MASTROGIANNI, Anna, Milan CHYTRÝ, Athanasios S. KALLIMANIS a Ioannis TSIRIPIDIS. Plant trait filtering is stronger in the herb layer than in the tree layer in Greek mountain forests. Ecological indicators. Amsterdam: Elsevier, 2021, roč. 131, November, s. "108229", 12 s. ISSN 1470-160X. Dostupné z: https://dx.doi.org/10.1016/j.ecolind.2021.108229.
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Základní údaje
Originální název Plant trait filtering is stronger in the herb layer than in the tree layer in Greek mountain forests
Autoři MASTROGIANNI, Anna, Milan CHYTRÝ (203 Česká republika, garant, domácí), Athanasios S. KALLIMANIS a Ioannis TSIRIPIDIS.
Vydání Ecological indicators, Amsterdam, Elsevier, 2021, 1470-160X.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor 10600 1.6 Biological sciences
Stát vydavatele Spojené státy
Utajení není předmětem státního či obchodního tajemství
WWW URL
Impakt faktor Impact factor: 6.263
Kód RIV RIV/00216224:14310/21:00119256
Organizační jednotka Přírodovědecká fakulta
Doi http://dx.doi.org/10.1016/j.ecolind.2021.108229
UT WoS 000704534800007
Klíčová slova anglicky Balkan Peninsula; Forest vegetation; Functional diversity; Functional identity; Functional structure; Greece; Plant traits
Štítky rivok
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnila: Mgr. Marie Šípková, DiS., učo 437722. Změněno: 6. 12. 2021 13:13.
Anotace
We studied the differentiation among plant communities of deciduous broadleaved and mountain coniferous forests in terms of functional diversity and identity at a regional scale (northern and central Greece). We asked if patterns of functional differentiation among communities are consistent between the overstorey and understorey layers and if they can be influenced by deep past environmental conditions. Functional Richness (FRic) and Functional Dispersion (FDis), as well as their standardized effect sizes, were employed to assess the multivariate functional diversity of the community types. In contrast, single-trait Community Weighted Means (CWMs) were used as surrogates of functional identity. The aforementioned indices were calculated for three datasets, namely all the vascular plant taxa found in individual vegetation plots (total community), all phanerophyte (tree and shrub) taxa (overstorey) and all non-phanerophyte vascular plant taxa (understorey). We found that community types and especially four broad forest types (beech, ravine, pine and oak forests) are well differentiated in terms of functional composition (identity), as indicated by Non-Metric Multidimensional Scaling (NMDS). After conducting an NMDS for the three datasets, functional identity based on the total floristic composition was found to be the best discriminator of the studied communities. However, contrasting patterns were found for some specific traits or their categories between overstorey and understorey layers. The patterns of functional diversity of the community types (based on multivariate indices), revealed by calculating the standardized effect sizes of FRic and FDis based on the richness null model, did not differ substantially from random expectations for most of the studied community types when the dataset of all the vascular plant taxa was analyzed. However, the patterns revealed for the overstorey layer differed from those for the understorey layer. For the latter layer, the clustered structure was revealed in many community types based on the ses.FDis metric. Indications of deep past influence on the functional composition were found for certain community types (i.e. ravine forests) based on single-trait metrics, but no indication of such influence was found based on multivariate indices. Our findings highlight the complementarity and the additive explanatory value of the simultaneous use of single- and multi-trait approaches and their application to different layers in forests.
Návaznosti
GX19-28491X, projekt VaVNázev: Centrum pro evropské vegetační syntézy (CEVS) (Akronym: CEVS)
Investor: Grantová agentura ČR, Centrum pro evropské vegetační syntézy (CEVS)
VytisknoutZobrazeno: 25. 8. 2024 09:22