TY - JOUR

T1 - Handbook of field sampling for multi-taxon biodiversity studies in European forests

AU - Burrascano, Sabina

AU - Trentanovi, Giovanni

AU - Paillet, Yoan

AU - Heilmann-Clausen, Jacob

AU - Giordani, Paolo

AU - Bagella, Simonetta

AU - Bravo-Oviedo, Andrés

AU - Campagnaro, Thomas

AU - Campanaro, Alessandro

AU - Francesco, Chianucci

AU - De Smedt, Pallieter

AU - Itziar, García Mijangos

AU - Matošević, Dinka

AU - Sitzia, Tommaso

AU - Aszalós, Réka

AU - Brazaitis, Gediminas

AU - Andrea, Cutini

AU - Ettore, D'Andrea A.

AU - Doerfler, Inken

AU - Hofmeister, Jeňýk

AU - Hošek, Jan

AU - Janssen, Philippe

AU - Kepfer Rojas, Sebastian

AU - Korboulewsky, Nathalie

AU - Kozák, Daniel

AU - Lachat, Thibault

AU - Lõhmus, Asko

AU - Lopez, Rosana

AU - Mårell, Anders

AU - Matula, Radim

AU - Mikoláš, Martin

AU - Munzi, Silvana

AU - Nordén, Björn

AU - Pärtel, Meelis

AU - Penner, Johannes

AU - Runnel, Kadri

AU - Schall, Peter

AU - Svoboda, Miroslav

AU - Tinya, Flóra

AU - Ujházyová, Mariana

AU - Vandekerkhove, Kris

AU - Verheyen, Kris

AU - Xystrakis, Fotios

AU - Ódor, Péter

N1 - Publisher Copyright: © 2021 The Authors

PY - 2021

Y1 - 2021

N2 - Forests host most terrestrial biodiversity and their sustainable management is crucial to halt biodiversity loss. Although scientific evidence indicates that sustainable forest management (SFM) should be assessed by monitoring multi-taxon biodiversity, most current SFM criteria and indicators account only for trees or consider indirect biodiversity proxies. Several projects performed multi-taxon sampling to investigate the effects of forest management on biodiversity, but the large variability of their sampling approaches hampers the identification of general trends, and limits broad-scale inference for designing SFM. Here we address the need of common sampling protocols for forest structure and multi-taxon biodiversity to be used at broad spatial scales. We established a network of researchers involved in 41 projects on forest multi-taxon biodiversity across 13 European countries. The network data structure comprised the assessment of at least three taxa, and the measurement of forest stand structure in the same plots or stands. We mapped the sampling approaches to multi-taxon biodiversity, standing trees and deadwood, and used this overview to provide operational answers to two simple, yet crucial, questions: what to sample? How to sample? The most commonly sampled taxonomic groups are vascular plants (83% of datasets), beetles (80%), lichens (66%), birds (66%), fungi (61%), bryophytes (49%). They cover different forest structures and habitats, with a limited focus on soil, litter and forest canopy. Notwithstanding the common goal of assessing forest management effects on biodiversity, sampling approaches differed widely within and among taxonomic groups. Differences derive from sampling units (plots size, use of stand vs. plot scale), and from the focus on different substrates or functional groups of organisms. Sampling methods for standing trees and lying deadwood were relatively homogeneous and focused on volume calculations, but with a great variability in sampling units and diameter thresholds. We developed a handbook of sampling methods (SI 3) aimed at the greatest possible comparability across taxonomic groups and studies as a basis for European-wide biodiversity monitoring programs, robust understanding of biodiversity response to forest structure and management, and the identification of direct indicators of SFM.

AB - Forests host most terrestrial biodiversity and their sustainable management is crucial to halt biodiversity loss. Although scientific evidence indicates that sustainable forest management (SFM) should be assessed by monitoring multi-taxon biodiversity, most current SFM criteria and indicators account only for trees or consider indirect biodiversity proxies. Several projects performed multi-taxon sampling to investigate the effects of forest management on biodiversity, but the large variability of their sampling approaches hampers the identification of general trends, and limits broad-scale inference for designing SFM. Here we address the need of common sampling protocols for forest structure and multi-taxon biodiversity to be used at broad spatial scales. We established a network of researchers involved in 41 projects on forest multi-taxon biodiversity across 13 European countries. The network data structure comprised the assessment of at least three taxa, and the measurement of forest stand structure in the same plots or stands. We mapped the sampling approaches to multi-taxon biodiversity, standing trees and deadwood, and used this overview to provide operational answers to two simple, yet crucial, questions: what to sample? How to sample? The most commonly sampled taxonomic groups are vascular plants (83% of datasets), beetles (80%), lichens (66%), birds (66%), fungi (61%), bryophytes (49%). They cover different forest structures and habitats, with a limited focus on soil, litter and forest canopy. Notwithstanding the common goal of assessing forest management effects on biodiversity, sampling approaches differed widely within and among taxonomic groups. Differences derive from sampling units (plots size, use of stand vs. plot scale), and from the focus on different substrates or functional groups of organisms. Sampling methods for standing trees and lying deadwood were relatively homogeneous and focused on volume calculations, but with a great variability in sampling units and diameter thresholds. We developed a handbook of sampling methods (SI 3) aimed at the greatest possible comparability across taxonomic groups and studies as a basis for European-wide biodiversity monitoring programs, robust understanding of biodiversity response to forest structure and management, and the identification of direct indicators of SFM.

KW - Biodiversity

KW - Field methods

KW - Forest stand structure

KW - Indicators

KW - Multi-taxon

KW - Sampling protocol

U2 - 10.1016/j.ecolind.2021.108266

DO - 10.1016/j.ecolind.2021.108266

M3 - Review

AN - SCOPUS:85116908286

VL - 132

JO - Ecological Indicators

JF - Ecological Indicators

SN - 1470-160X

M1 - 108266

ER -