Deep Learning Analysis of Climate-Induced Changes in Wetland Bird Breeding

Abstract

Introduction: Ecosystems of the wetlands offer important breeding sites to several species of birds, while climatic variability affects temperatures, rainfall, water supply, breeding conditions, and quality of habitat. Traditional ecological assessments can be time-consuming and have some drawbacks in dealing with big data on bird breeding activities. In this study, examine the potential for applying deep learning in studying the effects of climate on bird breeding in wetland areas.

Materials & Methods: The framework consists of integrating observations of birds from wetlands with weather and environmental factors, such as temperature, precipitation, water availability, and environment parameters. Data for birds go through several processing steps, which include data cleaning, resizing, normalizing, and preparing features, after which they are subjected to deep learning algorithms based on CNN to identify species of birds and breeding activities. Environmental factors are combined with bird features in order to analyze the connection between climate conditions and breeding activities. The dataset is divided into training, validation, and testing subsets, and model performance is evaluated using accuracy, precision, recall, and F1-score.

Results: The developed deep learning model had an accuracy of 89.7%, precision of 89.1%, recall of 88.6%, and F1-score of 88.8%, while the CNN approach had accuracy of 84.6% and F1-score of 83.3%. This shows that including the climate and habitat variables helps in detecting breeding behavior and a better depiction of environment impact on the wetland birds.

Conclusion: The results show that the use of deep learning could serve as an effective computational method for studying climatically driven changes in the breeding behavior of wetland birds. The suggested model could be used for automatic analysis, ecological understanding, and conservation purposes as well.