optistock (version 0.0.2)

mort_funs: Functions to produce mortality curves

Description

This family of functions produce different shapes of mortality curves across time

Usage

exp_mort(time, m_init, m_inf, alpha, t_scale = NULL)
decreasing_mort(time, m_init, m_inf, alpha)
constant_mort(time, m_init)
inv_mort(time, m_init, m_inf)
gaussian_mort(time, m_init, m_max, t_scale, alpha)
half_gaussian_mort(time, m_init, m_max, m_inf, t_scale, alpha)
linear_mort(time, alpha, m_init)
parabolic_mort(time, m_min, alpha, t_scale, beta)

Value

A vector of numeric values for mortality rate at time

Arguments

time

The time to calculate mortality at

m_init

Initial rate of mortality at time 0 (or time t for constant_mort)

m_inf

Final rate of mortality as time approaches infinity

alpha

The rate at which mortality decreases across time

t_scale

A horizontal scaling parameter

m_max

The maximum mortality that is achieved at time = t_scale

m_min

The lowest mortality that the curve should reach

beta

Slope on the quadratic term for parabolic_mort

Details

These functions produced different shapes of mortality curves that are commonly found in fisheries. Some of the more common are constant_mort (which returns constant mortality across time), exp_mort (S-shaped decreasing curve), and decreasing_mort (non-linear decreasing curve). Others are less common and represent specific scenarios such as gaussian_mort (implemented to represent a bottleneck).

Examples

# an example in years
curve(exp_mort(x, 0.2, 0.1, 0.25), 0, 20)
# an example in days
curve(exp_mort(x, (1 / 365), (0.2 / 365), 0.005), 0, 1000)