Movement

In Ecospace, the movement of organisms across space can be realized through dispersal, migration and advection [1] [2] [3]

Dispersal

Dispersal is the pseudo random movement of wandering. While dispersing, species in Ecospace by default have simple random movement. But an option is to have biased movement, with a higher chance of moving to a neighboring cell if feeding is better, and the risk of predation is lower. This is called Cell fitness, or the ratio between the ability to feed versus the risk of predation in a cell [4]. The main parameter to regulate dispersal is the Dispersal rate of a functional group. This is the absolute value of average individual annual movement distances as a result of random movements. The default value is 300 km year-1 for all groups apart from detritus groups (with a default of 10 km/year). General values can be assumed to be of different relative magnitudes (e.g. 3, 30 and 300 km year-1) representing essentially non-dispersing, and demersal and pelagic groups, respectively. It is recommended to use these general assumptions unless there are good reasons to believe otherwise. As is sometimes incorrectly quoted, Martell et al. [5] do not provide a way to calculate dispersal rates from swimming speeds. Instead, see Bradbury et al. [6].

 

An individual based model (IBM) to estimate Ecospace dispersal rates from movement parameters has been implemented as a Shiny app. See the dispersal app at  https://ecopath.app/ and the User Guide chapter.

Other parameters that influence movement are Relative vulnerability to predation (or grazing) outside their preferred habitat, which allows for increasing the strength of the prey-predation interaction outside the preferred habitat of a species grouping (Martell et al., 2005). In addition, the Relative movement to cell fitness or local fitness (per capita gain from net food intake minus predation mortality) is an experimental feature in EwE 6.6. Species move faster when a cell is less fit, trying to find food, avoiding predation, and improving chances of running into areas where conditions are better.

This Relative movement to cell fitness field partially replaces the Relative feeding rate in bad habitat, where dispersal rates were assumed to differ between preferred and non-preferred habitats, with higher values within non-preferred habitats than in preferred habitats. Organisms outside their preferred habitat may be conceived as less likely to consume as much appropriate food as within preferred habitat, due to the unavailability of such food or the danger associated with foraging. Organisms in non-preferred habitats will strive to leave these, and attempt to return as rapidly as possible to their preferred habitats. Ecospace users can reduce the feeding rate of ecosystem components down to 0.01 times the Ecopath baseline (i.e., the Q/B value). This mechanism can be turned off by setting the feeding rate multiplier equal to unity (1). Note that this mechanism has been integrated with the habitat foraging capacity model (EwE 6.4+). The default value for this multiplier is 5.0, the upper limit 10. A value of 1 will make this mechanism inoperative. Currently, this only applies to multi-stanza groups when running the IBM model [7]; (and see below).

Migration

This type of movement concerns directed forced movement unrelated to habitat or feeding preferences to represent ontogenetic annual migrations (e.g., [8]). A way to incorporate this movement is through migration target maps (in EwE 6.5+). Weight maps indicate migratory preferred areas for each month. Migration is different to dispersal, but migrating groups disperse during migration and when they reach their migration areas. To represent migrating species that leave the model area, users can designate a cell to be ‘outside’ and give this cell independent dynamics.

Advection

Ecospace also includes a rudimentary advection model to capture current drift of organisms like phytoplankton that tend to be transported with surface currents. Currents can be entered (monthly), can be calculated, or can be driven by an expert model. The advection field is calculated from wind/geostrophic forcing patterns for surface currents by solving the linearized pressure field and velocity equations across the Ecospace grid. Users can specify which groups are subject to advection velocities. Advected movement adds to dispersal and migration. Advection data is entered as u and v fields (x and y velocity), in cm/sec, where positive u = east, and positive v = south (not north, as is the common convention in current models).

While the simple built-in advection model is useful for testing purposes, we recommend that users obtain advection fields from 3D hydrodynamic models, and import those via the spatial temporal data framework for advanced applications.

Advection also affects environmental contaminant distributions in the EwE Ecotracer model [9] [10] [11]. For an illustrative example of the combined use of Ecospace, advection and Ecotracer see Tierney et al. [12].

Adaption

The chapter is in part adapted, with permission,  from:

De Mutsert K, Marta Coll, Jeroen Steenbeek, Cameron Ainsworth, Joe Buszowski, David Chagaris, Villy Christensen, Sheila J.J. Heymans, Kristy A. Lewis, Simone Libralato, Greig Oldford, Chiara Piroddi, Giovanni Romagnoni, Natalia Serpetti, Michael Spence, Carl Walters. 2023. Advances in spatial-temporal coastal and marine ecosystem modeling using Ecopath with Ecosim and Ecospace. Treatise on Estuarine and Coastal Science, 2nd Edition. Elsevier.


  1. Christensen, V., Walters, C.J., 2004. Ecopath with Ecosim: methods, capabilities and limitations. Ecological Modelling, Placing Fisheries in their Ecosystem Context 172, 109–139. https://doi.org/10.1016/j.ecolmodel.2003.09.003
  2. Coll, M., Bundy, A., Shannon, L.J., 2009. Ecosystem Modelling Using the Ecopath with Ecosim Approach, in: Megrey, B.A., Moksness, E. (Eds.), Computers in Fisheries Research. Springer Netherlands, Dordrecht, pp. 225–291. https://doi.org/10.1007/978-1-4020-8636-6_8
  3. Walters, C., Pauly, D., Christensen, V., 1999. Ecospace: Prediction of Mesoscale Spatial Patterns in Trophic Relationships of Exploited Ecosystems, with Emphasis on the Impacts of Marine Protected Areas. Ecosystems 2, 539–554. https://doi.org/10.1007/s100219900101
  4. Martell, S.J.D., Essington, T.E., Lessard, B., Kitchell, J.F., Walters, C.J., Boggs, C.H., 2005. Interactions of productivity, predation risk, and fishing effort in the efficacy of marine protected areas for the central Pacific. Can. J. Fish. Aquat. Sci. 62, 1320–1336. https://doi.org/10.1139/f05-114
  5. Martell, S.J.D., Essington, T.E., Lessard, B., Kitchell, J.F., Walters, C.J., Boggs, C.H., 2005. Interactions of productivity, predation risk, and fishing effort in the efficacy of marine protected areas for the central Pacific. Can. J. Fish. Aquat. Sci. 62, 1320–1336. https://doi.org/10.1139/f05-114
  6. Bradbury, I.R., Laurel, B., Snelgrove, P.V.R., Bentzen, P., Campana, S.E., 2008. Global patterns in marine dispersal estimates: the influence of geography, taxonomic category and life history. Proceedings of the Royal Society B: Biological Sciences 275, 1803–1809. https://doi.org/10.1098/rspb.2008.0216
  7. Walters, C., Christensen, V., Walters, W., Rose, K., 2010. Representation of multistanza life histories in Ecospace models for spatial organization of ecosystem trophic interaction patterns. Bulletin of Marine Science 86, 439–459.
  8. Fouzai, N., Coll, M., Palomera, I., Santojanni, A., Arneri, E., Christensen, V., 2012. Fishing management scenarios to rebuild exploited resources and ecosystems of the Northern-Central Adriatic (Mediterranean Sea). Journal of Marine Systems 102–104, 39–51. https://doi.org/10.1016/j.jmarsys.2012.05.003
  9. Boyer, J., Rubalcava, K., Booth, S., Townsend, H., 2022. Proof-of-concept model for exploring the impacts of microplastics accumulation in the Maryland coastal bays ecosystem. Ecological Modelling 464, 109849. https://doi.org/10.1016/j.ecolmodel.2021.109849
  10. Christensen, V., Walters, C.J., 2004. Ecopath with Ecosim: methods, capabilities and limitations. Ecological Modelling, Placing Fisheries in their Ecosystem Context 172, 109–139. https://doi.org/10.1016/j.ecolmodel.2003.09.003
  11. Walters, W.J., Christensen, V., 2018. Ecotracer: analyzing concentration of contaminants and radioisotopes in an aquatic spatial-dynamic food web model. Journal of Environmental Radioactivity 181, 118–127. https://doi.org/10.1016/j.jenvrad.2017.11.008
  12. Tierney, K.M., Heymans, J.J., Muir, G.K.P., Cook, G.T., Buszowski, J., Steenbeek, J., Walters, W.J., Christensen, V., MacKinnon, G., Howe, J.A., Xu, S., 2018. Modelling marine trophic transfer of radiocarbon (14C) from a nuclear facility. Environmental Modelling & Software 102, 138–154. https://doi.org/10.1016/j.envsoft.2018.01.013

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