Species Identification, Population Tracking, and Conservation AI

Module: Sustainable Tech | Difficulty: Premium

Species Distribution Model

Population Viability Analysis

Comparison

Python Implementation

import numpy as np

class PopulationModel:
    def __init__(self, initial_pop, growth_rate, carrying_capacity):
        self.N0 = initial_pop
        self.r = growth_rate
        self.K = carrying_capacity

    def logistic_growth(self, t):
        return self.K / (1 + (self.K - self.N0) / self.N0 * np.exp(-self.r * t))

    def stochastic_projection(self, years, n_simulations):
        projections = np.zeros((n_simulations, years))
        for sim in range(n_simulations):
            N = self.N0
            for t in range(years):
                growth = self.r * N * (1 - N / self.K)
                N = max(0, N + growth * np.exp(np.random.normal(0, 0.1)))
                projections[sim, t] = N
        return projections

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