Topic Editors

Forest Research Centre, Associate Laboratory TERRA, Instituto Superior de Agronomia, Universidade de Lisboa,1349-017 Lisboa, Portugal
Forest Research Centre, Associate Laboratory TERRA, Instituto Superior de Agronomia, Universidade de Lisboa,1349-017 Lisboa, Portugal

Intersection Between Macroecology and Data Science

Abstract submission deadline
30 September 2025
Manuscript submission deadline
30 November 2025
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1051

Topic Information

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

Keywords

  • macroecology
  • data science
  • species distribution and abundance
  • big data in ecology
  • machine learning in ecology
  • remote sensing and sensor technologies
  • species distribution modelling
  • biodiversity patterns and monitoring
  • conservation ecology
  • climate change and biodiversity
  • ecological data analytics
  • management and conservation strategies
  • ecosystem functioning
  • open science and data sharing
  • large-scale ecological processes

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Biology
biology
3.6 5.7 2012 16.4 Days CHF 2700 Submit
Data
data
2.2 4.3 2016 26.8 Days CHF 1600 Submit
Diversity
diversity
2.1 3.4 2009 18.3 Days CHF 2100 Submit
Fishes
fishes
2.1 1.9 2016 17.4 Days CHF 2600 Submit
Animals
animals
2.7 4.9 2011 16.1 Days CHF 2400 Submit
Conservation
conservation
- - 2021 35.6 Days CHF 1000 Submit
Hydrobiology
hydrobiology
- - 2022 21.1 Days CHF 1000 Submit

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Published Papers (1 paper)

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13 pages, 4706 KiB  
Data Descriptor
River Restoration Units: Riverscape Units for European Freshwater Ecosystem Management
by Gonçalo Duarte, Angeliki Peponi, Pedro Segurado, Tamara Leite, Florian Borgwardt, Andrea Funk, Sebastian Birk, Maria Teresa Ferreira and Paulo Branco
Data 2025, 10(4), 46; https://doi.org/10.3390/data10040046 - 28 Mar 2025
Viewed by 362
Abstract
Freshwater habitats and biota are among the most threatened worldwide. In Europe, significant efforts are being taken to counteract detrimental human impacts on nature. In line with these efforts, the MERLIN project funded by the H2020 program focuses on mainstreaming ecosystem restoration for [...] Read more.
Freshwater habitats and biota are among the most threatened worldwide. In Europe, significant efforts are being taken to counteract detrimental human impacts on nature. In line with these efforts, the MERLIN project funded by the H2020 program focuses on mainstreaming ecosystem restoration for freshwater-related environments at the landscape scale. Additionally, the Dammed Fish project focuses on one of the main threats affecting European Networks—artificial fragmentation of the river. Meeting the objectives of both projects to work on a large, pan-European scale, we developed a novel spatial database for river units. These spatial units, named River Restoration Units (R2Us), abide by river network functioning while creating the possibility of aggregating multiple data sources with varying resolutions to size-wise comparable units. To create the R2U, we set a methodological framework that departs from the Catchment Characterization and Modelling—River and Catchment Database v2.1 (CCM2)—together with the capabilities of the River Network Toolkit (v2) software (RivTool) to implement a seven-step methodological procedure. This enabled the creation of 11,557 R2U units in European sea outlet river basins along with their attributes. Procedure outputs were associated with spatial layers and then reorganized to create a relational database with normalized data. Under the MERLIN project, R2Us have been used as the spatial analysis unit for a large-scale analysis using multiple input datasets (e.g., ecosystem services, climate, and European Directive reporting data). This database will be valuable for river management and conservation planning, being particularly well suited for large-scale restoration planning in accordance with European Nature legislation. Full article
(This article belongs to the Topic Intersection Between Macroecology and Data Science)
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