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    Ecological variables for deep-ocean monitoring must include microbiota and meiofauna for effective conservation

    1.
    Danovaro, R. et al. Ecological variables for developing a global deep-ocean monitoring and conservation strategy. Nat. Ecol. Evol. 4, 181–192 (2020).
    Article  Google Scholar 
    2.
    Appeltans, W. et al. The magnitude of global marine species diversity. Curr. Biol. 22, 2189–2202 (2012).
    CAS  Article  Google Scholar 

    3.
    Sogin, M. L. et al. Microbial diversity in the deep sea and the underexplored ‘rare biosphere’. Proc. Natl Acad. Sci. USA 103, 12115–12120 (2006).
    CAS  Article  Google Scholar 

    4.
    Zeppilli, D. et al. Characteristics of meiofauna in extreme marine ecosystems: a review. Mar. Biodivers. 48, 35–71 (2018).
    Article  Google Scholar 

    5.
    Corinaldesi, C. New perspectives in benthic deep-sea microbial ecology. Front. Mar. Sci. 2, https://doi.org/10.3389/fmars.2015.00017 (2015).

    6.
    Boeuf, D. et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. Proc. Natl Acad. Sci. USA 116, 11824–11832 (2019).
    CAS  Google Scholar 

    7.
    López-García, P., Rodríguez-Valera, F., Pedrós-Alió, C. & Moreira, D. Unexpected diversity of small eukaryotes in deep-sea Antarctic plankton. Nature 409, 603–607 (2001).
    Article  Google Scholar 

    8.
    Schoenle, A., Nitsche, F., Werner, J. & Arndt, H. Deep-sea ciliates: recorded diversity and experimental studies on pressure tolerance. Deep Sea Res. Pt I 128, 55–66 (2017).
    CAS  Article  Google Scholar 

    9.
    Turley, C. Bacteria in the cold deep-sea benthic boundary layer and sediment—water interface of the NE Atlantic. FEMS Microbiol. Ecol. 33, 89–99 (2000).
    CAS  Google Scholar 

    10.
    Wei, C.-L. et al. Global patterns and predictions of seafloor biomass using random forests. PLoS ONE 5, e15323 (2010).
    CAS  Article  Google Scholar 

    11.
    Giere, O. Meiobenthology: The Microscopic Motile Fauna of Aquatic Sediments 2nd edn (Springer, 2009).

    12.
    Fenchel, T. Ecology of Protozoa: The Biology of Free-Living Phagotropic Protists (Springer, 2013).

    13.
    Glud, R. N. Oxygen dynamics of marine sediments. Mar. Biol. Res. 4, 243–289 (2008).
    Article  Google Scholar 

    14.
    Nascimento, F. J. A., Naslund, J. & Elmgren, R. Meiofauna enhances organic matter mineralization in soft sediment ecosystems. Limnol. Oceanogr. 57, 338–346 (2012).
    CAS  Article  Google Scholar 

    15.
    Bonaglia, S., Nascimento, F. J. A., Bartoli, M., Klawonn, I. & Brüchert, V. Meiofauna increases bacterial denitrification in marine sediments. Nat. Commun. 5, 5133 (2014).
    CAS  Article  Google Scholar 

    16.
    Sutherland, W. J., Pullin, A. S., Dolman, P. M. & Knight, T. M. The need for evidence-based conservation. Trends Ecol. Evol. 19, 305–308 (2004).
    Article  Google Scholar 

    17.
    Balsamo, M., Semprucci, F., Frontalini, F. & Coccioni, R. in Marine Ecosystems (ed. Cruzado, A.) 77–104 (InTech, 2012).

    18.
    Schratzberger, M. & Ingels, J. Meiofauna matters: the roles of meiofauna in benthic ecosystems. J. Exp. Mar. Biol. Ecol. 502, 12–25 (2018).
    Article  Google Scholar 

    19.
    Zeppilli, D. et al. Is the meiofauna a good indicator for climate change and anthropogenic impacts? Mar. Biodivers. 45, 505–535 (2015).
    Article  Google Scholar 

    20.
    Carugati, L., Corinaldesi, C., Dell’Anno, A. & Danovaro, R. Metagenetic tools for the census of marine meiofaunal biodiversity: an overview. Mar. Genom. 24, 11–20 (2015).
    Article  Google Scholar 

    21.
    Danovaro, R. et al. Implementing and innovating marine monitoring approaches for assessing marine environmental status. Front. Mar. Sci. 3, https://doi.org/10.3389/fmars.2016.00213 (2016).

    22.
    Dell’Anno, A., Carugati, L., Corinaldesi, C., Riccioni, G. & Danovaro, R. Unveiling the biodiversity of deep-sea nematodes through metabarcoding: are we ready to bypass the classical taxonomy? PLoS ONE 10, e0144928 (2015).
    Article  Google Scholar 

    23.
    Kitahashi, T., Watanabe, H. K., Tsuchiya, M., Yamamoto, H. & Yamamoto, H. A new method for acquiring images of meiobenthic images using the FlowCAM. MethodsX 5, 1330–1335 (2018).
    Article  Google Scholar 

    24.
    Pawlowski, J., Esling, P., Lejzerowicz, F., Cedhagen, T. & Wilding, T. A. Environmental monitoring through protist next-generation sequencing metabarcoding: assessing the impact of fish farming on benthic foraminifera communities. Mol. Ecol. Resour. 14, 1129–1140 (2014).
    CAS  Article  Google Scholar 

    25.
    Bik, H. M. et al. Sequencing our way towards understanding global eukaryotic biodiversity. Trends Ecol. Evol. 27, 233–243 (2012).
    Article  Google Scholar 

    26.
    Fenchel, T. The ecology of marine microbenthos IV. Structure and function of the benthic ecosystem, its chemical and physical factors and the microfauna commuities with special reference to the ciliated protozoa. Ophelia 6, 1–182 (1969).
    Article  Google Scholar 

    27.
    Worden, A. Z. et al. Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes. Science 347, 1257594 (2015).
    Article  Google Scholar 

    28.
    Gooday, A. J., Schoenle, A., Dolan, J. R. & Arndt, H. Protist diversity and function in the dark ocean—challenging the paradigms of deep-sea ecology with special emphasis on foraminiferans and naked protists. Eur. J. Protistol. 75, 125721 (2020).
    Article  Google Scholar 

    29.
    Schoenle, A. et al. Methodological studies on estimates of abundance and diversity of heterotrophic flagellates from the deep-sea floor. J. Mar. Sci. Eng. 4, 22 (2016).
    Article  Google Scholar 

    30.
    Lecroq, B. et al. Ultra-deep sequencing of foraminiferal microbarcodes unveils hidden richness of early monothalamous lineages in deep-sea sediments. Proc. Natl Acad. Sci. USA 108, 13177–13182 (2011).
    CAS  Article  Google Scholar  More

  • in

    The interspecific growth–mortality trade-off is not a general framework for tropical forest community structure

    1.
    Loehle, C. Tree life history strategies: the role of defenses. Can. J. For. Res. 18, 209–222 (1988).
    Article  Google Scholar 
    2.
    Kitajima, K. Relative importance of photosynthetic traits and allocation patterns as correlates of seedling shade tolerance of 13 tropical trees. Oecologia 98, 419–428 (1994).
    PubMed  Article  Google Scholar 

    3.
    Kobe, R. K., Pacala, S. W., Silander, J. A. & Canham, C. D. Juvenile tree survivorship as a component of shade tolerance. Ecol. Appl. 5, 517–532 (1995).
    Article  Google Scholar 

    4.
    Rees, M., Condit, R., Crawley, M., Pacala, S. & Tilman, D. Long-term studies of vegetation dynamics. Science 293, 650–655 (2001).
    CAS  PubMed  Article  Google Scholar 

    5.
    Russo, S. E., Brown, P., Tan, S. & Davies, S. J. Interspecific demographic trade-offs and soil-related habitat associations of tree species along resource gradients. J. Ecol. 96, 192–203 (2008).
    Article  Google Scholar 

    6.
    Wright, S. J. et al. Functional traits and the growth–mortality trade-off in tropical trees. Ecology 91, 3664–3674 (2010).
    PubMed  Article  Google Scholar 

    7.
    Hubbell, S. P. & Foster, R. B. Short-term dynamics of a neotropical forest: why ecological research matters to tropical conservation and management. Oikos 63, 48–61 (1992).
    Article  Google Scholar 

    8.
    Stephenson, N. L. et al. Causes and implications of the correlation between forest productivity and tree mortality rates. Ecol. Monogr. 81, 527–555 (2011).
    Article  Google Scholar 

    9.
    Adler, P. B., HilleRisLambers, J. & Levine, J. M. A niche for neutrality. Ecol. Lett. 10, 95–104 (2007).
    PubMed  Article  Google Scholar 

    10.
    Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton Univ. Press, 2001).

    11.
    Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).
    Article  Google Scholar 

    12.
    Poorter, L. et al. Are functional traits good predictors of demographic rates? Evidence from five neotropical forests. Ecology 89, 1908–1920 (2008).
    CAS  PubMed  Article  Google Scholar 

    13.
    Paine, C. E. T. et al. Globally, functional traits are weak predictors of juvenile tree growth, and we do not know why. J. Ecol. 103, 978–989 (2015).
    Article  Google Scholar 

    14.
    Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Change Biol. 23, 1675–1690 (2017).
    Article  Google Scholar 

    15.
    Wyckoff, P. H. & Clark, J. S. The relationship between growth and mortality for seven co-occurring tree species in the southern Appalachian Mountains. J. Ecol. 90, 604–615 (2002).
    Article  Google Scholar 

    16.
    Kobe, R. K. Intraspecific variation in sapling mortality and growth predicts geographic variation in forest composition. Ecol. Monogr. 66, 181–201 (1996).
    Article  Google Scholar 

    17.
    Kobe, R. K. Light gradient partitioning among tropical tree species through differential seedling mortality and growth. Ecology 80, 187–207 (1999).
    Article  Google Scholar 

    18.
    Chapin, F. S., Autumn, K. & Pugnaire, F. Evolution of suites of traits in response to environmental stress. Am. Nat. 142, S78–S92 (1993).
    Article  Google Scholar 

    19.
    Grime, J. P. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary biology. Am. Nat. 111, 1169–1194 (1977).
    Article  Google Scholar 

    20.
    Westoby, M., Warton, D. & Reich, P. B. The time value of leaf area. Am. Nat. 155, 649–656 (2000).
    PubMed  Article  Google Scholar 

    21.
    Zera, A. J. & Harshman, L. G. The physiology of life history trade-offs in animals. Annu. Rev. Ecol. Syst. 32, 95–126 (2003).
    Article  Google Scholar 

    22.
    Russo, S. E., Davies, S. J., King, D. A. & Tan, S. Soil-related performance variation and distributions of tree species in a Bornean rain forest. J. Ecol. 93, 879–889 (2005).
    CAS  Article  Google Scholar 

    23.
    Obeso, J. R. The costs of reproduction in plants. N. Phytol. 155, 321–348 (2002).
    Article  Google Scholar 

    24.
    Roxburgh, S. H., Shea, K. & Wilson, J. B. The intermediate disturbance hypothesis: patch dynamics and mechanisms of species coexistence. Ecology 85, 359–371 (2004).
    Article  Google Scholar 

    25.
    Lambers, H. & Poorter, H. Inherent variation in growth rate between higher plants: a search for physiological causes and ecological consequences. Adv. Ecol. Res. 34, 187–261 (1992).
    Article  Google Scholar 

    26.
    Metcalf, C. J. E. Invisible trade-offs: Van Noordwijk and de Jong and life-history evolution. Am. Nat. 187, iii–v (2016).
    PubMed  Article  Google Scholar 

    27.
    Van Noordwijk, A. J. & Jong, G. D. Acquisition and allocation of resources: their influence on variation in life history tactics. Am. Nat. 128, 137–142 (1986).
    Article  Google Scholar 

    28.
    Condit, R. et al. Importance of demographic niches to tree diversity. Science 313, 98–101 (2006).
    CAS  PubMed  Article  Google Scholar 

    29.
    Ricklefs, R. E. Community diversity: relative roles of local and regional processes. Science 235, 167–171 (1987).
    CAS  PubMed  Article  Google Scholar 

    30.
    Bormann, F. H. & Likens, G. E. Pattern and Process in a Forested Ecosystem (Springer, 1979).

    31.
    Salguero-Gómez, R. et al. Fast–slow continuum and reproductive strategies structure plant life-history variation worldwide. Proc. Natl Acad. Sci. USA 113, 230–235 (2016).
    PubMed  Article  CAS  Google Scholar 

    32.
    Rüger, N. et al. Beyond the fast–slow continuum: demographic dimensions structuring a tropical tree community. Ecol. Lett. 21, 1075–1084 (2018).
    PubMed  Article  Google Scholar 

    33.
    McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).
    PubMed  Article  Google Scholar 

    34.
    McMahon, S. M., Metcalf, C. J. E. & Woodall, C. W. High-dimensional coexistence of temperate tree species: functional traits, demographic rates, life-history stages, and their physical context. PLoS ONE 6, e16253 (2011).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    35.
    Reich, P. B. The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).
    Article  Google Scholar 

    36.
    Marks, C. O. & Lechowicz, M. J. Alternative designs and the evolution of functional diversity. Am. Nat. 167, 55–66 (2006).
    PubMed  Article  Google Scholar 

    37.
    Visser, M. D. et al. Functional traits as predictors of vital rates across the life cycle of tropical trees. Funct. Ecol. 30, 168–180 (2016).
    Article  Google Scholar 

    38.
    Detto, M. & Xu, X. Optimal leaf life strategies determine Vc,max dynamic during ontogeny. New Phytol. https://doi.org/10.1111/nph.16712 (2020).

    39.
    Poorter, L. & Bongers, F. Leaf traits are good predictors of plant performance across 53 rain forest species. Ecology 87, 1733–1743 (2006).
    PubMed  Article  Google Scholar 

    40.
    Anderson-Teixeira, K. J. et al. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Glob. Change Biol. 21, 528–549 (2015).
    Article  Google Scholar 

    41.
    R Core Team R: A Language and Environment for Statistical Computing version 3.6.1 (R Foundation for Statistical Computing, 2017).

    42.
    Warton, D. I., Wright, I. J., Falster, D. S. & Westoby, M. Bivariate line-fitting methods for allometry. Biol. Rev. 81, 259–291 (2006).
    PubMed  Article  Google Scholar 

    43.
    Warton, D. I., Duursma, R. A., Falster, D. S. & Taskinen, S. smatr 3— an R package for estimation and inference about allometric lines. Methods Ecol. Evol. 3, 257–259 (2012).
    Article  Google Scholar 

    44.
    Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. Bayesian Data Analysis 2nd edn (Chapman and Hall/CRC, 2004).

    45.
    Kenfack, D., Chuyong, G., Condit, R., Russo, S. & Thomas, D. Demographic variation and habitat specialization of tree species in a diverse tropical forest of Cameroon. For. Ecosyst. 1, 22 (2014).
    Article  Google Scholar 

    46.
    Condit, R. et al. Tropical forest dynamics across a rainfall gradient and the impact of an El Niño dry season. J. Trop. Ecol. 20, 51–72 (2004).
    Article  Google Scholar 

    47.
    Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.3.3.0 (2020).

    48.
    Robinson, D. broom: An R Package for Converting Statistical Analysis Objects Into Tidy Data Frames. R package version 2 (2014); https://arxiv.org/abs/1412.3565

    49.
    Nagelkerke, N. J. D. A note on a general definition of the coefficient of determination. Biometrika 78, 691–692 (1991).
    Article  Google Scholar 

    50.
    Long, J. S. Regression Models for Categorical and Limited Dependent Variables (Sage, 1997).

    51.
    Paul-Victor, C., Züst, T., Rees, M., Kliebenstein, D. J. & Turnbull, L. A. A new method for measuring relative growth rate can uncover the costs of defensive compounds in Arabidopsis thaliana. New Phytol. 187, 1102–1111 (2010).
    PubMed  Article  CAS  Google Scholar 

    52.
    Coomes, D. A. & Allen, R. B. Effects of size, competition and altitude on tree growth. Ecol. Lett. 95, 1084–1097 (2007).
    Google Scholar 

    53.
    Björklund, M. Are ‘comparative methods’ always necessary? Oikos 80, 607–612 (1997).
    Article  Google Scholar 

    54.
    Losos, J. B. Uncertainty in the reconstruction of ancestral character states and limitations on the use of phylogenetic comparative methods. Anim. Behav. 58, 1319–1324 (1999).
    CAS  PubMed  Article  Google Scholar 

    55.
    Losos, J. B. Seeing the forest for the trees: the limitations of phylogenies in comparative biology. Am. Nat. 177, 709–727 (2011).
    PubMed  Article  Google Scholar 

    56.
    Stearns, S. C. The Evolution of Life Histories (Oxford Univ. Press, 1992).

    57.
    Rose, K. E., Atkinson, R. L., Turnbull, L. A. & Rees, M. The costs and benefits of fast living. Ecol. Lett. 12, 1379–1384 (2009).
    PubMed  Article  Google Scholar 

    58.
    Makana, J.-R. et al. Demography and biomass change in monodominant and mixed old-growth forest of the Congo. J. Trop. Ecol. 27, 447–461 (2011).
    Article  Google Scholar  More

  • in

    Body size shapes thermal stress

    1.
    Sinclair, B. J. et al. Ecol. Lett. 19, 1372–1385 (2016).
    Article  Google Scholar 
    2.
    Deutsch, C. A. et al. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).
    CAS  Article  Google Scholar 

    3.
    Peralta-Maraver, I. & Rezende, E. L. Nat. Clim. Change https://doi.org/10.1038/s41558-020-00938-y (2020).

    4.
    Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Trends Ecol. Evol. 26, 285–291 (2011).
    Article  Google Scholar 

    5.
    Sheridan, J. A. & Bickford, D. Nat. Clim. Change 1, 401–406 (2011).
    Article  Google Scholar 

    6.
    Kingsolver, J. G. Am. Nat. 174, 755–768 (2009).
    Article  Google Scholar 

    7.
    Leiva, F. P., Calosi, P. & Verberk, W. C. Philos. T. R. Soc. B 374, 20190035 (2019).
    Article  Google Scholar 

    8.
    Rezende, E. L., Castañeda, L. E. & Santos, M. Funct. Ecol. 28, 799–809 (2014).
    Article  Google Scholar 

    9.
    Klockmann, M., Günter, F. & Fischer, K. Glob. Change Biol. 23, 686–696 (2017).
    Article  Google Scholar 

    10.
    Tseng, M. et al. J. Anim. Ecol. 87, 647–659 (2018).
    Article  Google Scholar 

    11.
    Dillon, M. E., Wang, G. & Huey, R. B. Nature 467, 704–706 (2010).
    CAS  Article  Google Scholar 

    12.
    Buckley, L. B., Cannistra, A. F. & John, A. Integr. Comp. Biol. 58, 38–51 (2018).
    Article  Google Scholar 

    13.
    Index of /pub/data/uscrn/products/subhourly01/2019/ (National Centers for Environmental Information, 2020); ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/subhourly01/2019/ More

  • in

    Heat tolerance in ectotherms scales predictably with body size

    1.
    Smith, J. J., Hasiotis, S. T., Kraus, M. J. & Woody, D. T. Transient dwarfism of soil fauna during the Paleocene–Eocene thermal maximum. Proc. Natl Acad. Sci. USA 106, 17655–17660 (2009).
    CAS  Article  Google Scholar 
    2.
    Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406 (2011).
    Article  Google Scholar 

    3.
    Daufresne, M., Lengfellner, K. & Sommer, U. Global warming benefits the small in aquatic ecosystems. Proc. Natl Acad. Sci. USA 106, 12788–12793 (2009).
    CAS  Article  Google Scholar 

    4.
    Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: a third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).
    Article  Google Scholar 

    5.
    Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).
    CAS  Article  Google Scholar 

    6.
    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 

    7.
    Martinez del Rio, C. & Karasov, W. H. Body size and temperature: why they matter. Nat. Educ. Knowl. 3, 10 (2010).
    Google Scholar 

    8.
    Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
    Article  Google Scholar 

    9.
    Klockmann, M., Günter, F. & Fischer, K. Heat resistance throughout ontogeny: body size constrains thermal tolerance. Glob. Change Biol. 23, 686–696 (2017).
    Article  Google Scholar 

    10.
    Leiva, F. P., Calosi, P. & Verberk, W. C. Scaling of thermal tolerance with body mass and genome size in ectotherms: a comparison between water-and air-breathers. Philos. T. R. Soc. B. 374, 20190035 (2019).
    Article  Google Scholar 

    11.
    Sinclair, B. J., Vernon, P., Klok, C. J. & Chown, S. L. Insects at low temperatures: an ecological perspective. Trends Ecol. Evol. 18, 257–262 (2003).
    Article  Google Scholar 

    12.
    Rezende, E. L., Castañeda, L. E. & Santos, M. Tolerance landscapes in thermal ecology. Funct. Ecol. 28, 799–809 (2014).
    Article  Google Scholar 

    13.
    Santos, M., Castañeda, L. E. & Rezende, E. L. Making sense of heat tolerance estimates in ectotherms: lessons from Drosophila. Funct. Ecol. 25, 1169–1180 (2011).
    Article  Google Scholar 

    14.
    Rezende, E. L., Tejedo, M. & Santos, M. Estimating the adaptive potential of critical thermal limits: methodological problems and evolutionary implications. Funct. Ecol. 25, 111–121 (2011).
    Article  Google Scholar 

    15.
    Strang, T. J. K. A review of published temperatures for the control of pest insects in museums. Coll. Forum 8, 41–67 (1992).
    Google Scholar 

    16.
    Sunday, J. M., Bates, A. E. & Dulvy, N. K. Global analysis of thermal tolerance and latitude in ectotherms. Proc. Natl Acad. Sci. USA 278, 1823–1830 (2010).
    Google Scholar 

    17.
    Hoffmann, A. A., Chown, S. L. & Clusella–Trullas, S. Upper thermal limits in terrestrial ectotherms: how constrained are they? Funct. Ecol. 27, 934–949 (2013).
    Article  Google Scholar 

    18.
    May, R. M. How many species are there on earth? Science 241, 1441–1449 (1988).
    CAS  Article  Google Scholar 

    19.
    Sunday, J. M. et al. Thermal–safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).
    CAS  Article  Google Scholar 

    20.
    Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).
    CAS  Article  Google Scholar 

    21.
    Kearney, M. R., Gillingham, P. K., Bramer, I., Duffy, J. P. & Maclean, I. M. A method for computing hourly, historical, terrain‐corrected microclimate anywhere on Earth. Methods Ecol. Evol. 11, 38–43 (2020).
    Article  Google Scholar 

    22.
    Rezende, E. L., Bozinovic, F., Szilágyi, A. & Santos, M. Predicting temperature mortality and selection in natural Drosophila populations. Science 369, 1242–1245 (2020).
    CAS  Article  Google Scholar 

    23.
    Glazier, D. S. A unifying explanation for diverse metabolic scaling in animals and plants. Biol. Rev. 85, 111–138 (2010).
    Article  Google Scholar 

    24.
    Schmid, P. E., Tokeshi, M. & Schmid-Araya, J. M. Relation between population density and body size in stream communities. Science 289, 1557–1560 (2000).
    CAS  Article  Google Scholar 

    25.
    Pörtner, H. O. & Knust, R. Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science 315, 95–97 (2007).
    Article  Google Scholar 

    26.
    Fan, Y. & van den Dool, H. A global monthly land surface air temperature analysis for 1948–present. J. Geophys. Res. Atmos. 113, 1–18 (2008).
    Article  Google Scholar 

    27.
    Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C. & Wang, W. An improved in situ and satellite SST analysis for climate. J. Clim. 15, 1609–1625 (2002).
    Article  Google Scholar 

    28.
    Crisp, D. J. Methods for the Study of Marine Benthos 2nd edn (eds Holme, N. A. & McIntyre, A. D) 284–366 (Blackwell, 1984).

    29.
    Reiss, J. & Schmid‐Araya, J. M. Existing in plenty: abundance, biomass and diversity of ciliates and meiofauna in small streams. Freshw. Bol. 53, 652–668 (2008).
    Article  Google Scholar 

    30.
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information–Theoretic Approach (Springer, 2002).

    31.
    Turkheimer, F. E., Hinz, R. & Cunningham, V. J. On the undecidability among kinetic models: from model selection to model averaging. J. Cereb. Blood Flow. Metab. 23, 490–498 (2003).
    Article  Google Scholar  More

  • in

    How to identify win–win interventions that benefit human health and conservation

    1.
    A Guide to SDG Interactions: from Science to Implementation (International Council for Science, 2017); https://go.nature.com/3o5nOD3
    2.
    IPBES Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).

    3.
    Schneider, F. et al. How can science support the 2030 Agenda for Sustainable Development? Four tasks to tackle the normative dimension of sustainability. Sustain. Sci. 14, 1593–1604 (2019).
    Article  Google Scholar 

    4.
    Barbier, E. B. & Burgess, J. C. Sustainable development goal indicators: analyzing trade-offs and complementarities. World Dev. 122, 295–305 (2019).
    Article  Google Scholar 

    5.
    Pradhan, P., Costa, L., Rybski, D., Lucht, W. & Kropp, J. P. A systematic study of Sustainable Development Goal (SDG) interactions. Earth’s Future 5, 1169–1179 (2017).
    Article  Google Scholar 

    6.
    Howe, C., Suich, H., Vira, B. & Mace, G. M. Creating win-wins from trade-offs? Ecosystem services for human well-being: a meta-analysis of ecosystem service trade-offs and synergies in the real world. Glob. Environ. Change 28, 263–275 (2014).
    Article  Google Scholar 

    7.
    Whitmee, S. et al. Safeguarding human health in the Anthropocene epoch: report of The Rockefeller Foundation–Lancet Commission on planetary health. Lancet 386, 1973–2028 (2015).
    Article  Google Scholar 

    8.
    Naidoo, R. & Fisher, B. Reset Sustainable Development Goals for a pandemic world. Nature 583, 198–201 (2020).
    CAS  Article  Google Scholar 

    9.
    Nilsson, M. et al. Mapping interactions between the sustainable development goals: lessons learned and ways forward. Sustain. Sci. 13, 1489–1503 (2018).
    Article  Google Scholar 

    10.
    Cohen-Shacham, E., Walters, G., Janzen, C. & Maginnis, S. (eds) Nature-based Solutions to Address Global Societal Challenges (IUCN, 2016).

    11.
    Allen, C., Metternicht, G. & Wiedmann, T. Prioritising SDG targets: assessing baselines, gaps and interlinkages. Sustain. Sci. 14, 421–438 (2019).
    Article  Google Scholar 

    12.
    Mayrhofer, J. P. & Gupta, J. The science and politics of co-benefits in climate policy. Environ. Sci. Policy 57, 22–30 (2016).
    Article  Google Scholar 

    13.
    Le Blanc, D. Towards Integration at Last? The Sustainable Development Goals as a Network of Targets (United Nations, Department of Economic and Social Affairs, 2015).

    14.
    Sokolow, S. H. et al. Nearly 400 million people are at higher risk of schistosomiasis because dams block the migration of snail-eating river prawns. Phil. Trans. R. Soc. B 372, 20160127 (2017).
    Article  Google Scholar 

    15.
    Steinmann, P., Keiser, J., Bos, R., Tanner, M. & Utzinger, J. Schistosomiasis and water resources development: systematic review, meta-analysis, and estimates of people at risk. Lancet Infect. Dis. 6, 411–425 (2006).
    Article  Google Scholar 

    16.
    Sokolow, S. H. et al. Global assessment of schistosomiasis control over the past century shows targeting the snail intermediate host works best. PLoS Negl. Trop. Dis. 10, e0004794 (2016).
    Article  Google Scholar 

    17.
    Martin, D. A. et al. Land-use history determines ecosystem services and conservation value in tropical agroforestry. Conserv. Lett. 13, e12740 (2020).
    Article  Google Scholar 

    18.
    Medlock, J. M. et al. A review of the invasive mosquitoes in Europe: ecology, public health risks, and control options. Vector Borne Zoonotic Dis. 12, 435–447 (2012).
    Article  Google Scholar 

    19.
    van Riper, C., van Riper, S. G., Goff, M. L. & Laird, M. The epizootiology and ecological significance of malaria in Hawaiian land birds. Ecol. Monogr. 56, 327–344 (1986).
    Article  Google Scholar 

    20.
    Franklin, B. Protection of Towns from Fire. The Pennsylvania Gazette (4 February 1735).

    21.
    Bauch, S. C., Birkenbach, A. M., Pattanayak, S. K. & Sills, E. O. Public health impacts of ecosystem change in the Brazilian Amazon. Proc. Natl Acad. Sci. USA 112, 7414–7419 (2015).
    CAS  Article  Google Scholar 

    22.
    Herrera, D. et al. Upstream watershed condition predicts rural children’s health across 35 developing countries. Nat. Commun. 8, 811 (2017).
    Article  Google Scholar 

    23.
    McShane, T. O. et al. Hard choices: making trade-offs between biodiversity conservation and human well-being. Biol. Conserv. 144, 966–972 (2011).
    Article  Google Scholar 

    24.
    Lengeler, C. Insecticide-treated bed nets and curtains for preventing malaria. Cochrane Database Syst. Rev. https://doi.org/10.1002/14651858.CD000363.pub2 (2004).

    25.
    Price, J., Richardson, M. & Lengeler, C. Insecticide-treated nets for preventing malaria. Cochrane Database Syst. Rev. https://doi.org/10.1002/14651858.CD000363.pub3 (2018).

    26.
    Short, R., Gurung, R., Rowcliffe, M., Hill, N. & Milner-Gulland, E. J. The use of mosquito nets in fisheries: a global perspective. PLoS ONE 13, e0191519 (2018).
    Article  Google Scholar 

    27.
    Markandya, A. et al. Counting the cost of vulture decline—an appraisal of the human health and other benefits of vultures in India. Ecol. Econ. 67, 194–204 (2008).
    Article  Google Scholar 

    28.
    Buechley, E. R. & Şekercioğlu, Ç. H. The avian scavenger crisis: looming extinctions, trophic cascades, and loss of critical ecosystem functions. Biol. Conserv. 198, 220–228 (2016).
    Article  Google Scholar 

    29.
    Gangoso, L. et al. Reinventing mutualism between humans and wild fauna: insights from vultures as ecosystem services providers. Conserv. Lett. 6, 172–179 (2013).
    Article  Google Scholar 

    30.
    Hampson, K. et al. Estimating the global burden of endemic canine rabies. PLoS Negl. Trop. Dis. 9, e0003709 (2015).
    Article  Google Scholar 

    31.
    Ogada, D. L., Torchin, M. E., Kinnaird, M. F. & Ezenwa, V. O. Effects of vulture declines on facultative scavengers and potential implications for mammalian disease transmission. Conserv. Biol. 26, 453–460 (2012).
    CAS  Article  Google Scholar 

    32.
    Breuer, E., Lee, L., De Silva, M. & Lund, C. Using theory of change to design and evaluate public health interventions: a systematic review. Implement. Sci. 11, 63 (2016).
    Article  Google Scholar 

    33.
    Constructing Theories of Change for Ecosystem-Based Adaptation Projects: A Guidance Document (Conservation International, 2013).

    34.
    de Wit, L. A. et al. Estimating burdens of neglected tropical zoonotic diseases on islands with introduced mammals. Am. J. Trop. Med. Hyg. 96, 749–757 (2017).
    Google Scholar 

    35.
    Morand, S. et al. Global parasite and Rattus rodent invasions: the consequences for rodent-borne diseases. Integr. Zool. 10, 409–423 (2015).
    Article  Google Scholar 

    36.
    Duron, Q., Shiels, A. B. & Vidal, E. Control of invasive rats on islands and priorities for future action. Conserv. Biol. 31, 761–771 (2017).
    Article  Google Scholar 

    37.
    Vanderwerf, E. A. Importance of nest predation by alien rodents and avian poxvirus in conservation of Oahu elepaio. J. Wildl. Manag. 73, 737–746 (2009).
    Article  Google Scholar 

    38.
    Pender, R. J., Shiels, A. B., Bialic-Murphy, L. & Mosher, S. M. Large-scale rodent control reduces pre- and post-dispersal seed predation of the endangered Hawaiian lobeliad, Cyanea superba subsp. superba (Campanulaceae). Biol. Invasions 15, 213–223 (2013).
    Article  Google Scholar 

    39.
    Hoare, J. M. & Hare, K. M. The impact of brodifacoum on non-target wildlife: gaps in knowledge. N. Z. J. Ecol. 30, 157–167 (2006).
    Google Scholar 

    40.
    DataBank (The World Bank, 2020); https://databank.worldbank.org/home.aspx

    41.
    Progress on Drinking Water and Sanitation: 2012 Update (World Health Organization and UNICEF, 2012); https://go.nature.com/2HOJFOR More

  • in

    Negative to positive shifts in diversity effects on soil nitrogen over time

    1.
    Vitousek, P. M. & Howarth, R. W. Nitrogen limitation on land and in the sea: how can it occur. Biogeochemistry 13, 87–115 (1991).
    Article  Google Scholar 
    2.
    Yuan, Z. Y. & Chen, H. Y. H. A global analysis of fine root production as affected by soil nitrogen and phosphorus. Proc. R. Soc. Lond. B 279, 3796–3802 (2012).
    CAS  Google Scholar 

    3.
    LeBauer, D. S. & Treseder, K. K. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89, 371–379 (2008).
    Article  Google Scholar 

    4.
    Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627 (2004).
    CAS  Article  Google Scholar 

    5.
    Marschner, H. Marschner’s Mineral Nutrition of Higher Plants 3rd edn (Academic Press, 2012).

    6.
    Niklaus, P. A., Wardle, D. A. & Tate, K. R. Effects of plant species diversity and composition on nitrogen cycling and the trace gas balance of soils. Plant Soil 282, 83–98 (2006).
    CAS  Article  Google Scholar 

    7.
    Li, Z. et al. Microbes drive global soil nitrogen mineralization and availability. Glob. Change Biol. 25, 1078–1088 (2019).
    Article  Google Scholar 

    8.
    Oelmann, Y. et al. Plant diversity effects on aboveground and belowground N pools in temperate grassland ecosystems: development in the first 5 years after establishment. Glob. Biogeochem. Cycles https://doi.org/10.1029/2010GB003869 (2011).

    9.
    Cong, W. F. et al. Plant species richness promotes soil carbon and nitrogen stocks in grasslands without legumes. J. Ecol. 102, 1163–1170 (2014).
    CAS  Article  Google Scholar 

    10.
    Mueller, K. E., Hobbie, S. E., Tilman, D. & Reich, P. B. Effects of plant diversity, N fertilization, and elevated carbon dioxide on grassland soil N cycling in a long-term experiment. Glob. Change Biol. 19, 1249–1261 (2013).
    Article  Google Scholar 

    11.
    von Felten, S. et al. Belowground nitrogen partitioning in experimental grassland plant communities of varying species richness. Ecology 90, 1389–1399 (2009).
    Article  Google Scholar 

    12.
    Le Roux, X. et al. Soil environmental conditions and microbial build-up mediate the effect of plant diversity on soil nitrifying and denitrifying enzyme activities in temperate grasslands. PLoS ONE https://doi.org/10.1371/journal.pone.0061069 (2013).

    13.
    Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
    CAS  Article  Google Scholar 

    14.
    Fornara, D. A. & Tilman, D. Plant functional composition influences rates of soil carbon and nitrogen accumulation. J. Ecol. 96, 314–322 (2008).
    CAS  Article  Google Scholar 

    15.
    Alberti, G. et al. Tree functional diversity influences belowground ecosystem functioning. Appl. Soil Ecol. 120, 160–168 (2017).
    Article  Google Scholar 

    16.
    McKane, R. B. et al. Resource-based niches provide a basis for plant species diversity and dominance in arctic tundra. Nature 415, 68–71 (2002).
    CAS  Article  Google Scholar 

    17.
    Meyer, S. T. et al. Effects of biodiversity strengthen over time as ecosystem functioning declines at low and increases at high biodiversity. Ecosphere https://doi.org/10.1002/ecs2.1619 (2016).

    18.
    Tilman, D., Wedin, D. & Knops, J. Productivity and sustainability influenced by biodiversity in grassland ecosystems. Nature 379, 718–720 (1996).
    CAS  Article  Google Scholar 

    19.
    Bessler, H. et al. Nitrogen uptake by grassland communities: contribution of N2 fixation, facilitation, complementarity, and species dominance. Plant Soil 358, 301–322 (2012).
    CAS  Article  Google Scholar 

    20.
    Chen, X. & Chen, H. Y. H. Plant diversity loss reduces soil respiration across terrestrial ecosystems. Glob. Change Biol. 25, 1482–1492 (2019).
    Article  Google Scholar 

    21.
    Zak, D. R., Holmes, W. E., White, D. C., Peacock, A. D. & Tilman, D. Plant diversity, soil microbial communities, and ecosystem function: are there any links? Ecology 84, 2042–2050 (2003).
    Article  Google Scholar 

    22.
    Hooper, D. U. & Vitousek, P. M. Effects of plant composition and diversity on nutrient cycling. Ecol. Monogr. 68, 121–149 (1998).
    Article  Google Scholar 

    23.
    Chen, X. et al. Effects of plant diversity on soil carbon in diverse ecosystems: a global meta-analysis. Biol. Rev. 95, 167–183 (2020).
    Article  Google Scholar 

    24.
    Chen, C., Chen, H. Y. H., Chen, X. & Huang, Z. Meta-analysis shows positive effects of plant diversity on microbial biomass and respiration. Nat. Commun. 10, 1332 (2019).
    Article  CAS  Google Scholar 

    25.
    Ma, Z. L. & Chen, H. Y. H. Positive species mixture effects on fine root turnover and mortality in natural boreal forests. Soil Biol. Biochem. 121, 130–137 (2018).
    CAS  Article  Google Scholar 

    26.
    Eisenhauer, N. et al. Plant diversity effects on soil microorganisms support the singular hypothesis. Ecology 91, 485–496 (2010).
    CAS  Article  Google Scholar 

    27.
    Lange, M. et al. How plant diversity impacts the coupled water, nutrient and carbon cycles. Adv. Ecol. Res. 61, 185–219 (2019).
    Article  Google Scholar 

    28.
    Forrester, D. I. & Bauhus, J. A review of processes behind diversity–productivity relationships in forests. Curr. For. Rep. 2, 45–61 (2016).
    Article  CAS  Google Scholar 

    29.
    Hisano, M., Chen, H. Y. H., Searle, E. B. & Reich, P. B. Species-rich boreal forests grew more and suffered less mortality than species-poor forests under the environmental change of the past half-century. Ecol. Lett. 22, 999–1008 (2019).
    Article  Google Scholar 

    30.
    Mueller, K. E., Tilman, D., Fornara, D. A. & Hobbie, S. E. Root depth distribution and the diversity–productivity relationship in a long-term grassland experiment. Ecology 94, 787–793 (2013).
    Article  Google Scholar 

    31.
    Oram, N. J. et al. Below-ground complementarity effects in a grassland biodiversity experiment are related to deep-rooting species. J. Ecol. 106, 265–277 (2018).
    CAS  Article  Google Scholar 

    32.
    Zhang, Y., Chen, H. Y. H. & Reich, P. B. Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis. J. Ecol. 100, 742–749 (2012).
    Article  Google Scholar 

    33.
    Ma, Z. L. & Chen, H. Y. H. Effects of species diversity on fine root productivity in diverse ecosystems: a global meta-analysis. Glob. Ecol. Biogeogr. 25, 1387–1396 (2016).
    Article  Google Scholar 

    34.
    Leimer, S. et al. Mechanisms behind plant diversity effects on inorganic and organic N leaching from temperate grassland. Biogeochemistry 131, 339–353 (2016).
    CAS  Article  Google Scholar 

    35.
    van Ruijven, J. & Berendse, F. Diversity–productivity relationships: initial effects, long-term patterns, and underlying mechanisms. Proc. Natl Acad. Sci. USA 102, 695–700 (2005).
    Article  CAS  Google Scholar 

    36.
    Manzoni, S., Jackson, R. B., Trofymow, J. A. & Porporato, A. The global stoichiometry of litter nitrogen mineralization. Science 321, 684–686 (2008).
    CAS  Article  Google Scholar 

    37.
    Howarth, R. W. & Marino, R. Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: evolving views over three decades. Limnol. Oceanogr. 51, 364–376 (2006).
    CAS  Article  Google Scholar 

    38.
    Bastin, J. F. et al. The global tree restoration potential. Science 365, 76–79 (2019).
    CAS  Article  Google Scholar 

    39.
    Post, W. M., Pastor, J., Zinke, P. J. & Stangenberger, A. G. Global patterns of soil-nitrogen storage. Nature 317, 613–616 (1985).
    Article  Google Scholar 

    40.
    Fowler, D., Pyle, J. A., Raven, J. A. & Sutton, M. A. The global nitrogen cycle in the twenty-first century: introduction. Phil. Trans. R. Soc. Lond. B https://doi.org/10.1098/rstb.2013.0165 (2013).

    41.
    Ratcliffe, S. et al. Biodiversity and ecosystem functioning relations in European forests depend on environmental context. Ecol. Lett. 20, 1414–1426 (2017).
    Article  Google Scholar 

    42.
    Santonja, M. et al. Plant litter mixture partly mitigates the negative effects of extended drought on soil biota and litter decomposition in a Mediterranean oak forest. J. Ecol. 105, 801–815 (2017).
    Article  Google Scholar 

    43.
    Groffman, P. M. et al. Earthworms increase soil microbial biomass carrying capacity and nitrogen retention in northern hardwood forests. Soil Biol. Biochem. 87, 51–58 (2015).
    CAS  Article  Google Scholar 

    44.
    Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & The, P. G. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 6, e1000097 (2009).
    Article  Google Scholar 

    45.
    Plot Digitizer v.2.0 (Faculty in the Department of Physics at the University of South Alabama, 2020); https://go.nature.com/2Gj5qW0

    46.
    Trabucco, A. & Zomer, R. J. Global Aridity Index (Global-Aridity) and Global Potential Evapo-transpiration (Global-PET) Geospatial Database (CGIAR, 2009); http://www.cgiar-csi.org

    47.
    UNEP World Atlas of Desertification (Edward Arnold Publication, 1997).

    48.
    Chen, H. Y. H. & Brassard, B. W. Intrinsic and extrinsic controls of fine root life span. Crit. Rev. Plant Sci. 32, 151–161 (2013).
    Article  Google Scholar 

    49.
    Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).
    Article  Google Scholar 

    50.
    Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).
    CAS  Article  Google Scholar 

    51.
    Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).
    CAS  Article  Google Scholar 

    52.
    Bates, D., Maechler, M., Bolker, B. & Walker, S. lme4: linear mixed-effects models using Eigen and S4. R package v.1.1-23 (2020); https://cran.r-project.org/web/packages/lme4/index.html

    53.
    Cohen, J., Cohen, P., West, S. G. & Alken, L. S. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (Routledge, 2013).

    54.
    Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
    Article  Google Scholar 

    55.
    Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).
    Article  Google Scholar 

    56.
    Whittingham, M. J., Stephens, P. A., Bradbury, R. B. & Freckleton, R. P. Why do we still use stepwise modelling in ecology and behaviour? J. Anim. Ecol. 75, 1182–1189 (2006).
    Article  Google Scholar 

    57.
    Bartoń, K. MuMIn: multi-model inference. R package v.1.42.1 (2018); https://cran.r-project.org/web/packages/MuMIn/index.html

    58.
    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).

    59.
    Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).

    60.
    Koricheva, J., Gurevitch, J. & Mengersen, K. Handbook of Meta-analysis in Ecology and Evolution (Princeton Univ. Press, 2013).

    61.
    Graham, M. H. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815 (2003).
    Article  Google Scholar 

    62.
    Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).
    CAS  Article  Google Scholar 

    63.
    Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package v.0.3.3.0 (2020); https://cran.r-project.org/web/packages/DHARMa/index.html

    64.
    Smith, J. L. & Doran, J. W. in Methods for Assessing Soil Quality (eds Doran, J. W. & Jones, A. J.) 169–185 (Soil Science Society of America, 1997).

    65.
    Adams, D. C., Gurevitch, J. & Rosenberg, M. S. Resampling tests for meta-analysis of ecological data. Ecology 78, 1277–1283 (1997).
    Article  Google Scholar 

    66.
    R Core Team R: A Language and Environment for Statistical Computing v.4.0.0 (R Foundation for Statistical Computing, 2020). More

  • in

    The importance of common and the irrelevance of rare species for partition the variation of community matrix: implications for sampling and conservation

    1.
    Hutchinson, G. E. Homage to santa rosalia or why are there so many kinds of animals?. Am. Nat. 93, 145–159 (1959).
    Article  Google Scholar 
    2.
    Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton University Press, Princeton, 2001).
    Google Scholar 

    3.
    Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).
    Article  Google Scholar 

    4.
    Legendre, P. & Legendre, L. Numerical Ecology (Elsevier, Amsterdam, 2012).
    Google Scholar 

    5.
    Cottenie, K. Integrating environmental and spatial processes in ecological community dynamics. Ecol. Lett. 8, 1175–1182 (2005).
    Article  PubMed  Google Scholar 

    6.
    Dray, S., Legendre, P. & Peres-Neto, P. R. Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Model. 196, 483–493 (2006).
    Article  Google Scholar 

    7.
    Leibold, M. A. & Chase, J. M. Metacommunity Ecology (Princeton University Press, Princeton, 2018).
    Google Scholar 

    8.
    Grinnell, J. Field tests of theories concerning distributional control. Am. Nat. 51, 115–128 (1917).
    Article  Google Scholar 

    9.
    Elton, C. Competition and the structure of ecological communities. J. Anim. Ecol. 15, 54–68 (1946).
    Article  Google Scholar 

    10.
    Griffith, D. A. & Peres-Neto, P. R. Spatial modeling in ecology: The flexibility of eigenfunction spatial analyses. Ecology 87, 2603–2613 (2006).
    Article  PubMed  Google Scholar 

    11.
    Peres-Neto, P. R., Legendre, P., Dray, S. & Borcard, D. Variation partitioning of species data matrices: Estimation and comparison of fractions. Ecology 87, 2614–2625 (2006).
    Article  PubMed  Google Scholar 

    12.
    Oksanen, J. et al. Vegan: community ecology package. R package version 2.4–5. https://cran.r-project.org/web/packages/vegan/index.html (2017).

    13.
    Montag, L. F. A. et al. Contrasting associations between habitat conditions and stream aquatic biodiversity in a forest reserve and its surrounding area in the Eastern Amazon. Hydrobiologia 826, 263–277 (2019).
    CAS  Article  Google Scholar 

    14.
    Juen, L. & De Marco, P. Odonate biodiversity in terra-firme forest streamlets in Central Amazonia: On the relative effects of neutral and niche drivers at small geographical extents. Insect Conserv. Divers. 4, 265–274 (2011).
    Article  Google Scholar 

    15.
    Brasil, L. S. et al. Spatial, biogeographic and environmental predictors of diversity in Amazonian Zygoptera. Insect Conserv. Divers. 11, 174–184 (2018).
    Article  Google Scholar 

    16.
    Dambros, C. S., Morais, J. W., Azevedo, R. A. & Gotelli, N. J. Isolation by distance, not rivers, control the distribution of termite species in the Amazonian rain forest. Ecography 40, 1242–1250 (2017).
    Article  Google Scholar 

    17.
    Hepp, L. U., Landeiro, V. L. & Melo, A. S. Experimental assessment of the effects of environmental factors and longitudinal position on alpha and beta diversities of aquatic insects in a neotropical stream. Int. Rev. Hydrobiol. 97, 157–167 (2012).
    Article  Google Scholar 

    18.
    Siqueira, T. et al. Common and rare species respond to similar niche processes in macroinvertebratemetacommunities. Ecography 35, 183–192 (2012).
    Article  Google Scholar 

    19.
    Alahuhta, J. & Heino, J. Spatial extent, regional specificity and metacommunity structuring in lake macrophytes. J. Biogeogr. 40, 1572–1582 (2013).
    Article  Google Scholar 

    20.
    Heino, J. & Alahuhta, J. Elements of regional beetle faunas: Faunal variation and compositional breakpoints along climate, land cover and geographical gradients. J. Anim. Ecol. 84, 427–441 (2015).
    Article  PubMed  Google Scholar 

    21.
    Algarte, V. M., Rodrigues, L., Landeiro, V. L., Siqueira, T. & Bini, L. M. Variance partitioning of deconstructed periphyton communities: Does the use of biological traits matter?. Hydrobiologia 722, 279–290 (2014).
    Article  Google Scholar 

    22.
    Brasil, L. S., Juen, L., Giehl, N. F. S. & Cabette, H. S. R. Effect of environmental and temporal factors on patterns of rarity of ephemeroptera in stream of the braziliancerrado. Neotrop. Entomol. 46, 29–35 (2017).
    CAS  Article  PubMed  Google Scholar 

    23.
    Gaston, K. J. Valuing common species. Science 327, 154–155 (2010).
    ADS  CAS  Article  PubMed  Google Scholar 

    24.
    Gaston, K. J. The importance of being rare. Ecology 487, 46–47 (2012).
    CAS  Google Scholar 

    25.
    Lários, M. C. et al. Evidence of cross-taxon congruence in Neotropical wetlands: Importance of environmental and spatial factors. Glob. Ecol. Conserv. 12, 108–118 (2017).
    Article  Google Scholar 

    26.
    Juen, L. et al. Effects of oil palm plantations on the habitat structure and biota of streams in eastern Amazon. River Res. Appl. 32, 2081–2094 (2016).
    Article  Google Scholar 

    27.
    Legendre, P. & Anderson, M. J. Distance-based redundancy analysis: Testing multispecies responses in multifactorial ecological experiments. Ecol. Monogr. 69, 1–24 (1999).
    Article  Google Scholar 

    28.
    Borcard, D., Gillet, F. & Legendre, P. Numerical Ecology with R (Springer, New York, 2018).
    Google Scholar 

    29.
    Barlow, J. et al. The future of hyperdiverse tropical ecosystems. Nature 559, 517–526 (2018).
    ADS  CAS  Article  PubMed  Google Scholar 

    30.
    Bini, L. M., Diniz-Filho, J. A. F., Rangel, T. F., Bastos, R. P. & Pinto, M. P. Challenging Wallacean and Linnean shortfalls: Knowledge gradients and conservation planning in a biodiversity hotspot. Divers. Distrib. 12, 475–482 (2006).
    Article  Google Scholar 

    31.
    Whittaker, R. J. et al. Conservation biogeography: Assessment and prospect. Divers. Distrib. 11, 3–23 (2005).
    Article  Google Scholar 

    32.
    Crouzeilles, R., Feltran-Barbieri, R., Ferreira, M. S. & Strassburg, B. B. Hard times for the Brazilian environment. Nature ecology & evolution. 1, 1213–1213 (2017).
    Article  Google Scholar 

    33.
    Vieira, T. B. et al. A multiple hypothesis approach to explain species richness patterns in neotropical stream-dweller fish communities. PLoS ONE 13, 1–17 (2018).
    Google Scholar 

    34.
    Brasil, L. S. et al. Net primary productivity and seasonality of temperature and precipitation are predictors of the species richness of the Damselflies in the Amazon. Basic Appl. Ecol. 35, 45–53 (2019).
    Article  Google Scholar 

    35.
    Kéry, M. & Schmid, H. Monitoring programs need to take into account imperfect species detectability. Basic Appl. Ecol. 5, 65–73 (2004).
    Article  Google Scholar 

    36.
    Leitão, R. P. et al. Rare species contribute disproportionately to the functional structure of species assemblages. Proc. R. Soc. B Biol. Sci. 2838, 20160084 (2016).
    Article  Google Scholar 

    37.
    Pereira, D. F. G., Oliveira-Junior, J. M. B. & Juen, L. Environmental changes promote larger species of Odonata (Insecta) in Amazonian streams. Ecol. Ind. 98, 179–192 (2019).
    Article  Google Scholar 

    38.
    Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403(6772), 853 (2000).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    39.
    Rodrigues, M. E. et al. Nonlinear responses in damselfly community along a gradient of habitat loss in a savanna landscape. Biol. Conserv. 194, 113–120 (2016).
    Article  Google Scholar 

    40.
    Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen–Geiger climate classification. Hydrol. Earth Syst. Sci. Discuss. 4, 439–473 (2007).
    ADS  Article  Google Scholar 

    41.
    Brasil, L. S. et al. Does the damming of streams in the southern Amazon basin affect dragonfly and damselfly assemblages (Odonata: Insecta)? A preliminary study. Int. J. Odonatol. 17, 187–197 (2014).
    Article  Google Scholar 

    42.
    Lencioni, F. A. A. The Damselflies of Brazil: An Illustrated Guide the Non Coenagrionidae Families (All Print Editora, São Paulo, 2005).
    Google Scholar 

    43.
    Lencioni, F. A. A. The Damselflies of Brazil: An Illustrated Guide—Coenagrionidae (All Print Editora, São Paulo, 2006).
    Google Scholar 

    44.
    Garrison, N. & Ellenrieder, J. A. L. Louton Damselfly Genera of the New World: An Illustrated and Annotated Key to the Zygoptera University Press (Johns Hopkins, Baltimore, 2010).
    Google Scholar 

    45.
    Frissell, C. R., Liss, W. J., Warren, C. E. & Hurley, M. D. A hierarchical framework for stream habitat classification: Viewing streams in a watershed context. Environ. Manag. 10, 199–214 (1986).
    ADS  Article  Google Scholar 

    46.
    Espírito-Santo, H. M. V., Magnusson, W. E., Zuanon, J., Mendonça, F. P. & Landeiro, V. L. Seasonal variation in the composition of fish assemblages in small Amazonian forest streams: Evidence for predictable changes. Freshw. Biol. 54, 536–548 (2009).
    Article  Google Scholar 

    47.
    Uieda, V. S. & Castro, R. M. C. Coleta e fixação de peixes de riachos: Ecologia de peixes de riacho (OecologiaAustralis, Rio de Janeiro, 1999).
    Google Scholar 

    48.
    Planquette, P., Keith, P. & Bail, P. Y. L. Atlas des poissons d’eau douce de Guyane (Service du patrimoine naturel, Paris, 1996).
    Google Scholar 

    49.
    Albert JS. Species Diversity and Phylogenetic Systematics of American Knifefishes (Gymnotiformes, Teleostei). (Miscellaneous Publications of the Museum of Zoology of the University of Michigan, Michigan, 2001).

    50.
    Kaufmann, P. R., Levine, P., Robison, E. G., Seeliger, C. & Peck, D. V. Quantifying Physical Habitat in Wadeable Streams (U.S. Environmental Protection Agency, Washington, 1999).
    Google Scholar 

    51.
    Peck, D. V. et al. Invironmental Monitoring and Assessment Program-Surface Waters: Western Pilot Study Field Operations Manual for Wadeable Streams (U.S. Environmental ProtectionAgency, Washington, 2006).
    Google Scholar 

    52.
    De Marco, P. & Nobrega, C. C. Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation. PLoS ONE 13, 1–25 (2018).
    Google Scholar 

    53.
    Legendre, P. Spatial autocorrelation: Trouble or new paradigm?. Ecology 74, 1659–1673 (1993).
    Article  Google Scholar  More

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    Audio long-read: The enigmatic organisms of the Ediacaran Period

    These bizarre ancient species are rewriting animal evolution – read by Benjamin Thompson
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    The Cambrian explosion, around 541 million years ago, has long been regarded as a pivotal point in evolutionary history, as this is when the ancient ancestors of most of today’s animals made their first appearances in the fossil record.
    Before this was a period known as the Ediacaran – a time when the world was believed to be populated by strange, simple organisms. But now, modern molecular research techniques, and some newly discovered fossils, are providing evidence that some of these organisms were actually animals, including ones with sophisticated features like legs and guts.
    This is an audio version of our feature: These bizarre ancient species are rewriting animal evolution
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