Integrating Multi-Model Simulations to Address Partial Observability in Population Dynamics: A Python-Based Ecological Tool

Species richness is a crucial factor in maintaining ecological balance and promoting ecosystem services. However, simulating population dynamics is a complex task that requires a comprehensive understanding of ecological systems. The current tools for wildlife research face three major challenges: i...

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Published in:Applied sciences Vol. 15; no. 1; p. 89
Main Authors: Yu, Yide, Li, Huijie, Liu, Yue, Ma, Yan
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.01.2025
Subjects:
Biodiversity
Climate change
Data science
Decision making
Ecological research
Ecologists
Interfaces
Libraries
Monte Carlo method
Monte Carlo simulation
Population ecology
Python-based framework
Researchers
Robots
Simulation methods
simulation software
Software
species richness
Wildlife conservation
ISSN:2076-3417, 2076-3417
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Abstract Species richness is a crucial factor in maintaining ecological balance and promoting ecosystem services. However, simulating population dynamics is a complex task that requires a comprehensive understanding of ecological systems. The current tools for wildlife research face three major challenges: insufficient multi-view assessment, a high learning curve, and a lack of seamless secondary development with Python. To address these issues, we developed a novel software tool named WAPET (Wildlife Analysis and Population Ecology Tool) (Python 3.10.12). WAPET integrates Monte Carlo simulation with ecological models, including Logistic Growth, Random Walk, and Cellular Automata, to provide a multi-perspective assessment of ecological systems. Our tool employs a fully parameterized input paradigm, allowing users without coding to easily explore simulations. Additionally, WAPET’s development is entirely Python-based, utilizing PySide6 and Mesa libraries and enabling seamless development in Python environments. Our contributions include the following: (I) integrating multiple ecological models for a comprehensive understanding of ecological processes, (II) developing a no-code mode of human–computer interaction for biodiversity stakeholders and researchers, and (III) implementing a Python-based framework for easy extension and customization. WAPET bridges the gap between comprehensive modeling capabilities and user-friendly interfaces, positioning itself as a versatile tool for both experienced researchers and non-computational stakeholders in biodiversity decision-making processes.
AbstractList Species richness is a crucial factor in maintaining ecological balance and promoting ecosystem services. However, simulating population dynamics is a complex task that requires a comprehensive understanding of ecological systems. The current tools for wildlife research face three major challenges: insufficient multi-view assessment, a high learning curve, and a lack of seamless secondary development with Python. To address these issues, we developed a novel software tool named WAPET (Wildlife Analysis and Population Ecology Tool) (Python 3.10.12). WAPET integrates Monte Carlo simulation with ecological models, including Logistic Growth, Random Walk, and Cellular Automata, to provide a multi-perspective assessment of ecological systems. Our tool employs a fully parameterized input paradigm, allowing users without coding to easily explore simulations. Additionally, WAPET’s development is entirely Python-based, utilizing PySide6 and Mesa libraries and enabling seamless development in Python environments. Our contributions include the following: (I) integrating multiple ecological models for a comprehensive understanding of ecological processes, (II) developing a no-code mode of human–computer interaction for biodiversity stakeholders and researchers, and (III) implementing a Python-based framework for easy extension and customization. WAPET bridges the gap between comprehensive modeling capabilities and user-friendly interfaces, positioning itself as a versatile tool for both experienced researchers and non-computational stakeholders in biodiversity decision-making processes.
Audience Academic
Author Yu, Yide
Li, Huijie
Liu, Yue
Ma, Yan
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SubjectTerms Biodiversity
Climate change
Data science
Decision making
Ecological research
Ecologists
Interfaces
Libraries
Monte Carlo method
Monte Carlo simulation
Population ecology
Python-based framework
Researchers
Robots
Simulation methods
simulation software
Software
species richness
Wildlife conservation
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