Survival of prey growing through gape-limited and apex predators

Jj, Anderson

doi:10.1101/686964

Cited by 1 publication

(3 citation statements)

References 25 publications

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“…The predation mortality rate was calculated by combining the probability of predator–prey interaction with the probability of prey falling within the predation size range during this interaction using the gape‐limited mortality framework of Anderson (2019):

μ_{i, t} goodbreak= λ \frac{1 - ϕ ((), x_{i, t})}{ϕ ((), \frac{m}{s})}

where, predation mortality, μ, for model individual i at time step t combines a term representing a daily encounter rate between predator and prey,

λ

, with a term representing the probability of prey being within a predator's gape range. ɸ , a standardized cumulative normal distribution, is calculated for prey in the numerator and predator in the denominator, in which it becomes a scaling factor that is near unity.…”

Section: Methodsmentioning

confidence: 99%

“…ɸ , a standardized cumulative normal distribution, is calculated for prey in the numerator and predator in the denominator, in which it becomes a scaling factor that is near unity.

x_{i, t}

represents the prey and is calculated using the prey size (

l_{i, t}

) normalized by the mean (m) and standard deviation (s) of the predator gape range (Anderson, 2019):

x_{i, t} goodbreak= \frac{l_{i, t} - m}{s}

…”

Section: Methodsmentioning

confidence: 99%

“…The predation mortality rate was calculated by combining the probability of predator-prey interaction with the probability of prey falling within the predation size range during this interaction using the gapelimited mortality framework of Anderson (2019):…”

Section: Predation Mortality Formulation and Parameterizationmentioning

confidence: 99%

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Size‐selective predation effects on juvenile Chinook salmon cohort survival off Central California evaluated with an individual‐based model

Vasbinder,

Fiechter,

Santora

et al. 2023

Fisheries Oceanography

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Variation in the recruitment of salmon is often found to be correlated with marine climate indices, but mechanisms behind environment–recruitment relationships remain unclear and correlations often break down over time. We used an ecosystem modeling approach to explore bottom‐up and top‐down mechanisms linking a variable environment to salmon recruitment variations. Our ecosystem model incorporates a regional ocean circulation submodel for hydrodynamics, a nutrient‐phytoplankton‐zooplankton submodel for producing planktonic prey fields, and an individual‐based model (IBM) representing juvenile Chinook salmon (Oncorhynchus tshawytscha), combined with observations of foraging distributions and diet of a seabird predator. The salmon IBM consists of modules, including a juvenile salmon growth module based on temperature and salmon–prey availability, a behavior‐based movement module, and a juvenile salmon predation mortality module based on juvenile salmon size distribution and predator–prey interaction probability. Seabird–salmon interactions depend on spatial overlap and juvenile salmon size, whereby salmon that grow past the size range of the prey distribution of the predator will escape predation. We used a 21‐year historical simulation to explore interannual variability in juvenile Chinook salmon growth and predation‐mediated survival under a range of ocean conditions for sized‐based mortality scenarios. We based a series of increasingly complex predation scenarios on seabird observational data to explore variability in predation mortality on juvenile Chinook salmon. We initially included information about the predator spatial distribution, then added population size, and finally the predator's diet percentage made up of juvenile salmon. Model agreement improves with added predator complexity, especially during periods when predator abundance is high. Overall, our model found that when the fraction of juvenile salmon in seabird diet increased relative to alternate prey (e.g., Northern anchovy Engraulis mordax, and juvenile rockfish Sebastes spp.), there was a concomitant decrease in salmon cohort survival during their first year at sea.

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μ_{i, t} goodbreak= λ \frac{1 - ϕ ((), x_{i, t})}{ϕ ((), \frac{m}{s})}

where, predation mortality, μ, for model individual i at time step t combines a term representing a daily encounter rate between predator and prey,

λ

Section: Methodsmentioning

confidence: 99%

“…ɸ , a standardized cumulative normal distribution, is calculated for prey in the numerator and predator in the denominator, in which it becomes a scaling factor that is near unity.

x_{i, t}

represents the prey and is calculated using the prey size (

l_{i, t}

) normalized by the mean (m) and standard deviation (s) of the predator gape range (Anderson, 2019):

x_{i, t} goodbreak= \frac{l_{i, t} - m}{s}

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Size‐selective predation effects on juvenile Chinook salmon cohort survival off Central California evaluated with an individual‐based model

Vasbinder,

Fiechter,

Santora

et al. 2023

Fisheries Oceanography

Self Cite

View full text Add to dashboard Cite

show abstract

Survival of prey growing through gape-limited and apex predators

Cited by 1 publication

References 25 publications

Size‐selective predation effects on juvenile Chinook salmon cohort survival off Central California evaluated with an individual‐based model

Size‐selective predation effects on juvenile Chinook salmon cohort survival off Central California evaluated with an individual‐based model

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