Ecological Informatics (Impact Factor 5.9)

https://www.sciencedirect.com/special-issue/320909/geoinformatics-and-machine-learning-for-studying-plant-functional-traits

Special Issue: Geo-informatics & Machine Learning for studying Plant Functional Traits

Submission deadline: 31 December 2025

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 [email protected]

Dr. Juan A. Blanco Dep. Sciences, IMAB, Universidad Publica de Navarra, Navarra, Spain [email protected] 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

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