Stand Structure and Local Landscape Variables Are the Dominant Factors Explaining Shrub and Tree Diversity in Mediterranean Forests

Abstract

Plant diversity is a core value of forests and is rapidly becoming a primary management goal under the threat of global environmental changes. Changing conditions, including forestry interventions, or lack of them, may endanger its preservation. Abandonment of management in forests previously subjected to a multipurpose silviculture and secondary succession is hypothesized to have altered the biodiverse Mediterranean forests in recent years and affected plant diversity. We used data in national forest inventory plots and local landscape ecology metrics from forest cartography, combined with artificial neural networks, to predict richness and Shannon diversity indices for the tree and shrub layers of several Mediterranean forest types. We found that richness and diversity depend on forest structure and on local landscape patterns, and also, though to a lesser degree, on site conditions (mainly soil pH), but not on forest intervention. In order to benefit plant diversity in the forest landscapes analyzed, forest management practices need to promote diameter variety, the presence of large trees, tree cover, variation in the height of trees and shrubs, and a heterogeneous local landscape at the stand level. Aleppo pine forests and Scots pine forests showed more consistent results in their models than cork oak and black pine forests, both of which require further research.

Nested plot designs used in forest inventory do not accurately capture tree species richness in Southwestern European forests

“…Tree species composition of Forest Europe" (Chirici et al 2012;Forest-Europe 2015;Traub and Wüest 2020). Tree species richness derived from NFIs has been widely used in various applications, such as monitoring forest diversity at different scales (Ghadban et al 2021;Bravo-Oviedo et al 2021) or as a predictor in modelling approaches (Ruiz-Benito et al 2012). However, the heterogeneity of NFI designs poses challenges for comparing tree species richness (Portier et al 2022) and even for estimating tree species richness within a single country (Lin et al 2020).…”

Forest Fire Spread Hazard and Landscape Pattern Characteristics in the Mountainous District, Beijing

“…Common methods include geostatistics, wavelet analysis, trend surface analysis, and autocorrelation analysis [37][38][39]. Analyses of landscape pattern indices can reflect the structural composition and spatial distribution characteristics of landscape elements, including the patch level, type level, and landscape level [40,41]. Long et al [42] analyzed the spatial pattern of the forest fire risk in Yunnan Province by using a spatial trend surface analysis and a spatial autocorrelation model.…”