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Naturalness and Tree Composition Determine the Abundance of Rare and Threatened Orchids in Mature and Old-Growth Abies alba Forests in the Northern Apennines (Italy)

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  • Antonio Pica

    (Department of Ecological and Biological Sciences (DEB), Tuscia University, 01100 Viterbo, Italy
    Italian Society of Forest Restoration (SIRF), Department of Agriculture and Forest Sciences (DAFNE), Tuscia University, 01100 Viterbo, Italy
    Department of Agriculture and Forest Sciences (DAFNE), Tuscia University, 01100 Viterbo, Italy)

  • Bartolomeo Schirone

    (Italian Society of Forest Restoration (SIRF), Department of Agriculture and Forest Sciences (DAFNE), Tuscia University, 01100 Viterbo, Italy)

  • Sara Magrini

    (Department of Ecological and Biological Sciences (DEB), Tuscia University, 01100 Viterbo, Italy)

  • Paolo Laghi

    (Valbonella Botanical Garden, Foreste Casentinesi, Monte Falterona and Campigna National Park, 52015 Pratovecchio, Italy)

  • Kevin Cianfaglione

    (ICL, Junia, Université Catholique de Lille, LITL, F-59000 Lille, France)

  • Alfredo Di Filippo

    (Italian Society of Forest Restoration (SIRF), Department of Agriculture and Forest Sciences (DAFNE), Tuscia University, 01100 Viterbo, Italy
    Department of Agriculture and Forest Sciences (DAFNE), Tuscia University, 01100 Viterbo, Italy)

Abstract

Forest Orchidaceae are important for European temperate forests, yet their distribution and abundance have so far interested limited research. In three pure or mixed silver fir stands in the Foreste Casentinesi National Park (NP) (Northern Apennines, Italy) we analysed how structural traits in mature and old-growth forests affected orchid communities in terms of abundance of the main genera, trophic strategy and rarity in the NP. We established three 20 × 60 m plots to quantify the structure of living and dead tree community, including a set of old-growth attributes connected to large trees, deadwood, and established regeneration. In each plot, we measured the abundance of all orchid species and explored their behaviour according to the trophic strategy (autotrophy/mixotrophy, obligate mycoheterotrophy), rarity within the NP, and threatened status according to the IUCN Red List. We used multivariate ordination and classification techniques to assess plot similarities according to forest structure and Orchid Community and identify the main structural factors related to orchid features. The main structural factors were used as predictors of community traits. Forest composition (i.e., the dominance/abundance of silver fir) affected the presence of the main orchid genera: Epipactis were abundant in silver fir-dominated forests, Cephalanthera in mixed beech and fir forests. Interestingly, Cephalanthera could become limited even in beech-dominated conditions if fir regeneration was abundant and established. Old-growth attributes like the density of deadwood and large tree volume were important determinants of the presence of rare and mycoheterotrophic species. Our results provided a first quantitative description of forest reference conditions to be used in the protection and restoration of threatened and rare orchid species.

Suggested Citation

  • Antonio Pica & Bartolomeo Schirone & Sara Magrini & Paolo Laghi & Kevin Cianfaglione & Alfredo Di Filippo, 2025. "Naturalness and Tree Composition Determine the Abundance of Rare and Threatened Orchids in Mature and Old-Growth Abies alba Forests in the Northern Apennines (Italy)," Land, MDPI, vol. 14(3), pages 1-27, March.
  • Handle: RePEc:gam:jlands:v:14:y:2025:i:3:p:579-:d:1608808
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    References listed on IDEAS

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    1. Simone Pesaresi & Diana Galdenzi & Edoardo Biondi & Simona Casavecchia, 2014. "Bioclimate of Italy: application of the worldwide bioclimatic classification system," Journal of Maps, Taylor & Francis Journals, vol. 10(4), pages 538-553, October.
    2. Peres-Neto, Pedro R. & Jackson, Donald A. & Somers, Keith M., 2005. "How many principal components? stopping rules for determining the number of non-trivial axes revisited," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 974-997, June.
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