Functions
double	edal::Individual::effective_carrying_capacity_quad_unnormalized () const
	\(K_e(I)\) with quadratic \(\Xi\) before normalization (v3u) More...

double	edal::Individual::effective_carrying_capacity_exp_unnormalized () const
	\(K_e(I)\) with exponential \(\Xi\)

double	edal::Individual::effective_carrying_capacity_old_exp_unnormalized () const
	\(K_e(I)\) with old resource distribution and exponential \(\Xi\) before normalization

double	edal::Individual::survival_probability (const double effective_num_competitors) const
	Probability of survival \(w(I)\). More...

double	edal::Individual::fitness (const double height, const double diameter) const
	Exponent of \(W(x_0, x_1 \mid u, v)\): measure adaptation to habitat. More...

void	edal::Patch::viability_selection ()
	Some individuals die depending on Individual::survival_probability()

std::vector< double >	edal::Patch::effective_num_competitors () const
	Calculate \(N_e(I)\) from \(C(I,J)\). More...

Detailed Description

Function Documentation

◆ effective_carrying_capacity_quad_unnormalized()

double edal::Individual::effective_carrying_capacity_quad_unnormalized ( ) const

\(K_e(I)\) with quadratic \(\Xi\) before normalization (v3u)

\[ K_e(I) = K_0 \int_0^1 \int_0^{1-u} F(u,v) W(x_0,x_1 \mid u,v) \Xi(y_0,y_1 \mid u,v) dv du \]

◆ effective_num_competitors()

std::vector< double > edal::Patch::effective_num_competitors ( ) const

private

Calculate \(N_e(I)\) from \(C(I,J)\).

Returns: \(N_e\) of the focal individual
\[ N_e(I) = \sum_J C(I,J) \]

◆ fitness()

double edal::Individual::fitness	(	const double	height,
		const double	diameter
	)		const

private

Exponent of \(W(x_0, x_1 \mid u, v)\): measure adaptation to habitat.

Parameters

height	habitat environmant
diameter	habitat environment

\[ W(x_0,x_1 \mid u,v) = \exp(-\frac{(x_0 - u)^2}{2s_0^2} -\frac{(x_1 - v)^2}{2s_1^2}) \]

◆ survival_probability()

double edal::Individual::survival_probability ( const double effective_num_competitors ) const

Probability of survival \(w(I)\).

Parameters

effective_num_competitors \( K_e(I) \)

Return values

1.0	if \( N_e(I) \ll K_e(I) \)
1/b	if \( N_e(I) = K_e(I) \)

See also: AVG_NUM_OFFSPINRGS_

The probability that an individual survives to the age of reproduction is

\[ w(I) = \frac 1 {1 + (b-1) \frac {N_e(I)^\alpha} {K_e(I)}} \]

where the parameter \( \alpha \) (= 1 for now) controls the strength of crowding, and \( b > 0 \) (Individual::AVG_NUM_OFFSPINRGS_) is a parameter (average number of offspring per female; see below). This is the Beverton-Holt model which represents a discrete-time analog of the logistic model