demovuln 0.1.0

Demographic vulnerability metrics for matrix population models under temporally structured perturbations.

demovuln

demovuln is a Python package for simulating temporally structured demographic perturbations in matrix population models and estimating integrated population vulnerability.

The package is designed for comparative demographic analyses in which perturbations differ in magnitude, duration, and recurrence. It provides tools to simulate individual perturbation trajectories, evaluate full perturbation grids, and compute an integrated vulnerability metric based on population reduction relative to an unperturbed baseline.

Installation

For local development:

git clone https://github.com/agimenezromero/demovuln.git
cd demovuln
python -m pip install -e ".[dev,docs]"

After publication on PyPI, the package will be installable with:

pip install demovuln

Basic usage

import numpy as np
from demovuln import MatrixPopulationModel, simulate_dynamics

A = np.array([
    [0.0, 2.0],
    [0.4, 0.7],
])

model = MatrixPopulationModel(A)

result = simulate_dynamics(
    model,
    target="adult_survival",
    magnitude=0.25,
    duration=1,
    period=3,
    t_max=50,
    recovery_steps=10,
)

print(result.reduction)
print(result.abundance)

Perturbation-grid analysis

import numpy as np
from demovuln import MatrixPopulationModel, PerturbationGrid, run_grid

A = np.array([
    [0.0, 2.0],
    [0.4, 0.7],
])

model = MatrixPopulationModel(A)

grid = PerturbationGrid(
    magnitudes=np.linspace(0, 1, 11),
    durations=[0, 1, 2, 3],
    periods=[1, 2, 3, 5, 10],
)

out = run_grid(
    model,
    target="adult_survival",
    grid=grid,
    t_max=50,
    recovery_steps=10,
)

print(out.vulnerability)
print(out.table.head())

Demographic targets

The package supports perturbations to:

• adult_survival
• juvenile_survival
• fecundity
• all
• custom

By default, adult stages are inferred as source-stage columns with at least one fecundity entry, and juvenile stages are inferred as the remaining source-stage columns. These definitions can be specified explicitly:

model = MatrixPopulationModel(
    A,
    adult_stages=[1],
    juvenile_stages=[0],
)

Custom perturbation targets can be defined with Boolean masks:

custom_mask = np.array([
    [False, False],
    [True, False],
])

result = simulate_dynamics(
    model,
    target="custom",
    custom_mask=custom_mask,
    magnitude=0.5,
    duration=1,
    period=3,
    t_max=50,
)

Conceptual summary

For a given perturbation regime, population reduction is computed as:

rho = 100 * (1 - N_perturbed(T) / N_baseline(T))

where N_perturbed(T) is the final population size under perturbed dynamics and N_baseline(T) is the final population size under the unperturbed baseline.

Integrated vulnerability is the mean population reduction across the simulated perturbation space:

Phi = mean(rho)

Development checks

Run:

pytest
python examples/basic_usage.py
ruff check demovuln tests examples
sphinx-build -W -b html docs docs/_build/html

Documentation

Local documentation can be built with:

sphinx-build -b html docs docs/_build/html

Then open:

xdg-open docs/_build/html/index.html

Citation

Citation metadata are provided in CITATION.cff.

License

This package is distributed under the MIT License.