An International Journal on Computational Ecology and Ecological Data Science

The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science, biogeography, and ecosystem analysis. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable ecosystem management in view of global environmental and climate change.

The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling of ecological data, and uncertainty analysis.

The journal invites papers on:

    novel concepts and tools for monitoring, acquisition, management, analysis, and synthesis of ecological data,
    innovative strategies and applications of eco-acoustics, eco-genomics, digital image processing, machine and deep learning,
    Bayesian inference and uncertainty analysis techniques,
    species distribution modelling,
    understanding and forecasting of ecosystem functioning and evolution, and
    use of quantitative tools to inform management decisions on environmental issues like ecosystem sustainability, climate change, and biodiversity.

Special Issue on Geoinformatics and Machine Learning for studying Plant Functional Traits
提出日: 2025-12-31

Traits are inherent attributes of individuals that directly influence their capacity to thrive, develop, produce, or navigate within a certain habitat. The plant distribution and performance based on traits has a long-standing history and continues to be an area of active research. Traits can help reveal the structure, function, evolution, and dynamics of individuals, communities, and ecosystems, if we properly understand their complex relationship with the environment. The evolving paradigm suggests that studying plant traits can strengthen our comprehensive understanding of how an ecosystem functions and the reciprocal relationship between climate and its impact.

The current serge in trait- environment studies has led to a reassessment of key concepts, such as monitoring of traits using remote sensing, functional grouping into Plant Functional Types (PFTs) and studying impacts at the levels of community and ecosystem by applying traits. To achieve the desired level of certainty in establishing trait-environment relationship, it is essential to establish a clear connection between traits and functions across different time scales, spatial scales, taxonomic groups, and trophic levels.

The large databases generated by remote sensing provide an opportunity to monitor traits such as tree height, shape and size of leaves, canopy structure, root structure, phenology, chemical concentration, etc. The incorporation of Geoinformatics and Machine Learning improves the capacity to observe and examine such traits on a broader scale, facilitating global environmental monitoring and decision-making. Nevertheless, the process of deriving valuable information from the vast quantities of satellite data presents considerable obstacles and necessitates the application of robust procedures, such as machine learning techniques. Hence, monitoring plant functional traits demands sophisticated computational techniques driven by Geoinformatics and Machine Learning.

Guest editors:

Dr. Manoj Kumar
Indian Council of Forestry Research & Education, New Forest Dehradun, India
kumarmanoj@icfre.gov.in 

Dr. Juan A. Blanco
Dep. Sciences, IMAB, Universidad Publica de Navarra, Navarra, Spain
juan.blanco@unavarra.es 

Special issue information:

This special issue intends to provide a thorough understanding of monitoring plant functional traits and grouping into PFTs by applying tools of Geoinformatics and Machine Learning. Manuscripts are invited to demonstrate a novel approach for monitoring plant traits, establishing trait-environment and trait- productivity relationships, and studying impacts of climate change using trait-based response. The tentative topic should cover:1. Measuring morphological, physiological, behavioral, or cultural traits at individual or other relevant level of organization using remote sensing
2. Applications of remote sensing-based observations and machine learning in studying and retrieval of phenological traits
3. Representing aggregative value of traits at the individual, population, community, or ecosystem level
4. Classifying ecosystems according to the aggregated values of traits into Plant Functional Types (PFTs)
5. Measuring soft traits such as plant life form, plant height, clonality, spinescence, flammability, leaf lifespan, leaf phenology; regenerative traits such as dispersal mode, dispersule shape, dispersule size/mass, seed mass, resprouting capacity; leaf traits such as SLA, leaf size (individual leaf area), leaf dry matter content, leaf nitrogen concentration, leaf phosphorus concentration, physical strength of leaves, photosynthetic pathway, leaf frost sensitivity; stem traits such as stem-specific density, twig dry matter content, twig drying time, bark thickness; belowground traits such as specific root length, fine root diameter, root depth distribution, 95% rooting depth (an estimate of the depth above which 95% of the root biomass is located), nutrient uptake strategy (categorical trait showing different nutrient uptake strategy such as nitrogen fixer, mycorrhiza, hairy root cluster, carnivorous).
6. Representation of traits in Dynamic Global Vegetation Models (DGVMs) and their integration with biophysical models for ecosystem studies
7. Relationship between plant functional traits and environment
8. Microclimate modifications and ecosystem functioning
9. Soil carbon dynamics and nutrient cycling
10. Climate and ecosystem functioning
11. Carnivorous plant traits
12. Trait based selection of species for meeting specific needs such as restoring degraded ecosystem
13. Role of inter- and intraspecific trait variations in community assembly processes
14. Role of functional traits in ameliorating urban climate and environmental pollution

Manuscript submission information:

When submitting your manuscript please select the article type “VSI: Plant Functional Traits Study” at https://www.editorialmanager.com/ecoinf/default.aspx. Please submit your manuscript before the submission deadline (31 December 2025).

All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.

Keywords:

Plant Functional Traits; Plant Functional Types; Trait Environment; Phenology; Machine Learning; Remote Sensing; Trait monitoring; Ecosystem Modelling; Geoinformatics; Machine Learning

Special Issue on Disruptive Technologies in Ecology and Sustainable Development
提出日: 2026-01-12

The Global South tends to be the receptacle where soot of development settles first. Ironically, the ‘wealthiest’ nations are the ‘poorest’. Rich in biodiversity and natural resources yet vulnerable to climate change and ecological degradation, these regions require innovative solutions tailored to their unique contexts. This Special Issue, " Disruptive Technologies in Ecology and Sustainable Development," aims to explore and highlight the development and application of cutting-edge technologies to address planetary challenges. The focus is on the intersection of emerging technologies and ecology, showcasing how advancements in science, engineering and computing can offer sustainable solutions to environmental problems such as biodiversity monitoring, renewable energy, climate-smart agriculture, water purification, and waste management.

This special issue seeks to be a catalyst to bridge the technologists, ecologists and policymakers, emphasizing the importance of context-specific solutions that consider economic, social, and cultural factors. This issue fosters a collaborative approach to ecological problem-solving, encouraging the exchange of knowledge and dissemination of best practices. We hope this collection will inspire new research, inform policy decisions, and contribute to more resilient and sustainable ecosystems in developing countries. Through shared knowledge and innovative approaches, we aim to empower communities and drive positive environmental change, working towards a future where technological innovation and ecological sustainability go hand in hand.

For considering your work to be published in the SI, please submit your manuscript to the EM system. Authors must select “Special Issue: DTESD in the submission process.

Guest editors:

Dr. Athira Kakkara
Kerala University of Digital Sciences, Innovation and Technology (Digital University Kerala), Kerala, India
athira.k@duk.ac.in 

Prof. Jaishanker Nair
Kerala University of Digital Sciences, Innovation and Technology (Digital University Kerala), Kerala, India; School of Ecology and Environment Studies, Nalanda University, Bihar, India
jrnair@duk.ac.in 

Special issue information:

We welcome contributions that investigate the intersection of technology and ecology, focusing on solutions that are scalable, adaptable, and context-specific. The following broad themes will be covered:

Biodiversity Monitoring and Conservation Technologies
Application of artificial intelligence (AI) and machine learning (ML) in species identification and habitat mapping
Remote sensing, GIS, and drone technologies for ecosystem surveillance
Bioacoustic monitoring and eDNA analysis for biodiversity assessment
Renewable Energy for Environmental Sustainability
Innovations in solar, wind, and bioenergy tailored for ecologically sensitive regions
Smart grids and decentralized energy solutions for sustainable rural development
Energy-efficient technologies reducing carbon footprints in industrial and urban settings
Climate-Smart Agriculture and Sustainable Food Systems
Precision agriculture using IoT and big data analytics
Hydroponics, aquaponics, and vertical farming for resource-efficient food production
Digital platforms and mobile technologies for farmer empowerment and climate adaptation
Water Purification and Management Technologies
Advanced filtration, desalination, and wastewater treatment solutions
Sensor-based water quality monitoring and predictive analytics
Community-driven water conservation strategies enabled by digital platforms
Waste Management and Circular Economy Innovations
AI-driven waste sorting and recycling technologies
Biodegradable materials and sustainable packaging solutions
Strategies for reducing e-waste and upcycling industrial byproducts
Digital and Computational Approaches in Ecology
Predictive modeling and simulation of ecological systems
Blockchain for environmental data security and sustainable supply chains
Citizen science initiatives leveraging mobile applications and open-source platforms
Policy, Governance, and Socio-Economic Implications
Integration of disruptive technologies into environmental policies and regulations
Socioeconomic impact assessments of technology-driven ecological solutions
Public-private partnerships fostering sustainable technology adoption

Manuscript submission information:

When submitting your manuscript please select the article type “VSI: DTESD” at https://www.editorialmanager.com/ecoinf/default.aspx. The submission portal will be open from 02 June 2025 to 12 January 2026.

All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.

Keywords:

DTESD, ECOLOGY, DISRUPTIVE TECHNOLOGY, SUSTAINABLE DEVELOPMENT GOALS, AI

Special Issue on Data Management and Data Analytics for Sustainable Agriculture
提出日: 2026-07-16

Agriculture is undergoing a profound transformation, driven by the increasing availability of heterogeneous, dynamic, and huge volumes of data from ground sensors, remote sensing, and autonomous robots. Leveraging these data for sustainable agricultural practices remains a challenge due to issues related to data integration, real-time analytics, scalability, and actionable decision-making. This Special Issue on Data Management and Data Analytics for Sustainable Agriculture, aims to address these challenges by exploring cutting-edge methods for managing and analyzing big data for sustainable agriculture.

The aim is to collect contributions that tackle the full data lifecycle in smart agriculture, from data acquisition (e.g., from the Internet of robotic things - IoRT, remote sensing, or autonomous platforms) to scalable storage solutions, efficient data fusion techniques, and advanced analytics. Emphasis will be placed on interdisciplinary approaches that bridge data science and agronomy to enhance productivity, while ensuring environmental sustainability. Attention will be also given to novel machine learning models that integrate domain knowledge, fog-edge-cloud computing frameworks for real-time processing, and federated learning strategies for collaborative data analysis in agricultural networks.

This Special Issue aims to highlight state-of-the-art advancements that enable data-driven, smart agriculture and support informed decision-making at different scales, from individual farms to regional food systems. By bringing together contributions from researchers in big data, AI, robotics and agricultural sciences, we seek to foster innovative solutions that contribute to biodiversity and environmental preservation, resource efficiency, climate resilience, green computing, and all other topics that contribute to sustainable agriculture.

Guest editors:

Dr. Sandro Bimonte
TSCF, INRAE, Ferrand, France
sandro.bimonte@inrae.fr 

Dr. Riccardo Bertoglio
TSCF, INRAE, Ferrand, France
riccardo.bertoglio@inrae.fr 

Prof. Piotr Skrzypczyński
Poznan University of Technology, Poznan, Poland
piotr.skrzypczynski@put.poznan.pl 

Prof. Robert Wrembel
Poznan University of Technology, Poznan, Poland
robert.wrembel@cs.put.poznan.pl 

Special issue information:

The Journal of Ecological Informatics is calling for submissions to a special issue on Data Management and Data Analytics for Sustainable Agriculture.

As agriculture embraces digital transformation, the integration of big data technologies is becoming essential to enhance sustainability, productivity, and resilience. With the rapid development of IoT sensors, autonomous machinery, and remote sensing technologies, vast amounts of agricultural data are being generated, creating both opportunities and challenges in data management and analysis.

We invite researchers to submit papers that explore innovative approaches to managing and analyzing big agricultural data, including topics such as data acquisition from IoRT, real time analytics, scalable storage systems, data fusion techniques, and AI-driven predictive modeling. We seek original research articles, survey papers, and case studies that address innovative datamanagement and analytics strategies promoting sustainable agricultural practices.

Topics of interest include, but are not limited to:

    IoT and robotics-enabled sensor networks for real-time soil, weather, life stock, and crop monitoring
    Data fusion techniques for combining heterogeneous agricultural data sources (e.g., UAVs, satellites, field sensors)
    Standardization and interoperability of agricultural data formats and platforms
    Scalable storage systems and distributed databases for agricultural data
    New database management systems based on multi-model, multi-modal, and polyglot approaches
    Data warehouse, data lake, data lakehouse, lambda, data mesh architectures
    Distributed and edge-fog-cloud computing frameworks for processing big agricultural data
    Federated learning for collaborative AI in agriculture, ensuring farmer data ownership
    Machine learning and deep learning approaches for decision support and automation in farming
    Explainable AI (XAI) techniques to enhance transparency and adoption of AI-driven agricultural solutions
    Interactive (geo)visualization tools for making complex data accessible to farmers and agronomists
    Business intelligence methods and systems for sustainable agriculture decision-making

Manuscript submission information:

When submitting your manuscript please select the article type “VSI: Sustainable Agriculture” at https://www.editorialmanager.com/ecoinf/default.aspx. 

The submission portal will be open from 15 December 2025 to 16 July 2026.

All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.

Keywords:

Big Data, Data Management, Data Analytics, AI, Sustainable Agriculture