44 External bio-economic models

Santiago de la Puente

No model is capable of doing everything, nor should it be. However, EwE models can be linked or coupled with external routines forming an ecological-social-economic modelling chain. For example, some authors have coupled Ecosim with bioeconomic models to get a better handle on fishing effort dynamics. For each time step, the bioeconomic model uses the predicted catches per fleet (Ecosim output) to estimate the fleets profitability and predict the next time steps’ fishing effort level, which are then reintroduced as inputs for Ecosim.[1] [2] [3]

In other cases, EwE models have been linked with Input-Output (I-O) models, [4] Social Accounting Matrices (SAM) [5] and Computable general equilibrium (CGE) models.[6] These tools also allow estimation of the broader economic impact of the fisheries across national or regional economies (much like the Value Chain plug-in). I-O models characterize the flows of goods in a symmetrical industry by industry format (i.e., goods supplied vs consumed) and are used to estimate direct (measures of actual expenditures by establishments operating in the sector), indirect (measures of economic activity of other industries supplying an industry or using its outputs) and induced economic effects of a particular industry (measures of economic impact derived from the expenditure of salaries gained in the sector on other sectors of the economy).[7] SAMs are extensions of input-output models, with their main advantage being that they consider the social-economic linkages as well as other transactions (such as linkages between production and household sectors).[8] Finally, CGE models provide an analytical framework to assess the impact of fishery policies on regional economies and social welfare.[9]

  1. Dichmont C.M., N. Ellis, R.H. Bustamante, R. Deng, S. Tickell, R. Pascual, H. Lozano‐Montes, S. Griffiths, Evaluating marine spatial closures with conflicting fisheries and conservation objectives, J. Appl. Ecol. 50 (2013) 1060–1070. https://doi.org/10.1111/1365-2664.12110 ↵
  2. Lee K., J. Apriesnig, H. Zhang, Socio-Ecological Outcomes of Single-Species Fisheries Management: The Case of Yellow Perch in Lake Erie, Front. Ecol. Evol. 9 (2021) 703813. https://doi.org/10.3389/fevo.2021.703813 ↵
  3. Apriesnig J.L., T.W. Warziniack, D.C. Finnoff, H. Zhang, K.D. Lee, D.M. Mason, E.S. Rutherford, The consequences of misrepresenting feedbacks in coupled human and environmental models, Ecol. Econ. 195 (2022) 107355. https://doi.org/10.1016/j.ecolecon.2022.107355 ↵
  4. Byron C.J., D. Jin, T.M. Dalton, An Integrated ecological–economic modeling framework for the sustainable management of oyster farming, AQC 447 (2015) 15–22. https://doi.org/10.1016/j.aquaculture.2014.08.030 ↵
  5. Wang Y., J. Hu, H. Pan, S. Li, P. Failler, An integrated model for marine fishery management in the Pearl River Estuary: Linking socio-economic systems and ecosystems, Marine Policy 64 (2016) 135–147. https://doi.org/10.1016/j.marpol.2015.11.014 ↵
  6. Wang Y., J. Hu, H. Pan, P. Failler, Ecosystem-based fisheries management in the Pearl River Delta: Applying a computable general equilibrium model, Marine Policy 112 (2020) 103784. https://doi.org/10.1016/j.marpol.2019.103784 ↵
  7. Byron et al. (2015) op. cit. https://doi.org/10.1016/j.aquaculture.2014.08.030 ↵
  8. Wang et al. (2016) op.cit. https://doi.org/10.1016/j.marpol.2015.11.014 ↵
  9. Wang et al. (2020) op. cit. https://doi.org/10.1016/j.marpol.2019.103784 ↵

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