opendrift.models.sealice

Attributes

logger

Classes

SeaLiceElement	Extending Lagrangian3DArray with specific properties for larval and
SeaLice	Particle trajectory model based on the OpenDrift framework.

Module Contents

opendrift.models.sealice.logger

class opendrift.models.sealice.SeaLiceElement(**kwargs)

Bases: opendrift.models.oceandrift.Lagrangian3DArray

Extending Lagrangian3DArray with specific properties for larval and juvenile stages of sea lice into super individuals

Initialises a LagrangianArray with given properties.

Args:

Keyword arguments (kwargs) with names corresponding to the OrderedDict ‘variables’ of the class, and corresponding values. The values must be ndarrays of equal length, or scalars. All (or none) variables must be given, unless a default value is specified in the OrderedDict ‘variables’ An empty object may be created by giving no input.

variables

class opendrift.models.sealice.SeaLice(*args, **kwargs)

Bases: opendrift.models.oceandrift.OceanDrift

Particle trajectory model based on the OpenDrift framework. Developed by Julien Moreau (Plastics@Bay CIC) Generic module for particles that are subject to vertical turbulent mixing with the possibility for positive or negative buoyancy Particles are sea-lice (Lepeophtheirus salmonis).

Initialise OpenDriftSimulation

Args:

seed: integer or None. A given integer will yield identical

random numbers drawn each simulation. Random numbers are e.g. used to distribute particles spatially when seeding, and may be used by modules (subclasses) for e.g. diffusion. Specifying a fixed value (default: 0) is useful for sensitivity tests. With seed = None, different random numbers will be drawn for subsequent runs, even with identical configuration/input.

iomodule: name of module used to export data

default: netcdf, see opendrift.io for more alternatives. iomodule is module/filename without preceeding io_

loglevel: set to 0 (default) to retrieve all debug information.

Provide a higher value (e.g. 20) to receive less output. Use the string ‘custom’ to configure logging from outside.

logtime: if True, a time stamp is given for each logging line.

logtime can also be given as a python time specifier (e.g. ‘%H:%M:%S’)

ElementType

Any trajectory model implementation must define an ElementType.

required_variables

Any trajectory model implementation must list needed variables.

prepare_run()

new_born()

Approach by Rittenhouse et al., (2016) [Rittenhouse, M.A., C.W. Revie and A. Hurford, 2016. A model for sea lice (Lepeophtheirus salmonis) dynamics in a seasonally changing environment. Energetics, 16, 8–16., whereby the numbers of lice nauplii released every day (NP) are estimated according to the following equation: NP(t)=ηενA(t) where η = 592 is the number of eggs per clutch (Heuch et al., 2000; Rittenhouse et al., 2016), ε = 0.0476 is the egg string production rate per day (Heuch et al., 2000; Rittenhouse et al., 2016), ν = 0.6 is the hatching success i.e. the proportion of eggs which produce viable nauplii (Johnson and Albright, 1991), and A(t) is the total number of adult female lice on each farm, derived here from lice target levels and number of fish on site.

population()

Simulate the population evolution according to the biological parameters Parameters ———- t: numpy array (float) Time abscissa in seconds through the experiment self.Mat: int Age in timestep when the nauplii start to become adult copepodids maturation_rate: float Probability of maturation by time-step (t>= Mat) death_rate: float Probability of death by time-step

Number of lice per particle when generated is standardised to 1 Returns ——- juv, adults, dead: np.arrays(int) Juveniles(Nauplii), adults(Copepodid), dead lices arrays through the duration of the experiment

SI_pop()

distribute the age fractions in the particles

sensing()

Lice sensing if above or bellow the conditions are better for them within a specified distance. moving the particles up and then down then back in their initial position

degree_days()

Calculate the degree days of a particles

XXX: under development

irradiance()

Distribute the daily energy from irradiance with a gaussian distribution. We use the twilight times for high sensitivity organisms Convert irradiance from W.m-2 to micromol photon.s-1.m-2 https://www.berthold.com/en/bioanalytic/knowledge/faq/irradiance-to-photon-flux calculate the photon flux in the water according to exponential decay from the sea surface

depth_test()

The natural range of the larvae is 0-50m

Lice_vertical_migration()

Make larval sea lice to migrate vertically according to salinity, light and temperature triggers.

update()

Update positions and properties of elements.