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Some refinements on species distribution models using tree-level National Forest Inventories for supporting forest management and marginal forest population detection

Maurizio Marchi   , Fulvio Ducci

iForest - Biogeosciences and Forestry, Volume 11, Issue 2, Pages 291-299 (2018)
doi: https://doi.org/10.3832/ifor2441-011
Published: Apr 13, 2018 - Copyright © 2018 SISEF

Research Articles

Collection/Special Issue: COST Action FP1202
Strengthening conservation: a key issue for adaptation of marginal/peripheral populations of forest trees to climate change in Europe (MaP-FGR)
Guest Editors: Fulvio Ducci, Kevin Donnelly


Spatial modelling is a fundamental tool to support forest management strategies. National Forest Inventories (NFIs) provide extensive and detailed data for spatial analysis. In this study, the most recent Italian NFI (INFC2005) was used to evaluate possible refinements on species distribution model (SDM) techniques and to derive the future scenarios for two target species (Fagus sylvatica L. and Abies alba Mill.) sharing a similar ecological environment and geographic range. A weighted SDM and a provenance distribution model (PDM) were tested, based on tree-level selection of NFI plots using species basal area as a filter. Two climate projections were analysed for 2050s according to the IPCC 5th Assessment Report (AR5). The results were evaluated as possible guidelines for management of the Italian region of the EUFGIS network, where many marginal forest populations (MaPs) are currently included as genetic conservation units (GCUs). The uncertainty of coordinates of inventory points did not affect the results of SDM. No statistical differences were found when comparing the niche realization for the two model species (ANOVA p>0.05) mainly due to spatial autocorrelation between the environmental predictors. Based on the classic SDM evaluation method (True Skill Statistic - TSS) little improvements in predictions were observed when weighting each presence/absence records, possibly due to the lack of adequate ancillary data but also to the evaluation method. A higher accuracy of predictions (TSS>0.85) was obtained when different “provenances” were modelled separately, due to the reduction in the “background noise”. We showed that for classical SDM, the prevalence of certain ecological features of some locations may drive algorithms to produce coarse averaged predictions. Provenance distribution modelling may represent a valuable step forward in spatial analysis, particularly for the detection of marginal peripheral populations. The exact spatial co-ordinates of plots and additional information on site quality (e.g., stand age, site index, etc.) in NFI data could greatly help in better weighting presence/absence data and properly test the new evaluation methods.

  Keywords


INFC2005, European Beech, Silver Fir, Modelling Uncertainties, Provenance Modelling, Climate Change, Mediterranean Area

Authors’ address

(1)
Maurizio Marchi
Fulvio Ducci
CREA - Research Centre for Forestry and Wood, v.le S. Margherita 80, I-52100 Arezzo (Italy)

Corresponding author

 
Maurizio Marchi
maurizio.marchi@crea.gov.it

Citation

Marchi M, Ducci F (2018). Some refinements on species distribution models using tree-level National Forest Inventories for supporting forest management and marginal forest population detection. iForest 11: 291-299. - doi: 10.3832/ifor2441-011

Academic Editor

Piermaria Corona

Paper history

Received: Mar 23, 2017
Accepted: Feb 06, 2018

First online: Apr 13, 2018
Publication Date: Apr 30, 2018
Publication Time: 2.20 months

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