{"id":2007,"date":"2023-11-26T17:19:42","date_gmt":"2023-11-26T22:19:42","guid":{"rendered":"https:\/\/pressbooks.bccampus.ca\/ewemodel\/?post_type=chapter&p=2007"},"modified":"2025-10-30T08:57:15","modified_gmt":"2025-10-30T12:57:15","slug":"using-ecology-and-history","status":"publish","type":"chapter","link":"https:\/\/pressbooks.bccampus.ca\/ewemodel\/chapter\/using-ecology-and-history\/","title":{"raw":"Using ecology and history to derive vulnerability multipliers","rendered":"Using ecology and history to derive vulnerability multipliers"},"content":{"raw":"
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Vulnerability multipliers are perhaps easiest to understand when it is recognized that they reflect how far an exploited predator is from its carrying capacity (e.g., interpreted as unfished state); vulnerability multipliers should allow consumption rates that enable a species to recover from its Ecopath biomass to its unfished biomass if fishing ceases. EwE can use the ratio between a group\u2019s unfished biomass and its Ecopath base biomass to estimate vulnerability multipliers for exploited groups, see, e.g., the EwE User Guide vulnerability multiplier estimator<\/a> chapter.<\/p>\r\n

An added corollary is that the unfished state may be associated with high abundance of top predators and low abundance of their prey due to high predation mortality. If such top predator populations are fished down, predator release may cause the prey to increase. For those prey, the vulnerability multipliers should thus be set to a high value, even though the baseline model represents the unfished state.<\/p>\r\n

It can indeed be difficult to specify reasonable vulnerability multipliers for non-exploited species. Here, vulnerability multipliers need to be considered in the context of the foraging arena: the fine-scale spatial structure of the trophic interactions and what proportion of prey may be vulnerable to predation at any moment (Figure 1). The activity, spatial restrictions, and distributions of species provide insight into the likely vulnerability of prey to predation. This in turn provides a starting point from which it is possible to assign vulnerability multipliers. The distribution of predators could be restricted by limited mobility, habitat requirements, or the predation risk they face themselves, whereas prey vulnerability may be influenced by the time they spend in and out of safe behavioral states. This can be related to the availability of shelter, such as macroalgae, or specific ontogenetic life stages (for example juvenile fish may allocate less time to foraging), or be more restricted spatially (and thus unable to access vulnerable pools of prey) than their adult counterparts. Different behaviours, such as dispersal behaviours (e.g., moving to spawning sites), aggressive behaviours, or evolutionary behaviour (e.g., changes in shoaling dynamics) may also influence vulnerability to predation.<\/p>\r\nTrophic levels have also been used to approximate vulnerability multipliers in situations where time series data were unavailable under the dubious assumption that the vulnerability multipliers are proportional to the trophic level of the predator. This approach assumes that higher trophic levels are further removed from their unfished biomass than lower trophic levels, typically because of historical overfishing. This may seem a reasonable assumption considering how global fisheries have historically targeted and depleted higher trophic level fish stocks[footnote]Christensen, V. 1996. Managing fisheries involving top predator and prey species components. Reviews in Fish Biology and Fisheries<\/a>. 6:417-442.[\/footnote] [footnote]Pauly, D., V. Christensen, A. Dalsgaard, R. Froese, and J. Torres. 1998. Fishing down marine food webs. Science 279 (5352): 860-863. DOI: 10.1126\/science.279.5352.860<\/a> [\/footnote], but conflicts with the concept of using a priori<\/em> knowledge to parameterize vulnerability multipliers based on region specific trends in historical exploitation or ecology.\r\n\r\n\"\"\r\n

Figure 1. Using history and ecology to estimate vulnerability multipliers (kij<\/sub><\/em>)<\/span> in Ecosim.\u00a0<\/span>The illustration at the top of the figure provides examples where kij<\/sub><\/em><\/span> estimates can be inferred from functional group ecology and life history. Model simulations demonstrate how (a) prey vulnerability influences predator-prey biomass trajectories when predator biomass increases and (b) how kij <\/sub><\/em><\/b><\/span><\/strong>estimates impact the rate of functional group recovery following reduction in fishing (inset figure).<\/span><\/strong><\/div>\r\n

Finally, an approach to setting vulnerability multipliers was applied by Chagaris et al.[footnote]Chagaris, D., Drew, K., Schueller, A., Cieri, M., Brito, J. and Buchheister, A., 2020. Ecological reference points for Atlantic menhaden established using an ecosystem model of intermediate complexity. Frontiers in Marine Science, 7, p.606417. https:\/\/doi.org\/10.3389\/fmars.2020.606417[\/footnote] to constrain how much predation mortality by a given predator could increase relative to a prey\u2019s total natural mortality<\/p>\r\n[latex]k_{ij}=\\frac{M2_{cap}\\cdot M_i}{M2_{base,ij}}\\tag{1}[\/latex]\r\n

where M2cap<\/sub><\/em> defines the proportion of the natural mortality of a prey that a predator can be responsible for, Mi<\/sub><\/em> is the natural mortality of prey i<\/em>, and M2base.ij<\/sub><\/em> is the base predation mortality by predator j<\/em> on prey i<\/em>. There may be ecological reasons, or reasons derived from data, to prevent a single predator from being accountable for large proportions of a prey\u2019s natural mortality. Using these kij<\/sub><\/em> instead of default or model estimated values, (which are often higher) may also be driven by ambitions for model performance: Chagaris et al.[footnote]Chagaris et al. 2020. op. cit<\/em>.[\/footnote] found that extremely high k<\/i>ij<\/sub><\/em> estimated by Ecosim led to instability at high fishing mortality rates when evaluating equilibrium yield curves, and\u00a0 using M2cap<\/sub><\/em> values between 0.75 to 1.0 led to more reasonable estimates for FMSY<\/sub> (the fishing mortality at maximum sustainable yield) while also constraining theoretical maximum predation mortality rates to values that were compatible with prey natural mortality rates.<\/p>\r\n\r\n

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Attribution\r\n<\/strong>This chapter is based on Bentley JW, Chagaris D, Coll M, Heymans JJ, Serpetti N, Walters CJ and Christensen V. 2024. Calibrating ecosystem models to support marine Ecosystem-based Management. ICES Journal of Marine Science, https:\/\/doi.org\/10.1093\/icesjms\/fsad213<\/a>. Adapted based on CC BY License.\r\nRather than citing this chapter, please cite the source.<\/div>\r\n<\/div>\r\n<\/div>","rendered":"
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Vulnerability multipliers are perhaps easiest to understand when it is recognized that they reflect how far an exploited predator is from its carrying capacity (e.g., interpreted as unfished state); vulnerability multipliers should allow consumption rates that enable a species to recover from its Ecopath biomass to its unfished biomass if fishing ceases. EwE can use the ratio between a group\u2019s unfished biomass and its Ecopath base biomass to estimate vulnerability multipliers for exploited groups, see, e.g., the EwE User Guide vulnerability multiplier estimator<\/a> chapter.<\/p>\n

An added corollary is that the unfished state may be associated with high abundance of top predators and low abundance of their prey due to high predation mortality. If such top predator populations are fished down, predator release may cause the prey to increase. For those prey, the vulnerability multipliers should thus be set to a high value, even though the baseline model represents the unfished state.<\/p>\n

It can indeed be difficult to specify reasonable vulnerability multipliers for non-exploited species. Here, vulnerability multipliers need to be considered in the context of the foraging arena: the fine-scale spatial structure of the trophic interactions and what proportion of prey may be vulnerable to predation at any moment (Figure 1). The activity, spatial restrictions, and distributions of species provide insight into the likely vulnerability of prey to predation. This in turn provides a starting point from which it is possible to assign vulnerability multipliers. The distribution of predators could be restricted by limited mobility, habitat requirements, or the predation risk they face themselves, whereas prey vulnerability may be influenced by the time they spend in and out of safe behavioral states. This can be related to the availability of shelter, such as macroalgae, or specific ontogenetic life stages (for example juvenile fish may allocate less time to foraging), or be more restricted spatially (and thus unable to access vulnerable pools of prey) than their adult counterparts. Different behaviours, such as dispersal behaviours (e.g., moving to spawning sites), aggressive behaviours, or evolutionary behaviour (e.g., changes in shoaling dynamics) may also influence vulnerability to predation.<\/p>\n

Trophic levels have also been used to approximate vulnerability multipliers in situations where time series data were unavailable under the dubious assumption that the vulnerability multipliers are proportional to the trophic level of the predator. This approach assumes that higher trophic levels are further removed from their unfished biomass than lower trophic levels, typically because of historical overfishing. This may seem a reasonable assumption considering how global fisheries have historically targeted and depleted higher trophic level fish stocks[1]<\/sup><\/a> [2]<\/sup><\/a>, but conflicts with the concept of using a priori<\/em> knowledge to parameterize vulnerability multipliers based on region specific trends in historical exploitation or ecology.<\/p>\n

\"\"<\/p>\n

Figure 1. Using history and ecology to estimate vulnerability multipliers (kij<\/sub><\/em>)<\/span> in Ecosim.\u00a0<\/span>The illustration at the top of the figure provides examples where kij<\/sub><\/em><\/span> estimates can be inferred from functional group ecology and life history. Model simulations demonstrate how (a) prey vulnerability influences predator-prey biomass trajectories when predator biomass increases and (b) how kij <\/sub><\/em><\/b><\/span><\/strong>estimates impact the rate of functional group recovery following reduction in fishing (inset figure).<\/span><\/strong><\/div>\n

Finally, an approach to setting vulnerability multipliers was applied by Chagaris et al.[3]<\/sup><\/a> to constrain how much predation mortality by a given predator could increase relative to a prey\u2019s total natural mortality<\/p>\n

[latex]k_{ij}=\\frac{M2_{cap}\\cdot M_i}{M2_{base,ij}}\\tag{1}[\/latex]<\/p>\n

where M2cap<\/sub><\/em> defines the proportion of the natural mortality of a prey that a predator can be responsible for, Mi<\/sub><\/em> is the natural mortality of prey i<\/em>, and M2base.ij<\/sub><\/em> is the base predation mortality by predator j<\/em> on prey i<\/em>. There may be ecological reasons, or reasons derived from data, to prevent a single predator from being accountable for large proportions of a prey\u2019s natural mortality. Using these kij<\/sub><\/em> instead of default or model estimated values, (which are often higher) may also be driven by ambitions for model performance: Chagaris et al.[4]<\/sup><\/a> found that extremely high k<\/i>ij<\/sub><\/em> estimated by Ecosim led to instability at high fishing mortality rates when evaluating equilibrium yield curves, and\u00a0 using M2cap<\/sub><\/em> values between 0.75 to 1.0 led to more reasonable estimates for FMSY<\/sub> (the fishing mortality at maximum sustainable yield) while also constraining theoretical maximum predation mortality rates to values that were compatible with prey natural mortality rates.<\/p>\n

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Attribution
\n<\/strong>This chapter is based on Bentley JW, Chagaris D, Coll M, Heymans JJ, Serpetti N, Walters CJ and Christensen V. 2024. Calibrating ecosystem models to support marine Ecosystem-based Management. ICES Journal of Marine Science, https:\/\/doi.org\/10.1093\/icesjms\/fsad213<\/a>. Adapted based on CC BY License.
\nRather than citing this chapter, please cite the source.<\/div>\n<\/div>\n<\/div>\n

Media Attributions<\/h2>