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+---
+id: wiki-2026-0508-ecology-and-ecosystem-modeling
+title: Ecology and Ecosystem Modeling
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [ecosystem modeling, agent-based ecology, Lotka-Volterra, food web, population dynamics]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.92
+verification_status: applied
+tags: [ecology, ecosystem, abm, lotka-volterra, simulation, biodiversity, food-web]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python / NetLogo / Mesa
+  framework: NumPy / scipy / Mesa
+---
+
+# Ecology and Ecosystem Modeling
+
+## 매 한 줄
+> **"매 species 매 environment 의 interaction 의 mathematical / computational simulate"**. 매 Lotka-Volterra (predator-prey), 매 food web, 매 ABM (agent-based). 매 modern: 매 deep learning + 매 climate model coupling.
+
+## 매 핵심
+
+### 매 model type
+- **Differential equation**: 매 Lotka-Volterra, SIR-like.
+- **Matrix model** (Leslie): 매 age-structured.
+- **Individual-based** (IBM/ABM): 매 agent.
+- **Network-based**: 매 food web.
+- **Spatial**: 매 PDE / cellular automata.
+
+### 매 famous
+- **Lotka-Volterra**: 매 predator-prey oscillation.
+- **NetLogo Wolves-Sheep**: 매 ABM textbook.
+- **Madingley Model**: 매 global-scale ecosystem.
+- **EwE (Ecopath with Ecosim)**: 매 marine fisheries.
+
+### 매 응용
+1. **Conservation**: 매 endangered species.
+2. **Fisheries**: 매 stock assessment.
+3. **Invasive species**: 매 spread.
+4. **Climate adaptation**: 매 range shift.
+5. **Disease ecology**: 매 zoonotic.
+6. **Restoration**: 매 rewilding scenario.
+
+### 매 modern AI
+- **Species distribution**: 매 ML predict.
+- **Image classification**: 매 camera trap.
+- **Bioacoustic**: 매 bird ID.
+- **Foundation model**: 매 GeoCLIP, BioCLIP.
+
+## 💻 패턴
+
+### Lotka-Volterra
+```python
+import numpy as np
+from scipy.integrate import odeint
+
+def lv(state, t, alpha, beta, delta, gamma):
+    prey, pred = state
+    dprey = alpha * prey - beta * prey * pred
+    dpred = delta * prey * pred - gamma * pred
+    return [dprey, dpred]
+
+t = np.linspace(0, 50, 1000)
+sol = odeint(lv, [10, 5], t, args=(0.5, 0.1, 0.05, 0.5))
+```
+
+### Logistic growth (carrying capacity)
+```python
+def logistic(N, t, r, K):
+    return r * N * (1 - N / K)
+
+t = np.linspace(0, 30, 300)
+N = odeint(logistic, 5, t, args=(0.5, 100))
+```
+
+### Leslie matrix (age-structured)
+```python
+def leslie_step(pop, fecundity, survival):
+    L = np.zeros((len(pop), len(pop)))
+    L[0, :] = fecundity
+    for i in range(len(pop) - 1):
+        L[i + 1, i] = survival[i]
+    return L @ pop
+
+# 매 example
+pop = np.array([100, 80, 50, 20])
+fec = np.array([0, 0.5, 1.5, 0.8])
+surv = np.array([0.6, 0.7, 0.5])
+for _ in range(20): pop = leslie_step(pop, fec, surv)
+```
+
+### Agent-based (Mesa)
+```python
+from mesa import Agent, Model
+from mesa.space import MultiGrid
+from mesa.time import RandomActivation
+
+class Sheep(Agent):
+    def step(self):
+        self.energy -= 1
+        if grass_at(self.pos): self.energy += 5
+        # 매 move
+        x, y = self.random.choice(self.model.grid.get_neighborhood(self.pos, True))
+        self.model.grid.move_agent(self, (x, y))
+        # 매 reproduce
+        if self.energy > 20:
+            self.energy /= 2
+            self.model.add_sheep(self.pos, self.energy)
+        # 매 die
+        if self.energy <= 0: self.model.kill(self)
+
+class Wolf(Agent):
+    def step(self):
+        # 매 hunt sheep
+        sheep_nearby = self.model.grid.get_cell_list_contents([self.pos])
+        sheep_nearby = [a for a in sheep_nearby if isinstance(a, Sheep)]
+        if sheep_nearby:
+            self.energy += 20
+            self.model.kill(sheep_nearby[0])
+
+class Ecosystem(Model):
+    def __init__(self, n_sheep, n_wolves, w, h):
+        self.grid = MultiGrid(w, h, torus=True)
+        self.schedule = RandomActivation(self)
+        for _ in range(n_sheep): self.add_sheep((random_pos), 10)
+        for _ in range(n_wolves): self.add_wolf((random_pos), 20)
+    def step(self): self.schedule.step()
+```
+
+### Food web (network)
+```python
+import networkx as nx
+
+G = nx.DiGraph()
+# 매 edge: prey → predator
+G.add_edges_from([
+    ('grass', 'rabbit'), ('grass', 'mouse'),
+    ('rabbit', 'fox'), ('mouse', 'fox'),
+    ('mouse', 'owl'), ('rabbit', 'eagle'),
+])
+
+# 매 trophic level
+def trophic_level(G, species):
+    if G.in_degree(species) == 0: return 1
+    preys = list(G.predecessors(species))
+    return 1 + np.mean([trophic_level(G, p) for p in preys])
+```
+
+### Species distribution model (MaxEnt-style)
+```python
+from sklearn.ensemble import RandomForestClassifier
+
+def fit_sdm(presences, absences, env_features):
+    X = np.vstack([presences, absences])
+    y = np.array([1] * len(presences) + [0] * len(absences))
+    clf = RandomForestClassifier(n_estimators=200).fit(X, y)
+    return clf
+
+def project(clf, future_env_grid):
+    return clf.predict_proba(future_env_grid)[:, 1]  # 매 suitability
+```
+
+### Spatial spread (cellular automaton)
+```python
+def spread_step(grid, infected_value=1, p_spread=0.3):
+    new_grid = grid.copy()
+    H, W = grid.shape
+    for i in range(1, H - 1):
+        for j in range(1, W - 1):
+            if grid[i, j] == 0:
+                neighbors = grid[i-1:i+2, j-1:j+2].sum() - grid[i, j]
+                if neighbors > 0 and np.random.rand() < 1 - (1 - p_spread) ** neighbors:
+                    new_grid[i, j] = infected_value
+    return new_grid
+```
+
+### Camera trap classifier (BioCLIP)
+```python
+from transformers import CLIPModel, CLIPProcessor
+model = CLIPModel.from_pretrained('imageomics/bioclip')
+
+def classify_animal(image, candidate_species):
+    inputs = processor(text=candidate_species, images=image, return_tensors='pt')
+    out = model(**inputs)
+    return candidate_species[out.logits_per_image.argmax().item()]
+```
+
+### Bird sound (BirdNET-style)
+```python
+def detect_birds(audio, sr=48000, window_s=3):
+    # 매 chunked inference
+    chunks = chunk(audio, window_s * sr)
+    return [birdnet_model.predict(c) for c in chunks]
+```
+
+### Sensitivity analysis
+```python
+from SALib.sample import saltelli
+from SALib.analyze import sobol
+
+problem = {
+    'num_vars': 4,
+    'names': ['alpha', 'beta', 'delta', 'gamma'],
+    'bounds': [[0.1, 1], [0.05, 0.2], [0.01, 0.1], [0.1, 1]],
+}
+param_values = saltelli.sample(problem, 1024)
+Y = np.array([simulate(p)[-1, 0] for p in param_values])  # 매 final prey
+Si = sobol.analyze(problem, Y)
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| 2-3 species | Lotka-Volterra ODE |
+| Age structure | Leslie matrix |
+| Behavioral | ABM (Mesa / NetLogo) |
+| Network | Food web graph |
+| Range / spatial | SDM + CA |
+| Fisheries | Ecopath / EwE |
+| Camera trap data | BioCLIP / DL |
+
+**기본값**: 매 question-driven — 매 quick population → ODE; 매 behavioral → ABM; 매 modern data → ML SDM.
+
+## 🔗 Graph
+- 부모: [[Systems Biology]]
+- 변형: [[Lotka-Volterra]] · [[Food-Web]]
+- Adjacent: [[Cellular Automata]]
+
+## 🤖 LLM 활용
+**언제**: 매 conservation. 매 invasive species. 매 climate adaptation.
+**언제 X**: 매 hard physics (use mechanistic instead).
+
+## ❌ 안티패턴
+- **Ignore stochasticity**: 매 small population.
+- **Single-scale**: 매 cross-scale interaction 의 miss.
+- **Calibrate to single dataset**: 매 over-fit.
+- **No sensitivity analysis**: 매 parameter uncertainty.
+
+## 🧪 검증 / 중복
+- Verified (May, Levin Mathematical Ecology).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-04-20 | Auto-reinforced |
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — ODE / ABM / food web / SDM / sensitivity code |