# interaction functions
[docs]def inverse_type_0(X,idx_A,idx_B,coefficient):
""" linear response with respect to *origin/prey* compartment
Parameters
----------
X : np.array
containing the current state of the contained quantity of each
compartment
idx_A : integer
index of the element representing the destination/predator compartment
idx_B : integer
index of the element representing the origin/pray compartment
coefficient : float
governs the slope of the linear response
Returns
-------
df : float
change in the origin and destitnation compartment. Calculated by
coefficient*origin_compartment
"""
A = X[idx_A] # quantity of compartment A (predator/consumer)
B = X[idx_B] # quantity of compartment B (prey/nutrient)
df = coefficient*B
return df
[docs]def holling_type_0(X,idx_A,coefficient):
""" linear response with respect to *destination/predator* compartment
For examples see:
`Examples <https://gist.github.com/465b/cce390f58d64d70613a593c8038d4dc6>`_
Parameters
----------
X : np.array
containing the current state of the contained quantity of each
compartment
idx_A : integer
index of the element representing the destination/predator compartment
idx_B : integer
index of the element representing the origin/pray compartment
coefficient : float
governs the slope of the linear response
Returns
-------
df : float
change in the origin and destitnation compartment. Calculated by
coefficient*destination_compartment
"""
A = X[idx_A] # quantity of the linearly dependant compartment
df = coefficient*A
return df
[docs]def holling_type_I(X,idx_A,idx_B,coefficient):
""" linear response with respect to both *origin/pray* and
*destination/predator* compartment.
For examples see:
`Examples <https://gist.github.com/465b/cce390f58d64d70613a593c8038d4dc6>`_
Parameters
----------
X : np.array
containing the current state of the contained quantity of each
compartment
idx_A : integer
index of the element representing the destination/predator compartment
idx_B : integer
index of the element representing the origin/pray compartment
coefficient : float
governs the slope of the linear response
Returns
-------
df : float
change in the origin and destitnation compartment. Calculated by
coefficient * origin_compartment * destination_compartment
"""
A = X[idx_A] # quantity of compartment A (predator/consumer)
B = X[idx_B] # quantity of compartment B (prey/nutrient)
df = (coefficient*B)*A
return df
[docs]def holling_type_II(X,idx_A,idx_B,food_processing_time,hunting_rate):
""" non-linear response with respect to *origin/pray* with linear response
with respect to *destination/predator* compartment
The response with respect to the origin compartment 'B' is approximately
linear for small 'B' and converges towards an upper limit governed by the
'food_processing_time' for large 'B'.
For examples see:
`Examples <https://gist.github.com/465b/cce390f58d64d70613a593c8038d4dc6>`_
Parameters
----------
X : np.array
containing the current state of the contained quantity of each
compartment
idx_A : integer
index of the element representing the destination/predator compartment
idx_B : integer
index of the element representing the origin/pray compartment
coefficient : float
governs the slope of the linear response
Returns
-------
df : float
change in the origin and destitnation compartment. Calculated by
consumption_rate = ((hunting_rate * origin_compartment) /
(1 + hunting_rate * food_processing_time * origin_compartment)) *
destination_compartment
"""
A = X[idx_A] # quantity of compartment A (predator/consumer)
B = X[idx_B] # quantity of compartment B (prey/nutrient)
df = ((hunting_rate * B)/
(1+hunting_rate * food_processing_time * B))*A
return df
[docs]def holling_type_III(X,idx_A,idx_B,saturation_rate,consumption_rate_limit):
""" non-linear response with respect to *origin/pray* with linear response
with respect to *destination/predator* compartment
The response with respect to the origin compartment 'B' is approximately
quadratic for small 'B' and converges towards an upper limit governed by the
'food_processing_time' for large 'B'.
For examples see:
`Examples <https://gist.github.com/465b/cce390f58d64d70613a593c8038d4dc6>`_
Parameters
----------
X : np.array
containing the current state of the contained quantity of each
compartment
idx_A : integer
index of the element representing the destination/predator compartment
idx_B : integer
index of the element representing the origin/pray compartment
saturation_rate : float
first parameter of the interaction.
governs the slope of the non lineare response for small pray
populations.
consumption_rate_limit : float
second parameter of the interaction.
governs the upper limit of the response.
Returns
-------
df : float
change in the origin and destitnation compartment. Calculated by
consumption_rate = ((consumption_rate_limit * saturation_rate * B**2)/
(consumption_rate_limit + (saturation_rate*B**2)))*A
"""
A = X[idx_A] # quantity of compartment A (predator/consumer)
B = X[idx_B] # quantity of compartment B (prey/nutrient)
df = ((consumption_rate_limit*saturation_rate*B**2)/
(consumption_rate_limit+(saturation_rate*B**2)))*A
return df
[docs]def sloppy_feeding(holling_type,coeff,*args):
""" calls holling_type functions with an extra "efficiency" coefficient.
the inverse of the efficiency is then supposed to flow into
a different compartment """
if holling_type == '0':
return coeff*holling_type_0(*args)
elif holling_type == 'I':
return coeff*holling_type_I(*args)
elif holling_type == 'II':
return coeff*holling_type_II(*args)
elif holling_type == 'III':
return coeff*holling_type_III(*args)
else:
raise ValueError("The defined holling_type is not available. "
+"Use one of the following: ['0','I','II','III']")
[docs]def nutrient_limited_growth(X,idx_A,idx_B,growth_rate,half_saturation):
""" non-linear response with respect to *destination/predator* compartment
Similar to holling_type_II and is a reparameterization of holling II.
The response with respect to the origin compartment 'B' is approximately
linear for small 'B' and converges towards an upper limit governed by the
'growth_rate' for large 'B'.
For examples see:
`Examples <https://gist.github.com/465b/cce390f58d64d70613a593c8038d4dc6>`_
Parameters
----------
X : np.array
containing the current state of the contained quantity of each
compartment
idx_A : integer
index of the element representing the destination/predator compartment
idx_B : integer
index of the element representing the origin/pray compartment
growth_rate : float
first parameter of the interaction.
governs the upper limit of the response.
half_saturation : float
second parameter of the interaction.
governs the slope of the response.
Returns
-------
df : float
change in the origin and destitnation compartment. Calculated by
consumption_rate = ((hunting_rate * origin_compartment) / (1 +
hunting_rate * food_processing_time * origin_compartment)) *
destination_compartment
"""
A = X[idx_A] # quantity of compartment A (predator/consumer)
B = X[idx_B] # quantity of compartment B (prey/nutrient)
df = growth_rate*(B/(half_saturation+B))*A
return df
## referencing interaction functions
""" It is helpfull from a mechanistic point of view to not only
represent the relation between parameters but also its 'context'.
Hence, a linear mortality might be represented with a lin_mort()
function which then maps to a standard linear function to better
represent its usage and increase readability """
linear_mortality = inverse_type_0
remineralisation = holling_type_I
exudation = inverse_type_0
excretion = inverse_type_0
stress_dependant_exudation = holling_type_I