IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v427y2020ics0304380020301290.html
   My bibliography  Save this article

Conducting reproducible ecosystem modeling using the open source mass balance model Rpath

Author

Listed:
  • Lucey, Sean M.
  • Gaichas, Sarah K.
  • Aydin, Kerim Y.

Abstract

Ecosystem models are important tools for conducting ecosystem-based management. A particularly useful method of characterizing the flow of energy through an ecosystem and the subsequent direct and indirect implications of management actions is mass balance modeling. Here we outline the equations as utilized in Rpath, an R implementation of the mass balance algorithms popularized by Ecopath with Ecosim that are designed to work with fisheries data sources. We believe that common practices in R will aid in the reproducibility of conducting analysis using a mass balance model as all of the code is contained within a single script file. This includes the built-in statistical and graphical functions of R. In addition to added reproducibility, R is a coding language with which ecologists are familiar. This familiarity offers greater flexibility for practitioners to tailor the model to their needs. We have made the code available on an open software development platform which should aid in continuous community development of the tool.

Suggested Citation

  • Lucey, Sean M. & Gaichas, Sarah K. & Aydin, Kerim Y., 2020. "Conducting reproducible ecosystem modeling using the open source mass balance model Rpath," Ecological Modelling, Elsevier, vol. 427(C).
  • Handle: RePEc:eee:ecomod:v:427:y:2020:i:c:s0304380020301290
    DOI: 10.1016/j.ecolmodel.2020.109057
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380020301290
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2020.109057?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Eddelbuettel, Dirk & Francois, Romain, 2011. "Rcpp: Seamless R and C++ Integration," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i08).
    2. Heymans, Johanna Jacomina & Coll, Marta & Link, Jason S. & Mackinson, Steven & Steenbeek, Jeroen & Walters, Carl & Christensen, Villy, 2016. "Best practice in Ecopath with Ecosim food-web models for ecosystem-based management," Ecological Modelling, Elsevier, vol. 331(C), pages 173-184.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Whitehouse, George A. & Aydin, Kerim Y., 2020. "Assessing the sensitivity of three Alaska marine food webs to perturbations: an example of Ecosim simulations using Rpath," Ecological Modelling, Elsevier, vol. 429(C).
    2. Calhoun-Grosch, Stacy & Ruzicka, Jim J. & Robinson, Kelly L. & Wang, Verena H. & Sutton, Tracey & Ainsworth, Cameron & Hernandez, Frank, 2024. "Simulating productivity changes of epipelagic, mesopelagic, and bathypelagic taxa using a depth-resolved, end-to-end food web model for the oceanic Gulf of Mexico," Ecological Modelling, Elsevier, vol. 489(C).
    3. Heinichen, Margaret & McManus, M. Conor & Lucey, Sean M. & Aydin, Kerim & Humphries, Austin & Innes-Gold, Anne & Collie, Jeremy, 2022. "Incorporating temperature-dependent fish bioenergetics into a Narragansett Bay food web model," Ecological Modelling, Elsevier, vol. 466(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wilson J. Wright & Peter N. Neitlich & Alyssa E. Shiel & Mevin B. Hooten, 2022. "Mechanistic spatial models for heavy metal pollution," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
    2. Woodstock, Matthew S. & Sutton, Tracey T. & Frank, Tamara & Zhang, Yuying, 2021. "An early warning sign: trophic structure changes in the oceanic Gulf of Mexico from 2011—2018," Ecological Modelling, Elsevier, vol. 445(C).
    3. Booth, Shawn & Walters, William J & Steenbeek, Jeroen & Christensen, Villy & Charmasson, Sabine, 2020. "An Ecopath with Ecosim model for the Pacific coast of eastern Japan: Describing the marine environment and its fisheries prior to the Great East Japan earthquake," Ecological Modelling, Elsevier, vol. 428(C).
    4. Fernández de Marcos Giménez de los Galanes, Alberto, 2022. "Data-driven stabilizations of goodness-of-fit tests," DES - Working Papers. Statistics and Econometrics. WS 35324, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Sloot Henrik, 2022. "Implementing Markovian models for extendible Marshall–Olkin distributions," Dependence Modeling, De Gruyter, vol. 10(1), pages 308-343, January.
    6. Cindy Frascolla & Guillaume Lecuelle & Pascal Schlich & Hervé Cardot, 2022. "Two sample tests for Semi-Markov processes with parametric sojourn time distributions: an application in sensory analysis," Computational Statistics, Springer, vol. 37(5), pages 2553-2580, November.
    7. Samrachana Adhikari & Tracy Sweet & Brian Junker, 2021. "Analysis of longitudinal advice‐seeking networks following implementation of high stakes testing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1475-1500, October.
    8. François Bachoc & Marc G Genton & Klaus Nordhausen & Anne Ruiz-Gazen & Joni Virta, 2020. "Spatial blind source separation," Biometrika, Biometrika Trust, vol. 107(3), pages 627-646.
    9. Bill Venables, 2017. "JOHN M. CHAMBERS . Extending R . Boca Raton : CRC Press," Biometrics, The International Biometric Society, vol. 73(2), pages 709-710, June.
    10. Borrett, Stuart R. & Sheble, Laura & Moody, James & Anway, Evan C., 2018. "Bibliometric review of ecological network analysis: 2010–2016," Ecological Modelling, Elsevier, vol. 382(C), pages 63-82.
    11. Anoek Castelein & Dennis Fok & Richard Paap, 2020. "A multinomial and rank-ordered logit model with inter- and intra-individual heteroscedasticity," Tinbergen Institute Discussion Papers 20-069/III, Tinbergen Institute.
    12. Tesfaye, Gashaw & Wolff, Matthias, 2018. "Modeling trophic interactions and the impact of an introduced exotic carp species in the Rift Valley Lake Koka, Ethiopia," Ecological Modelling, Elsevier, vol. 378(C), pages 26-36.
    13. Virginia X. He & Matt P. Wand, 2024. "Bayesian generalized additive model selection including a fast variational option," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 108(3), pages 639-668, September.
    14. Adrien Ickowicz & Jessica Ford & Keith Hayes, 2019. "A Mixture Model Approach for Compositional Data: Inferring Land-Use Influence on Point-Referenced Water Quality Measurements," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 719-739, December.
    15. James Joseph Balamuta & Steven Andrew Culpepper, 2022. "Exploratory Restricted Latent Class Models with Monotonicity Requirements under PÒLYA–GAMMA Data Augmentation," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 903-945, September.
    16. Athanasios C. Micheas & Jiaxun Chen, 2018. "sppmix: Poisson point process modeling using normal mixture models," Computational Statistics, Springer, vol. 33(4), pages 1767-1798, December.
    17. Martinetti, Davide & Geniaux, Ghislain, 2017. "Approximate likelihood estimation of spatial probit models," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 30-45.
    18. Jin, Shaobo & Moustaki, Irini & Yang-Wallentin, Fan, 2018. "Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case," LSE Research Online Documents on Economics 88118, London School of Economics and Political Science, LSE Library.
    19. Martina Sundqvist & Julien Chiquet & Guillem Rigaill, 2023. "Adjusting the adjusted Rand Index," Computational Statistics, Springer, vol. 38(1), pages 327-347, March.
    20. Prellezo, Raúl & Corrales, Xavier & Andonegi, Eider & Bald, Carlos & Fernandes-Salvador, Jose A. & Iñarra, Bruno & Irigoien, Xabier & Martin, Adrian & Murillas-Maza, Arantza & Tasdemir, Deniz, 2024. "Economic trade-offs of harvesting the ocean twilight zone: An ecosystem services approach," Ecosystem Services, Elsevier, vol. 67(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:427:y:2020:i:c:s0304380020301290. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.