The condition of forest ecosystems depends on the temporal and spatial pattern of management interventions and natural disturbances. Remnants of previous conditions persisting after disturbances, or ecosystem legacies, collectively comprise ecosystem memory. Ecosystem memory in turn contributes to resilience and possibilities of ecosystem reorganization following further disturbance. Understanding the role of disturbance and legacies is a prerequisite for maintaining resilience in the face of global change. Several legacy concepts discussed in the peer‐reviewed literature, including disturbance, biological, soil, land‐use, and silvicultural legacies, overlap in complex ways. Here, we review these established legacy concepts and propose that the new terms “material legacy” (individuals or matter, e.g., survivors, coarse woody debris, nutrients left after disturbance) and “information legacy” (adaptations to historical disturbance regimes) cut across these previous concepts and lead to a new classification of legacies. This includes six categories: material legacies with above‐ and belowground, and biotic and abiotic categories, and information legacies with above‐ and belowground categories. These six legacies are influenced by differential patterns of editing and conditioning by “legacy syndromes” that result from natural or human‐manipulated disturbance regimes that can be arranged along a gradient of naturalness. This scheme is applied to a case study of hemiboreal forests in the Baltic States of Estonia, Latvia, and Lithuania, where natural disturbance, traditional clearcut silviculture, and afforestation of abandoned agricultural lands constitute the three main legacy syndromes. These legacy syndromes in turn influence forest response to management actions and constrain resilience, leading to a mosaic of natural, manipulated, and artificial (novel) ecosystems across the landscape, depending on how the legacies in each syndrome affect ecological memory.

Aim of study: Modelling of forest growth and dynamics has focused mainly on pure stands. Mixed-forest management lacks systematic procedures to forecast the impact of silvicultural actions. The main objective of the present work is to review current knowledge and forest model developments that can be applied to mixed forests.Material and methods: Primary research literature was reviewed to determine the state of the art for modelling tree species mixtures, focusing mainly on temperate forests.Main results: The essential principles for predicting stand growth in mixed forests were identified. Forest model applicability in mixtures was analysed. Input data, main model components, output and viewers were presented. Finally, model evaluation procedures and some of the main model platforms were described.Research highlights: Responses to environmental changes and management activities in mixed forests can differ from pure stands. For greater insight into mixed-forest dynamics and ecology, forest scientists and practitioners need new theoretical frameworks, different approaches and innovative solutions for sustainable forest management in the context of environmental and social changes.Keywords: dynamics, ecology, growth, yield, empirical, classification.

Establishment of the SMEAR Estonia at a hemiboreal mixed deciduous broad-leaved-evergreen needle-leaved forest at Järvselja, South-Eastern Estonia, has strongly enhanced the possibilities for national and international cooperation in the fields of forest ecosystem – atmosphere research and impacts of climatic changes on forest ecosystems, atmospheric trace gases, aerosols and air ions. The station provides a multitude of comprehensive continuously measured data covering key climatic and atmospheric characteristics (state and dynamics of solar radiation, trace gases, aerosols and air ions, meteorological parameters) and forest ecosystem traits (net primary productivity, individual tree growth, gas-exchange characteristics, soil variables). The station follows a multidisciplinary and multiscale approach covering processes in spatial dimensions ranging from nanometres to several hundred square kilometres, being thus able to significantly contribute to worldwide measurement networks and the SMEAR network. Here we present an overview of the station, its data produced and we envision future developments towards sustainable research and development of the large-scale scientific infrastructure SMEAR Estonia.