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Stand, landscape and climatic attributes contributing to the probability of Ips typographus damage in Finland

Pulgarín Díaz, John Alexander; Melin, Markus; Mehtätalo, Lauri; Polade, Suraj; Aalto, Juha; Peltola, Heli; Tikkanen, Olli-Pekka
Forest ecology and management (Elsevier, 2026)

Large-scale outbreaks of Ips typographus L. (SBB) have caused extensive damage to Norway spruce (Picea abies (L.) H. Karst.) forests. Under climate change, SBB damage is expected to increase in Northern Europe and especially in Finland, where Norway spruce is one of the most ecologically and economically important tree species. We developed spatially-explicit probability models and SBB damage risk maps using hierarchical logistic regression modelling. We considered various stand, landscape and climatic attributes, including disturbances by routine forestry activities (i.e. new clear-cuts), wind and SBB damage. The analysis drew on detailed, open-access, stand-level forest data collected in 2020–2022 for managed Norway spruce stands in the southern half of Finland (study area), where damage is most prevalent. The study area of 11.4 million ha with more than two million stands Norway spruce stands was split into northern and southern sub-areas to develop sub-area-specific generalised linear mixed effect models for predicting the probability of stand-level SBB damage. We found a generally low probability of SBB damage, higher in the southern sub-area. Landscape attributes showed the strongest effect on SBB damage predisposition, followed by stand and climatic attributes, though the effects differed in sub-areas. The top predictors of SBB damage were proximity to clear-cuts, followed by stand mean diameter at breast height, distance to previous SBB damage and the maximum number of consecutive days with temperature above 25°C. However, careful planning of proactive risk management actions is required, as clear-cuts – including SBB-related salvage loggings – may induce new SBB infestations.

Inventario forestal de precisión basado en nubes d epuntos terrestres: validación en parcelas de estudio del género Pinus situadas en Asturias

Prendes-Pérez, Covadonga; Cabo-Gómez, Carlos; Ordóñez-Galán, Celestino; Acuna, Mauricio; Canga-Líbano, Elena
Montes : 161 (2025)

A bi-objective mixed-integer linear programming model to optimize thinning schedules in wildfire-prone Pinus canariensis forests

Navarro-Cerrillo, Rafael M.; Acuna, Mauricio; Ariza-Salamanca, Antonio Jesús; Martínez, M. Ángeles Varo; Cedrés, Eva Padrón
Science of the total environment (Elsevier, 2025)

Nutrient recycling in the Baltic Sea Region - State-of-the-art and the way forward

Luostarinen, Sari; Tampio, Elina; Laakso, Johanna; Köster, Tiina; Vettik, Raivo; Tamm, Kalvi; Melnalksne, Zanda; Grudovska, Iveta; Akstinas, Edmundas; Wach, Damian; Skowron, Piotr; Borzecka, Magdalena; Mocny, Krystian; Schick, Judith; Foged, Henning Lyngsø; de Morais Lima, Priscila; Dzemedzionaite, Vaida; Cordeiro, Cheryl Marie; Sindhöj, Erik (Natural Resources Institute Finland (Luke), 2025)

An Integrated GIS-MILP Framework for Cost-Optimal Forest Biomass-to-Bioenergy Supply Chains: A Case Study in Queensland, Australia

Van Holsbeeck, Sam; Acuna, Mauricio; Ezzati, Sättar
Forests : 9 (MDPI, 2025)

Renewable energy expansion requires cost-effective strategies to integrate underutilized biomass resources into energy systems. In Australia, forest residues represent a significant but largely untapped feedstock that could contribute to a more diversified energy portfolio. This study presents an integrated geospatial and optimization decision-support model designed to minimize the total cost of forest biomass-to-bioenergy supply chains through optimal facility selection and network design. The model combined geographic information systems with mixed-integer linear programming to identify the optimal candidate facility sites based on spatial constraints, biomass availability and infrastructure proximity. These inputs then informed an optimization framework that determined the number, size, and geographical distribution of bioenergy plants. The model was applied to a case study in Queensland, Australia, evaluating two strategic scenarios: (i) a biomass-driven approach that maximizes the use of forest residues; (ii) an energydriven approach that aligns facilities with regional energy consumption patterns. Results indicated that increasing the minimum facility size reduced overall costs by capitalizing on economies of scale. Biomass collection accounted for 81%–83% of total supply chain costs (excluding capital installation), emphasizing the need for logistically efficient sourcing strategies. Furthermore, the system exhibited high sensitivity to transportation distance and biomass availability; energy demands exceeding 400 MW resulted in sharply escalating transport expenses. This study provides a scalable, data-driven framework for the strategic planning of forest-based bioenergy systems. It offers actionable insights for policymakers and industry stakeholders to support the development of robust, cost-effective, and sustainable bioenergy supply chains in Australia and other regions with similar biomass resources.