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

Abstract

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 5 th 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. 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

Reviewing climatic traits for the main forest tree species in Italy

“…The new quantitative data provided by this study can be used to identify stands that may be adversely affected by the effects of climate change effects earlier than those located in the core of the geographic distribution. This information can be fundamental in Italy and more generally in the Mediterranean region, both of which are considered important European biodiversity hotspots featuring unique species richness (Médail & Quézel 1997, Hampe & Petit 2005, Marchi & Ducci 2018. Moreover, the Mediterranean region is also considered to be seriously threatened by future climate change effects (Resco De Dios et al 2007, Lelieveld et al 2012.…”

“…NFIs are the most extensive and comprehensive source of forest information suitable for spatial analysis, ecological modelling and statistical mapping of forest attributes (Johnson et al 2014, Di Biase et al 2018, Marchi & Ducci 2018. Raw georefer-enced data for sampling units obtained in the field are fundamental for many research activities in the forestry field and are becoming publicly available in most countries (Borghetti & Chirici 2016, Mauri et al 2017.…”

Evaluating WorldClim Version 1 (1961–1990) as the Baseline for Sustainable Use of Forest and Environmental Resources in a Changing Climate

“…One of the main aims for researchers dealing with environmental resources has become to forecast possible impacts of climate change on organisms and to evaluate possible mitigation [6][7][8][9]. In the past few decades, many conservation strategies have been suggested in order to maintain human well-being and ensure an adequate level of welfare [10] from (relatively) simple management strategies [4,11], including "assisted migration" [12][13][14], a controversial protocol that includes translocating more adapted or resilient genotypes for conservation or to improve the resilience of ecosystems. Such efforts are often driven by statistical models [14][15][16][17] and management…”

Exploring Nonlinear Intra-Annual Growth Dynamics in Fagus sylvatica L. Trees at the Italian ICP-Forests Level II Network

“…The spatial distribution of forest tree species (range) is often recognized as a good proxy to derive their ecological requirements and to build up statistical models [9,13]. Analysis of field data and tree-level information allows a comprehensive understanding of stand-level health as well as the Most plots are grown-up high forest stands.…”