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Marine Biology

, 166:14 | Cite as

Predicting the performance of cosmopolitan species: dynamic energy budget model skill drops across large spatial scales

  • Cristián J. Monaco
  • Erika M. D. Porporato
  • Justin A. Lathlean
  • Morgana Tagliarolo
  • Gianluca Sarà
  • Christopher D. McQuaid
Original paper

Abstract

Individual-based models are increasingly used by marine ecologists to predict species responses to environmental change on a mechanistic basis. Dynamic Energy Budget (DEB) models allow the simulation of physiological processes (maintenance, growth, reproduction) in response to variability in environmental drivers. High levels of computational capacity and remote-sensing technologies provide an opportunity to apply existing DEB models across global spatial scales. To do so, however, we must first test the assumption of stationarity, i.e., that parameter values estimated for populations in one location/time are valid for populations elsewhere. Using a validated DEB model parameterized for the cosmopolitan intertidal mussel Mytilus galloprovincialis, we ran growth simulations for native, Mediterranean Sea, populations and non-native, South African populations. The model performed well for native populations, but overestimated growth for non-native ones. Overestimations suggest that: (1) unaccounted variables may keep the physiological performance of non-native M. galloprovincialis in check, and/or (2) phenotypic plasticity or local adaptation could modulate responses under different environmental conditions. The study shows that stationary mechanistic models that aim to describe dynamics in complex physiological processes should be treated carefully when implemented across large spatial scales. Instead, we suggest placing the necessary effort into identifying the nuances that result in non-stationarity and explicitly accounting for them in geographic-scale mechanistic models.

Notes

Acknowledgements

This research was funded by the South African Research Chairs Initiative of the Department of Science and Technology and the National Research Foundation to CDM, and the Italian Minister of University and Research (PRIN TETRIS 2010, Grant no. 2010PBMAXP_003) to GS. CJM and MT were supported by a Rhodes University post-doctoral fellowship. The Group for High Resolution Sea Surface Temperature (GHRSST) Multi-scale Ultra-high Resolution (MUR) SST data were obtained from the NASA EOSDIS Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the Jet Propulsion Laboratory, Pasadena, CA (http://dx.doi.org/10.5067/GHGMR-4FJ01). Chlorophyll-a data were generated using CMEMS Products, production centre ACRI-ST. We thank Jaqui Trassierra for assistance during fieldwork. An anonymous reviewer and the Associate Editor, Sandra Shumway, provided insightful comments that improved our manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

Supplementary material

227_2018_3462_MOESM1_ESM.pdf (139 kb)
Appendix S1. Mytilus galloprovincialis Dynamic Energy Budget (DEB) parameter values used to perform model simulations (PDF 139 kb)
227_2018_3462_MOESM2_ESM.pdf (118 kb)
Appendix S2. Linear regression coefficients from relationships between satellite-derived sea surface temperature and in situ measurements (data available in Appendix S5) (PDF 118 kb)
227_2018_3462_MOESM3_ESM.pdf (111 kb)
Appendix S3. Linear regression coefficients from relationship between weather station air temperature (www.weatherunderground.com/weather/api) and in situ measurements taken with aerially exposed “robomussels” (data available in Appendix S6) (PDF 111 kb)
227_2018_3462_MOESM4_ESM.pdf (179 kb)
Appendix S4. Dynamic Energy Budget (DEB) model scrip for Mytilus galloprovincialis (PDF 179 kb)
227_2018_3462_MOESM5_ESM.csv (114 kb)
Appendix S5. Satellite-derived sea surface temperature and in situ measurements used to estimate submerged mussel body temperature (CSV 114 kb)
227_2018_3462_MOESM6_ESM.csv (62 kb)
Appendix S6. Weather station air temperature and in situ “robomussels” measurements used to estimate aerially exposed mussel body temperature (CSV 61 kb)
227_2018_3462_MOESM7_ESM.csv (9 mb)
Appendix S7. Site-specific environmental data used to run the DEB models. Columns are: region, site, local time (SAST or CEST), estimated body temperature in water (°C), estimated body temperature in air (°C), chlorophyll-a concentration (μg/L), tide height (m), and tide flag (1 = mussel submerged, 0 = mussel exposed to air) (CSV 9198 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Cristián J. Monaco
    • 1
    • 6
  • Erika M. D. Porporato
    • 2
  • Justin A. Lathlean
    • 3
  • Morgana Tagliarolo
    • 1
    • 4
  • Gianluca Sarà
    • 5
  • Christopher D. McQuaid
    • 1
  1. 1.Department of Zoology and EntomologyRhodes UniversityGrahamstownSouth Africa
  2. 2.Department of Environmental Sciences, Informatics and StatisticsCa’ Foscari University of VeniceVenice MestreItaly
  3. 3.School of Biological SciencesQueen’s University BelfastBelfastUK
  4. 4.UMSR LEEISA (CNRS, UG, Ifremer)CayenneFrench Guiana
  5. 5.Dipartimento di Scienze della Terra e del MareUniversity of PalermoPalermoItaly
  6. 6.Southern Seas Ecology Laboratories, School of Biological SciencesUniversity of AdelaideAdelaideAustralia

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