Species Mixing Proportion and Aridity Influence in the Height–Diameter Relationship for Different Species Mixtures in Mediterranean Forests

Exploring climate-smart forestry in Mediterranean forests through an innovative composite climate-smart index

In recent years, Climate-Smart Forestry (CSF) has emerged as an innovative approach to sustainable forest management, aiming to enhance forest resilience and to balance the provision of ecosystem services facing climate-related threats. This study introduces for the first time a new composite climate-smart index (ICSF) to assess CSF. The methodological approach comprises the following steps: (i) the selection and evaluation of CSF indicators; (ii) the weighting of these indicators; and (iii) the assessment of CSF for Mediterranean forests in two distinct periods, specifically 2005 and 2015. Eight indicators were selected from a systematic literature review. The Analytic Hierarchy Process was applied to translate the preferences obtained through an online questionnaire from a network of CSF-expert stakeholders into weights, at both indicators and criteria levels (i.e., adaptation, mitigation, and the social dimension). Results reveals that indicators "tree species composition", "forest damage", and "regeneration" are of crucial importance for CSF assessment. The comparison of the CSF value between the years 2005 and 2015, shows a slight increase in CSF ratings. The ICSF serves as a comprehensive index of CSF covering all aspects of that concept, i.e. adaptation, mitigation, and the social dimension (including production). The national-scale analysis provides an overview of the dynamics that involve forest management of Mediterranean forests against climate change. The study offers a practicable method for CSF evaluation with its allover set of indicators, representing a suitable tool for supporting forest managers to mitigate the negative impacts of climate change.

Unpunctual in diversity: The effect of stand species richness on spring phenology of deciduous tree stands varies among species and years

Climatic drivers alone do not adequately explain the regional variation in budburst timing in deciduous forests across Europe. Stand-level factors, such as tree species richness, might affect budburst timing by creating different microclimates under the same site macroclimate. We assessed different phases of the spring phenology (start, midpoint, end, and overall duration of the budburst period) of four important European tree species (Betula pendula, Fagus sylvatica, Quercus robur and Tilia cordata) in monocultures and four-species mixture stands of a common garden tree biodiversity experiment in Belgium (FORBIO) in 2021 and 2022. Microclimatic differences between the stands in terms of bud chilling, temperature forcing, and soil temperature were considerable, with four-species mixtures being generally colder than monocultures in spring, but not in winter. In the colder spring of 2021, at the stand level, the end of the budburst period was advanced, and its overall duration shortened, in the four-species mixtures. At species level, this response was significant for F. sylvatica. In the warmer spring of 2022, advances in spring phenology in four-species stands were observed again in F. sylvatica and, less markedly, in B. pendula but without a general response at the stand level. Q. robur showed specific patterns with delayed budburst start in 2021 in the four-species mixtures and very short budburst duration for all stands in 2022. Phenological differences between monocultures and four-species mixtures were linked to microclimatic differences in light availability rather than in temperature as even comparatively colder microclimates showed an advanced phenology. Compared to weather conditions, tree species richness had a lower impact on budburst timing, but this impact can be of importance for key species like F. sylvatica and colder springs. These results indicate that forest biodiversity can affect budburst phenology, with wider implications, especially for forest- and land surface models.