{"id":4064,"date":"2025-01-27T11:40:51","date_gmt":"2025-01-27T16:40:51","guid":{"rendered":"https:\/\/pressbooks.bccampus.ca\/ewemodel\/?post_type=chapter&p=4064"},"modified":"2026-02-18T18:23:30","modified_gmt":"2026-02-18T23:23:30","slug":"environmental-impacts","status":"publish","type":"chapter","link":"https:\/\/pressbooks.bccampus.ca\/ewemodel\/chapter\/environmental-impacts\/","title":{"raw":"Environmental impacts","rendered":"Environmental impacts"},"content":{"raw":"One of the clear lessons from time series fitting with Ecosim is that in order to fit models convincingly to time series data, we have to consider food web impacts, environmental change as well as human impacts, (see the On modelling<\/a> chapter).\r\n\r\nThe basic structure of EwE is designed so that trophic impacts (be they direct or indirect) and direct human impacts (notably through exploitation) are dealt with through the food web and exploitation parts as detailed in the baseline Ecopath and Ecosim chapters of this text book. Indirect food web effects of a behavioural character, such as when one group impacts the feeding interactions between two other groups can be handled with mediation \u2013 as described in more detail in the Mediation<\/a> chapter. \u00a0This leaves us with describing how environmental impacts are represented in EwE, and that's the topic of this chapter.\r\n\r\nAs a starting point, environmental impacts are dynamic factors, i.e. they change over time, and they impact organisms in different ways. This in essence means that there are two steps to be considered for inclusion of environmental impacts: how they change and how they impact.\r\n\r\nThe first factor, how an environmental factor changes over time, is modelled with forcing functions. In Ecosim, these are temporal while in Ecospace they have to be both temporal and spatial, so reading in a spatial map for each time step. Other than that aspect, Ecosim and Ecospace work the same way with incorporating environmental impacts.\r\n\r\nThe second factor, how to model the direct impact of the forcing function on functional groups is done with <\/span>environmental preference functions, The details of this are described in the Habitat Capacity<\/a> chapter to which we refer. \u00a0That chapter describes the spatial implementation of habitat capacity, and this functionality was indeed developed for Ecospace initially, but it is now also implemented in Ecosim with the same approach and functionality as described in the chapter.\r\n\r\nFor details of how to use the forcing functions and define the habitat capacity preference functions, see the tutorial on Incorporating environmental forcing<\/a> (available online and in pdf version only).\r\n
\r\n\r\nScaling your forcing functions?<\/strong>\r\n\r\nWhen you have loaded a forcing function in Ecosim, you have two choices:\r\n
    \r\n \t
  1. Use it for direct forcing, (Ecosim > Input > Forcing function), <\/em>with the option of forcing \u00a0predator-prey interactions, primary production or detritus import. \u00a0For this option, the forcing functions should be scaled relative to the Ecopath baseline (so as a rule with the value of 1 as the first value);<\/li>\r\n \t
  2. Use it through environmental preference functions that impact foraging arena size, e.g., for temperature, salinity, pH, or O2<\/sub>. \u00a0Such preference functions should be used with the corresponding values, (e.g., 10o<\/sup>C as temperature value).<\/li>\r\n<\/ol>\r\n<\/div>\r\n

    Inner workings in Ecosim<\/h2>\r\nThe description below of how forcing functions are considered in Ecosim is also applicable for mediation functions.\r\n\r\nThe basic Ecosim prediction for consumption or flow rate (unit: biomass\/time, e.g., t km-2<\/sup> year-1<\/sup>) of type i<\/em> prey biomass to type j<\/em> predator is of the functional form\r\n\r\n[latex]\\text{flow rate} = a_{ij} V_{ij} B_j \\tag{1}[\/latex]<\/a>\r\n\r\nwhere aij<\/sub><\/em> is a \u201crate of effective search\u201d parameter, Vij<\/sub><\/em> is vulnerable prey biomass, and B<\/em>j<\/sub> is effective predator abundance[footnote]For biomass pool functional groups B<\/em>j<\/sub> is just predator biomass; for multi-stanza groups it is the sum over ages in that group of numbers at age times body weight to the 2\/3 power, an index of per-predator search rate[\/footnote]. If vulnerable prey were randomly distributed over the modelled area, and V, Bj<\/sub> <\/em>were expressed per unit area, then a<\/em>ij<\/sub> can be interpreted as a volume (or area) swept per unit predator abundance per unit time, corrected for the proportion of time actually spent searching for food[footnote]Foraging time and handling time adjustments reduce aij<\/sub><\/em> from its theoretical maximum value for a predator that searched continuously for food[\/footnote].\r\n\r\nTo understand how effects of habitat changes may impact trophic flow rates, consider the a<\/em>ij<\/sub> parameter. For most trophic interactions, predators search for prey only over restricted spatial foraging arenas, and hence V<\/em>ij<\/sub> is distributed only over such areas rather than at random over the whole system.\r\n\r\nSuppose the (practically unmeasurable) restricted area where foraging by j<\/em> on prey i<\/em> takes place is A<\/em>ij<\/sub> per unit total model area. Suppose that while in this area, each unit of predator abundance searches an effective area a<\/em>ij<\/sub>* for prey. On average, each such area searched should result in capture of V<\/em>ij<\/sub>\/A<\/em>ij<\/sub> prey, since this ratio is prey density in the arena area. In other words, the flow rate could be modelled more precisely (if we could measure A<\/em>ij<\/sub>) as\r\n\r\n[latex]\\text{flow rate}=a_{ij}^{*} A_{ij} V_{ij} B_j \\tag{2}[\/latex]\r\n\r\ni.e., the aij<\/sub><\/em> in Eq. 1<\/a> Equation \u00a0can be interpreted as a<\/em>ij<\/sub> = \u00a0a<\/em>ij<\/sub>*\/A<\/em>ij<\/sub>. Expressed this way, we see that time forcing[footnote]Or mediation effects[\/footnote] can influence the flow rate in at least three quite distinct ways:\r\n