Improving estimations of life history parameters of small animals in mesocosm experiments: A case study on mosquitoes

These files accompany the paper "Improving estimations of life history parameters of small animals in mesocosm experiments: A case study on mosquitoes".

The code provided enables replication of our results.

If you wish to apply this methodology to your own research project then this code should provide a good starting point.

To run our model and the optimisation, use one of the following:
- normal_model_plus_optimisation.py
- mixed_model_plus_optimisation.py
The normal version should run on a regular laptop. The mixed version will require a supercomputer or computing cluster.
The mixed version requires Python3.7 or later.

The mixed version uses initial conditions defined in a separate text tile (initial_contidions_for_mixed.txt). 
We originally used very basic initial conditions(x0 = [0.5 for i in range(0,96)]+[1,1,1,2]), but found that this gave a non-optimal result for treatment 1.
To fix this we ran the optimisation again, with initial conditions for treatments 2-12 as the optimised values we had just calculated, and the initial 
conditions for treatment 1 as the optimised values from the all-normal approach.

The optimised probabilities for each approach are stored in:
- normal_probabilities.txt
- mixed_probabilities.txt

To run the validation (based on experimental data) use:
- validation.py

For the simulation analysis:
To generate input files:
- simulation_analysis_create_inputs.py
To run the simulation anaysis, you can use the normal_model_plus_optimisation.py and mixed_model_plus_optimisation.py files, just change the name of the observations ('main_data.csv') file (be warned, this takes a LOT of computing resources when running for the mixed-distribution approach).
All inputs and outputs for the simulation analysis are available in simulation_analysis_inputs_and_outputs.zip
To analyse the results use:
- simulation_analysis_analyse_results.py

To make graphs of adult emergence, proportions of larvae and pupae, and survival and development probabilities use:
- make_plots.py

Data for the main and validation experiments is stored in:
- main_data.csv
- validation_data.csv

To save the predicted populations and optimised probabilities to a csv file use:
- write_outputs_normal.py
- write_outputs_mixed.py

Funding provided by: Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100003246
Award Number: NWA.1160.1S.210

Funding provided by: Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100003246
Award Number: 613.009.103