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Annual Review of Marine Science

Volume 16, 2024

Designing More Informative Multiple-Driver Experiments

Abstract

For decades, multiple-driver/stressor research has examined interactions among drivers that will undergo large changes in the future: temperature, pH, nutrients, oxygen, pathogens, and more. However, the most commonly used experimental designs—present-versus-future and ANOVA—fail to contribute to general understanding or predictive power. Linking experimental design to process-based mathematical models would help us predict how ecosystems will behave in novel environmental conditions. We review a range of experimental designs and assess the best experimental path toward a predictive ecology. Full factorial response surface, fractional factorial, quadratic response surface, custom, space-filling, and especially optimal and sequential/adaptive designs can help us achieve more valuable scientific goals. Experiments using these designs are challenging to perform with long-lived organisms or at the community and ecosystem levels. But they remain our most promising path toward linking experiments and theory in multiple-driver research and making accurate, useful predictions.

Keyword(s): anthropogenic change, experimental design, interactions, multiple stressors, predictive ecology, theory–experiment integration
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/content/journals/10.1146/annurev-marine-041823-095913
2024-01-17
2024-04-27
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Literature Cited

  1. Advani M, Bunin G, Mehta P. 2018. Statistical physics of community ecology: a cavity solution to MacArthur's consumer resource model. J. Stat. Mech. Theory Exp. 2018:3033406
     [Google Scholar]
  2. Atkinson AC. 1981. A comparison of two criteria for the design of experiments for discriminating between models. Technometrics 23:3301–5
     [Google Scholar]
  3. Atkinson AC, Donev AN, Tobias RD. 2007. Optimum Experimental Designs, with SAS Oxford, UK: Oxford Univ. Press
  4. Atkinson AC, Fedorov VV. 1975a. Optimal design: experiments for discriminating between several models. Biometrika 62:2289–303
     [Google Scholar]
  5. Atkinson AC, Fedorov VV. 1975b. The design of experiments for discriminating between two rival models. Biometrika 62:157–70
     [Google Scholar]
  6. Auguie B, Antonov A. 2017. gridExtra: miscellaneous functions for “grid” graphics. Comprehensive R Archive Network. https://CRAN.R-project.org/package=gridExtra
    [Google Scholar]
  7. Berger MPF, Wong WK. 2009. An Introduction to Optimal Designs for Social and Biomedical Research Chichester, UK: Wiley & Sons
  8. Blasius B, Rudolf L, Weithoff G, Gaedke U, Fussmann GF. 2020. Long-term cyclic persistence in an experimental predator-prey system. Nature 577:7789226–30
     [Google Scholar]
  9. Bolker B, R Dev. Core Team 2022. Bbmle: tools for general maximum likelihood estimation. Comprehensive R Archive Network. https://CRAN.R-project.org/package=bbmle
    [Google Scholar]
  10. Box GEP. 1954. The exploration and exploitation of response surfaces: some general considerations and examples. Biometrics 10:116–60
     [Google Scholar]
  11. Box GEP, Draper NR. 1959. A basis for the selection of a response surface design. J. Am. Stat. Assoc. 54:287622–54
     [Google Scholar]
  12. Box GEP, Draper NR. 1963. The choice of a second order rotatable design. Biometrika 50:3–4335–52
     [Google Scholar]
  13. Box GEP, Hill WJ. 1967. Discrimination among mechanistic models. Technometrics 9:157–71
     [Google Scholar]
  14. Box GEP, Hunter JS. 1961a. The 2k−p fractional factorial designs part I. Technometrics 3:3311–51
     [Google Scholar]
  15. Box GEP, Hunter JS. 1961b. The 2k−p fractional factorial designs part II. Technometrics 3:4449–58
     [Google Scholar]
  16. Box GEP, Hunter JS, Hunter WG. 2005. Statistics for Experimenters: Design, Innovation, and Discovery Hoboken, NJ: Wiley-Intersci. , 2nd ed..
  17. Box GEP, Wilson K. 1951. On the experimental designs for exploring response surfaces. Ann. Math. Stat. 13:1–45
     [Google Scholar]
  18. Box GEP, Youle PV. 1955. The exploration and exploitation of response surfaces: an example of the link between the fitted surface and the basic mechanism of the system. Biometrics 11:3287–323
     [Google Scholar]
  19. Boyd PW, Collins S, Dupont S, Fabricius K, Gattuso J-P et al. 2018. Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change—a review. Glob. Change Biol. 24:62239–61
     [Google Scholar]
  20. Boyd PW, Lennartz ST, Glover DM, Doney SC. 2015. Biological ramifications of climate-change-mediated oceanic multi-stressors. Nat. Clim. Change 5:171–79
     [Google Scholar]
  21. Brown JH, Gillooly JF, Allen AP, Savage VM, West GB. 2004. Toward a metabolic theory of ecology. Ecology 85:71771–89
     [Google Scholar]
  22. Burson A, Stomp M, Greenwell E, Grosse J, Huisman J. 2018. Competition for nutrients and light: testing advances in resource competition with a natural phytoplankton community. Ecology 99:51108–18
     [Google Scholar]
  23. Carnell R. 2022. lhs: Latin hypercube samples. Comprehensive R Archive Network https://CRAN.R-project.org/package=lhs
    [Google Scholar]
  24. Chalcraft DR. 2019. To replicate, or not to replicate – that should not be a question. Ecol. Lett. 22:71174–75
     [Google Scholar]
  25. Chen DG, Irvine JR. 2001. A semiparametric model to examine stock recruitment relationships incorporating environmental data. Can. J. Fish Aquat. Sci. 58:61178–86
     [Google Scholar]
  26. Collins S, Whittaker H, Thomas MK. 2022. The need for unrealistic experiments in global change biology. Curr. Opin. Microbiol. 68:102151
     [Google Scholar]
  27. Cottingham KL, Lennon JT, Brown BL. 2005. Knowing when to draw the line: designing more informative ecological experiments. Front. Ecol. Environ. 3:3145–52
     [Google Scholar]
  28. Crain CM, Kroeker K, Halpern BS. 2008. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11:121304–15
     [Google Scholar]
  29. Crombecq K, Laermans E, Dhaene T. 2011. Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling. Eur. J. Oper. Res. 214:3683–96
     [Google Scholar]
  30. Currie DJ, Mittelbach GG, Cornell HV, Field R, Guegan J-F et al. 2004. Predictions and tests of climate-based hypotheses of broad-scale variation in taxonomic richness. Ecol. Lett. 7:121121–34
     [Google Scholar]
  31. D'Arzenio DZ. 1990. Incorporating prior parameter uncertainty in the design of sampling schedules for pharmacokinetic parameter estimation experiments. Math. Biosci. 99:105–18
     [Google Scholar]
  32. Dewar RC, Porté A. 2008. Statistical mechanics unifies different ecological patterns. J. Theor. Biol. 251:3389–403
     [Google Scholar]
  33. Dietze MC. 2017. Prediction in ecology: a first-principles framework. Ecol. Appl. 27:72048–60
     [Google Scholar]
  34. Eilers PHC, Peeters JCH. 1988. A model for the relationship between light intensity and the rate of photosynthesis in phytoplankton. Ecol. Model. 42:3–4199–215
     [Google Scholar]
  35. Fedorov V. 1972. Theory of Optimal Experiments New York: Academic
  36. Fitzpatrick MC, Hargrove WW. 2009. The projection of species distribution models and the problem of non-analog climate. Biodivers. Conserv. 18:82255–61
     [Google Scholar]
  37. Friedman J, Higgins LM, Gore J. 2017. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1:50109
     [Google Scholar]
  38. Frisch D, Morton PK, Chowdhury PR, Culver BW, Colbourne JK et al. 2014. A millennial-scale chronicle of evolutionary responses to cultural eutrophication in Daphnia. Ecol. Lett. 17:3360–68
     [Google Scholar]
  39. Galic N, Sullivan LL, Grimm V, Forbes VE. 2018. When things don't add up: quantifying impacts of multiple stressors from individual metabolism to ecosystem processing. Ecol. Lett. 21:4568–77
     [Google Scholar]
  40. Geider RJ, MacIntyre HL, Kana TM. 1998. A dynamic regulatory model of phytoplanktonic acclimation to light, nutrients, and temperature. Limnol. Oceanogr. 43:4679–94
     [Google Scholar]
  41. Gerhard M, Koussoroplis A-M, Raatz M, Pansch C, Fey SB et al. 2023. Environmental variability in aquatic ecosystems: avenues for future multifactorial experiments. Limnol. Oceanogr. Lett. 8:2247–66
     [Google Scholar]
  42. Gunst RF, Mason RL. 2009. Fractional factorial design. Wiley Interdiscip. Rev. Comput. Stat. 1:2234–44
     [Google Scholar]
  43. Halpern BS, Frazier M, Afflerbach J, Lowndes JS, Micheli F et al. 2019. Recent pace of change in human impact on the world's ocean. Sci. Rep. 9:111609
     [Google Scholar]
  44. Hanson PC, Stillman AB, Jia X, Karpatne A, Dugan HA et al. 2020. Predicting lake surface water phosphorus dynamics using process-guided machine learning. Ecol. Model. 430:109136
     [Google Scholar]
  45. Hanson PJ, Walker AP. 2020. Advancing global change biology through experimental manipulations: Where have we been and where might we go?. Glob. Change Biol. 26:1287–99
     [Google Scholar]
  46. Harman R, Filova L. 2019. OptimalDesign: a toolbox for computing efficient designs of experiments. Comprehensive R Archive Network. https://CRAN.R-project.org/package=OptimalDesign
    [Google Scholar]
  47. Hashemi SH, Kaykhaii M, Jamali Keikha A, Sajjadi Z, Mirmoghaddam M 2019. Application of response surface methodology for silver nanoparticle stir bar sorptive extraction of heavy metals from drinking water samples: a Box-Behnken design. Analyst 144:113525–32
     [Google Scholar]
  48. Hastings A, Hom CL, Ellner S, Turchin P, Godfray HCJ. 1993. Chaos in ecology: Is Mother Nature a strange attractor?. Annu. Rev. Ecol. Syst. 24:1–33
     [Google Scholar]
  49. Higgins K, Hastings A, Sarvela JN, Botsford LW. 1997. Stochastic dynamics and deterministic skeletons: population behavior of Dungeness crab. Science 276:53171431–35
     [Google Scholar]
  50. Holling CS. 1959. The components of predation as revealed by a study of small-mammal predation of the European pine sawfly. Can. Entomol. 91:5293–320
     [Google Scholar]
  51. Holling CS. 1966. The functional response of invertebrate predators to prey density. Mem. Entomol. Soc. Can. 98:5–86
     [Google Scholar]
  52. Hollowed AB, Bax N, Beamish R, Collie J, Fogarty M et al. 2000. Are multispecies models an improvement on single-species models for measuring fishing impacts on marine ecosystems?. ICES J. Mar. Sci. 57:3707–19
     [Google Scholar]
  53. Houlahan JE, McKinney ST, Anderson TM, McGill BJ. 2017. The priority of prediction in ecological understanding. Oikos 126:11–7
     [Google Scholar]
  54. Huey RB, Kingsolver JG. 2019. Climate warming, resource availability, and the metabolic meltdown of ectotherms. Am. Nat. 194:6E140–50
     [Google Scholar]
  55. Huisman J, Jonker RR, Zonneveld C, Weissing FJ. 1999. Competition for light between phytoplankton species: experimental tests of mechanistic theory. Ecology 80:1211–22
     [Google Scholar]
  56. Hunter WG, Reiner AM. 1965. Designs for discriminating between two rival models. Technometrics 7:3307–23
     [Google Scholar]
  57. IPCC (Intergov. Panel Clim. Change) 2021. Climate Change 2021: The Physical Science Basis; Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change V Masson-Delmotte, P Zhai, A Pirani, SL Connors, C Péan et al. Cambridge UK: Cambridge Univ. Press
  58. Jackson MC, Loewen CJG, Vinebrooke RD, Chimimba CT. 2016. Net effects of multiple stressors in freshwater ecosystems: a meta-analysis. Glob. Change Biol. 22:1180–89
     [Google Scholar]
  59. Johnson FH, Lewin I. 1946. The growth rate of E. coli in relation to temperature, quinine and coenzyme. J. Cell Comp. Physiol. 28:147–75
     [Google Scholar]
  60. Joseph VR. 2016. Space-filling designs for computer experiments: a review. Qual. Eng. 28:128–35
     [Google Scholar]
  61. Joseph VR, Gu L, Ba S, Myers WR. 2019. Space-filling designs for robustness experiments. Technometrics 61:124–37
     [Google Scholar]
  62. Kay JJ, Schneider E. 1994. Embracing complexity: the challenge of the ecosystem approach. Perspectives on Ecological Integrity, Vol. 5 L Westra, J Lemons 49–59. Dordrecht, Neth.: Springer
     [Google Scholar]
  63. Keyl F, Wolff M. 2008. Environmental variability and fisheries: What can models do?. Rev. Fish Biol. Fish. 18:3273–99
     [Google Scholar]
  64. Kiefer J, Wolfowitz J. 1960. The equivalence of two extremum problems. Can. J. Math. 12:363–66
     [Google Scholar]
  65. Kingsolver JG. 2009. The well-temperatured biologist (American Society of Naturalists Presidential Address). Am. Nat. 174:6755–68
     [Google Scholar]
  66. Krenek S, Berendonk TU, Petzoldt T. 2011. Thermal performance curves of Paramecium caudatum: a model selection approach. Eur. J. Protistol. 47:2124–37
     [Google Scholar]
  67. Kreyling J, Schweiger AH, Bahn M, Ineson P, Migliavacca M et al. 2018. To replicate, or not to replicate – that is the question: how to tackle nonlinear responses in ecological experiments. Ecol. Lett. 21:111629–38
     [Google Scholar]
  68. Läuter E. 1974. Experimental design in a class of models. Math. Oper. Stat. 5:4–5379–98
     [Google Scholar]
  69. Lenth RV. 2009. Response-surface methods in R, using rsm. J. Stat. Softw. 32:71–17
     [Google Scholar]
  70. Letten AD, Dhami MK, Ke P-J, Fukami T. 2018. Species coexistence through simultaneous fluctuation-dependent mechanisms. PNAS 115:266745–50
     [Google Scholar]
  71. Lind EM, Borer E, Seabloom E, Adler P, Bakker JD et al. 2013. Life-history constraints in grassland plant species: a growth-defence trade-off is the norm. Ecol. Lett. 16:4513–21
     [Google Scholar]
  72. Matthijs HCP, Visser PM, Reeze B, Meeuse J, Slot PC et al. 2012. Selective suppression of harmful cyanobacteria in an entire lake with hydrogen peroxide. Water Res 46:51460–72
     [Google Scholar]
  73. Mehdizadeh Allaf M, Trick CG. 2019. Multiple-stressor design-of-experiment (DOE) and one-factor-at-a-time (OFAT) observations defining Heterosigma akashiwo growth and cell permeability. J. Appl. Phycol. 31:63515–26
     [Google Scholar]
  74. Michaelis L, Menten ML. 1913. Die kinetik der invertinwirkung. Biochem. Z. 49:333–69
     [Google Scholar]
  75. Moffat H, Hainy M, Papanikolaou NE, Drovandi C. 2020. Sequential experimental design for predator-prey functional response experiments. J. R. Soc. Interface 17:16620200156
     [Google Scholar]
  76. Monod J. 1949. The growth of bacterial cultures. Annu. Rev. Microbiol. 3:371–94
     [Google Scholar]
  77. Moran J, Tikhonov M. 2022. Defining coarse-grainability in a model of structured microbial ecosystems. Phys. Rev. X 12:2021038
     [Google Scholar]
  78. Munch SB, Rogers TL, Johnson BJ, Bhat U, Tsai C-H. 2022. Rethinking the prevalence and relevance of chaos in ecology. Annu. Rev. Ecol. Evol. Syst. 53:227–49
     [Google Scholar]
  79. Myers RH, Montgomery DC, Anderson-Cook CM. 2009. Response Surface Methodology: Process and Product Optimization Using Designed Experiments Hoboken, NJ: Wiley. , 3rd ed..
  80. Nash JC. 2014. On best practice optimization methods in R. J. Stat. Softw. 60:21–14
     [Google Scholar]
  81. Nash JC, Varadhan R. 2011. Unifying optimization algorithms to aid software system users: optimx for R. J. Stat. Softw. 43:91–14
     [Google Scholar]
  82. Newman EA, Kennedy MC, Falk DA, McKenzie D. 2019. Scaling and complexity in landscape ecology. Front. Ecol. Evol. 7:293
     [Google Scholar]
  83. Norberg J. 2004. Biodiversity and ecosystem functioning: a complex adaptive systems approach. Limnol. Oceanogr. 49:41269–77
     [Google Scholar]
  84. Orr JA, Vinebrooke RD, Jackson MC, Kroeker KJ, Kordas RL et al. 2020. Towards a unified study of multiple stressors: divisions and common goals across research disciplines. Proc. R. Soc. Edinb. B 287:192620200421
     [Google Scholar]
  85. Ospici M, Sys K, Guegan-Marat S. 2022. Prediction of fish location by combining fisheries data and sea bottom temperature forecasting. Image Analysis and Processing – ICIAP 2022437–48. Cham, Switz.: Springer
     [Google Scholar]
  86. Padfield D, O'Sullivan H, Pawar S 2021. rTPC and nls.multstart: a new pipeline to fit thermal performance curves in R. Methods Ecol. Evol. 12:61138–43
     [Google Scholar]
  87. Pennekamp F, Adamson MW, Petchey OL, Poggiale J-C, Aguiar M et al. 2017. The practice of prediction: What can ecologists learn from applied, ecology-related fields?. Ecol. Complex. 32:156–67
     [Google Scholar]
  88. Piggott JJ, Townsend CR, Matthaei CD. 2015. Reconceptualizing synergism and antagonism among multiple stressors. Ecol. Evol. 5:71538–47
     [Google Scholar]
  89. Platt T, Gallegos CL, Harrison WG. 1980. Photoinhibition of photosynthesis in natural assemblages of marine phytoplankton. J. Mar. Res. 38:103–11
     [Google Scholar]
  90. Porter WP, Busch RL. 1978. Fractional factorial analysis of growth and weaning success in Peromyscus maniculatus. Science 202:4370907–10
     [Google Scholar]
  91. Pottier J, Dubuis A, Pellissier L, Maiorano L, Rossier L et al. 2013. The accuracy of plant assemblage prediction from species distribution models varies along environmental gradients: climate and species assembly predictions. Glob. Ecol. Biogeogr. 22:152–63
     [Google Scholar]
  92. Pronzato L, Walter E. 1985. Robust experiment design via stochastic approximation. Math. Biosci. 75:1103–20
     [Google Scholar]
  93. Qin M, Li Z, Du Z. 2017. Red tide time series forecasting by combining ARIMA and deep belief network. Knowl.-Based Syst 125:39–52
     [Google Scholar]
  94. Quinn TJ. 2003. Ruminations on the development and future of population dynamics models in fisheries. Nat. Resour. Model. 16:4341–92
     [Google Scholar]
  95. R Core Team 2022. R: A Language and Environment for Statistical Computing Vienna: R Found. Stat. Comput. https://www.R-project.org
  96. Ratkowsky DA, Lowry RK, McMeekin TA, Stokes AN, Chandler RE. 1983. Model for bacterial culture growth rate throughout the entire biokinetic temperature range. J. Bacteriol. 154:31222–26
     [Google Scholar]
  97. Rezende EL, Bozinovic F. 2019. Thermal performance across levels of biological organization. Philos. Trans. R. Soc. Lond. B 374:177820180549
     [Google Scholar]
  98. Ryan EG, Drovandi CC, McGree JM, Pettitt AN. 2016. A review of modern computational algorithms for Bayesian optimal design. Int. Stat. Rev. 84:1128–54
     [Google Scholar]
  99. Schindler DW. 1978. Factors regulating phytoplankton production and standing crop in the world's freshwaters. Limnol. Oceanogr. 23:3478–86
     [Google Scholar]
  100. Schindler DW, Hecky RE, Findlay DL, Stainton MP, Parker BR et al. 2008. Eutrophication of lakes cannot be controlled by reducing nitrogen input: results of a 37-year whole-ecosystem experiment. PNAS 105:3211254–58
     [Google Scholar]
  101. Schoolfield RM, Sharpe PJH, Magnuson CE. 1981. Non-linear regression of biological temperature-dependent rate models based on absolute reaction-rate theory. J. Theor. Biol. 88:4719–31
     [Google Scholar]
  102. Seifert M, Rost B, Trimborn S, Hauck J. 2020. Meta-analysis of multiple driver effects on marine phytoplankton highlights modulating role of pCO2. Glob. Change Biol. 26:126787–6804
     [Google Scholar]
  103. Shaw RG. 2019. From the past to the future: considering the value and limits of evolutionary prediction. Am. Nat. 193:11–10
     [Google Scholar]
  104. Smith WF. 2005. Experimental Design for Formulation Philadelphia: Soc. Ind. Appl. Math.
  105. Soetaert K, Herman PMJ, eds. 2009. A Practical Guide to Ecological Modelling: Using R as a Simulation Platform Dordrecht, Neth.: Springer
  106. Soetaert K, Hindmarsh AC, Eisenstat SC, Moler C, Dongarra J, Saad Y. 2021. rootSolve: nonlinear root finding, equilibrium and steady-state analysis of ordinary differential equations. Comprehensive R Archive Network https://CRAN.R-project.org/package=rootSolve
    [Google Scholar]
  107. Steinberg DM, Hunter WG. 1984. Experimental design: review and comment. Technometrics 26:271–97
     [Google Scholar]
  108. Thomas MK, Aranguren-Gassis M, Kremer CT, Gould MR, Anderson K et al. 2017. Temperature-nutrient interactions exacerbate sensitivity to warming in phytoplankton. Glob. Change Biol. 23:83269–80
     [Google Scholar]
  109. Tilman D. 1977. Resource competition between plankton algae: an experimental and theoretical approach. Ecology 58:2338–48
     [Google Scholar]
  110. Vallino JJ. 2010. Ecosystem biogeochemistry considered as a distributed metabolic network ordered by maximum entropy production. Philos. Trans. R. Soc. Lond. B 365:15451417–27
     [Google Scholar]
  111. Wagner T, Schliep EM, North JS, Kundel H, Custer CA et al. 2023. Predicting climate change impacts on poikilotherms using physiologically guided species abundance models. PNAS 120:15e2214199120
     [Google Scholar]
  112. Ward BA, Dutkiewicz S, Jahn O, Follows MJ. 2012. A size-structured food-web model for the global ocean. Limnol. Oceanogr. 57:61877–91
     [Google Scholar]
  113. Warner SC, Travis J, Dunson WA. 1993. Effect of pH variation of interspecific competition between two species of hylid tadpoles. Ecology 74:1183–94
     [Google Scholar]
  114. Wheeler B. 2022. AlgDesign: algorithmic experimental design. Comprehensive R Archive Network https://CRAN.R-project.org/package=AlgDesign
    [Google Scholar]
  115. Wickham H, Averick M, Bryan J, Chang W, D'Agostino McGowan L et al. 2019. Welcome to the tidyverse. J. Open Source Softw. 4:431686
     [Google Scholar]
  116. Wilke CO. 2020. Cowplot: streamlined plot theme and plot annotations for “ggplot2. .” Comprehensive R Archive Network. https://CRAN.R-project.org/package=cowplot
    [Google Scholar]
  117. Wolfram Res 2021. Mathematica Champaign, IL: Wolfram Res. https://www.wolfram.com/mathematica
  118. Wu C-F, Hamada M. 2009. Experiments: Planning, Analysis, and Optimization Hoboken, NJ: Wiley. , 2nd ed..
  119. Yoshida T, Jones LE, Ellner SP, Fussmann GF, Hairston NG. 2003. Rapid evolution drives ecological dynamics in a predator-prey system. Nature 424:6946303–6
     [Google Scholar]
  120. Yvon-Durocher G, Allen AP. 2012. Linking community size structure and ecosystem functioning using metabolic theory. Philos. Trans. R. Soc. Lond. B 367:16052998–3007
     [Google Scholar]
  121. Zakem EJ, Polz MF, Follows MJ. 2020. Redox-informed models of global biogeochemical cycles. Nat. Commun. 11:15680
     [Google Scholar]
/content/journals/10.1146/annurev-marine-041823-095913
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Designing More Informative Multiple-Driver Experiments
Annual Review of Marine Science 16, 513 (2024); https://doi.org/10.1146/annurev-marine-041823-095913
/content/journals/10.1146/annurev-marine-041823-095913
/content/journals/10.1146/annurev-marine-041823-095913
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  • Article Type: Review Article

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