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foreMast: an R package for predicting beech (Fagus sylvatica L.) masting events in European countries
Chiavetta , U. , Marzini , Sebastian
forest management climatic cues fagus sylvatica l. (european beech) mast event r package seed production
Mostra abstract
Key message: Forecasting annual seed production will improve the management of forests across Europe. The foreMast R package we developed predicts current year masting probability in beech (Fagus sylvatica L.) using climate data easily accessible by any stakeholder. Context: Modelling and predicting forest masting is one of the most challenging tasks in forest management, as it is a strategy shared by several species, very important for tree dispersion and forest regeneration, mainly related to climate and ecological processes. Aims: As many studies focus on European beech (Fagus sylvatica L.) masting without simple practical implementations, we developed a tool capable of predicting beech masting years. Methods: The tool is an R package (foreMast) made by three functions, which relies mainly on climate data. The algorithm performance is compared with the records of the MASTREE database, which gather several beech seed production series for various sites across European countries. Results: Overall, the results show a tight correlation with the compared sites (ρ = 0.50 to 0.61, p-value < 0.0001, respectively), especially when temperatures weigh three times more than precipitation. Nevertheless, in some sites, seed production seems to be more related to precipitation dynamics than to temperatures. Conclusion: foreMast can be used both for studying changes in mast events in relation to climate changes and in operative forest management and planning. It is flexible and thus amenable to future implementation of additional predicting variables or target species. © 2021, INRAE and Springer-Verlag France SAS, part of Springer Nature.
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2021
Rivista
Annals of Forest Science
Dettagli
Vol. 78 (4)
DOI
Handbook of field sampling for multi-taxon biodiversity studies in European forests
Burrascano , Sabina , Trentanovi , Giovanni , Paillet , Yoan , Heilmann-Clausen , Jacob , Giordani , P. , Bagella , Simonetta , Bravo-Oviedo , Andrés , Campagnaro , Thomas , Campanaro , Alessandro , Chianucci , Francesco , de Smedt , Pallieter , Itziar , García Mijangos , Matošević , Dinka , Sitzia , Tommaso , Aszalós , Réka , Brazaitis , Gediminas , Cutini , Andrea , D'Andrea , Ettore , Doerfler , Inken , Hofmeister , Jeňýk , Hošek , Jan , Janssen , Philippe , Kepfer-Rojas , Sebastian , Korboulewsky , Nathalie , Kozák , Daniel , Lachat , Thibault , Lõhmus , Asko , López , Rosana , Mårell , Anders , Matula , Radim , Mikoláš , Martin , Munzi , Silvana , Nordén , Björn , Pärtel , Meelis , Penner , Johannes , Runnel , Kadri , Schall , Peter , Svoboda , Miroslav , Tinya , Flóra , Ujházyová , Mariana , Vandekerkhove , Kris , Verheyen , Kris , Xystrakis , Fotios , Ódor , Péter
biodiversity forest stand structure multi-taxon field methods indicators sampling protocol
Mostra abstract
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. © 2021 The Authors
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2021
Rivista
Ecological Indicators
Dettagli
Vol. 132
DOI