Modelo multivariante para predecir abundancia y diversidad de aves según variables climáticas en el campus de la ESPOL

Abstract

The main objective of this work is to develop a multivariate predictive model to forecast the abundance and diversity of birds based on climatic variations in the ESPOL campus. The underlying hypothesis is that climatic conditions directly influence the probability of bird sightings. This model aims to provide tools for environmental management and biodiversity monitoring in areas of interest. For this purpose, climatic data and bird sighting records from the eBird platform were collected for the period 2010–2025. Multivariate data analysis techniques such as logistic regression, principal component methods, and Random Forest were applied to model the relationship between climatic variables (temperature, humidity, precipitation, among others) and the presence of different bird species. The data were cleaned, filtered, preprocessed, scaled, and grouped by species and climatic conditions. The results showed that certain climatic variables, such as mean temperature and precipitation, have a significant impact on the probability of bird sightings in the region. The trained models achieved good performance in predicting bird presence as a function of climatic conditions. The conclusions indicate that the predictive model is a useful tool to anticipate the presence of birds under specific climatic scenarios, which can be applied to environmental management projects and species conservation. Keywords: Predictive model, Birds, Climatic Variables, Conservation