Vegetation Classification and Survey (VCS): Editors’ Choice of the second quarter of 2025
Prepared by Jürgen Dengler (on behalf of VCS Chief Editor)
In the second quarter of its 6th volume, Vegetation Classification and Survey (VCS) has published six articles (see list below). Among these, the Chief Editors selected the contribution by Monteiro-Henriques (2025) as the Editors’ Choice article.
In his VCS Methods paper, Tiago Monteiro-Henriques (Portugal) introduces a new method for vegetation classification, called TDV-optimization. TDV stands for Total Differential Value, the average of the DifferentialValues (DiffVal) of all species in a vegetation table. The author makes an interesting distinction between stochastic and differentiating absences of species. The DiffVal of each species ranges from 0 to 1 and indicates how well a certain species reflects the separation of relevé groups of any number in a vegetation table. Using TDV as a measure how well a partition of relevés in a table into vegetation types is supported by differential species, makes it feasible to search for the “optimal” partitioning by a computer, and the author developed an R package for this purpose. In the paper the author further tests the performance of his new method compared to previous automatic and manual methods of partitioning used in vegetation science with a real and with an artificial dataset and generally found that TDV-optimization does well. The paper convinces by the clarity of concepts and presentation and a broad and informed review of previous numerical approaches for this core task of vegetation classification, such as k-means clustering, modified TWINSPAN, DIANA or ISOPAM. With these properties, Monteiro-Henriques (2025) has the potential to become a seminal text in our scientific discipline and highly cited as several other VCS Methods papers before. We look forward to seeing TDV-optimization being both applied and tested comprehensively, also in comparison to more recent approaches not included in the current comparison, such as GRIMP (Group Improvement; Tichý et al. 2019) and ICO-HES (Iterative Cluster Optimisation for Hierarchical Expert Systems; Vassilev et al. 2024). It also would be interesting to see whether and how TDV-optimization could be extended to hierarchical classification systems, which are a fundamental property of phytosociology. Without any doubt, VCS and its article category VCS Methods would be very eager to publish such follow-up studies.
- Monteiro-Henriques T (2025) TDV-optimization: A novel numerical method for phytosociological tabulation. Vegetation Classification and Survey 6: 99–127. https://doi.org/10.3897/VCS.140466, see also https://vegsciblog.org/2025/07/26/twelve-years-with-diffval-and-tdv/
- Tichý L, Chytrý M, Landucci F (2019) GRIMP: a machine-learning method for improving diagnostic species groups in expert systems for vegetation classification. Journal of Vegetation Science 30: 5–17. https://doi.org/10. 1111/jvs.12696
- Vassilev K, Bergmeier E, Boch S, Pedashenko H, Sopotlieva D, Tsiripidis I, Apostolova I, Fotiadis G, Ganeva A … Dengler J (2024) Classification of the high-rank syntaxa of the Balkan dry grasslands with a new hierarchical expert system approach. Applied Vegetation Science 27:e12779. https://doi.org/10.1111/avsc.12779, see also https://vegsciblog.org/2024/06/04/syntaxonomy-of-balkan-dry-grasslands/
All articles of Volume 6, 2nd quarter
- Naqinezhad, A., Biurrun, I., Chepinoga, V., Dengler, J., Nowak, A. (2025) Advancing vegetation classification of grassland ecosystems across Asia: current status and way forward. Vegetation Classification and Survey 6: 79–97. https://doi.org/10.3897/VCS.151773, see https://vegsciblog.org/2025/04/25/classification-of-grassland-ecosystems-across-asia/
- Monteiro-Henriques, T. (2025) TDV-optimization: A novel numerical method for phytosociological tabulation. Vegetation Classification and Survey 6: 99–127. https://doi.org/10.3897/VCS.140466
- Uogintas, D., Petrulaitis, L., Kitrytė, N., Šimanskaitė, V. (2025) KELVEG – Roadside Vegetation of Lithuania. Vegetation Classification and Survey 6: 129–130. https://doi.org/10.3897/VCS.151991
- Świerkosz, K., Wójcicka-Rosińska, A., Kuras-Hilares, I., Pech, P., Reczyńska, K. (2025) LAURA: a resurvey database of forest vegetation in southwestern Poland. Vegetation Classification and Survey 6: 131–132. https://doi.org/10.3897/VCS.150538
- Dembicz, I., Dengler, J. (2025) Should we estimate plant cover in percent or on ordinal scales? II – Diversity indices. Vegetation Classification and Survey 6: 133–140. https://doi.org/10.3897/VCS.144252, see https://vegsciblog.org/2025/05/21/ordinal-cover-scales-bias-biodiversity-metrics/
- Biurrun, I., Belmonte, J., Sanz-Zubizarreta, I., Campos, J.A. (2025) Should we estimate plant cover in percent or on ordinal scales? II – Diversity indices. Vegetation Classification and Survey 6: 141–161. https://doi.org/10.3897/VCS.145406
