et al. Microbiome-driven identification of microbial indicators for postharvest diseases of sugar beets. Microbiome 7, 112 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    56.
    Lundberg, D. S., Yourstone, S., Mieczkowski, P., Jones, C. D. & Dangl, J. L. Practical innovations for high-throughput amplicon sequencing. Nat. Methods 10, 999–1002 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    57.
    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    58.
    Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    59.
    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    60.
    Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahe, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    61.
    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    62.
    Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    63.
    Ayyagari, V. S. & Sreerama, K. Evaluation of haplotype diversity of Achatina fulica (Lissachatina) [Bowdich] from Indian sub-continent by means of 16S rDNA sequence and its phylogenetic relationships with other global populations. 3 Biotech 7, 252 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    64.
    Lu, J. et al. Induced jasmonate signaling leads to contrasting effects on root damage and herbivore performance. Plant Physiol. 167, 1100–1116 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    65.
    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    66.
    Anders, S., Pyl, P. T. & Huber, W. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    67.
    Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    68.
    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    69.
    Deng, X., Zhou, Y., Zheng, W., Bai, L. & Zhou, X. Dissipation dynamic and final residues of oxadiargyl in paddy fields using high-performance liquid chromatography-tandem mass spectrometry coupled with modified QuEChERS method. Int. J. Environ. Res. Public Health 15, 1680 (2018).
    PubMed Central  Article  CAS  Google Scholar 

    70.
    Lang, Z. et al. Isolation and characterization of a quinclorac-degrading Actinobacteria Streptomyces sp. strain AH-B and its implication on microecology in contaminated soil. Chemosphere 199, 210–217 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    71.
    Sun, M., Li, H. & Jaisi, D. P. Degradation of glyphosate and bioavailability of phosphorus derived from glyphosate in a soil–water system. Water Res. 163, 114840 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar  More

  • in

    Substrate thermal properties influence ventral brightness evolution in ectotherms

    1.
    Endler, J. A., Westcott, D. A., Madden, J. R. & Robson, T. Animal visual systems and the evolution of color patterns: sensory processing illuminates signal evolution. Evolution 59, 1795–1818 (2005).
    PubMed  Article  Google Scholar 
    2.
    Norris, K. S. & Lowe, C. H. An analysis of background color-matching in amphibians and reptiles. Ecology 45, 565–580 (1964).
    Article  Google Scholar 

    3.
    Allen, J. J., Mäthger, L. M., Barbosa, A. & Hanlon, R. T. Cuttlefish use visual cues to control three-dimensional skin papillae for camouflage. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 195, 547–555 (2009).
    PubMed  Article  Google Scholar 

    4.
    Cuthill, I. C. et al. The biology of color. Science https://doi.org/10.1126/science.aan0221 (2017).

    5.
    Seehausen, O., Van Alphen, J. J. M. & Lande, R. Color polymorphism and sex ratio distortion in a cichlid fish as an incipient stage in sympatric speciation by sexual selection. Ecol. Lett. 2, 367–378 (1999).
    Article  Google Scholar 

    6.
    Pérez-Rodríguez, L., Jovani, R. & Stevens, M. Shape matters: animal colour patterns as signals of individual quality. Proc. R. Soc. Lond. Ser. B Biol. Sci. 284, 20162446 (2017).
    Google Scholar 

    7.
    Tanaka, K. Thermal biology of a colour-dimorphic snake, Elaphe quadrivirgata, in a montane forest: Do melanistic snakes enjoy thermal advantages? Biol. J. Linn. Soc. 92, 309–322 (2007).
    Article  Google Scholar 

    8.
    Smith, K. R. et al. Colour change on different body regions provides thermal and signalling advantages in bearded dragon lizards. Proc. R. Soc. Lond. Ser. B Biol. Sci. 283, 20160626 (2016).
    Google Scholar 

    9.
    Christian, K. A. & Tracy, C. R. The effect of the thermal environment on the ability of hatchling galapagos land iguanas to avoid predation during dispersal. Oecologia 49, 218–223 (1981).
    PubMed  Article  Google Scholar 

    10.
    Clusella-Trullas, S., van Wyk, J. H. & Spotila, J. R. Thermal melanism in ectotherms. J. Therm. Biol. 32, 235–245 (2007).
    Article  Google Scholar 

    11.
    Moreno Azócar, D. L. et al. Effect of body mass and melanism on heat balance in Liolaemus lizards of the goetschi clade. J. Exp. Biol. 219, 1162–1171 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    12.
    Farouki, O. T. Thermal properties of soils. U.S. Army Corps of Engineers, Cold Regions Research and Engineering Laboratory. https://doi.org/10.4236/ojss.2011.13011 (1981).

    13.
    Porter, W. P. & Gates, D. M. Thermodynamic equilibria of animals with environment. Ecol. Monogr. 39, 227–244 (1969).
    Article  Google Scholar 

    14.
    Miller, G. E. in Introduction to Biomedical Engineering (3rd edn.) (eds. Enderle, J., & Bronzino, J.) pp. 937–993 (Academic press, 2012).

    15.
    Prota, G. Melanins and Melanogenesis (Academic Press, New York, 1992).

    16.
    Meredith, P. et al. Towards structure–property–function relationships for eumelanin. Soft Matter 2, 37–44 (2006).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    17.
    Geen, M. R. S. & Johnston, G. R. Coloration affects heating and cooling in three color morphs of the Australian Bluetongue Lizard, Tiliqua scincoides. J. Therm. Biol. 43, 54–60 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    18.
    Cordero, R. J. & Casadevall, A. Melanin. Curr. Biol. 30, R142–R143 (2020).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    19.
    Jastrzebska, M. M., Isotalo, H., Paloheimo, J. & Stubb, H. Electrical conductivity of synthetic DOPA-melanin polymer for different hydration states and temperatures. J. Biomater. Sci. Polym. Ed. 7, 577–586 (1996).
    Article  Google Scholar 

    20.
    Mostert, A. B. et al. Role of semiconductivity and ion transport in the electrical conduction of melanin. Proc. Natl Acad. Sci. USA 109, 8943–8947 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    21.
    Mostert, A. B. et al. Understanding melanin: a nano-based material for the future. In Nanomaterials: Science and Applications (eds. D. M. Kane, A. Micolich & P. Roger) 175–202 (New York: Jenny Stanford Publishing, 2016).

    22.
    Kellicker, J., DiMarzio, C. A. & Kowalski, G. J. Computational model of heterogeneous heating in melanin. Optical Interact. Tissue Cells XXVI 9321, 93210H (2015).
    Google Scholar 

    23.
    Jastrzebska, M. M., Isotalo, H., Paloheimo, J. & Stubb, H. Electrical conductivity of synthetic dopa-melanin polymer for different hydration states and temperatures. J. Biomater. Sci. 7, 577–586 (1995).
    CAS  Article  Google Scholar 

    24.
    Wünsche, J. et al. Protonic and electronic transport in hydrated thin films of the pigment eumelanin. Chem. Mater. 27, 436–442 (2015).
    Article  CAS  Google Scholar 

    25.
    Rienecker, S. B., Mostert, A. B., Schenk, G., Hanson, G. R. & Meredith, P. Heavy water as a probe of the free radical nature and electrical conductivity of melanin. J. Phys. Chem. B 119, 14994–15000 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    26.
    Migliaccio, L. et al. Evidence of unprecedented high electronic conductivity in mammalian pigment based eumelanin thin films after thermal annealing in vacuum. Front. Chem. 7, 162 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    27.
    Rosenblum, E. B., Hoekstra, H. E. & Nachman, M. Adaptive reptile color variation and the evolution of the Mc1r gene. Evolution 58, 1794–1808 (2004).
    CAS  PubMed  PubMed Central  Google Scholar 

    28.
    Jackson, J. F., Iii, W. I. & Campbell, H. W. The dorsal pigmentation pattern of snakes as an antipredator strategy: a multivariate approach. Am. Naturalist 110, 1029 (1976).
    Article  Google Scholar 

    29.
    Wüster, W. et al. Do aposematism and Batesian mimicry require bright colours? A test, using European viper markings. Proc. R. Soc. Lond. Ser. B Biol. Sci. 271, 2495–2499 (2004).
    Article  Google Scholar 

    30.
    Allen, W. L., Baddeley, R., Scott-Samuel, N. E. & Cuthill, I. C. The evolution and function of pattern diversity in snakes. Behav. Ecol. 24, 1237–1250 (2013).
    Article  Google Scholar 

    31.
    Clause, A. G. & Becker, R. N. Temperature shock as a mechanism for color pattern aberrancy in snakes. Herpetol. Notes 8, 331–334 (2015).
    Google Scholar 

    32.
    Ressel, S. & Schall, J. J. Parasites and showy males: malarial infection and color variation in fence lizards. Oecologia 78, 158–164 (1989).
    CAS  PubMed  Article  Google Scholar 

    33.
    Morrison, R. L., Rand, M. S. & Frost-Mason, S. K. Cellular basis of color differences in three morphs of the lizard Sceloporus undulatus erythrocheilus. Copeia 1995, 397–408 (1995).

    34.
    Stuart-Fox, D. M. & Ord, T. J. Sexual selection, natural selection and the evolution of dimorphic coloration and ornamentation in agamid lizards. Proc. R. Soc. Lond. Ser. B Biol. Sci. 271, 2249–2255 (2004).
    Article  Google Scholar 

    35.
    Langkilde, T. & Boronow, K. E. Hot boys are blue: temperature-dependent color change in male eastern fence lizards. J. Herpetol. 46, 461–465 (2012).

    36.
    Moreno Azócar, D. L. et al. Variation in body size and degree of melanism within a lizards clade: is it driven by latitudinal and climatic gradients? J. Zool. 295, 243–253 (2014).
    Article  Google Scholar 

    37.
    Pearson, O. P. The effect of substrate and of skin color on thermoregulation of a lizard. Comp. Biochem. Physiol. Part A Physiol. 58, 353–358 (1977).
    Article  Google Scholar 

    38.
    Hutchinson, V. H. & Larimer, J. L. Reflectivity of the integuments of some lizards from different habitats. Ecology 41, 199–209 (1960).
    Article  Google Scholar 

    39.
    Norris, K. S. in Lizard Ecology: A Symposium (ed. W. W. Milstead) 162–229 (University of Missouri Press, 1967).

    40.
    Barry, R. G., & Chorley, R. J. Atmosphere, Weather and Climate (Routledge, 2003).

    41.
    Olalla‐Tarraga, M. Á. & Rodríguez, M. Á. Energy and interspecific body size patterns of amphibian faunas in Europe and North America: anurans follow Bergmann’s rule, urodeles its converse. Glob. Ecol. Biogeogr. 16, 606–617 (2007).
    Article  Google Scholar 

    42.
    Uetz, P., Freed, P. & Hošek, J. (eds.). The Reptile Database. http://www.reptile-database.org (2020).

    43.
    Ohta, Y. I., Kanade, T. & Sakai, T. Color information for region segmentation. Comput. Graph. Image Process. 13, 222–241 (1980).
    Article  Google Scholar 

    44.
    Gueymard, C. A., Myers, D. & Emery, K. Proposed reference irradiance spectra for solar energy systems testing. Sol. Energy 73, 443–467 (2002).
    Article  Google Scholar 

    45.
    Shawkey, M. D. et al. Beyond colour: consistent variation in near infrared and solar reflectivity in sunbirds (Nectariniidae). Sci. Nat. (Naturwissenschaften) 104, 78 (2017).
    Article  CAS  Google Scholar 

    46.
    Shine, R. & Kearney, M. Field studies of reptile thermoregulation: how well do physical models predict operative temperatures? Funct. Ecol. 15, 282–288 (2001).
    Article  Google Scholar 

    47.
    Reguera, S., Zamora-Camacho, F. J. & Moreno-Rueda, G. The lizard Psammodromus algirus (Squamata: Lacertidae) is darker at high altitudes. Biol. J. Linn. Soc. 112, 132–141 (2014).
    Article  Google Scholar 

    48.
    Martínez-Freiría, F., Toyama, K. S., Freitas, I. & Kaliontzopoulou, A. Thermal melanism explains macroevolutionary variation of dorsal pigmentation in Eurasian vipers. Sci. Rep. 10, 1–10 (2020).
    Article  CAS  Google Scholar 

    49.
    Pizzigalli, C. et al. Eco-geographical determinants of ornamentation in vipers. Biol. J. Linnean Soc. 130, 1–14 (2020).

    50.
    Kurschner, W. M., Kvacek, Z. & Dilcher, D. L. The impact of Miocene atmospheric carbon dioxide fluctuations on climate and the evolution of terrestrial ecosystems. Proc. Natl Acad. Sci. USA 105, 449–453 (2008).
    PubMed  Article  PubMed Central  Google Scholar 

    51.
    Schraft, H. A., Goodman, C. & Clark, R. W. Do free-ranging rattlesnakes use thermal cues to evaluate prey? J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 204, 295–303 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    52.
    Alencar, L. R. V. et al. Diversification in vipers: phylogenetic relationships, time of divergence and shifts in speciation rates. Mol. Phylogenet. Evol. 105, 50–62 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    53.
    Zhang, Z. et al. Aridification of the Sahara desert caused by Tethys Sea shrinkage during the Late Miocene. Nature 513, 401–404 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    54.
    Pokorny, L. et al. Living on the edge: timing of Rand Flora disjunctions congruent with ongoing aridification in Africa. Front. Genet. 6, 154 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    55.
    Barlow, A. et al. Ancient habitat shifts and organismal diversification are decoupled in the African viper genus Bitis (Serpentes: Viperidae). J. Biogeogr. 46, 1234–1248 (2019).
    Article  Google Scholar 

    56.
    Senut, B., Pickford, M. & Ségalen, L. Neogene desertification of Africa. C. R. Geosci. 341, 591–602 (2009).
    CAS  Article  Google Scholar 

    57.
    Douglas, M. E., Douglas, M. R., Schuett, G. W. & Porras, L. W. Evolution of rattlesnakes (Viperidae; Crotalus) in the warm deserts of western North America shaped by Neogene vicariance and Quaternary climate change. Mol. Ecol. 15, 3353–3374 (2006).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    58.
    Zhisheng, A., Kutzbach, J. E., Prell, W. L. & Porter, S. C. Evolution of Asian monsoons and phased uplift of the Himalaya–Tibetan plateau since Late Miocene times. Nature 411, 62 (2001).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    59.
    Janis, C. M., Damuth, J. & Theodor, J. M. The species richness of Miocene browsers, and implications for habitat type and primary productivity in the North American grassland biome. Palaeogeogr. Palaeoclimatol. Palaeoecol. 207, 371–398 (2004).
    Article  Google Scholar 

    60.
    Walters, K. A., & Roberts, M. S. The structure and function of Skin. https://doi.org/10.1002/yea (2002).

    61.
    Wüster, W., Peppin, L., Pook, C. E. & Walker, D. E. A nesting of vipers: phylogeny and historical biogeography of the Viperidae (Squamata: Serpentes). Mol. Phylogenet. Evol. 49, 445–459 (2008).
    PubMed  Article  PubMed Central  Google Scholar 

    62.
    Shine, R. & Li-Xin, S. Arboreal ambush site selection by pit-vipers Gloydius shedaoensis. Anim. Behav. 63, 565–576 (2002).
    Article  Google Scholar 

    63.
    Ursenbacher, S. et al. Postglacial recolonization in a cold climate specialist in western europe: patterns of genetic diversity in the adder (Vipera berus) support the central-marginal hypothesis. Mol. Ecol. 24, 3639–3651 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    64.
    Blumthaler, M., Ambach, W. & Ellinger, R. Increase in solar UV radiation with altitude. J. Photochem. Photobiol. B Biol. 39, 130–134 (1997).
    CAS  Article  Google Scholar 

    65.
    Gaston, K. J. Global patterns in biodiversity. Nature 405, 220–227 (2000).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    66.
    Körner, C. et al. in Ecosystems and Human Well-being, Chapter 24, vol. 1. (Island Press, 2005).

    67.
    Tuniyev, B. et al. Gloydius halys. The IUCN Red List of Threatened Species 2009: e.T157282A5069394. https://www.iucnredlist.org/species/157282/5069394 (2009).

    68.
    Salter, C., Hobbs, J., Wheeler, J., Kostbade, J. T. Essentials of World Regional Geography 2nd edn. (Harcourt Brace, New York, 2005) pp. 464–465.

    69.
    Couplan, F., & Ligeon, J. C. Fleurs des Alpes: balade d’un botaniste, des plaines aux sommets (Nathan, 2005).

    70.
    Solórzano, A., Porras, L. W., Chaves, G., Bonilla, F. & Batista, A. Atropoides picadoi. The IUCN Red List of Threatened Species 2014: e.T203657A2769424. https://doi.org/10.2305/IUCN.UK.2014-1.RLTS.T203657A2769424.en. (2014).

    71.
    Canseco-Márquez, L. & Muñoz-Alonso, A. Bothriechis rowleyi. The IUCN Red List of Threatened Species 2007: e.T64304A12761506. https://doi.org/10.2305/IUCN.UK.2007.RLTS.T64304A12761506.en. (2020).

    72.
    Feldman, A., Sabath, N., Pyron, R. A., Mayrose, I. & Meiri, S. Body sizes and diversification rates of lizards, snakes, amphisbaenians and the tuatara. Glob. Ecol. Biogeogr. 25, 187–197 (2016).
    Article  Google Scholar 

    73.
    Hill, N. Description of cranial elements and ontogenetic change within Tropidolaemus wagleri (Serpentes: Crotalinae). PLoS ONE 14, e0206023 (2019).

    74.
    Savage, J. M. The Amphibians and Reptiles of Costa Rica: A Herpetofauna between two Continents, between two Seas. (University of Chicago Press, Chicago, 2002).

    75.
    Fathinia, B., Rastegar-Pouyani, N., Rastegar-Pouyani, E., Todehdehghan, F. & Amiri, F. Avian deception using an elaborate caudal lure in Pseudocerastes urarachnoides (Serpentes: Viperidae). Amphib. Reptilia 36, 223–231 (2015).
    Article  Google Scholar 

    76.
    Menegon, M., Davenport, T. R. & Howell, K. M. Description of a new and critically endangered species of Atheris (Serpentes: Viperidae) from the Southern Highlands of Tanzania, with an overview of the country’s tree viper fauna. Zootaxa 3120, 43–54 (2011).
    Article  Google Scholar 

    77.
    Goldenberg, J., D’Alba, L. Bisschop, K., Vanthournout, B., Shawkey, M. “Replication Data for: Substrate thermal properties influence ventral brightness evolution in ectotherms”; MacroBright v.0.1, https://doi.org/10.34894/FZ66NU, DataverseNL, V2. (2020).

    78.
    R-Core-Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/ (2019)

    79.
    Pennell, M. W. et al. geiger v2.0: an expanded suite of methods for fitting macroevolutionary models to phylogenetic trees. Bioinformatics 15, 2216–2218 (2014).
    Article  CAS  Google Scholar 

    80.
    Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evolution 3, 217–223 (2012).
    Article  Google Scholar 

    81.
    Stayton, C. T. convevol: Analysis of Convergent Evolution. R package version 1.3. https://CRAN.R-project.org/package=convevol (2018).

    82.
    Stayton, C. T. The definition, recognition, and interpretation of convergent evolution, and two new measures for quantifying and assessing the significance of convergence. Evolution 69, 2140–2153 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    83.
    Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R Package. J. Stat. Softw. 33, 1–22 (2010).
    Article  Google Scholar 

    84.
    Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.15. https://CRAN.R-project.org/package=MuMIn (2019).

    85.
    Marchetti, M. P., Light, T., Moyle, P. B. & Viers, J. H. Fish invasions in California watersheds: testing hypotheses using landscape patterns. Ecol. Appl. 14, 1507–1525 (2004).
    Article  Google Scholar 

    86.
    Buxton, A. S., Groombridge, J. J., Zakaria, N. B. & Griffiths, R. A. Seasonal variation in environmental DNA in relation to population size and environmental factors. Sci. Rep. 7, 1–9 (2017).
    Article  CAS  Google Scholar 

    87.
    Hadfield, J. MCMC Course Notes. https://cran.r-project.org/web/packages/MCMCglmm/vignettes/CourseNotes.pdf (2018).

    88.
    Gelman, A. & Rubin, B. D. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–511 (1992).
    Article  Google Scholar 

    89.
    Porter, W. P., Mitchell, J. W., Beckman, W. A. & DeWitt, C. B. Behavioral implications of mechanistic ecology – Thermal and behavioral modeling of desert ectotherms and their microenvironment. Oecologia 13, 1–54 (1973).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    90.
    Orlov, N. L., Sundukov, Y. N. & Kropachev, I. I. Distribution of pitvipers of “Gloydius blomhoffii” complex in Russia with the first records of Gloydius blomhoffii blomhoffii at Kunashir island (Kuril archipelago, Russian far east). Russ. J. Herpetol. 21, 169–178 (2014). More

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    Reciprocal interactions between tumour cell populations enhance growth and reduce radiation sensitivity in prostate cancer

    Biological experiments
    Cell culture
    We obtained unlabelled parental (sensitive) and radiation-resistant populations from two prostate cell lines, PC3 and DU145, from the Liu laboratory (University of Toronto, Canada). Radioresistant cell populations comprised pooled cells from the parental line that survived a clinically-relevant course of radiotherapy23,24. To produce stable, fluorescent cell lines, we transduced cells using lentiviral particles containing the vectors, pCDH1-CMV-GFP-EF1-Hygro or pCHD1-CMV-DsRed-EF1-Hygro (Systems Biosciences), collected the top 30% of brightest cells by flow cytometry, and used hygromycin B (50 mg/mL, Gibco) for selection (200 µg/mL for PC3 and 250 µg/mL for DU145). Cell lines were cultured in DMEM medium (low glucose, pyruvate, GlutaMAX, Gibco) supplemented with 25 mM HEPES (Gibco), 10% foetal bovine serum (Sigma or Pan-Biotech), and 1% penicillin/streptomycin. Authentication was performed using STR profiling (Promega PowerPlex 21 PCR kit, Eurofins), and mycoplasma checks were performed routinely using MycoAlert Mycoplasma Detection Kit (Lonza). All cells were maintained in an incubator (37 °C, 5% CO2). Unlabelled cells were cultured for up to 10 passages (~6 weeks) for transduction; labelled cells were cultured for up to 10 passages (~6 weeks). No additional courses of radiation were used to maintain resistance. We measured clonogenic survival at 2 Gy (SF2) to verify that the labelled cells maintained the resistance phenotype for up to 12 passages (~8 weeks) in culture.
    Monolayer growth experiments
    Single cells (1 × 103 cells/well, 200 μL medium) were seeded in triplicate in flat-bottom 96-well plates, allowed to attach overnight, irradiated (0, 2, or 6 Gy), and imaged daily using brightfield (Incucyte Live Cell Imaging System, Sartorius). The medium was changed every 2 days. Cell confluence was determined using Incucyte Base Software (Sartorius).
    Clonogenic assays
    Survival of cell lines after radiation was measured using a clonogenic assay36. Briefly, cells were seeded in triplicate in six-well plates and irradiated using a Cs-137 (dose rate of 0.89 Gy/min) or an X-ray irradiator (195 kV, 10 mA). Surviving colonies were stained after 10 days with crystal violet and counted. The surviving fraction was calculated as (number of colonies/number of seeded cells) × plating efficiency.
    Response to cisplatin
    To check whether the RR cells had altered DNA damage response, we measured the cell viability of each population in response to a range of concentrations of cisplatin using a modified cytotoxicity assay37. Briefly, single cells (2 × 103 cells in 100 μL/well) were seeded as monolayers in triplicate in a 96-well plate and allowed to attach for 36 h before treatment. Increasing concentrations of cisplatin (made in 100 μL/well) were added to each well resulting in a final volume of 200 μL/well; cisplatin (Sigma) was prepared fresh for each treatment by dissolving the powder in 0.9% sterile-filtered saline to a stock concentration of 3.3 mM. Treated cells were then cultured for 72 h in cisplatin before they were fixed with 10% formalin. Cell confluence was determined using Incucyte Base Software (Sartorius) and reformatted to a concentration–response curve by normalising cell confluence values to the untreated well.
    Spheroid generation and culture
    Homogeneous and mixed spheroids were generated in 96-well, ultra-low attachment plates (7007, Corning) by seeding different ratios of parental and radioresistant cell populations (2 × 103 total cells/well) using Matrigel (5% v/v, Corning) to promote spheroid formation38. For all spheroid experiments, after a formation phase of 3 days, spheroids were fed every 2 days by replacing 50% of the medium in each well with fresh medium (200 μL total/well). Culture medium and incubation conditions were as described under ‘Cell culture’. Spheroid volumes were calculated using SpheroidSizer39.
    Unirradiated spheroid growth experiments
    Spheroids were generated as described in ‘Spheroid generation and culture’ and monitored for growth by brightfield imaging (Leica DM IRBE, Hamamatsu).
    Flow cytometry
    The proportions, survival, and cell cycle of each population from spheroids were measured by flow cytometry. Mixed unirradiated or irradiated spheroids (seeded 1:1 parental:RR, 6–8 pooled/group) were incubated with EdU (10 µM final concentration) 12 h prior to dissociation, dissociated (100 μL Accumax, Millipore) for 20 min at 37 °C, washed with phosphate-buffered saline (PBS), centrifuged (300 × g, 5 min), and incubated with efluor-780 (1 μL/mL PBS; ThermoFisher Scientific) for 30 min on ice in the dark to distinguish live/dead cells. After washing in PBS, samples were fixed for 10 min in IC Fixation Buffer (ThermoFisher Scientific), and permeabilized and stained with Click-iT Plus EdU Alexa Fluor 647 (ThermoFisher Scientific) according to manufacturer’s instructions. Following a wash in 1× saponin, cells were incubated 30 min with FxCycle Violet Stain (1:1000, 300 µL of 1× saponin; ThermoFisher Scientific) before being run on the BD LSR Fortessa X-20 Cytometer or the Attune NxT Flow Cytometer using the 405, 488, 561, and 633 lasers. Data were analysed using FlowJo (Treestar, Inc.) as described in Supplementary Figs. 2 and 4.
    Spheroid growth experiments after radiation
    To determine bulk radiation response of spheroids, PC3 cells were seeded as spheroids (n = 15 per dose per group) with 4 groups as described in ‘Spheroid generation and culture’: parental, 9:1 parental:RR, 1:1 parental:RR, and RR. After formation, spheroids were irradiated (0, 2.5, 5, 7.5, 10, 15, and 20 Gy) on day 4 and imaged for up to 48 days to monitor regrowth using brightfield (Celigo Imaging Cytometer, Nexelcom). After log-transforming the volume data, we calculated the radiation-induced growth delay (days) relative to untreated spheroids as the time for each irradiated spheroid to reach a volume endpoint (2.5 times the starting volume right before irradiation); we selected the lowest endpoint that was still within the exponential growth phase of all spheroids in the experiment. The average time for untreated spheroids to reach endpoint was estimated in R by local regression using the loess function with “direct” surface estimation to allow extrapolation for the parental spheroids (R project, v. 3.6.2).
    Regrowth experiments were repeated by irradiating day 3 spheroids from three groups (parental, mixed and RR) of PC3 cells (6 Gy, n = 17–18/ group) and of DU145 cells (6 Gy, n = 12/group; 10 Gy, n = 20/group). Spheroids were imaged using brightfield (Leica DM IRBE, Hamamatsu) for up to 27 days (PC3) and up to 23 days (DU145). The radiation-induced growth delay (delays) was calculated as above, but with different endpoints (3.5 times starting volume for PC3 and 4 times starting volume for DU145) to ensure the endpoint was within the exponential growth phase. Data from Fig. 2 were used to estimate average time of untreated spheroids; the ‘span’ parameter of the loess function was reduced from the default of 0.75–0.5 for the unirradiated DU145 spheroids to better estimate the average time of reaching the endpoint.
    To measure changes in the radiation response of PC3 cell populations within spheroids, untreated homogeneous and mixed spheroids were grown until day 5 or 11, dissociated using Accumax, seeded as single cells for clonogenic experiments, and allowed to attach for 6 h prior to radiation. Fluorescent colonies were counted using the Celigo Cytometer.
    Immunofluorescence
    For immunofluorescence and H&E experiments, spheroids were treated and fixed prior to staining40. To investigate the spatial distribution of fluorescent populations, sections were hydrated in PBS, stained for 10 min with Hoechst (1 μg/mL in PBS, Sigma) to visualise nuclei, and mounted using ProLong Diamond Antifade Mountant (ThermoFisher). For hypoxia, spheroids were pre-treated with 300 μM of the hypoxia drug EF5 (gift from Dr. Cameron Koch, University of Pennsylvania) prior to fixation. They were then permeabilized (PBS containing 0.3% Tween-20, 10 min), blocked (5% goat serum in PBS containing 0.1% Tween-20, 30 min), stained using anti-EF5 antibody (75 μg/mL; from Dr. Cameron Koch) overnight at 4 °C, washed (ice-cold PBS containing 0.3% Tween-20, 2 × 45 min)40, stained for nuclei as above, and mounted. For Ki67, spheroid sections were permeabilized (PBS containing 0.3% Tween-20, 10 min), blocked (5% goat serum in PBS containing 0.1% Tween-20, 30 min), and incubated overnight at 4 °C with primary antibody (clone SP6, 1:100, Vector Laboratories). After washing in PBS, sections were incubated for 1 h with goat anti-rabbit Alexa Fluor 647 (4 μg/mL, ThermoFisher), washed, and stained with Hoechst 33342 (5 μg/mL, Sigma) for 10 min. Slides were mounted and imaged using epifluorescence microscopy (20× objective; 0.30 NA; 0.64 μm resolution; excitation lasers: 395, 470, 555, and 640; Nikon Ti-E). Sections were stained using H&E and imaged using a Bright Field Slide Scanner (Aperio CS2, Leica) to visualise necrosis. To quantify the ratio of parental to radioresistant populations in spheroid cross-sections (n = 16 spheroids from 4 batches), we measured the number of pixels from each population (i.e., signal) by applying a threshold value 5 times higher than the median value of the background (i.e., noise) (Octave 4.4.1).
    Oxygen consumption measurements
    OCR was measured from each population using the Seahorse assay. Cells (1.2 × 104/well) were seeded in triplicate using the normal culture medium in a Seahorse XF 96-well microplate (Agilent) and allowed to attach overnight. Prior to the assay, cells were washed with and incubated in assay medium (DMEM basal medium containing 5 mM glucose, 4 mM glutamine, 5 mM pyruvate, pH 7.4; 200 μL/well) for 2 h at 37 °C without CO2 to degas the medium. Calibrant buffer (200 μL/well) was added to wells of the probe plate and also left at 37 °C without CO2 to degas. After OCR was measured on the Seahorse XF Analyser (Agilent Biosciences), cells were fixed using 4% paraformaldehyde, stained using Hoechst 33342, and counted (Celigo Cytometer, Nexelcom).
    Transwell experiments
    Co-culture experiments were performed to measure whether transferred factors between cell populations enhanced survival under hypoxia. Cells were seeded in triplicate (3.0 × 104/bottom well and 1.0 × 104/insert) in 12-well plates and in Transwell inserts, and allowed to attach overnight. Once the medium was changed, the plates were placed into normoxia or hypoxia (0.1% O2) for 24 and 120 h. Cells were fixed using 4% paraformaldehyde, stained with Hoechst (5 μg/mL), and counted (Celigo Cytometer, Nexelcom).
    Statistics and reproducibility
    Data were evaluated for equal variance using homoscedasticity plots (absolute value of residual vs predicted value) and for normality using Q–Q plots (Prism 8.0, GraphPad). Unless otherwise indicated, statistical significance was evaluated using one-way ANOVA, two-way ANOVA, or a mixed-effects model followed by multiple testing correction (α = 0.05). For clonogenic assays, the radiation protection factor was calculated as the area under the dose–response curve (AUC) for the RR cell populations divided by that of the parentals; AUC values were analysed for significance using a Student’s t test (unpaired, one-tailed, α = 0.05). For cisplatin cytotoxicity assays, IC50 values were calculated using a normalised response, variable slope, dose–response model (Prism, 8.0, GraphPad) and evaluated for statistical significance using extra sum-of-squares F-test. For post-radiation growth experiments, survival curves were analysed using the Mantel–Cox (log-rank) test and adjusted for multiple testing using Holm’s correction; spheroids that did not reach the endpoint during the timeframe of the experiment were marked as ‘censored’ on the final day of the experiment (please see Supplementary Methods section 3 for further details). Due to heteroscedasticity, cell counts from flow cytometry experiments involving cell cycle and death, and from co-culture Transwell assays were analysed using overdispersed Poisson or binomial regression models (please see Supplementary Methods section 3 for further details). For quantification of population proportions in microscopy images, pixel numbers were analysed using a two-tailed, Wilcoxon matched-pairs signed-rank test. Adjusted P values (Padj) are reported in the main text for experiments where multiple comparisons were performed.
    Data points represent biological replicates; experiments were performed using at least two separate batches of cells. We note the following data exclusions: missing data from some time points due to technical failures in imaging (Fig. 1), one excluded mixed DU145 spheroid because its growth did not resemble that of the other 35 spheroids (Fig. 2a), and one excluded plate of PC3 spheroids from survival analysis (Fig. 4) because of irregular growth that did not match the other nine plates. Sample sizes were approximated using effect sizes from pilot studies to ensure power (approximate β = 0.8); randomisation and blinding were not possible.
    Mathematical experiments
    Non-spatial mathematical models
    We used the logistic growth model to describe the growth of homogeneous tumour spheroids26. Thus, the rate of change of spheroid volume V at time t is given by

    $$frac{{dV}}{{dt}} = rV left(1 – frac{V}{K}right),$$
    (1)

    where r  > 0 represents the growth rate, K  > 0 is the carrying capacity (the limiting volume of the spheroid) and V(t = 0) = V0 denotes the spheroid volume at t = 0. The analytical solution to the logistic model is given by

    $$Vleft( t right) = frac{{V_0Ke^{rt}}}{{K + V_0(e^{rt} – 1)}}.$$
    (2)

    The Lotka–Volterra model was used to describe the growth of mixtures of parental and RR cell populations

    $$left. {begin{array}{*{20}{c}} {frac{{dV_P}}{{dt}} = r_PK_Pleft( {1 – frac{{V_P}}{{K_P}} – lambda _{RR}frac{{V_{RR}}}{{K_P}}} right)} \ {frac{{dV_{RR}}}{{dt}} = r_{RR}K_{RR}left( {1 – frac{{V_{RR}}}{{K_{RR}}} – lambda _Pfrac{{V_P}}{{K_{RR}}}} right)} end{array}} right},$$
    (3)

    with VP(t = 0) = VP0 and VRR(t = 0) = VRR0. In these equations, VP and VRR represent respectively the volumes of parental and RR populations, rP and rRR their initial growth rates, KP and KRR their carrying capacities, and VP0 and VRR0 their initial volumes. The parameters λP and λRR describe the effect that parental cells have on RR cells, and vice versa. These type of interactions, found in ecology12, may be competitive (λP  > 0 and λRR  > 0), mutualistic (λP  More

  • in

    Dynamic allometric scaling of tree biomass and size

    1.
    Weiskittel, A. R. et al. A call to improve methods for estimating tree biomass for regional and national assessments. J. For. 113, 414–424 (2015).
    Google Scholar 
    2.
    Huang, H., Liu, C., Wang, X., Zhou, X. & Gong, P. Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China. Remote Sens. Environ. 221, 225–234 (2019).
    Article  Google Scholar 

    3.
    Zianis, D. & Seura, S. Biomass and stem volume equations for tree species in Europe. Silva Fenn. Monogr. 4, 1–63 (2005).
    Google Scholar 

    4.
    Henry, M. et al. Estimating tree biomass of sub-Saharan African forests: a review of available allometric equations. Silva Fenn. 45, 477–569 (2011).
    Article  Google Scholar 

    5.
    Jenkins, J. C., Chojnacky, D. C., Heath, L. S. & Birdsey, R. A. Comprehensive Database of Diameter-Based Biomass Regressions for North American Tree Species (USDA Forest Service, 2003).

    6.
    Yuen, J. Q., Fung, T. & Ziegler, A. D. Review of allometric equations for major land covers in SE Asia: uncertainty and implications for above- and below-ground carbon estimates. For. Ecol. Manag. 360, 323–340 (2016).
    Article  Google Scholar 

    7.
    Liu, C. et al. Separating regressions for model fitting to reduce the uncertainty in forest volume–biomass relationship. Forests 10, 658 (2019).
    Article  Google Scholar 

    8.
    Niklas, K. J. A phyletic perspective on the allometry of plant biomass-partitioning patterns and functionally equivalent organ-categories. New Phytol. 171, 27–40 (2006).
    PubMed  Article  Google Scholar 

    9.
    Smith, J. E., Heath, L. S. & Jenkins, J. C. Forest Volume-to-Biomass Models and Estimates of Mass for Live and Standing Dead Trees of U.S. Forests (USDA Forest Service, 2003).

    10.
    Jalkanen, A., Mäkipää, R., Ståhl, G., Lehtonen, A. & Petersson, H. Silviculture-driven vegetation change in a European temperate deciduous forest. Ann. For. Sci. 62, 313–323 (2005).
    Article  Google Scholar 

    11.
    Guo, Z., Fang, J., Pan, Y. & Birdsey, R. Inventory-based estimates of forest biomass carbon stocks in China: a comparison of three methods. For. Ecol. Manag. 259, 1225–1231 (2010).
    Article  Google Scholar 

    12.
    Chave, J. et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Change Biol. 20, 3177–3190 (2014).
    Article  Google Scholar 

    13.
    Ishihara, M. I. et al. Efficacy of generic allometric equations for estimating biomass: a test in Japanese natural forests. Ecol. Appl. 25, 1433–1446 (2015).
    PubMed  Article  Google Scholar 

    14.
    Xiang, W. et al. General allometric equations and biomass allocation of Pinus massoniana trees on regional scale in southern China. Ecol. Res. 26, 697–711 (2011).
    Article  Google Scholar 

    15.
    Parresol, B. R. Assessing tree and stand biomass: a review with examples and critical comparisons. For. Sci. 45, 573–593 (1999).
    Google Scholar 

    16.
    Wirth, C., Schumacher, J. & Schulze, E.-D. Generic biomass functions for Norway spruce in Central Europe—a meta-analysis approach toward prediction and uncertainty estimation. Tree Physiol. 24, 121–139 (2004).
    PubMed  Article  Google Scholar 

    17.
    Rutishauser, E. et al. Generic allometric models including height best estimate forest biomass and carbon stocks in Indonesia. For. Ecol. Manag. 307, 219–225 (2013).
    Article  Google Scholar 

    18.
    Chave, J. et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145, 87–99 (2005).
    CAS  PubMed  Article  Google Scholar 

    19.
    Gonzalez-Benecke, C. A. et al. Local and general above-stump biomass functions for loblolly pine and slash pine trees. For. Ecol. Manag. 334, 254–276 (2014).
    Article  Google Scholar 

    20.
    Sileshi, G. W. A critical review of forest biomass estimation models, common mistakes and corrective measures. For. Ecol. Manag. 329, 237–254 (2014).
    Article  Google Scholar 

    21.
    Picard, N., Rutishauser, E., Ploton, P., Ngomanda, A. & Henry, M. Should tree biomass allometry be restricted to power models? For. Ecol. Manag. 353, 156–163 (2015).
    Article  Google Scholar 

    22.
    Sheil, D. et al. Does biomass growth increase in the largest trees? Flaws, fallacies and alternative analyses. Funct. Ecol. 31, 568–581 (2017).
    Article  Google Scholar 

    23.
    Muller-Landau, H. C. et al. Testing metabolic ecology theory for allometric scaling of tree size, growth and mortality in tropical forests. Ecol. Lett. 9, 575–588 (2006).
    PubMed  Article  Google Scholar 

    24.
    Schafer, J. L. & Mack, M. C. Growth, biomass, and allometry of resprouting shrubs after fire in scrubby flatwoods. Am. Midl. Nat. 172, 266–284 (2014).
    Article  Google Scholar 

    25.
    Poorter, H. et al. How does biomass distribution change with size and differ among species? An analysis for 1200 plant species from five continents. New Phytol. 208, 736–749 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    26.
    Smith, R. J. Rethinking allometry. J. Theor. Biol. 87, 97–111 (1980).
    CAS  PubMed  Article  Google Scholar 

    27.
    Dassot, M. et al. Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment. Comput. Electron. Agric. 89, 86–93 (2012).
    Article  Google Scholar 

    28.
    Disney, M. I. et al. Weighing trees with lasers: advances, challenges and opportunities. Interface Focus 8, 201700484 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    29.
    Sarrus, P. F. & Rameaux, J.-F. Application des sciences accessoires et principalement des mathématiques à la physiologie générale. Bull. Acad. R. Méd. 3, 1094–1100 (1838).
    Google Scholar 

    30.
    Huxley, J. S. & Teissier, G. Terminology of relative growth. Nature 137, 780–781 (1936).
    Article  Google Scholar 

    31.
    Gayon, J. History of the concept of allometry. Am. Zool. 40, 748–758 (2000).
    Google Scholar 

    32.
    Rubner, M. Über den einfluss der körpergrösse auf stoff- und kraftwechsel. Z. Biol. 19, 536–562 (1883).
    Google Scholar 

    33.
    von Bertalanffy, L. General System Theory: Foundations, Development, Applications (George Braziller, 1973).

    34.
    Kleiber, M. Body size and metabolism. Hilgardia 6, 315–353 (1932).
    CAS  Article  Google Scholar 

    35.
    West, G. B., Brown, J. H. & Enquist, B. J. A general model for the origin of allometric scaling laws in biology. Science 276, 122–126 (1997).
    CAS  PubMed  Article  Google Scholar 

    36.
    West, G. B., Brown, J. H. & Enquist, B. J. A general model for ontogenetic growth. Nature 413, 628–631 (2001).
    CAS  PubMed  Article  Google Scholar 

    37.
    Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
    Article  Google Scholar 

    38.
    Bokma, F. Evidence against universal metabolic allometry. Funct. Ecol. 18, 184–187 (2004).
    Article  Google Scholar 

    39.
    Dodds, P. S., Rothman, D. H. & Weitz, J. S. Re-examination of the “3/4-law” of metabolism. J. Theor. Biol. 209, 9–27 (2001).
    CAS  PubMed  Article  Google Scholar 

    40.
    Kozłowski, J. & Konarzewski, M. Is West, Brown and Enquist’s model of allometric scaling mathematically correct and biologically relevant? Funct. Ecol. 18, 283–289 (2004).
    Article  Google Scholar 

    41.
    Henry, M. et al. Wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa. For. Ecol. Manag. 260, 1375–1388 (2010).
    Article  Google Scholar 

    42.
    Satoo, T. Notes on Kittredge’s method of estimation of amount of leaves of forest stand. Jpn. J. For. 44, 267–272 (1962).
    Google Scholar 

    43.
    Ruark, G. A., Martin, G. L. & Bockheim, J. G. Comparison of constant and variable allometric ratios for estimating populus tremuloides biomass. For. Sci. 33, 294–300 (1987).
    Google Scholar 

    44.
    Mori, S. et al. Mixed-power scaling of whole-plant respiration from seedlings to giant trees. Proc. Natl Acad. Sci. USA 107, 1447–1451 (2010).
    CAS  PubMed  Article  Google Scholar 

    45.
    Tjørve, E. Shapes and functions of species-area curves (II): a review of new models and parameterizations. J. Biogeogr. 36, 1435–1445 (2009).
    Article  Google Scholar 

    46.
    Luo, Y., Wang, X., Zhang, X. & Lu, F. Biomass and Its Allocation of Forest Ecosystems in China [in Chinese] (Chinese Forestry Publishing House, 2013).

    47.
    Stovall, A. E. L., Shugart, H. H., Stovall, A. E. L. & Anderson-Teixeira, K. J. Assessing terrestrial laser scanning for developing non-destructive biomass allometry. For. Ecol. Manag. 427, 217–229 (2018).
    Article  Google Scholar 

    48.
    Packard, G. C. Is logarithmic transformation necessary in allometry? Biol. J. Linn. Soc. 109, 476–486 (2013).
    Article  Google Scholar 

    49.
    Mascaro, J., Litton, C. M., Hughes, R. F., Uowolo, A. & Schnitzer, S. A. Is logarithmic transformation necessary in allometry? Ten, one-hundred, one-thousand-times yes. Biol. J. Linn. Soc. 111, 230–233 (2014).
    Article  Google Scholar 

    50.
    Sprugel, D. G. Correcting for bias in log-transformed allometric equations. Ecology 64, 209–210 (1983).
    Article  Google Scholar 

    51.
    Peichl, M. & Arain, M. A. Allometry and partitioning of above- and belowground tree biomass in an age-sequence of white pine forests. For. Ecol. Manag. 253, 68–80 (2007).
    Article  Google Scholar 

    52.
    Wolf, A., Field, C. B. & Berry, J. A. Allometric growth and allocation in forests: a perspective from FLUXNET. Ecol. Appl. 21, 1546–1556 (2011).
    PubMed  Article  Google Scholar 

    53.
    Litton, C. M., Raich, J. W. & Ryan, M. G. Carbon allocation in forest ecosystems. Glob. Change Biol. 13, 2089–2109 (2007).
    Article  Google Scholar 

    54.
    Vallet, P., Dhôte, J. F., Moguédec, G. L. E., Ravart, M. & Pignard, G. Development of total aboveground volume equations for seven important forest tree species in France. For. Ecol. Manag. 229, 98–110 (2006).
    Article  Google Scholar 

    55.
    Cannell, M. G. R. World Forest Biomass and Primary Production Data (Academic Press, 1982).

    56.
    Usoltsev, V. A. Forest Biomass and Primary Production Database for Eurasia (Ural State Forest Engineering Univ., 2013).

    57.
    West, G. B. & Brown, J. H. The origin of allometric scaling laws in biology from genomes to ecosystems: towards a quantitative unifying theory of biological structure and organization. J. Exp. Biol. 208, 1575–1592 (2005).
    PubMed  Article  Google Scholar 

    58.
    Reich, P. B. et al. Universal scaling of respiratory metabolism, size and nitrogen in plants. Nature 439, 457–461 (2006).
    CAS  PubMed  Article  Google Scholar 

    59.
    Li, H., Han, X. & Wu, J. Lack of evidence for 3/4 scaling of metabolism in terrestrial plants. J. Integr. Plant Biol. 47, 1173–1183 (2005).
    Article  Google Scholar 

    60.
    Zhou, X. et al. Correcting the overestimate of forest biomass carbon on the national scale. Method Ecol. Evol. 7, 447–455 (2016).
    Article  Google Scholar 

    61.
    Enquist, B. J., Brown, J. H. & West, G. B. Allometric scaling of plant energetics and population density. Nature 395, 163–165 (1998).
    CAS  Article  Google Scholar  More

  • in

    Empirical support for the biogeochemical niche hypothesis in forest trees

    1.
    Tracy, C. R. & Christian, K. A. Ecological relations among space, time, and thermal niche axes. Ecology 67, 609–615 (1986).
    Article  Google Scholar 
    2.
    Peterson, A. T., Soberon, J. & Sanchez-Cordero, V. Conservatism of ecological niches in evolutionary time. Science 285, 1265–1267 (1999).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    3.
    Hirzel, A. H., Hausser, J., Chessel, D. & Perrin, N. Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83, 2017–2036 (2002).
    Article  Google Scholar 

    4.
    Sexton, J. P., Montiel, J., Shay, J. E., Stephens, M. R. & Slatyer, R. A. Evolution of ecological niche breadth. Rev. Ecol. Evol. Syst. 48, 183–206 (2017).
    Article  Google Scholar 

    5.
    Wright, J. W., Davies, K. F., Lau, J. A., McCall, A. C. & McKay, J. K. Experimental verification of ecological niche modelling in a heterogeneous environment. Ecology 87, 2433–2439 (2006).
    PubMed  Article  PubMed Central  Google Scholar 

    6.
    Swanson, H. K. et al. A new probabilistic method for quantifying n-dimensional ecological niches and niche overlap. Ecology 96, 318–324 (2015).
    PubMed  Article  PubMed Central  Google Scholar 

    7.
    Grubb, P. J. The maintenance of species-richness in plant communities: the importance of the regeneration niche. Biol. Rev. 52, 107–145 (1977).
    Article  Google Scholar 

    8.
    Herrel, A., Spithoven, L., Van Damme, V. & De Vree, F. Sexual dimorphism of head size in Gallotia galloti: testing the divergence hypothesis by functional analyses. Funct. Ecol. 13, 289–297 (1999).
    Article  Google Scholar 

    9.
    Mouillot, D. et al. Niche overlap estimates based on quantitative functional traits: a new family of non-parametric indices. Oecologia 145, 345–353 (2005).
    PubMed  Article  PubMed Central  Google Scholar 

    10.
    Kraft, N., Valencia, R. & Ackerly, D. D. Functional traits and niche based tree community assembly in an Amazonian forest. Science 322, 580–582 (2008).
    CAS  PubMed  Article  Google Scholar 

    11.
    Peñuelas, J., Sardans, J., Ogaya, R. & Estiarte, M. Nutrient stoichiometric relations and biogeochemical niche in coexisting plant species: effect of simulated climate change. Pol. J. Ecol. 56, 613–622 (2008).
    Google Scholar 

    12.
    Peñuelas, J. et al. Faster returns on “leaf economics” and different biogeochemical niche in invasive compared with native plant species. Glob. Change Biol. 16, 2171–2185 (2010).
    Article  Google Scholar 

    13.
    Peñuelas, J. et al. The bioelements, the elementome and the “biogeochemical niche”. Ecology 100, e02652 (2019).
    PubMed  Article  Google Scholar 

    14.
    Sardans, J. et al. Factors influencing the foliar elemental composition and stoichiometry in forest trees in Spain. Persp. Plant Ecol. Evol. Syst. 18, 52–69 (2016).
    Article  Google Scholar 

    15.
    Sardans, J. et al. Foliar elemental composition of European forest tree species associated with evolutionary traits and present environmental and competitive conditions. Glob. Ecol. Biogeogr. 24, 240–255 (2015).
    Article  Google Scholar 

    16.
    Urbina, I. et al. Shifts in the elemental composition of plants during a very severe drought. Environ. Exp. Bot. 111, 63–73 (2015).
    CAS  PubMed  Article  Google Scholar 

    17.
    Urbina, I. et al. Plant community composition affects the species biogeochemical niche. Ecosphere 8, e01801 (2017).
    Article  Google Scholar 

    18.
    White, P. J. et al. Testing distinctness of shoot ionomes of angiosperm families using the Rothamsted Park grass continuous hay experiment. N. Phytol. 196, 101–109 (2012).
    CAS  Article  Google Scholar 

    19.
    Kerkhoff, A. J., Fagan, W. F., Elser, J. J. & Enquist, B. J. Phylogenetic and growth form variation in the scaling of nitrogen and phosphorus in the seed plants. Am. Nat. 168, E103–E122 (2006).
    PubMed  Article  Google Scholar 

    20.
    Sun, L. K. et al. Leaf elemental stoichiometry of Tamarix Lour. Species in relation to geographic, climatic, soil, and genetic components in China. Ecol. Eng. 106, 448–457 (2017).
    Article  Google Scholar 

    21.
    Neugebauer, K. et al. Variation in the angiosperm ionome. Physiol. Plant. 163, 306–322 (2018).
    CAS  Article  Google Scholar 

    22.
    Gillman, L. N., Keeling, D. J., Gardner, R. C. & Wright, S. D. Faster evolution of highly conserved in tropical plants. J. Evol. Biol. 23, 1327–1330 (2010).
    CAS  PubMed  Article  Google Scholar 

    23.
    Puurtinen, M. et al. Temperature-dependent mutational robustness can explain faster molecular evolution at warm temperatires, affecting speciation rate and global patterns of species diversity. Ecography 39, 1025–1033 (2016).
    Article  Google Scholar 

    24.
    Kellner, A., Ritz, C. M., Schlittenhaedt, P. & Hellwig, F. H. Genetic differentiation in the genus Lithops L. (Ruschoideae, Aizoaceae) reveals a high level of convergent evolution and reflects geographic distribution. Plant Biol. 13, 368–380 (2011).
    CAS  PubMed  Article  Google Scholar 

    25.
    Jwa, N. S. & Hwang, B. K. Convergent evolution of pathogen effectors toward reactive oxygen species signaling networks in plants. Front. Plant Sci. 8, 1687 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    26.
    Molina-Montenegro, M. A. et al. Is the success of plant invasions the result of rapid adaptive evolution in seed traits? Evidence from a latitudinal rainfall gradient. Front. Plant Sci. 9, 208 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    27.
    Anacker, B. L. & Strauss, S. Y. Ecological similarity is related to phylogenetic distance between species in a cross-niche field transplant experiment. Ecology 97, 1807–1818 (2016).
    PubMed  Article  Google Scholar 

    28.
    Reich, P. B. & Oleksyn, J. Global patterns of plant leaf N and P in relation to temperature and latitude. Proc. Natl Acad. Sci. USA 101, 11001–11106 (2004).
    CAS  PubMed  Article  Google Scholar 

    29.
    Ordoñez, J. C. et al. A global study of relationships between leaf traits, climate and soil measures of nutrient fertility. Glob. Ecol. Biogeogr. 18, 137–149 (2009).
    Article  Google Scholar 

    30.
    Kerkhoff, A. J., Enquist, B. J., Elser, J. J. & Fagan, W. F. Plantallometry, stoichiometry and the temperature-dependence of primary productivity. Glob. Ecol. Biogeogr. 14, 585–598 (2005).
    Article  Google Scholar 

    31.
    Yuan, Z. Y. & Chen, H. Y. H. Global trends in senesced-leaf nitrogen and phosphorus. Glob. Ecol. Biogeogr. 18, 532–542 (2009).
    Article  Google Scholar 

    32.
    Vitousek, P. M., Porder, S., Houlton, B. Z. & Chadwick, O. A. Terrestrial phosphorus limitation: mechanisms, implications and nitrogen–phosphorus interactions. Ecol. Appl. 20, 5–15 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    33.
    McGroddy, M. E., Daufresne, T. & Hedin, L. O. Scaling of C/N/P stoichiometry in forest worldwide: implications of terrestrial Redfield-type ratios. Ecology 85, 2390–2401 (2004).
    Article  Google Scholar 

    34.
    Townsend, A. R., Cleveland, C. C., Asner, G. P. & Bustamante, M. M. C. Controls over foliar N:P ratios in tropical rainforest. Ecology 88, 107–118 (2007).
    PubMed  Article  PubMed Central  Google Scholar 

    35.
    Lovelock, C. E., Feller, I. C., Ball, M. C., Ellis, J. & Sorell, B. Testing the growth rate vs. geochemical hypothesis for latitudinal variation in plant nutrients. Ecol. Lett. 10, 1154–1163 (2007).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    36.
    Marschner, H. Mineral Nutrition of Higher Plants (Academic Press, 1995).

    37.
    Zhang, Y. et al. Log-term trends in total inorganic nitrogen and sulfur deposition in US from 1990 to 2010. Atmos. Chem. Phys. 18, 9091–9106 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    38.
    Horn, K. J. et al. Growth and survival relationships of 71 tree species with nitrogen and sulfur deposition across the conterminous U.S. PLoS ONE 14, e0212984 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    39.
    Papanikolaou, N., Britton, A. J., Helliwell, R. C. & Johnson, D. Nitrogen deposition, vegetation burning and climate warming act independently on microbial community structure and enzyme activity associated with decomposing litter in low-alpine heath. Glob. Change Biol. 16, 3120–3132 (2010).
    Google Scholar 

    40.
    Marklein, A. R. & Houlton, B. Z. Nitrogen inputs accelerate phosphorus cycling rates across a wide variety of terrestrial ecosystems. N. Phytol. 193, 696–704 (2012).
    CAS  Article  Google Scholar 

    41.
    Sardans, J. et al. Foliar and soil concentrations and stoichiometry of nitrogen and phosphorus across European Pinus sylvestris forests: relationships with climate, N deposition and tree growth. Funct. Ecol. 30, 676–689 (2016).
    Article  Google Scholar 

    42.
    Peñuelas, J. et al. Human-induced nitrogen–phosphorus imbalances alter natural and managed ecosystems across the globe. Nat. Commun. 4, 2934 (2013).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    43.
    Penuelas, J. et al. Increasing atmospheric CO2 concentrations correlate with declining nutritional status of European forests. Commun. Biol. 3, 125 (2020).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    44.
    Ahmad, N. & Mermut, A. Vertisols and Technologies for their Development 1st edn, Vol. 24 (Elsevier, 1996).

    45.
    Nishiue, A., Nanzyo, M., Kanno, H. & Takahashi, T. Properties and classification of volcanic ash soils around Lake Kuwanuma on the eastern footslope of Mt. Funagata in Miyagi prefecture, northeastern Japan. Soil Sci. Plant Nutr. 60, 848–862 (2014).
    CAS  Article  Google Scholar 

    46.
    De la Riva, E. G. et al. Biogeochemical and ecomorphological niche segregation of Mediterranean woody species along a local gradient. Fron. Plant Sci. 8, 1242 (2017).
    Article  Google Scholar 

    47.
    Yu, Q. et al. Stoichiometry homeostasis of vascular plants in the inner Mongolia grassland. Oecologia 166, 1–10 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    48.
    Sardans, J., Albert Rivas-Ubach, A. & Peñuelas, J. The elemental stoichiometry of aquatic and terrestrial ecosystems and its relationships with organismic lifestyle and ecosystem structure and function: a review. Biogeochemistry 111, 1–39 (2012).
    Article  Google Scholar 

    49.
    Gracia, C., Burriel, J. A., Ibàñez, J. J., Mata, T. & Vayreda, J. Inventari ecològic i forestal de Catalunya: regió forestal V (CREAF, 2004).

    50.
    Fick, A. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
    Article  Google Scholar 

    51.
    Lang, R. Verwitterung und Bodenbildung als Einfuehrung in die Bodenkunde (Schweizerbart Science Publishers, 1920).

    52.
    Köppen, W. Klassification der Klimate nach Tempertur, Niederschlag and Jahreslauf. Petermanns Geog. Mitt. 64, 243–248 (1918).
    Google Scholar 

    53.
    De Martonne, E. Nouvelle carte mondiale de l’indece d’aridité. Ann. Géogr. 51, 242–250 (1942).
    Google Scholar 

    54.
    Emberger, L. La vegetation de la región Mèditerranéenne, essai d’une classification des groupements vegetaux. Rev. Gén. Bot. 42, 641–662, 705–721 (1930).

    55.
    Vorösmarty, C. J. et al. Global threats to human water security and river biodiversity. Nature 467, 555–561 (2010).
    PubMed  Article  CAS  Google Scholar 

    56.
    R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2011).

    57.
    Qian, H. & Jin, Y. An updated megaphylogeny of plants, a tool for generating plant phylogenies, and an analysis of phylogenetic community structure. J. Plant Ecol. 9, 233–239 (2016).
    Article  Google Scholar 

    58.
    Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).

    59.
    Revell, L. J. Phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
    Article  Google Scholar 

    60.
    Blomberg, S. P., Garland, T. & Ives, A. R. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution 57, 717–745 (2003).
    PubMed  Article  Google Scholar 

    61.
    Münkemüller, T. et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 3, 743–756 (2012).
    Article  Google Scholar 

    62.
    Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).
    CAS  PubMed  Article  Google Scholar 

    63.
    Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).
    Article  Google Scholar 

    64.
    Revell, L. J. Two new graphical methods for mapping trait evolution on phylogenies. Methods Ecol. Evol. 4, 754–759 (2013).
    Article  Google Scholar 

    65.
    Raamsdonk, L. M. et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 19, 45–50 (2001).
    CAS  PubMed  Article  Google Scholar 

    66.
    Hadfield, J. D. MCMC methods for multi-response generalised linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 2 (2010). More

  • in

    Author Correction: Tree mode of death and mortality risk factors across Amazon forests

    School of Geography, Earth and Enviornmental Sciences, University of Birmingham, Birmingham, UK
    Adriane Esquivel-Muelbert & Thomas A. M. Pugh

    School of Geography, University of Leeds, Leeds, UK
    Adriane Esquivel-Muelbert, Oliver L. Phillips, Roel J. W. Brienen, Martin J. P. Sullivan, Timothy R. Baker, Emanuel Gloor, Aurora Levesley, Simon L. Lewis, Karina Liana Lisboa Melgaço Ladvocat, Gabriela Lopez-Gonzalez, Nadir Pallqui Camacho, Julie Peacock, Georgia Pickavance & David Galbraith

    Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
    Adriane Esquivel-Muelbert & Thomas A. M. Pugh

    School of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth, UK
    Sophie Fauset

    Department of Natural Sciences, Manchester Metropolitan University, Manchester, UK
    Martin J. P. Sullivan

    International Master Program of Agriculture, National Chung Hsing University, Taichung, Taiwan
    Kuo-Jung Chao

    Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK
    Ted R. Feldpausch

    Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil
    Niro Higuchi, Adriano José Nogueira Lima & Carlos Quesada

    School of Mathematics, University of Leeds, Leeds, UK
    Jeanne Houwing-Duistermaat & Haiyan Liu

    Faculty of Natural Sciences, Department of Life, Imperial College London Sciences, London, UK
    Jon Lloyd

    Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK
    Yadvinder Malhi & Simone Matias de Almeida Reis

    UNEMAT – Universidade do Estado de Mato Grosso PPG-Ecologia e Conservação, Campus de Nova Xavantina, Nova Xavantina, MT, Brazil
    Beatriz Marimon, Ben Hur Marimon Junior, Paulo Morandi, Edmar Almeida de Oliveira & Simone Matias de Almeida Reis

    Jardín Botánico de Missouri, Oxapampa, Peru
    Abel Monteagudo-Mendoza, Victor Chama Moscoso, Luis Valenzuela Gamarra & Rodolfo Vasquez Martinez

    Forest Ecology and Forest Management Group, Wageningen University and Research, Wageningen, Netherlands
    Lourens Poorter, Frans Bongers, Marielos Peña-Claros & Pieter Zuidema

    Centro de Ciências Biológicas e da Natureza, Universidade Federal do Acre, Rio Branco, AC, Brazil
    Marcos Silveira

    Instituto de Investigaciones para el Desarrollo Forestal (INDEFOR), Universidad de Los Andes, Mérida, Venezuela
    Emilio Vilanova Torre & Julio Serrano

    University of California, Berkeley, CA, USA
    Emilio Vilanova Torre

    Escuela de Ciencias Agropecuarias y Ambientales, Universidad Nacional Abierta y a Distancia, Boyacá, Colombia
    Esteban Alvarez Dávila

    Fundación ConVida, Medellín, Colombia
    Esteban Alvarez Dávila

    Instituto de Investigaciones de la Amazonia Peruana, Iquitos, Peru
    Jhon del Aguila Pasquel, Nallaret Davila Cardozo & Eurídice Honorio Coronado

    Instituto de Biodiversidade e Florestas, Universidade Federal do Oeste do Pará, Santarém, Brazil
    Everton Almeida

    Center for Tropical Conservation, Nicholas School of the Environment, University in Durham, Durham, NC, USA
    Patricia Alvarez Loayza

    Projeto Dinâmica Biológica de Fragmentos, Instituto Nacional de Pesquisas da Amazônia Florestais, Manaus, AM, Brazil
    Ana Andrade & José Luís Camargo

    National Institute for Space Research (INPE), São José dos Campos, SP, Brazil
    Luiz E. O. C. Aragão

    Museo de Historia Natural Noel Kempff Mercado, Universidad Autónoma Gabriel Rene Moreno, Santa Cruz de la Sierra, Bolivia
    Alejandro Araujo-Murakami & Marisol Toledo

    Wageningen Environmental Research, Wageningen University and Research, Wageningen, Netherlands
    Eric Arets

    Dirección de la Carrera de Biología, Universidad Autónoma Gabriel René Moreno, Santa Cruz de la Sierra, Bolivia
    Luzmila Arroyo

    UNELLEZ-Guanare, Herbario Universitario (PORT), Portuguesa, Venezuela Compensation International Progress S.A. Ciprogress–Greenlife, Bogotá, D.C., Colombia
    Gerardo A. Aymard C.

    INRAE, UMR EcoFoG, CNRS, Cirad, AgroParisTech, Université des Antilles, Université de Guyane, Kourou, France
    Michel Baisie, Damien Bonal, Benoit Burban, Aurélie Dourdain, Maxime Rejou-Machain & Clement Stahl

    Department of Biological Sciences, International Center for Tropical Botany, Florida International University, Miami, FL, USA
    Christopher Baraloto

    Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, Piracicaba, Brazil
    Plínio Barbosa Camargo

    Universidade Federal do Acre, Campus Floresta, Cruzeiro do Sul, Brazil
    Jorcely Barroso

    UR Forest & Societies, CIRAD, Montpellier, France
    Lilian Blanc

    Department of Biology, Utrecht, Netherlands
    René Boot

    Woods Hole Research Center, Falmouth, MA, USA
    Foster Brown

    Laboratório de Botânica e Ecologia Vegetal, Universidade Federal do Acre, Rio Branco, AC, Brazil
    Wendeson Castro

    Laboratoire Evolution et Diversite Biologique, CNRS, Toulouse, France
    Jerome Chave

    Inventory and Monitoring Program, National Park Service, Fort Collins, CO, USA
    James Comiskey

    Proyecto Castaña, Madre de Dios, Peru
    Fernando Cornejo Valverde

    Instituto de Geociências, Faculdade de Meteorologia, Universidade Federal do Para, Belém, Brazil
    Antonio Lola da Costa

    Department of Anthropology and Primate Molecular Ecology and Evolution Laboratory, University of Texas, Austin, TX, USA
    Anthony Di Fiore

    National Museum of Natural History, Smithsonian Institute, Washington, DC, USA
    Terry Erwin

    Universidad Nacional Jorge Basadre de Grohmann, Tacna, Peru
    Gerardo Flores Llampazo

    Museu Paraense Emílio Goeldi, Belém, Brazil
    Ima Célia Guimarães Vieira & Rafael Salomão

    Instituto Venezolano de Investigaciones Científicas (IVIC), Caracas, Venezuela
    Rafael Herrera

    IIAMA, Universitat Politécnica de València, València, Spain
    Rafael Herrera

    Universidad Nacional de San Antonio Abad del Cusco, Cusco, Peru
    Isau Huamantupa-Chuquimaco

    Instituto Amazónico de Investigaciones Imani, Universidad Nacional de Colombia Sede Amazonia, Leticia, Colombia
    Eliana Jimenez-Rojas

    Agteca, Santa Cruz, Bolivia
    Timothy Killeen

    College of Science and Engineering, James Cook University, Cairns, QLD, Australia
    Susan Laurance & William Laurance

    Department of Geography, University College London, London, UK
    Simon L. Lewis

    Environmental Science and Policy, George Mason University, Fairfax, VA, USA
    Thomas Lovejoy

    Research School of Biology, Australian National University, Canberra, ACT, Australia
    Patrick Meir

    School of Geosciences, University of Edinburgh, Edinburgh, UK
    Patrick Meir

    Escuela de Ciencias Forestales, Unidad Académica del Trópico, Universidad Mayor de San Simón, Cochabamba, Bolivia
    Casimiro Mendoza

    Facultad de Ingeniería Ambiental, Universidad Estatal Amazónica, Puyo, Ecuador
    David Neill

    Universidad Nacional de San Antonio Abad del Cusco, Cusco, Perú
    Percy Nuñez Vargas, Nadir Pallqui Camacho & Javier Silva Espejo

    Universidad Autónoma del Beni José Ballivián, Trinidad, Bolivia
    Guido Pardo & Vincent Vos

    Universidad Regional Amazónica Ikiam, Ikiam, Ecuador
    Maria Cristina Peñuela-Mora

    Broward County Parks Recreation, Oakland Park, FL, USA
    John Pipoly

    Keller Science Action Center, Field Museum, Chicago, IL, USA
    Nigel Pitman

    Instituto de Ciencias Naturales, Universidad Nacional de Colombia, Bogotá, Colombia
    Adriana Prieto & Agustín Rudas

    Institute of Research for Forestry Development (INDEFOR), Universidad de los Andes, Mérida, Venezuela
    Hirma Ramirez-Angulo

    Socioecosistemas y Cambio Climatico, Fundacion Con Vida, Medellín, Colombia
    Zorayda Restrepo Correa

    Centro de Conservacion, Investigacion y Manejo de Areas Naturales, CIMA Cordillera Azul, Lima, Peru
    Lily Rodriguez Bayona

    Universidade Federal Rural da Amazônia, Belém, Brazil
    Rafael Salomão & Natalino Silva

    Departamento de Biología, Universidad de La Serena, La Serena, Chile
    Javier Silva Espejo

    Guyana Forestry Commission, Georgetown, Guyana
    James Singh

    Federal University of Alagoas, Maceió, Brazil
    Juliana Stropp

    Institute for Conservation Research, Escondido, CA, USA
    Varun Swamy

    Institute for Transport Studies, University of Leeds, Leeds, UK
    Joey Talbot

    Biodiversity Dynamics, Naturalis Biodiversity Center, Leiden, The Netherlands
    Hans ter Steege

    Systems Ecology, Free University, De Boelelaan 1087, Amsterdam, Netherlands
    Hans ter Steege

    Department of Biology, University of Florida, Gainesville, FL, USA
    John Terborgh

    Iwokrama International Centre for Rainforest Conservation and Development, Georgetown, Guyana
    Raquel Thomas

    Universidad de los Andes, Mérida, Venezuela
    Armando Torres-Lezama

    School of Geography, University of Nottingham, Nottingham, UK
    Geertje van der Heijden

    Van Hall Larenstein University of Applied Sciences, Leeuwarden, Netherlands
    Peter van der Meer

    Van der Hoult Forestry Consulting, Leeuwarden, The Netherlands
    Peter van der Hout

    Núcleo de Estudos e Pesquisas Ambientais – Universidade Estadual de Campinas, Campinas, Brazil
    Simone Aparecida Vieira

    Herbario del Sur de Bolivia, Universidad de San Francisco Xavier de Chuquisaca, Sucre, Bolivia
    Jeanneth Villalobos Cayo

    Tropenbos International, Wageningen, Netherlands
    Roderick Zagt More