Intersection Between Macroecology and Data Science

Dear Colleagues,

Macroecology examines large-scale ecological patterns and processes, focusing on questions related to species distribution, biodiversity, and ecosystem function across spatial and temporal scales. With the increasing ability to procure, harvest, create and store data, ranging from satellite imagery, sensor technologies, global biodiversity and citizen science initiatives, there is a growing need for sophisticated analytical techniques to extract meaningful insights from large datasets. Data Science, with its suite of computational tools and statistical methods, is critical for managing, analyzing, and visualizing large and complex datasets. It has accelerated our ability to uncover patterns and understand underlying mechanisms and trends across environmental or pressure gradients that only meaningful in large extents. By integrating data science methodologies into macroecology, researchers can better understand ecological dynamics, predict species responses to environmental changes, address critical biodiversity questions and inform management and conservation strategies. Moreover, the rise of open science and data-sharing initiatives has broadened opportunities for collaborative, large-scale ecological research. This Topic invites contributions at the interface of macroecology and data science, applying innovative approaches to understand ecological patterns at large scales. We encourage submissions that utilize machine learning, statistical modelling, and data mining to address fundamental questions in ecology, conservation, environmental monitoring and climate change impacts or adaptation.

Dr. Paulo Branco
Dr. Gonçalo Duarte
Topic Editors