Ecology

Department of Life and Environmental Sciences, Polytechnic University of Marche, Ancona, Italy
Roberto Danovaro, Emanuela Fanelli, Laura Carugati & Antonio Dell’Anno

Stazione Zoologica Anton Dohrn, Naples, Italy
Roberto Danovaro, Emanuela Fanelli & Jacopo Aguzzi

Instituto de Ciencias del Mar (ICM-CSIC), Barcelona, Spain
Jacopo Aguzzi

National Oceanography Centre, Southampton, UK
David Billett & Henry A. Ruhl

Department of Sciences and Engineering of Materials, Environment and Urban Planning (SIMAU), Polytechnic University of Marche, Ancona, Italy
Cinzia Corinaldesi

IUCN Global Marine and Polar Programme, Cambridge, MA, USA
Kristina Gjerde

School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK
Alan J. Jamieson

The Biodiversity Research Group, The School of Biological Sciences, Centre for Biodiversity and Conservation Science, The University of Queensland, Brisbane, Queensland, Australia
Salit Kark

Louisiana Universities Marine Consortium, Chauvin, LA, USA
Craig McClain

Center for Marine Biodiversity and Conservation and Integrative Oceanography Division, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA
Lisa A. Levin

Department of Geography, The Hebrew University of Jerusalem, Jerusalem, Israel
Noam Levin

Remote Sensing Research Centre, School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia
Noam Levin

Norwegian Institute for Water Research, Oslo, Norway
Eva Ramirez-Llodra

Monterey Bay Aquarium Research Institute, Moss Landing, CA, USA
Henry A. Ruhl

Department of Oceanography, University of Hawaii at Mano’a, Honolulu, HI, USA
Craig R. Smith

Departments of Ocean Sciences and Biology, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada
Paul V. R. Snelgrove

Jacobs University, Bremen, Germany
Laurenz Thomsen

Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University, Durham, NC, USA
Cindy L. Van Dover

School of Biological Sciences and Swire Institute of Marine Science, The University of Hong Kong, Hong Kong SAR, China
Moriaki Yasuhara

State Key Laboratory of Marine Pollution, City University of Hong Kong, Hong Kong SAR, China
Moriaki Yasuhara

R.D., E.F., J.A., D.B., L.C., C.C., A.D., K.G., A.J.J., S.K., C.M., L.A.L., N.L., E.R.-L., H.A.R., C.R.S., P.V.R.S., L.T., C.L.V.D. and M.Y. equally contributed to the work, critically revised the final version and gave approval for publication. More

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

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