Philippa Kaur

1.
Fernández, M. V., Heras, S., Maltagliati, F., Turco, A. & Roldán, M. I. Genetic structure in the blue and red shrimp Aristeus antennatus and the role played by hydrographical and oceanographical barriers. Mar. Ecol. Prog. Ser. 421, 163–171 (2011).
ADS  Article  Google Scholar 
2.
Campillo, A. Bio-ecology of Aristeus antennatus in the French Mediterranean. in Proceedings of the International Workshop on Life Cycles and Fisheries of the Deep-water Red Shrimps Aristaeomorpha foliacea and Aristeus antennatus (ed. Bianchini, M. L. & Ragonese, S.) 25–26 (I.T.P.P. Spec. Publ., Mazara del Vallo, Italy, 1994).
Google Scholar 

3.
Sardà, F. et al. An introduction to Mediterranean deep-sea biology. Sci. Mar. 68, 7–38 (2004).
Article  Google Scholar 

4.
Sardà, F., Company, J. B. & Castellón, A. Intraspecific aggregation structure of a shoal of a Western Mediterranean (Catalan coast) deep-sea shrimp, Aristeus antennatus (Risso, 1816), during the reproductive period. J. Shellfish Res. 22, 569–579 (2003).
Google Scholar 

5.
García-Rodriguez, M. & Esteban, A. On the biology and fishery of Aristeus antennatus (Risso, 1816), (Decapoda, Dendrobranchiata) in the Ibiza Channel (Balearic Islands, Spain). Sci. Mar. 63, 27–37 (1999).
Article  Google Scholar 

6.
Carreton, M. et al. Morphological identification and molecular confirmation of the deep-sea blue and red shrimp Aristeus antennatus larvae. PeerJ 7, e6063 (2019).
PubMed  PubMed Central  Article  Google Scholar 

7.
Carbonell, A., Carbonell, M., Demestre, M., Grau, A. & Monserrat, S. The red shrimp Aristeus antennatus (Risso, 1816) fishery and biology in the Balearic Islands, Western Mediterranean. Fish. Res. 44, 1–13 (1999).
Article  Google Scholar 

8.
Maynou, F. Environmental causes of the fluctuations of red shrimp (Aristeus antennatus) landings in the Catalan Sea. J. Mar. Syst. 71, 294–302 (2008).
Article  Google Scholar 

9.
Massutí, E. et al. The influence of oceanographic scenarios on the population dynamics of demersal resources in the western Mediterranean; hypothesis for hake and red shrimp off Balearic Islands. J. Mar. Syst. 71, 421–438 (2008).
Article  Google Scholar 

10.
Food and Agriculture Organization. General Fisheries Commission for the Mediterranean; Report of the ninth session of the Scientific Advisory Committee. FAO Fish. Rep. 814, 1–106 (2006).

11.
Boletín Oficial del Estado. Orden AAA/2808/2012, de 21 de diciembre, por la que se establece un Plan de Gestión Integral para la conservación de los recursos pesqueros en el Mediterráneo afectados por las pesquerías realizadas con redes de cerco, redes de arrastre y artes fijos y menores, para el período 2013–2017. BOE 313, 89468–89475 (2012).
Google Scholar 

12.
Boletín Oficial del Estado. Orden AAA/923/2013, de 16 de mayo, por la que se regula la pesca de gamba rosada (Aristeus antennatus) con arte de arrastre de fondo en determinadas zonas marítimas próximas a Palamós. BOE 126, 40016–40022 (2013).
Google Scholar 

13.
Boletín Oficial del Estado. Orden APM/532/2018, de 25 de mayo, por la que se regula la pesca de gamba rosada (Aristeus antennatus) con arte de arrastre de fondo en determinadas zonas marítimas próximas a Palamós. BOE 128, 55045–55051 (2018).
Google Scholar 

14.
Waples, R. S., Punt, A. E. & Cope, J. M. Integrating genetic data into management of marine resources: how can we do it better?. Fish. Fish. 9, 423–449 (2008).
Article  Google Scholar 

15.
Sardà, F., Bas, C., Roldán, M. I., Pla, C. & Lleonart, J. Enzymatic and morphometric analyses in mediterranean populations of the rose shrimp, Aristeus antennatus (Risso, 1816). J. Exp. Mar. Biol. Ecol. 221, 131–144 (1998).
Article  Google Scholar 

16.
Roldán, M. I., Heras, S., Patellani, R. & Maltagliati, F. Analysis of genetic structure of the red shrimp Aristeus antennatus from the Western Mediterranean employing two mitochondrial regions. Genetica 136, 1–4 (2009).
PubMed  Article  CAS  PubMed Central  Google Scholar 

17.
Lo Brutto, S., Maggio, T., Delana, A. M., Cannas, R. & Arculeo, M. Further investigations on populations of the Deep-water blue and red shrimp Aristeus antennatus (Risso, 1816) (Decapoda, Dendrobranchiata), as inferred from amplified fragment length polymorphism (AFLP) and mtDNA analyses. Crustaceana 85, 1393–1408 (2012).
Article  Google Scholar 

18.
Wright, J. M. & Bentzen, P. Microsatellites: genetic markers for the future. Rev. Fish. Biol. Fish. 4, 384–388 (1994).
Article  Google Scholar 

19.
Cannas, R. et al. Genetic variability of the blue and red shrimp Aristeus antennatus in the Western Mediterranean Sea inferred by DNA microsatellite loci. Mar. Ecol. 33, 350–363 (2012).
ADS  Article  Google Scholar 

20.
You, E.-M. et al. Microsatellite and mitochondrial haplotype diversity reveals population differentiation in the tiger shrimp (Penaeus monodon) in the Indo-Pacific region. Anim. Genet. 39, 267–277 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Robainas-Barcia, A. et al. Spatiotemporal genetic differentiation of Cuban natural populations of the pink shrimp Farfantepenaeus notialis. Genetica 133, 283–294 (2008).
PubMed  Article  PubMed Central  Google Scholar 

22.
Hauser, L. & Carvalho, G. R. Paradigm shifts in marine fisheries genetics: ugly hypotheses slain by beautiful facts. Fish. Fish. 9, 333–362 (2008).
Article  Google Scholar 

23.
Schunter, C. et al. Matching genetics with oceanography: directional gene flow in a Mediterranean fish species. Mol. Ecol. 20, 5167–5181 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

24.
Vera, M. et al. Current genetic status, temporal stability and structure of the remnant wild European flat oyster populations: conservation and restoring implications. Mar. Biol. 163, 239 (2016).
Article  Google Scholar 

25.
García-Ladona, E. Currents in the Western Mediterranean basin. In Atlas of Bedforms in the Western Mediterranean (eds Guillén, J. et al.) 41–47 (Springer, Cham, 2017).
Google Scholar 

26.
Fernández, V., Dietrich, D. E., Haney, R. L. & Tintoré, J. Mesoscale, seasonal and interannual variability in the Mediterranean Sea using a numerical ocean model. Prog. Oceanogr. 66, 321–340 (2005).
ADS  Article  Google Scholar 

27.
Pinot, J.-M., López-Jurado, J. L. & Riera, M. The Canales experiment (1996–1998). Interannual, seasonal and mesoscale variability of the circulation in the Balearic Channels. Prog. Oceanogr. 55, 335–370 (2002).
ADS  Article  Google Scholar 

28.
García-Merchán, V. H. et al. Phylogeographic patterns of decapod crustaceans at the Atlantic-Mediterranean transition. Mol. Phylogenet. Evol. 62, 664–672 (2012).
PubMed  Article  PubMed Central  Google Scholar 

29.
Cartes, J. E., Madurell, T., Fanelli, E. & López-Jurado, J. L. Dynamics of suprabenthos-zooplankton communities around the Balearic Islands (western Mediterranean): influence of environmental variables and effects on the biological cycle of Aristeus antennatus. J. Mar. Syst. 71, 316–335 (2008).
Article  Google Scholar 

30.
Company, J. B. et al. Climate influence on deep sea populations. PLoS ONE 3, e1431 (2008).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

31.
Direcció General d’Agricultura, Ramadería, Pesca i Alimentació. Programa d’Acció Marítima. Estratègia marítima de Catalunya 2030—Pla Estratègic 2018–2021 (2018).

32.
Food and Agriculture Organization. General Fisheries Commission for the Mediterranean; Report of the nineteenth session of the Scientific Advisory Committee on Fisheries. FAO Fish. Rep. 1209, 1–174 (2017).

33.
Planella, L., Vera, M., García-Marín, J.-L., Heras, S. & Roldán, M. I. Mating structure of the blue and red shrimp, Aristeus antennatus (Risso, 1816) characterized by relatedness analysis. Sci. Rep. 9, 7227 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

34.
Heras, S., Planella, L., García-Marín, J.-L., Vera, M. & Roldán, M. I. Genetic structure and population connectivity of the blue and red shrimp Aristeus antennatus. Sci. Rep. 9, 13531 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Jorquera, E., Anstey, L., Paterson, I., Kenchington, E. & Ruzzante, D. E. Isolation and characterization of 26 novel microsatellite loci in the deep-sea shrimp Acanthephyra pelagica. Conserv. Genet. Resour. 6, 731–733 (2014).
Article  Google Scholar 

36.
Jacobson, A., Plouviez, S., Thaler, A. D. & Van Dover, C. L. Characterization of 13 polymorphic microsatellite loci in Rimicaris hybisae, a shrimp from deep-sea hydrothermal vents. Conserv. Genet. Resour. 5, 449–451 (2013).
Article  Google Scholar 

37.
Arculeo, M., Pellerito, R. & Bonhomme, F. Isolation and use of microsatellite loci in Melicertus kerathurus (Crustacea, Penaeidae). Aquat. Living Resour. 23, 103–107 (2010).
CAS  Article  Google Scholar 

38.
Benzie, J. A. H. Population genetic structure in penaeid prawns. Aquac. Res. 31, 95–119 (2000).
Article  Google Scholar 

39.
Maggio, T., Lo Brutto, S., Cannas, R., Deiana, A. M. & Arculeo, M. Environmental features of deep-sea habitats linked to the genetic population structure of a crustacean species in the Mediterranean Sea. Mar. Ecol. 30, 354–365 (2009).
ADS  CAS  Article  Google Scholar 

40.
Palumbi, S. R. Marine reserves and ocean neighbourhoods: the spatial scale of marine populations and their management. Annu. Rev. Environ. Resour. 29, 31–68 (2004).
Article  Google Scholar 

41.
Anger, K. Contributions of larval biology to crustacean research: a review. Invertebr. Reprod. Dev. 49, 175–205 (2006).
Article  Google Scholar 

42.
Palmas, F., Olita, A., Addis, P., Sorgente, R. & Sabatini, A. Modelling giant red shrimp larval dispersal in the Sardinian seas: density and connectivity scenarios. Fish. Oceanogr. 26, 364–378 (2017).
Article  Google Scholar 

43.
Mokhtar-Jamaï, K. et al. From global to local genetic structuring in the red gorgonian Paramuricea clavata: the interplay between oceanographic conditions and limited larval dispersal. Mol. Ecol. 20, 3291–3305 (2011).
PubMed  Article  PubMed Central  Google Scholar 

44.
Schunter, C. et al. Genetic connectivity patterns in an endangered species: The dusky grouper (Epinephelus marginatus). J. Exp. Mar. Biol. Ecol. 401, 126–133 (2011).
Article  Google Scholar 

45.
Carbonell, A. Evaluación de la gamba rosada, Aristeus antennatus (Risso, 1816), en el Mar Balear. (University of the Balearic Islands, Mallorca, Spain, 2005).

46.
Sardà, F., Company, J. B., Rotllant, G. & Coll, M. Biological patterns and ecological indicators for Mediterranean fish and crustaceans below 1,000 m: a review. Rev. Fish. Biol. Fish. 19, 329–347 (2009).
Article  Google Scholar 

47.
Paradis, S. et al. Spatial distribution of sedimentation-rate increases in Blanes Canyon caused by technification of bottom trawling fleet. Prog. Oceanogr. 169, 241–252 (2018).
ADS  Article  Google Scholar 

48.
Clavel-Henry, M. et al. Influence of the summer deep-sea circulations on passive drifts among the submarine canyons in the northwestern Mediterranean Sea. Ocean. Sci. 15, 1745–1759 (2019).
ADS  Article  Google Scholar 

49.
Masó, M. & Tintoré, J. Variability of the shelf water off the northeast Spanish coast. J. Mar. Syst. 1, 441–450 (1991).
Article  Google Scholar 

50.
Ahumada-Sempoal, M.-A., Flexas, M. M., Bernardello, R., Bahamon, N. & Cruzado, A. Northern Current variability and its impact on the Blanes Canyon circulation: a numerical study. Prog. Oceanogr. 118, 61–70 (2013).
ADS  Article  Google Scholar 

51.
Fernandez-Arcaya, U. et al. Ecological role of submarine canyons and need for canyon conservation: a review. Front. Mar. Sci. 4, 1–26 (2017).
ADS  Article  Google Scholar 

52.
Zúñiga, D. et al. Particle fluxes dynamics in Blanes submarine canyon (Northwestern Mediterranean). Prog. Oceanogr. 82, 239–251 (2009).
ADS  Article  Google Scholar 

53.
Durrieu de Madron, X. et al. Interaction of dense shelf water cascading and open-sea convection in the northwestern Mediterranean during winter 2012. Geophys. Res. Lett. 40, 1379–1385 (2013).
ADS  Article  Google Scholar 

54.
Cisneros, M. et al. Deep-water formation variability in the north-western Mediterranean Sea during the last 2500 yr: a proxy validation with present-day data. Glob. Planet. Change 177, 56–68 (2019).
ADS  Article  Google Scholar 

55.
Román, S. et al. High spatiotemporal variability in meiofaunal assemblages in Blanes Canyon (NW Mediterranean) subject to anthropogenic and natural disturbances. Deep. Sea Res Part I Oceanogr. Res. Pap. 117, 70–83 (2016).
ADS  Article  CAS  Google Scholar 

56.
Fernández, M. V., Heras, S., Maltagliati, F. & Roldán, M. I. Deep genetic divergence in giant red shrimp Aristaeomorpha foliacea (Risso, 1827) across a wide distributional range. J. Sea. Res. 76, 146–153 (2013).
ADS  Article  Google Scholar 

57.
Heras, S. et al. Development and characterization of novel microsatellite markers by Next Generation Sequencing for the blue and red shrimp Aristeus antennatus. PeerJ 4, e2200 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

58.
Goudet, J. FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3). Accessed 20 Nov 2019; https://www2.unil.ch/popgen/softwares/fstat.htm (2001).

59.
Rousset, F. GENEPOP´007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).
PubMed  Article  PubMed Central  Google Scholar 

60.
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).
CAS  PubMed  PubMed Central  Google Scholar 

61.
Guo, S. W. & Thompson, E. A. Performing the exact test of Hardy–Weinberg proportion for multiple alleles. Biometrics 48, 361–372 (1992).
CAS  PubMed  MATH  Article  PubMed Central  Google Scholar 

62.
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. Micro-Checker: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
Article  CAS  Google Scholar 

63.
Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).
Article  Google Scholar 

64.
Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

65.
Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).
CAS  PubMed  PubMed Central  Google Scholar 

66.
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

67.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

68.
Earl, D. A. & vonHoldt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).
Article  Google Scholar 

69.
Ryman, N. & Palm, S. POWSIM: a computer program for assessing statistical power when testing for genetic differentiation. Mol. Ecol. Notes 6, 600–602 (2006).
Article  Google Scholar 

70.
Hill, W. G. Estimation of effective population size from data on linkage disequilibrium. Genet. Res. 38, 209–216 (1981).
Article  Google Scholar 

71.
Waples, R. S. A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conserv. Genet. 7, 167–184 (2006).
Article  Google Scholar 

72.
Do, C. et al. NeEstimator V2: re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

73.
Rousset, F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145, 1219–1228 (1997).
CAS  PubMed  PubMed Central  Google Scholar 

74.
Rohlf, F. J. NTSYS-pc. Numerical Taxonomy and Multivariate Analysis System, Version 2.1. Setauket, New York (1993).

75.
Sundqvist, L., Keenan, K., Zackrisson, M., Prodöhl, P. & Kleinhans, D. Directional genetic differentiation and relative migration. Ecol. Evol. 6, 3461–3475 (2016).
PubMed  PubMed Central  Article  Google Scholar  More

1.
Schlesinger, W. H. et al. Biological feedbacks in global desertification. Science. (80-) 247, 1043–1048 (1990).
ADS  CAS  Article  Google Scholar 
2.
Durán, J. et al. Temperature and aridity regulate spatial variability of soil multifunctionality in drylands across the globe. Ecology 99, 1184–1193 (2018).
Article  Google Scholar 

3.
Zuo, X. A. et al. Spatial pattern and heterogeneity of soil organic carbon and nitrogen in sand dunes related to vegetation change and geomorphic position in Horqin Sandy Land Northern China. Environ. Monit. Assess. 164, 29–42 (2010).
CAS  Article  Google Scholar 

4.
Farley, R. A. & Fitter, A. H. Temporal and spatial variation in soil resources in a deciduous woodland. J. Ecol. 87, 688–696 (1999).
Article  Google Scholar 

5.
Jackson, R. B. & Caldwell, M. M. The scale of nutrient heterogeneity around individual plants and its quantification with geostatistics. Ecology 74, 612–614 (1993).
Article  Google Scholar 

6.
Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).
ADS  CAS  Article  Google Scholar 

7.
Delgado-Baquerizo, M. et al. Soil microbial communities drive the resistance of ecosystem multifunctionality to global change in drylands across the globe. Ecol. Lett. 20, 1295–1305 (2017).
Article  Google Scholar 

8.
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science (80-). 346, 1256688–1256688 (2014).
Article  Google Scholar 

9.
Fierer, N. Embracing the unknown: Disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).
CAS  Article  Google Scholar 

10.
Delgado-Baquerizo, M. et al. Plant attributes explain the distribution of soil microbial communities in two contrasting regions of the globe. New Phytol. 219, 574–587 (2018).
Article  Google Scholar 

11.
Delgado-Baquerizo, M. et al. Changes in belowground biodiversity during ecosystem development. Proc. Natl. Acad. Sci. 116, 6891–6896 (2019).
CAS  Article  Google Scholar 

12.
Delgado-Baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science (80-) 359, 320–325 (2018).
ADS  CAS  Article  Google Scholar 

13.
Franklin, R. B. & Mills, A. L. Importance of spatially structured environmental heterogeneity in controlling microbial community composition at small spatial scales in an agricultural field. Soil Biol. Biochem. 41, 1833–1840 (2009).
CAS  Article  Google Scholar 

14.
Day, K. J., Hutchings, M. J. & John, E. A. The effects of spatial pattern of nutrient supply on yield, structure and mortality in plant populations. J. Ecol. 91, 541–553 (2003).
Article  Google Scholar 

15.
Maestre, F. T. & Reynolds, J. F. Biomass responses to elevated CO2, soil heterogeneity and diversity: An experimental assessment with grassland assemblages. Oecologia 151, 512–520 (2007).
ADS  Article  Google Scholar 

16.
Tsunoda, T., Kachi, N. & Suzuki, J.-I. Interactive effects of soil nutrient heterogeneity and belowground herbivory on the growth of plants with different root foraging traits. Plant Soil 384, 327–334 (2014).
CAS  Article  Google Scholar 

17.
Schlesinger, W. H. & Bernhardt, E. S. Biogeochemistry: An Analysis of Global Change (Elsevier Science, Amsterdam, 2012).
Google Scholar 

18.
Ochoa-Hueso, R. et al. Soil fungal abundance and plant functional traits drive fertile island formation in global drylands. J. Ecol. 106, 242–253 (2018).
CAS  Article  Google Scholar 

19.
Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science (80-). 335, 214–218 (2012).
ADS  CAS  Article  Google Scholar 

20.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Article  Google Scholar 

21.
Wardle, D. A., Walker, L. R. & Bardgett, R. D. Ecosystem properties and forest decline in contrasting long-term chronosequences. Science (80-). 305, 509–513 (2004).
ADS  CAS  Article  Google Scholar 

22.
Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. U.S.A. 103, 626–631 (2006).
ADS  CAS  Article  Google Scholar 

23.
Alfaro, F. D., Manzano, M., Marquet, P. A. & Gaxiola, A. Microbial communities in soil chronosequences with distinct parent material: the effect of soil pH and litter quality. J. Ecol. 105, 1709–1722 (2017).
CAS  Article  Google Scholar 

24.
Wardle, D. A. et al. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633 (2004).
ADS  CAS  Article  Google Scholar 

25.
Lauber, C. L., Hamady, M., Knight, R. & Fierer, N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).
CAS  Article  Google Scholar 

26.
Ramirez, K. S. et al. Biogeographic patterns in below-ground diversity in New York City’s Central Park are similar to those observed globally. Proc. R. Soc. B Biol. Sci. 281, 2 (2014).
Google Scholar 

27.
Duarte, C. M. Variance and the description of nature. In Comparative Analyses of Ecosystems 301–318 (Springer, New York, 1991). https://doi.org/10.1007/978-1-4612-3122-6_15.
Google Scholar 

28.
Fraterrigo, J. M. & Rusak, J. A. Disturbance-driven changes in the variability of ecological patterns and processes. Ecol. Lett. 11, 756–770 (2008).
Article  Google Scholar 

29.
Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9, 683–693 (2006).
Article  Google Scholar 

30.
Anderson, M., Gorley, R. & Clarke, K. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods (Primer-E Ltd, Plymouth, 2008).
Google Scholar 

31.
Grace, J. B. Structural Equation Modeling and Natural Systems (Cambridge University Press, Cambridge, 2006). https://doi.org/10.1017/CBO9780511617799.
Google Scholar 

32.
Schermelleh-Engel, K., Moosbrugger, H. & Müller, H. H. Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit measures. (2003).

33.
Moles, A. T. et al. Global patterns in plant height. J. Ecol. 97, 923–932 (2009).
Article  Google Scholar 

34.
Deraison, H., Badenhausser, I., Loeuille, N., Scherber, C. & Gross, N. Functional trait diversity across trophic levels determines herbivore impact on plant community biomass. Ecol. Lett. 18, 1346–1355 (2015).
Article  Google Scholar 

35.
García-Palacios, P., Shaw, E. A., Wall, D. H. & Hättenschwiler, S. Temporal dynamics of biotic and abiotic drivers of litter decomposition. Ecol. Lett. 19, 554–563 (2016).
Article  Google Scholar 

36.
Le Bagousse-Pinguet, Y. et al. Testing the environmental filtering concept in global drylands. J. Ecol. 105, 1058–1069 (2017).
Article  Google Scholar 

37.
Dougill, A. J. & Thomas, A. D. Kalahari sand soils: Spatial heterogeneity, biological soil crusts and land degradation. L. Degrad. Dev. 15, 233–242 (2004).
Article  Google Scholar 

38.
Hook, P. B., Burke, I. C. & Lauenroth, W. K. Heterogeneity of soil and plant N and C associated with individual plants and openings in North American shortgrass steppe. Plant Soil 138, 247–256 (1991).
CAS  Article  Google Scholar 

39.
D’Odorico, P., Bhattachan, A., Davis, K. F., Ravi, S. & Runyan, C. W. Global desertification: Drivers and feedbacks. Adv. Water Resour. 51, 326–344 (2013).
ADS  Article  Google Scholar 

40.
Wardle, D. A. The influence of biotic interactions on soil biodiversity. Ecol. Lett. 9, 870–886 (2006).
Article  Google Scholar 

41.
Jurena, P. N. & Archer, S. Woody plant establishment and spatial heterogeneity in grasslands. Ecology 84, 907–919 (2003).
Article  Google Scholar 

42.
Zhou, Y., Boutton, T. W. & Wu, X. B. Woody plant encroachment amplifies spatial heterogeneity of soil phosphorus to considerable depth. Ecology 99, 136–147 (2018).
Article  Google Scholar  More

Fish culture
Fertilized eggs of chum salmon [Oncorhynchus keta (Walbaum, 1792)] were obtained from Unosumai hatchery, Iwate, Japan. The eggs were fertilized artificially on 4th December 2018 and eye-stage embryos were transferred to the Atmosphere and Ocean Research Institute, Chiba, Japan. Hatched salmon fries were fed with commercial diet after emergence in freshwater. Natural seawater (SW, salinity 35 ‰) was obtained from the Kuroshio Current at Hachijō-jima. The SW was stored in underground facilities and salmon eDNA was not detectable from the SW stock from in-house experiment. Salmon juveniles were transferred to seawater tanks after 2-months culture in freshwater. The experimental tank contained 500 L seawater with recirculation and temperature control at 14 °C. Twenty-five individuals (fork-length: ca. 20 cm; weight: ca. 50 g) were kept in the tank and the water served as a source of eDNA in the present study. All animal studies were performed according to the Guideline for Care and Use of Animals approved by the Animal Experiment Committee of The University of Tokyo.
Filtration of water
Two concentrations of salmon water were prepared. “Neat salmon water” was obtained directly from the stock tank. “Diluted salmon water” was prepared by diluting the tank water 10 times with seawater that had not been used for any previous fish culture. To standardize filtration conditions, 1 L of salmon water at either concentration was filtered through a Sterivex cartridge (0.45 μm, Millipore SVHV010RS, Merck, Tokyo, Japan) using a vacuum manifold (Manifold KMP-3, Advantec, Tokyo, Japan) attached to an aspirator (Eyela A-1000S, Tokyo Rikakikai, Tokyo, Japan) (Fig. 7). After filtration, the outlet of the Sterivex cartridge was sealed by Parafilm and 1.6 mL of RNAlater was introduced into the cartridge through the inlet. The inlet was subsequently sealed by Parafilm and the cartridge was stored at − 20 °C until further processing. All the Sterivex cartridges were then randomly assigned to different extraction protocols after the filtration step to reduce bias on filtration order.
Figure 7

Representative photos of Sterivex cartridges with different extraction protocols. (A) A cartridge extracted by 0.44 mL lysis mix protocol. Note the water mark that is present on the filter surface (white arrow), suggesting insufficient contact with the lysis buffer mix. (B) A cartridge extracted by 2.0 mL lysis buffer mix with backwash. Note that no water mark is found as in (A). (C) A cartridge centrifuged by angled rotor. Note that remaining lysate is present (black arrow).

Full size image

DNA extraction and purification
All the connectors, stoppers, and silicon tubes were decontaminated by bleaching before use5. The RNAlater in the Sterivex cartridges was thawed on ice and removed by aspiration through the outlet. A silicon tube (1.5 cm; i.d. 3 mm; o.d. 9 mm) was used to connect a Luer-Lock adaptor (VRF306, AS ONE, Osaka, Japan) to the outlet such that both inlet and outlet can be sealed by Luer-Lock stoppers (VRMP6, AS ONE, Osaka, Japan). We tested three protocols that were modified from a published method that was widely used in the eDNA field13. The following reagents are based on the Qiagen DNeasy Blood and Tissue Kit (ThermoFisher Scientific, Waltham, MA, USA), with additional reagents (e.g. Buffer AL and proteinase K) purchased from the same sources when necessary. The major modifications are summarized in Fig. 7. For Protocol 1, we followed the original extraction protocol13. A lysis buffer mix (PBS 220 μL, Buffer AL 200 μL, Proteinase K 20 μL; 440 μL total volume) was introduced to the cartridge through the inlet. Both ends of the cartridge were sealed by Luer-Lock stoppers and the cartridge was incubated at 56 °C for 30 min with mild rotation. In Protocol 2, approximately four-times volume (2 mL) of lysis buffer mix (PBS 990 μL, Buffer AL 910 μL, Proteinase K 100 μL) was introduced through the inlet. After sealing the ends, the cartridge was incubated at 56 °C for 30 min without rotation. The rotation was omitted because the filter surface was completely covered by the lysis buffer mix. In Protocol 3, 2 mL lysis buffer mix (PBS 990 μL, Buffer AL 910 μL, Proteinase K 100 μL) was introduced into the cartridge through the outlet using a 2.5 mL syringe (Terumo Corporation, Tokyo, Japan) to flush the cartridge in a reverse direction. After sealing the ends, the cartridge was incubated at 56 °C for 30 min without rotation.
After the incubation, each Sterivex cartridge was placed in a spin column (maxi spin, flat bottom, Ciro Manufacturing Corporation, Deerfield Beach, FL, USA) attached to a 50 mL centrifuge tube, with the cartridge inlet facing downward. The unit was centrifuged with a swing-type rotor at 2500g for 10 min at 25 °C to elute the content. The cartridge and spin column were removed and molecular grade ethanol (99.5%, Fujifilm Wako Pure Chemical Corporation, Osaka, Japan) was added to one-third of the final volume (200 μL for Protocol 1 and 1 mL for Protocols 2 and 3). The mixture was loaded on a DNeasy Blood and Tissue Kit column attached to a vacuum manifold and the column was washed by 0.8 mL AW1 buffer and 0.8 mL AW2 buffer sequentially. The DNeasy column was dried by centrifugation at 17,700g for 2 min. Adsorbed DNA was eluted from the column with 75 μL AE buffer twice to maximize recovery. A total of 150 μL sample was collected from each extraction. Six replicates of Sterivex cartridges were used for each protocol. To test the extraction efficiency, each Sterivex cartridge was extracted 3 times and the eluted samples were analyzed separately.
An aliquot of DNA sample (100 μL) was further purified by Qiagen DNeasy PowerClean Pro Cleanup Kit (ThermoFisher Scientific, Waltham, MA, USA) according to the manufacturer’s protocol.
To test the binding capacity of the DNeasy column, we performed an additional experiment to determine whether the quantity of sample input will have a saturating effect on the column binding. We prepared a high sample input group by filtering 2 L of neat salmon seawater per Sterivex cartridge, while 1 L of 10×-diluted salmon seawater per cartridge was considered as a low sample input group. The extraction was performed using Protocol 3. Since we hypothesized that the binding capacity of one DNeasy column could be saturated by high input, we compared the eDNA extracted by a single column or double columns connected in series. In the case of double columns, the elution was performed individually for the upper and lower columns, and the eluates were analyzed separately.
Chum salmon eDNA quantification
The eDNA was measured by quantitative PCR (qPCR) as described in a previous study5, with slight modification. Instead of using ABI TaqMan Environmental Master Mix 2.0 (Applied Biosystems, Foster City, USA), Takara Probe qPCR Mix (Takara Bio Incorporation, Kusatsu, Japan) was used as we showed that this reagent has a higher capacity to resist inhibition by environmental contaminants in the PCR reaction (see Results). We compared the resistance to contaminants by Environmental Master Mix and Takara Probe qPCR Mix according to the spiking/dilution methods described previously5, using the environmental samples collected from Murohama Bay of Otsuchi, which contained high levels of PCR inhibitors.
Method validations in field samples
Thirty-four random seawater samples (1 L each) were collected from the Otsuchi River mouth (141′26.917 E, 39′55.021 N), Iwate Prefecture between March 15th–19th, 2020. Negative control was prepared by filtering distilled water (1 L). The water samples were filtered and treated with RNAlater as described in previous section. The cartridges were extracted according to the Protocol 3 described in previous section. To test the extraction and purification efficiencies, 1010 copies of a control plasmid (self-ligated pGEMT-easy, Promega, Madison, WI, USA) were added to each 2 mL lysis buffer mix (PBS 990 μL, Buffer AL 910 μL, Proteinase K 100 μL) at the beginning of the extraction. The extraction efficiency was calculated from ratio of control plasmid quantified from the DNeasy-extracted samples relative to input quantity. Real-time PCR for control plasmid was designed at the M13 regions: forward primer (pGEMT-F), 5′-TTTCCCAGTCACGACGTT-3′; reverse primer (pGEMT-R), 5′-TTCACACAGGAAACAGCTATGA-3′; probe (pGEMT-probe), 56/FAM/ACGCGTTGG/ZEN/ATGCATAGCTTGAGTA/3lABkFQ (Integrated DNA Technologies Inc., Coralville, IO, USA). Chum salmon eDNA concentrations of both DNeasy extracted samples and samples further purified by PowerClean Pro Cleanup Kit were measured to estimate the recovery rate of PowerClean purification. To estimate the PCR inhibitor effects from samples without purification by PowerClean Pro Cleanup Kit, an additional assay was performed with 105 copies of chum salmon standard spiked into each PCR reaction. The chum salmon standard used in spiking is the same plasmid DNA5 used for constructing the standard curve. A reduction in spiked values calculated by subtracting the spiked copy number to original copy number indicates that PCR reaction was not optimal.

$$ {text{Detection}},{text{rate}},{text{of}},{text{spiked}},{text{DNA}} = frac{{left( {{text{copy}},{text{in}},{text{spiked}},{text{sample}} – {text{copy}},{text{in}},{text{original}},{text{sample}}} right)}}{{left( {10^{5} ,{text{copy}},{text{of}},{text{spiked}},{text{DNA}}} right)}} times 100% $$

Detection rates lower than 100% indicate that the PCR reaction could be affected by PCR inhibitors in environmental samples.
To test whether addition of bovine serum albumin (BSA) can further protect the real-time PCR reaction from non-specific inhibition, a final concentration of 1 μg/μL BSA (A4161, Sigma-Aldrich, St. Louis, MO, USA) was added to each Takara Probe qPCR Mix reaction. Real-time PCR was performed on the serially-diluted inhibitor-rich sample and the slope of the dilution curve was determined. PCR efficiencies were calculated according to the following equation (Efficiency = 10−1/slope)27. As we found improved quantification from BSA experiment, we further tested chemical additives including 2% DMSO (D2650-5X Sigma-Aldrich, St. Louis, MO, USA), 0.5% Tween 20 (P9416 Sigma-Aldrich, St. Louis, MO, USA), 0.01% Formamide (F9037 Sigma-Aldrich, St. Louis, MO, USA), and a protein-based additive GP-32 protein (Nippon Gene, Toyama, Japan) at 0.1 μg/μL final concentration28. A pooled eDNA sample was made from mixing some inhibitor-rich samples from the 34 random environmental samples, and subsequently used in this preliminary test. The PCR amplification plots were compared between chum salmon standard and the pooled sample. Slope of the PCR amplification plot was obtained by linear regression on the steepest linear portion of the curve. PCR reaction was compromised by the inhibitor when we observed a reduction in saturated fluorescence and a flatten slope of the linear portion of the amplification plot of the pooled sample in comparison to those of standard. We considered that the rescue from reduction in saturated fluorescence and slope change are indicating an increase in resistance to PCR inhibitors by the additive.
Statistical analysis
The concentrations of salmon eDNA extracted by various protocols were analyzed by two-way ANOVA followed by Tukey’s multiple comparisons (GraphPad Prism Ver. 6 for Windows, San Diego, CA, USA). Statistical significance (p  More

1.
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 
2.
Tomlinson, S. et al. Applications and implications of ecological energetics. Trends Ecol. Evol. 29, 280–290 (2014).
PubMed  Article  PubMed Central  Google Scholar 

3.
Wilson, R. P. et al. Estimates for energy expenditure in free-living animals using acceleration proxies; a reappraisal. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13040 (2018).
Article  PubMed  PubMed Central  Google Scholar 

4.
Stearns, S. C. The Evolution of Life Histories (OUP Oxford, Oxford, 1992).
Google Scholar 

5.
Green, J. A., Boyd, I. L., Woakes, A. J., Warren, N. L. & Butler, P. J. Evaluating the prudence of parents: Daily energy expenditure throughout the annual cycle of a free-ranging bird, the macaroni penguin Eudyptes chrysolophus. J. Avian Biol. 40, 529–538 (2009).
Article  Google Scholar 

6.
Halsey, L. G. et al. Flexibility, variability and constraint in energy management patterns across vertebrate taxa revealed by long-term heart rate measurements. Funct. Ecol. 33, 260–272 (2019).
Article  Google Scholar 

7.
Butler, P. J., Green, J. A., Boyd, I. L. & Speakman, J. R. Measuring metabolic rate in the field: The pros and cons of the doubly labelled water and heart rate methods. Funct. Ecol. 18, 168–183 (2004).
Article  Google Scholar 

8.
Green, J. A. The heart rate method for estimating metabolic rate: Review and recommendations. Comp. Biochem. Physiol. A. Mol. Integr. Physiol. 158, 287–304 (2011).
PubMed  Article  CAS  PubMed Central  Google Scholar 

9.
Green, J. A., Halsey, L. G., Wilson, R. P. & Frappell, P. B. Estimating energy expenditure of animals using the accelerometry technique: Activity, inactivity and comparison with the heart-rate technique. J. Exp. Biol. 212, 471–482 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Speakman, J. R. Doubly Labelled Water: Theory and Practice (Chapman and Hall, London, 1997).
Google Scholar 

11.
Yoda, K. et al. A new technique for monitoring the behaviour of free-ranging Adélie penguins. J. Exp. Biol. 204, 685–690 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

12.
Wilson, R. P. et al. Moving towards acceleration for estimates of activity-specific metabolic rate in free-living animals: The case of the cormorant. J. Anim. Ecol. 75, 1081–1090 (2006).
PubMed  Article  PubMed Central  Google Scholar 

13.
Gleiss, A. C., Wilson, R. P. & Shepard, E. L. C. Making overall dynamic body acceleration work: On the theory of acceleration as a proxy for energy expenditure. Methods Ecol. Evol. 2, 23–33 (2011).
Article  Google Scholar 

14.
Ropert-Coudert, Y. & Wilson, R. P. Trends and perspectives in animal-attached remote sensing. Front. Ecol. Environ. 3, 437–444 (2005).
Article  Google Scholar 

15.
Gatt, M. C., Quetting, M., Cheng, Y. & Wikelski, M. Dynamic body acceleration increases by 20% during flight ontogeny of Greylag Geese (Anser anser). J. Avian Biol. 1, 2235 (2019).
Google Scholar 

16.
Van Walsum, T. A. et al. Exploring the relationship between flapping behaviour and accelerometer signal during ascending flight, and a new approach to calibration. Ibis (Lond. 1859) 162, 13–26 (2020).
Article  Google Scholar 

17.
Elliott, K. H. Measurement of flying and diving metabolic rate in wild animals: Review and recommendations. Comparat. Biochem. Physiol. Part A Mol. Integr. Physiol. 202, 63–77 (2016).
CAS  Article  Google Scholar 

18.
Fahlman, A., Svärd, C., Rosen, D. S., Jones, D. R. & Trites, A. W. Metabolic costs of foraging and the management of O2 and CO2 stores in Steller sea lions. J. Exp. Biol. 211, 3573–3580 (2008).
PubMed  Article  PubMed Central  Google Scholar 

19.
Payne, N. L. et al. Accelerometry estimates field metabolic rate in giant Australian cuttlefish Sepia apama during breeding. J. Anim. Ecol. 80, 422–430 (2011).
PubMed  Article  PubMed Central  Google Scholar 

20.
Wright, S., Metcalfe, J. D., Hetherington, S. & Wilson, R. P. Estimating activity-specific energy expenditure in a teleost fish, using accelerometer loggers. Mar. Ecol. Prog. Ser. 496, 19–32 (2014).
ADS  Article  Google Scholar 

21.
Bidder, O. R. et al. Does the treadmill support valid energetics estimates of field locomotion?. Integr. Comp. Biol. 57, 301–319 (2017).
PubMed  Article  PubMed Central  Google Scholar 

22.
Pagano, A. M. & Williams, T. M. Estimating the energy expenditure of free-ranging polar bears using tri-axial accelerometers: A validation with doubly labeled water. Ecol. Evol. 00, 1–10 (2019).
Google Scholar 

23.
Jeanniard-du-Dot, T., Trites, A. W., Arnould, J. P. Y., Speakman, J. R. & Guinet, C. Activity-specific metabolic rates for diving, transiting, and resting at sea can be estimated from time–activity budgets in free-ranging marine mammals. Ecol. Evol. 7, 2969–2976 (2017).
PubMed  PubMed Central  Article  Google Scholar 

24.
Hicks, O. et al. Validating accelerometry estimates of energy expenditure across behaviours using heart rate data in a free-living seabird. J. Exp. Biol. 220, 1875–1881 (2017).
PubMed  PubMed Central  Article  Google Scholar 

25.
Elliott, K. H., Le Vaillant, M., Kato, A., Speakman, J. R. & Ropert-Coudert, Y. Accelerometry predicts daily energy expenditure in a bird with high activity levels. Biol. Lett. 9, 1–4 (2013).
Article  Google Scholar 

26.
Bishop, C. M. et al. The roller coaster flight strategy of bar-headed geese conserves energy during Himalayan migrations. Science 147, 250–254 (2015).
ADS  Article  CAS  Google Scholar 

27.
Stothart, M. R., Elliott, K. H., Wood, T., Hatch, S. A. & Speakman, J. R. Counting calories in cormorants: Dynamic body acceleration predicts daily energy expenditure measured in pelagic cormorants. J. Exp. Biol. 219, 2192–2200 (2016).
PubMed  Article  PubMed Central  Google Scholar 

28.
Karasov, W. H. Daily energy expenditure and the cost of activity in mammals. Integr. Comp. Biol. 32, 238–248 (1992).
Google Scholar 

29.
Lovvorn, J. R. Thermal substitution and aerobic efficiency: Measuring and predicting effects of heat balance on endotherm diving energetics. Philos. Trans. R. Soc. B Biol. Sci. 362, 2079–2093 (2007).
CAS  Article  Google Scholar 

30.
Lewden, A., Enstipp, M. R., Picard, B., Van Walsum, T. & Handrich, Y. High peripheral temperatures in king penguins while resting at sea: Thermoregulation versus fat deposition. J. Exp. Biol. 220, 3084–3094 (2017).
PubMed  Article  PubMed Central  Google Scholar 

31.
Halsey, L. G., Shepard, E. L. C. & Wilson, R. P. Assessing the development and application of the accelerometry technique for estimating energy expenditure. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 158, 305–314 (2011).
Article  CAS  Google Scholar 

32.
Elliott, K. H. Measurement of flying and diving metabolic rate in wild animals: Review and recommendations. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 202, 63–77 (2016).
CAS  Article  Google Scholar 

33.
Wilson, R. P. & Culik, B. M. The cost of a hot meal: Facultative specific dynamic action may ensure temperature homeostasis in post-ingestive endotherms. Comp. Biochem. Physiol. Part A Physiol. 100, 151–154 (1991).
CAS  Article  Google Scholar 

34.
Halsey, L. G. et al. Assessing the validity of the accelerometry technique for estimating the energy expenditure of diving double-crested cormorants Phalacrocorax auritus. Physiol. Biochem. Zool. 84, 230–237 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Ladds, M. A., Rosen, D. A. S., Slip, D. J. & Harcourt, R. G. Proxies of energy expenditure for marine mammals: An experimental test of ‘the time trap’. Sci. Rep. 7, 1–10 (2017).
CAS  Article  Google Scholar 

36.
Qasem, L. et al. Tri-axial dynamic acceleration as a proxy for animal energy expenditure; should we be summing values or calculating the vector?. PLoS ONE 7, e31187 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Sparling, C. E., Thompson, D., Fedak, M. A., Gallon, S. L. & Speakman, J. R. Estimating field metabolic rates of pinnipeds: Doubly labelled water gets the seal of approval. Funct. Ecol. 22, 245–254 (2008).
Article  Google Scholar 

38.
Ropert-Coudert, Y. et al. Two recent massive breeding failures in an adélie penguin colony call for the creation of a Marine Protected area in D’Urville Sea/Mertz. Front. Mar. Sci. 5, 1–7 (2018).
Article  Google Scholar 

39.
Chappell, M. A., Shoemaker, V. H., Janes, D. N., Maloney, S. K. & Bucher, T. L. Energetics of foraging in breeding Adélie penguins. Ecology 74, 2450–2461 (1993).
Article  Google Scholar 

40.
Nagy, K. A. & Obst, B. S. Food and energy requirements of Adelie penguins (Pygoscelis adeliae) on the Antarctic Peninsula. Physiol. Zool. 65, 1271–1284 (1992).
Article  Google Scholar 

41.
Culik, B. Y. B. & Wilson, R. P. Swimming energetics and performance of instrumented Adélie penguins (Pygoscelis Adeliae). J. Exp. Biol. 158, 355–368 (1991).
Google Scholar 

42.
Kooyman, G. L., Gentry, R. L., Bergman, W. P. & Hammel, H. T. Heat loss in penguins during immersion and compression. Comp. Biochem. Physiol. Part A Physiol. 54, 75–80 (1976).
CAS  Article  Google Scholar 

43.
Fahlman, A., Wilson, R., Svärd, C., Rosen, D. A. S. & Trites, A. W. Activity and diving metabolism correlate in Steller sea lion Eumetopias jubatus. Aquat. Biol. 2, 75–84 (2008).
Article  Google Scholar 

44.
Gleiss, A. C., Dale, J. J., Holland, K. N. & Wilson, R. P. Accelerating estimates of activity-specific metabolic rate in fishes: Testing the applicability of acceleration data-loggers. J. Exp. Mar. Bio. Ecol. 385, 85–91 (2010).
Article  Google Scholar 

45.
Halsey, L. G., Jones, T. T., Jones, D. R., Liebsch, N. & Booth, D. T. Measuring energy expenditure in sub-adult and hatchling sea turtles via accelerometry. PLoS ONE 6, 2 (2011).
Article  CAS  Google Scholar 

46.
Halsey, L. G. et al. The relationship between oxygen consumption and body acceleration in a range of species. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 152, 197–202 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Halsey, L. G. et al. Acceleration versus heart rate for estimating energy expenditure and speed during locomotion in animals: Tests with an easy model species, Homo sapiens. Zoology. https://doi.org/10.1016/j.zool.2007.07.011 (2008).
Article  PubMed  PubMed Central  Google Scholar 

48.
Gleiss, A. C., Gruber, S. H. & Wilson, R. P. Multi-channel data-logging: Towards determination of behaviour and metabolic rate in free-swimming sharks. Tag. Track. Mar. Anim. Electron. Dev. 9, 211–228 (2009).
Google Scholar 

49.
Gómez-Laich, A., Wilson, R. P., Quintana, F. & Shepard, E. Identification of imperial cormorant Phalacrocorax atriceps behaviour using accelerometers. Endanger. Species Res. 10, 29–37 (2008).
Article  Google Scholar 

50.
Jeanniard-du-Dot, T., Guinet, C., Arnould, J. P. Y., Speakman, J. R. & Trites, A. W. Accelerometers can measure total and activity-specific energy expenditures in free-ranging marine mammals only if linked to time-activity budgets. Funct. Ecol. 31, 377–386 (2016).
Article  Google Scholar 

51.
Gómez-Laich, A., Wilson, R. P., Gleiss, A. C., Shepard, E. L. C. & Quintana, F. Use of overall dynamic body acceleration for estimating energy expenditure in cormorants. J. Exp. Mar. Bio. Ecol. 399, 151–155 (2011).
Article  Google Scholar 

52.
Fahlman, A., Schmidt, A., Handrich, Y., Woakes, A. J. & Butler, P. J. Metabolism and thermoregulation during fasting in king penguins, Aptenodytes patagonicus, in air and water. Am. J. Physiol. Integr. Comp. Physiol. 289, R670–R679 (2005).
CAS  Article  Google Scholar 

53.
Ciancio, J. E., Quintana, F., Sala, J. E. & Wilson, R. P. Cold birds under pressure: Can thermal substitution ease heat loss in diving penguins?. Mar. Biol. 163, 1–15 (2016).
Article  CAS  Google Scholar 

54.
Wilson, R. P. et al. Estimates for energy expenditure in free-living animals using acceleration proxies: A reappraisal. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13040 (2019).
Article  PubMed  PubMed Central  Google Scholar 

55.
Lifson, N. & McClintock, R. Theory of use of the turnover rates of body water for measuring energy and material balance. J. Theor. Biol. 12, 46–74 (1966).
CAS  PubMed  Article  PubMed Central  Google Scholar 

56.
Speakman, J. R. & Król, E. Comparison of different approaches for the calculation of energy expenditure using doubly labeled water in a small mammal. Physiol. Biochem. Zool. 78, 650–667 (2005).
PubMed  Article  PubMed Central  Google Scholar 

57.
Beaulieu, M. et al. Sex-specific parental strategies according to the sex of offspring in the Adélie penguin. Behav. Ecol. 20, 878–883 (2009).
Article  Google Scholar 

58.
Berman, E. S. F. et al. Direct analysis of δ2H and δ18O in natural and enriched human urine using laser-based, off-axis integrated cavity output spectroscopy. Anal. Chem. 84, 9768–9773 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Nagy, K. A. Doubly labeled water method (3 HH 18 O): A guide to its use. Oecologia 59, 1–45 (1983).
Article  Google Scholar 

60.
Speakman, J. R. How should we calculate CO2 production in DLW studies of mammals. Funct. Ecol. 7, 746–750 (1993).
Google Scholar 

61.
Visser, G. H. & Schekkerman, H. Validation of the doubly labeled water method in growing precocial birds: The importance of assumptions concerning evaporative water loss. Physiol. Biochem. Zool. 72, 740–749 (1999).
CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Van Trigt, R. et al. Validation of the DLW method in Japanese quail at different water fluxes using laser and IRMS. J. Appl. Physiol. 93, 2147–2154 (2002).
PubMed  Article  PubMed Central  Google Scholar 

63.
Culik, B. et al. Energy requirements of Adélie penguin (Pygoscelis adeliae) chicks. J. Comp. Physiol. B 160, 61–70 (1990).
CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Wilson, R. P. et al. Long-term attachment of transmitting and recording devices to penguins and other seabirds. Wildl. Soc. Bull. 25, 101–105 (1997).
Google Scholar 

65.
Collins, P. M. et al. Interpreting behaviors from accelerometry: A method combining simplicity and objectivity. Ecol. Evol. 5, 4642–4654 (2015).
PubMed  PubMed Central  Article  Google Scholar 

66.
Patterson, A., Gilchrist, H. G., Chivers, L., Hatch, S. & Elliott, K. A comparison of techniques for classifying behavior from accelerometers for two species of seabird. Ecol. Evol. https://doi.org/10.1002/ece3.4740 (2019).
Article  PubMed  PubMed Central  Google Scholar 

67.
R Core Team. R: A language and environment for statistical computing. (2019). More

On a summer morning in 2019, Andy Suhrbier pilots a small aluminium boat out to a mussel raft in a quiet cove on the eastern shore of Puget Sound in Washington State. As the boat approaches, a mother seal and her pup resting on the raft slip into the water. Suhrbier climbs from his boat onto the raft; the only sign of life is a vague smell.
Suhrbier tugs on a couple of ropes attached to one of the raft’s beams. Soon, a mesh-lined plastic cage emerges with water and silt pouring out of it. He picks off several sea stars and tosses them back into the water, then flips open the lid like a pirate opening a treasure chest.
Inside is more dark sediment — mostly waste from the mussels, the source of the smell. Suhrbier sifts through it. He is looking for something.
“Look at this monster!” he says, holding up a sea cucumber nearly a foot long. Its deep red body covered in orange bumps stands out from the muck like a gold doubloon. “That’s definitely market size.”

Suhrbier is a biologist with the Pacific Shellfish Institute in Olympia, Washington, a non-profit research organization that works to promote healthy wild shellfish populations and sustainable shellfish aquaculture along the US west coast. Two years earlier, he had put sea cucumbers in cages and suspended them beneath the mussel raft, as part of an effort to develop aquaculture in Puget Sound. The hefty size of the cucumbers is a promising sign.
Suhrbier and his colleagues think that sea-cucumber farming could have two benefits. First, the animals could help to prevent excess waste from building up underneath aquaculture installations, such as mussel rafts or net pens used to hold bony fish such as salmon. (Sea cucumbers, soft-bodied animals related to sea urchins, move slowly over the sea floor eating detritus — the vacuum cleaners of the ocean.) Second, a ready source of farmed sea cucumbers could reduce the poaching of wild stocks to feed the growing market in east and southeast Asia.
Globally, aquaculture produced 82.1 million tonnes of aquatic animals in 2018, and wild fisheries produced 97 million tonnes, according to the United Nations’ Food and Agriculture Organization (FAO). But the value of farmed fish was higher, around US$250 billion compared with $151 billion for wild-caught fish. Aquaculture production of animals is projected to increase by one-third by 2030, reaching 109 million tonnes, and will supply the majority of aquatic protein in people’s diets by 2050.
“We need to grow the amount of seafood available, as world populations grow, to provide enough protein for everybody,” says Monica Jain, founder of Fish2.0 in Carmel, California, an organization that promotes investment in sustainable seafood businesses. With the catches from wild fisheries remaining largely flat and some stocks already overexploited, “aquaculture is really the only way to do that”. But as the industry grows, Jain and other aquaculture advocates want to make sure that it does so sustainably.
Double alchemy
Aquaculture is a relatively small proportion of the global food system — terrestrial meat production (both livestock and wild game) totalled around 342 million tonnes in 2018, and production of grains and cereals was 2.7 billion tonnes. However, aquaculture is more diverse, particularly in terms of the animals farmed. These range widely across taxonomic groups, including bony fish (carp, tilapia and salmon, for example), crustaceans (shrimp, prawns and crayfish), molluscs (clams, oysters and mussels) and echinoderms (sea cucumbers). Various species of seaweed are also gathered. There are freshwater, saltwater, brackish water and self-contained terrestrial aquaculture systems. And each has its own sustainability benefits and challenges.
One subsector that offers huge environmental advantages and has no equivalent in terrestrial agriculture is non-fed aquaculture. Marine bivalves, such as clams, mussels and oysters, get their nourishment by filtering microscopic plants, detritus and nutrients from the water that surrounds them. They require minimal inputs and can even improve the water quality. In this sense, Suhrbier’s sea cucumbers represent a kind of double alchemy: non-fed aquaculture species grown on the wastes of other non-fed aquaculture species.
Similarly, cultivated seaweeds can remove excess nutrients, such as nitrogen, that contribute to the formation of areas of oxygen-poor water where marine life has difficulty surviving, known as dead zones. By taking up carbon they can also help to alleviate ocean acidification at a local scale. Moreover, “seaweeds are incredibly nutritious,” says Alecia Bellgrove, head of the DeakinSeaweed Research Group at Deakin University in Melbourne, Australia. “They are, for example, fantastic sources of trace minerals, which are often lacking in our diets based on terrestrial foods.”

Sea cucumbers are retrieved from the mesh-lined cages at Puget Sound in Washington state. Credit: Sarah DeWeerdt

Aquatic animals that require feed — mainly prawns and bony fish — also have an environmental advantage over animals raised in terrestrial agriculture. Because most are cold-blooded, they convert food into body mass more efficiently than birds and mammals, which need energy to help regulate their body temperature. So it takes less feed to produce a kilogram of salmon, for example, than it does to produce a kilogram of, say, beef or pork.
However, some of the most lucrative aquaculture species are carnivorous, and therefore sit higher in the food chain than any terrestrial species raised in agriculture. Take the Atlantic salmon (Salmo salar), for example. In the mid-2000s, salmon aquaculture, now a $15.4-billion industry, was growing rapidly. Feeding the salmon demanded an increasing share of the world’s fish meal and fish oil, which was sourced from small forage fish, such as anchovies, sardines and capelin. But while demand from the salmon farms grew, fishing yield for the forage species remained relatively flat. It took at least 4 kilograms of wild-caught forage fish to produce just 1 kilogram of salmon.
From an environmental point of view, “It made no sense,” says Scott Nichols, founder of Food’s Future, a consultancy in West Chester, Pennsylvannia, which promotes the development of sustainable aquaculture businesses. As a biochemist working at US chemical company DuPont in the mid 2000s, Nichols helped to develop a way to produce omega-3 fatty acids from yeast. The fatty acids were then incorporated into salmon feed to replace some of the wild-fish component. The new feed was tested through a partnership between DuPont and the aquaculture firm AquaChile based in Puerto Montt, Chile, in the form of the salmon producer Verlasso in Miami, Florida.
“We were able, after a couple of years of production, to get to the point where for every kilo of salmon that was produced, we were using less than a kilo of wild-caught fish,” Nichols says. “So our farming practices resulted in the net production of fish on the planet.”
Other companies soon joined in, producing omega-3s in genetically engineered canola oil or single-celled algae. Meanwhile, fish-oil and fish-meal producers are increasingly making use of fish trimmings and other by-products that previously went to waste. Fish meal and fish oil, which are still used in a variety of aquaculture feeds, as well as in products such as food supplements, accounted for around 10% of the world’s total fish production in 2018, according to the FAO. But nonetheless, Nichols takes heart from developments. “What looked on the face of it to be dismal in 2006 now looks to be very promising,” he says.
Disease detectives
An increasingly important threat to aquaculture sustainability is disease, which affects all subsectors of aquaculture and causes an estimated $6 billion worth of aquatic animal losses every year. Diseases include parasites called sea lice in salmon; white spot syndrome virus in prawns, which emerged in the early 1990s and devastated prawn farming throughout Asia before spreading to the Americas; and tilapia lake virus, which threatens the economic and nutritional gains that freshwater aquaculture has made possible in many low- and middle-income countries.
As aquaculture is scaled up, the problem of disease will also become greater. “As you expand the volume of production, you are going to get significant losses,” says Grant Stentiford, a pathologist and head of aquatic animal health at the Centre for Environment Fisheries and Aquaculture Science, Weymouth, UK. “You’ve used up potentially large amounts of resource to get absolutely nowhere.”
To deal with such threats, some large producers who supply the export market are moving to self-contained, land-based systems. Others are moving away from the coast into deeper waters that might dilute the threat of disease. Vaccines have also made a difference, reducing not only the threat of many fish diseases, but also antibiotic use — another major environmental concern about the industry. And high-throughput sequencing of the microbial DNA in aquaculture systems could provide early warning of disease outbreaks.
But many of these solutions are expensive and, therefore, out of reach for the small and medium-sized producers who make up the majority of the global aquaculture industry, producing food for subsistence or local markets in low- and middle-income countries. Moreover, diseases that threaten aquaculture are emerging every three to five years on average. The dearth of knowledge about aquatic pathogens makes diseases hard to predict and spot.
It can also be a challenge to deduce their cause. For example, ice-ice disease results in bleaching of Kappaphycus seaweed, which is grown in large amounts in southeast Asia and Tanzania for the production of food additives, such as the thickening agent carrageenan. The disease has caused yields to plummet over the past decade, but “the causative agent is still not known”, says Valéria Montalescot, senior project manager for GlobalSeaweedSTAR, a four-year research project based at the Scottish Association for Marine Science in Oban, UK, which aims to boost knowledge about seaweed cultivation in low- and middle-income countries. Kappaphycus is usually grown from cuttings, so the whole crop across multiple countries might be the result of just a few clones, possibly making it more vulnerable to disease, Montalescot adds.
Diverse yields
Climate change is complicating efforts to fight disease. Higher water temperatures can alter the microbial community of a body of water, encouraging the growth of pathogens, as well as stressing organisms and making them more vulnerable to disease. One suggested cause of ice-ice disease is that temperature-stressed seaweeds release compounds that attract bacteria, for example.
And temperature is not the only issue. Both increased rainfall and salinity intrusion from sea-level rise can alter water chemistry in ways that are detrimental to aquaculture organisms. Storms can destroy aquaculture crops or infrastructure in the water and on land. “From an environmental point of view I think climate change is the greatest challenge” for the sustainability of aquaculture systems, says Nesar Ahmed, who studies global seafood sustainability at Deakin University.
Climate change also intersects with aquaculture’s pressure on water and land resources. Inland aquaculture demands 429 cubic kilometres of fresh water each year — much less than the demand from terrestrial agriculture, but still enough to pose a strain on increasingly drought-prone areas.
In south and southeast Asia, prawn cultivation has contributed to the destruction of 38% of the world’s mangrove habitats, which have a variety of important ecological functions, including sequestering carbon and buffering coastlines from storms and sea-level rise. The loss of mangroves has also resulted in saltwater intrusion rendering inland areas unsuitable for terrestrial agriculture.
Some farmers are now producing prawns among intact mangrove stands. Although there are concerns that this practice might also damage the health of the mangroves, it is part of a larger trend to create aquaculture systems that include multiple species and involve interrelationships more like the ones that keep natural ecosystems in balance.
Some examples of this integrated aquaculture are long-established, such as stocking rice paddy fields with fish or prawns. The animals eat pests and fertilize the rice crop, increasing rice yields and providing an extra source of protein or income for small-scale farmers, Ahmed says. Growing two species in a single body of water also reduces overall water use.
This type of rice–fish system has been practised for hundreds of years in China and has been designated a Globally Important Agricultural Heritage System by the FAO — a designation that aims to preserve agricultural knowledge that can contribute to a more sustainable and resilient food system. Large-scale aquaculture operations, such as Cooke Aquaculture based in Blacks Harbour, Canada, have also been experimenting with multi-species systems. The company keeps salmon in net pens near both mussel and kelp rafts in the Bay of Fundy, Canada.

In theory, integrated aquaculture can help to increase yields, decrease risk by diversifying operations, and is generally a more environmentally sound form of aquaculture. But in practice, it can be difficult to quantify these benefits. For example, because nitrogen moves freely through water, it is difficult to track uptake of excess nitrogen produced by bony fish by seaweed growing nearby. And then there are the complexities of managing an operation with multiple species — not just producing them but also harvesting, processing and marketing them.
Suhrbier knows such difficulties well. The sea cucumbers he and his team harvested from under the mussel raft were the right size, weight and colour for the export market, but the mussel producer he was working with was unable to renew its permit at that location. The raft was lost, and with it Suhrbier’s chance of follow-up experiments to develop sea-cucumber aquaculture techniques. “I was really shocked and saddened to see that go because it was one of those places where it just makes a lot of sense for sea cucumbers to be,” Suhrbier says. The new location of the producer’s rafts isn’t a good habitat for sea cucumbers.
Suhrbier is still experimenting growing sea cucumbers alongside other types of aquaculture operation around the Puget Sound area. But, like an increasing number of aquaculture researchers, he is beginning to think that producing the animals needs to move in a simpler and more radical direction. Growing sea cucumbers in cages is labour intensive. What if the animals are placed in the vicinity of aquaculture operations and left to roam freely — like a marine equivalent of a ranch or even a permaculture system?
“If we could mainly enhance the wild population around these areas, I think that would be a great benefit for everybody,” Suhrbier says. “I’m trying to have something that fits in: easy, cost effective and as passive as it can be.” More

1.
Gause, G. F. The Struggle for Existence (Williams & Wilkins, Baltimore, 1934).
Google Scholar 
2.
Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).
Article  Google Scholar 

3.
Volterra, V. Variations and fluctuations of the number of individuals in marine intertidal species living together. J. Conseil. 3, 3–51 (1928).
Article  Google Scholar 

4.
Darwin, C. On the Origin of Species by Natural Selection, or the Preservation of Favoured Races in the Struggle for Life (John Murray, London, 1859).
Google Scholar 

5.
Hardin, G. The competitive exclusion principle. Science 131, 1292–1297 (1960).
ADS  CAS  Article  Google Scholar 

6.
Alley, T. R. Competition theory, evolution, and the concept of an ecological niche. Acta Biotheor. 31, 165–179 (1982).
CAS  PubMed  Article  Google Scholar 

7.
Chase, J. M. & Leibold, M. A. Ecological Niches: Linking Classical and Contemporary Approaches (University of Chicago Press, Chicago, 2003).
Google Scholar 

8.
Pianka, E. R. Competition and niche theory. In Theoretical Ecology: Principles and Applications (ed. May, R. M.) 114–141 (W.B. Saunders, Philadelphia, 1976).
Google Scholar 

9.
Gerking, S. D. The Feeding Ecology of Fish (Academic, San Diego, 1994).
Google Scholar 

10.
Ross, S. T. Resource partitioning in fish assemblages: a review of field studies. Copeia 2, 352–388 (1986).
Article  Google Scholar 

11.
Persson, L. Asymmetrical competition: are larger animals competitively superior?. Am. Nat. 126, 261–266 (1985).
Article  Google Scholar 

12.
Boecklen, W. J., Yarnes, C. T., Cook, B. A. & James, A. C. On the use of stable isotopes in trophic ecology. Annu. Rev. Ecol. Evol. Syst. 42, 411–440 (2011).
Article  Google Scholar 

13.
Layman, C. A. et al. Applying stable isotopes to examine food-web structure: an overview of analytical tools. Biol. Rev. Camb. Philos. 87, 545–562 (2012).
Article  Google Scholar 

14.
Bearhop, S., Adams, C. E., Waldron, S., Fuller, R. A. & MacLeod, H. Determining trophic niche width: a novel approach using stable isotope analysis. J. Anim. Ecol. 73, 1007–1012 (2004).
Article  Google Scholar 

15.
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER-stable isotope Bayesian ellipses in R. J. Anim. Ecol. 80, 595–602 (2011).
PubMed  Article  Google Scholar 

16.
Layman, C. A., Arrington, D. A., Montaña, C. G. & Post, D. M. Can stable isotope ratios provide for community-wide measures of trophic structure?. Ecology 88, 42–48 (2007).
PubMed  Article  Google Scholar 

17.
Newsome, S. D., del Rio, C. M., Bearhop, S. & Phillips, D. L. A niche for isotopic ecology. Front. Ecol. Environ. 5, 429–436 (2007).
Article  Google Scholar 

18.
Hette-Tronquart, N. Isotopic niche is not equal to trophic niche. Ecol. Lett. 22, 1987–1989 (2019).
PubMed  Article  Google Scholar 

19.
Parnell, C. A. et al. Bayesian stable isotope mixing models. Environmetrics 24, 387–399 (2013).
MathSciNet  Google Scholar 

20.
Phillips, D. L. et al. Best practices for use of stable isotope mixing models in food-web studies. Can. J. Zool. 92, 823–835 (2014).
Article  Google Scholar 

21.
Knickle, D. C. & Rose, G. A. Dietary niche partitioning in sympatric gadid species in coastal Newfoundland: evidence from stomachs and C-N isotopes. Environ. Biol. Fish. 97, 343–355 (2014).
Article  Google Scholar 

22.
Simpson, S. J., Sims, D. W. & Trueman, C. M. Ontogenetic trends in resource partitioning and trophic geography of sympatric skates (Rajidae) inferred from stable isotope composition across eye lenses. Mar. Ecol. Prog. Ser. 624, 103–116 (2019).
ADS  CAS  Article  Google Scholar 

23.
Bearhop, S. et al. Stable isotopes indicate sex-specific and long-term individual foraging specialisation in diving seabirds. Mar. Ecol. Prog. Ser. 311, 157–164 (2006).
ADS  Article  Google Scholar 

24.
Young, H. S., McCauley, D. J., Dirzo, R., Dunbar, R. B. & Shaffer, S. A. Niche partitioning among and within sympatric tropical seabirds revealed by stable isotope analysis. Mar. Ecol. Prog. Ser. 416, 285–294 (2010).
ADS  CAS  Article  Google Scholar 

25.
Botta, S. et al. Isotopic niche overlap and partition among three Antarctic seals from the Western Antarctic Peninsula. Deep-Sea Res. II(149), 240–249 (2018).
Google Scholar 

26.
Kiszka, J. et al. Ecological niche segregation within a community of sympatric dolphins around a tropical island. Mar. Ecol. Prog. Ser. 433, 273–288 (2011).
ADS  Article  Google Scholar 

27.
Ogloff, W. R., Yurkowski, D. J., Davoren, G. K. & Ferguson, S. H. Diet and isotopic niche overlap elucidate competition potential between seasonally sympatric phocids in the Canadian Arctic. Mar. Biol. 166, 103 (2019).
Article  CAS  Google Scholar 

28.
Dubois, S., Orvain, F., Marin-Léal, J. C., Ropert, M. & Lefebvre, S. Small-scale spatial variability of food partitioning between cultivated oysters and associated suspension feeding species, as revealed by stable isotopes. Mar. Ecol. Prog. Ser. 336, 151–160 (2007).
ADS  CAS  Article  Google Scholar 

29.
Karlson, A. M. L., Gorokhova, E. & Elmgren, R. Do deposit-feeders compete? Isotopic niche analysis of an invasion in a species-poor system. Sci. Rep. 5, 9715 (2015).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

30.
Taupp, T., Hellmann, C., Gergs, R., Winkelmann, C. & Wetzel, M. A. Life under exceptional conditions—isotopic niches of benthic invertebrates in the estuarine maximum turbidity zone. Estuar. Coast. 40, 502–512 (2017).
CAS  Article  Google Scholar 

31.
Bennice, C. O., Rayburn, A. R., Brooks, W. R. & Hanlon, R. T. Fine-scale habitat partitioning facilitates sympatry between two octopus species in a shallow Florida lagoon. Mar. Ecol. Prog. Ser. 609, 151–161 (2019).
Article  Google Scholar 

32.
Matias, R. S. et al. Show your beaks and we tell you what you eat: different ecology in sympatric Antarctic benthic octopods under a climate change context. Mar. Environ. Res. 150, 104757 (2019).
CAS  PubMed  Article  Google Scholar 

33.
Rosas-Luis, R., Navarro, J., Sánchez, P. & del Río, J. L. Assessing the trophic ecology of three sympatric squid in the marine ecosystem off the Patagonian Shelf by combining stomach content and stable isotopic analyses. Mar. Biol. Res. 12, 402–411 (2016).
Article  Google Scholar 

34.
Boyle, P. R. & Rodhouse, P. G. Cephalopods: Ecology and Fisheries (Wiley-Blackwell, Oxford, 2005).
Google Scholar 

35.
Rodhouse, P. G. & Nigmatullin, Ch. M. Role as consumers. Philos. Trans. R. Soc. B 351, 1003–1022 (1996).
ADS  Article  Google Scholar 

36.
Jereb, P. & Roper, C.F.E. Cephalopods of the world. An annotated and illustrated catalogue of cephalopod species known to date. Volume 1. Chambered nautiluses and sepioids (Nautilidae, Sepiidae, Sepiolidae, Sepiadariidae, Idiosepiidae and Spirulidae). FAO Species Catalogue for Fishery Purposes, No. 4. Rome: FAO (2005).

37.
Golikov, A. V., Sabirov, R. M., Lubin, P. A. & Jørgensen, L. L. Changes in distribution and range structure of Arctic cephalopods due to climatic changes of the last decades. Biodiversity 14, 28–35 (2013).
Article  Google Scholar 

38.
Nesis, K. N. Cephalopod mollusks of the Arctic Ocean and its seas. In Fauna and Distribution of Molluscs: North Pacific and Arctic Basin (ed. Kafanov, A. I.) 115–136 (USSR Academy of Sciences, Vladivostok, 1987) (in Russian).
Google Scholar 

39.
Xavier, J. C. et al. A review on the biodiversity, distribution and trophic role of cephalopods in the Arctic and Antarctic marine ecosystems under a changing ocean. Mar. Biol. 165, 93 (2018).
Article  Google Scholar 

40.
Golikov, A. V. et al. Reproductive biology and ecology of the boreoatlantic armhook squid Gonatus fabricii (Cephalopoda: Gonatidae). J. Mollus. Stud. 85, 287–299 (2019).
Article  Google Scholar 

41.
Golikov, A. V. et al. Food spectrum and trophic position of an Arctic cephalopod, Rossia palpebrosa (Sepiolida), inferred by stomach contents and stable isotope (δ13C and δ15N) analyses. Mar. Ecol. Prog. Ser. 632, 131–144 (2019).
ADS  Article  Google Scholar 

42.
Golikov, A. V. et al. Ontogenetic changes in stable isotope (δ13C and δ15N) values in squid Gonatus fabricii (Cephalopoda) reveal its important ecological role in the Arctic. Mar. Ecol. Prog. Ser. 606, 65–78 (2018).
ADS  CAS  Article  Google Scholar 

43.
Golikov, A. V., Sabirov, R. M. & Lubin, P. A. First assessment of biomass and abundance of cephalopods Rossia palpebrosa and Gonatus fabricii in the Barents Sea. J. Mar. Biol. Assoc. UK 97, 1605–1616 (2017).
Article  Google Scholar 

44.
Nesis, K. N. Oceanic Cephalopods: Distribution, Life Forms, Evolution (Nauka, Moscow, 1985) (in Russian).
Google Scholar 

45.
Overland, J. E., Wang, M., Walsh, J. E. & Stroeve, J. C. Future Arctic climate changes: adaptation and mitigation time scales. Earth’s Future 2, 68–74 (2014).
ADS  Article  Google Scholar 

46.
Dalpadado, P. et al. Climate effects on temporal and spatial dynamics of phytoplankton and zooplankton in the Barents Sea. Prog. Oceanogr. 185, 102320 (2020).
Article  Google Scholar 

47.
Laidre, K. L. et al. Arctic marine mammal population status, sea ice habitat loss, and conservation recommendations for the 21st century. Conserv. Biol. 29, 724–737 (2015).
PubMed  PubMed Central  Article  Google Scholar 

48.
Mercer, M.C. Systematics of the Sepiolid Squid Rossia Owen 1835 in Canadian Waters with a Preliminary Review of the Genus and Notes on Biology (MSc thesis). St. Johns: Memorial University of Newfoundland (1968).

49.
Golikov, A.V. Distribution and reproductive biology of ten-armed cephalopods (Sepiolida, Teuthida) in the Barents Sea and adjacent areas (PhD thesis). Moscow: Moscow State University (2015) (in Russian).

50.
Golikov, A.V., Sabirov, R.M., Gudmundsson, G. Cephalopoda (Smokkdýr), Rossia megaptera Verrill, 1881. (2018). http://www.ni.is/biota/animalia/mollusca/cephalopoda/rossia-megaptera. Accessed 04 June 2020.

51.
Golikov, A. V., Morov, A. R., Sabirov, R. M., Lubin, P. A. & Jørgensen, L. L. Functional morphology of reproductive system of Rossia palpebrosa (Cephalopoda, Sepiolida) in Barents Sea. Proc. Kazan Univ. Nat. Sci. Ser. 155, 116–129 (2013) (in Russian with English abstract).
Google Scholar 

52.
Cherel, Y., Ducatez, S., Fontaine, C., Richard, P. & Guinet, C. Stable isotopes reveal the trophic position and mesopelagic fish diet of female southern elephant seals breeding on the Kerguelen Islands. Mar. Ecol. Prog. Ser. 370, 239–247 (2008).
ADS  Article  Google Scholar 

53.
Cherel, Y. & Hobson, K. A. Stable isotopes, beaks and predators: a new tool to study the trophic ecology of cephalopods, including giant and colossal squids. Proc. R. Soc. B. 272, 1601–1607 (2005).
PubMed  Article  Google Scholar 

54.
Golikov, A. V. et al. The first global deep-sea stable isotope assessment reveals the unique trophic ecology of Vampire Squid Vampyroteuthis infernalis (Cephalopoda). Sci. Rep. 9, 19099 (2019).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

55.
Cherel, Y., Fontaine, C., Jackson, G. D., Jackson, C. H. & Richard, P. Tissue, ontogenic and sex-related differences in δ13C and δ15N values of the oceanic squid Todarodes filippovae (Cephalopoda: Ommastrephidae). Mar. Biol. 156, 699–708 (2009).
Article  Google Scholar 

56.
Zar, J. H. Biostatistical Analysis 5th edn. (Prentice Hall, Upper Saddle River, 2010).
Google Scholar 

57.
Ruiz-Cooley, R. I., Garcia, K. Y. & Hetherington, E. D. Effects of lipid removal and preservatives on carbon and nitrogen stable isotope ratios of squid tissues: implications for ecological studies. J. Exp. Mar. Biol. Ecol. 407, 101–107 (2011).
CAS  Article  Google Scholar 

58.
Hobson, K. A. & Cherel, Y. Isotopic reconstruction of marine food webs using cephalopod beaks: new insight from captively raised Sepia officinalis. Can. J. Zool. 84, 766–770 (2006).
Article  Google Scholar 

59.
Post, D. M. Using stable isotopes to estimate trophic position: models, methods and assumptions. Ecology 83, 703–718 (2002).
Article  Google Scholar 

60.
Hobson, K. A. et al. A stable isotope (δ13C, δ15N) model for the North Water food web: implications for evaluating trophodynamics and the flow of energy and contaminants. Deep-Sea Res. II 49, 5131–5150 (2002).
ADS  CAS  Article  Google Scholar 

61.
Van der Zanden, M. J., Cabana, G. & Rasmussen, J. B. Comparing trophic position of freshwater fish calculated using stable nitrogen isotope ratios (δ15N) and literature dietary data. Can. J. Fish. Aquat. Sci. 54, 1142–1158 (1997).
Article  Google Scholar 

62.
Hussey, N. E. et al. Rescaling the trophic structure of marine food webs. Ecol. Lett. 17, 239–250 (2014).
PubMed  Article  Google Scholar 

63.
Hussey, N. E. et al. Corrigendum to Hussey et al. (2014). Ecol. Lett. 17, 768 (2014).
PubMed Central  Article  PubMed  Google Scholar 

64.
Linnebjerg, J. F. et al. Deciphering the structure of the West Greenland marine food web using stable isotopes (δ13C, δ15N). Mar. Biol. 163, 230 (2016).
Article  Google Scholar 

65.
Søreide, J. E. et al. Sympagic-pelagic-benthic coupling in Arctic and Atlantic waters around Svalbard revealed by stable isotopic and fatty acid tracers. Mar. Biol. Res. 9, 831–850 (2013).
Article  Google Scholar 

66.
Sokolowski, A. et al. Trophic structure of the macrobenthic community of Hornsund, Spitsbergen, based on the determination of stable carbon and nitrogen isotopic signatures. Polar Biol. 37, 1247–1260 (2014).
Article  Google Scholar 

67.
Tamelander, T. et al. Trophic relationships and pelagic-benthic coupling during summer in the Barents Sea marginal ice zone, revealed by stable carbon and nitrogen isotope measurements. Mar. Ecol. Prog. Ser. 310, 33–46 (2006).
ADS  CAS  Article  Google Scholar 

68.
R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. (2019). http://www.r-project.org/. Accessed 04 June 2020.

69.
Syväranta, J., Lensu, A., Marjomaki, T. J., Oksanen, S. & Jones, R. I. An empirical evaluation of the utility of convex hull and standard ellipse areas for assessing population niche widths from stable isotope data. PLoS ONE 8, e56094 (2013).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

70.
Langton, R. W. Diet overlap between Atlantic cod, Gadus morphua, silver hake, Merluccius bilinearis, and fifteen other northwest Atlantic finfish. Fish. B NOAA 80, 745–759 (1982).
Google Scholar 

71.
Parnell, C.A. simmr: A Stable Isotope Mixing Model. Version 0.4.1. (2019). https://cran.r-project.org/web/packages/simmr/. Accessed 04 June 2020.

72.
Smith, J. A., Mazumder, D., Suthers, I. M. & Taylor, M. D. To fit or not to fit: evaluating stable isotope mixing models using simulated mixing polygons. Methods Ecol. Evol. 4, 612–618 (2013).
Article  Google Scholar 

73.
Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 1–9 (2001).
Google Scholar 

74.
Gruber, N. et al. Spatiotemporal patterns of carbon-13 in the global surface oceans and the oceanic Suess effect. Glob. Biogeochem. Cycl. 13, 307–335 (1999).
ADS  CAS  Article  Google Scholar 

75.
Yurkowski, D. J., Hussey, N. E., Ferguson, S. H. & Fisk, A. T. A temporal shift in trophic diversity among a predator assemblage in a warming Arctic. R. Soc. Open. Sci. 5, 180259 (2018).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

76.
Guerra, A. et al. Life-history traits of the giant squid Architeuthis dux revealed from stable isotope signatures recorded in beaks. ICES J. Mar. Sci. 67, 1425–1431 (2010).
Article  Google Scholar 

77.
Queirós, J. P. et al. Ontogenic changes in habitat and trophic ecology in the Antarctic squid Kondakovia longimana derived from isotopic analysis on beaks. Polar Biol. 41, 2409–2421 (2018).
Article  Google Scholar 

78.
Queirós, J. P. et al. Ontogenetic changes in habitat and trophic ecology of the giant Antarctic octopus Megaleledone setebos inferred from stable isotope analyses in beaks. Mar. Biol. 167, 56 (2020).
Article  CAS  Google Scholar 

79.
Hansen, H. J., Hedeholm, R. B., Sünksen, K., Christensen, J. T. & Grønkjær, P. Spatial variability of carbon (δ13C) and nitrogen (δ15N) stable isotope ratios in an Arctic marine food web. Mar. Ecol. Prog. Ser. 467, 47–59 (2012).
ADS  CAS  Article  Google Scholar 

80.
Cherel, Y., Ridoux, V., Spitz, J. & Richard, P. Stable isotopes document the trophic structure of a deep-sea cephalopod assemblage including giant octopod and giant squid. Biol. Lett. 5, 364–367 (2009).
CAS  PubMed  PubMed Central  Article  Google Scholar 

81.
Chouvelon, T. et al. Revisiting the use of δ15N in meso-scale studies of marine food webs by considering spatio-temporal variations in stable isotopic signatures—the case of an open ecosystem: the Bay of Biscay (North-East Atlantic). Prog. Oceanogr. 101, 92–105 (2012).
ADS  Article  Google Scholar 

82.
Das, K., Lepoint, G., Leroy, Y. & Bouquegneau, J. M. Marine mammals from the southern North Sea: feeding ecology data from δ13C and δ15N measurements. Mar. Ecol. Prog. Ser. 263, 287–298 (2003).
ADS  Article  Google Scholar 

83.
Gong, Y., Ruiz-Cooley, R. I., Hunsicker, M. E., Li, Y. & Chen, X. Sexual dimorphism in feeding apparatus and niche partitioning in juvenile jumbo squid Dosidicus gigas. Mar. Ecol. Prog. Ser. 607, 99–112 (2018).
ADS  CAS  Article  Google Scholar 

84.
Trasviña-Carrillo, L. D. et al. Spatial and trophic preferences of jumbo squid Dosidicus gigas (D’Orbigny, 1835) in the central Gulf of California: ecological inferences using stable isotopes. Rapid Commun. Mass. Spectrom. 32, 1225–1236 (2018).
ADS  PubMed  Article  CAS  Google Scholar 

85.
Guerreiro, M. et al. Habitat and trophic ecology of Southern Ocean cephalopods from stable isotope analyses. Mar. Ecol. Prog. Ser. 530, 119–134 (2015).
ADS  CAS  Article  Google Scholar 

86.
Kato, Y. et al. Stable isotope analysis of the gladius to investigate migration and trophic patterns of the neon flying squid (Ommastrephes bartramii). Fish. Res. 173, 169–174 (2016).
Article  Google Scholar  More

1.
Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).
CAS  Article  Google Scholar 
2.
Mehrabi, Z. & Ramankutty, N. Synchronized failure of global crop production. Nat. Ecol. Evol. 3, 780–786 (2019).
Article  Google Scholar 

3.
Cottrell, R. S. et al. Food production shocks across land and sea. Nat. Sustain. 2, 130–137 (2019).
Article  Google Scholar 

4.
The Food and Agriculture Organization of the United Nations Statistics (FAO, accessed 22 November 2019); https://www.fao.org/faostat/en/#data/.

5.
Marshall, M. G. Codebook: Major Episodes of Political Violence (MEPV) and Conflict Regions, 1946–2015. http://www.systemicpeace.org/inscr/MEPVcodebook2016.pdf (2016).

6.
Klein Goldewijk, K., Beusen, A., Doelman, J. & Stehfest, E. New anthropogenic land use estimates for the Holocene – HYDE 3.2. Earth Syst. Sci. Data 9, 927–953 (2017).
ADS  Article  Google Scholar 

7.
Sacks, W. J., Deryng, D. & Foley, J. A. Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).
Google Scholar 

8.
Willmott, C. J. & Matsuura, K. Terrestrial Air Temperature and Precipitation: Monthly and Annual Time Series (1950–1999). http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_ts2.html (2001).

9.
Food balance sheets: A handbook (FAO, 2001).

10.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).

11.
Loreau, M. & de Mazancourt, C. Species synchrony and its drivers: neutral and nonneutral community dynamics in fluctuating environments. Am. Nat. 172, E48–E66 (2008).
Article  Google Scholar 

12.
Hallett, L. M. et al. codyn : An R package of community dynamics metrics. Methods Ecol. Evol. 7, 1146–1151 (2016).
Article  Google Scholar 

13.
Khoury, C. K. et al. Increasing homogeneity in global food supplies and the implications for food security. Proc. Natl Acad. Sci. USA 111, 4001–4006 (2014).
ADS  CAS  Article  Google Scholar 

14.
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

15.
Knapp, S. & van der Heijden, M. G. A. A global meta-analysis of yield stability in organic and conservation agriculture. Nat. Commun. 9, 3632 (2018).
ADS  Article  Google Scholar  More

We thank the Bren School of Environment Science and Management of the University of California Santa Barbara for support leading to the initial publication. This work was also supported by a grant overseen by the French ‘Programme Investissement d’Avenir’ as part of the ‘Make Our Planet Great Again’ programme (reference: 17-MPGA-0004) and by a National Science Foundation grant (LTER-1831944). More