Tree growth modelling and predictions are essential components of forest planning and ecological research. Airborne laser scanning data can provide detailed information about tree crowns, where primary photosynthesis happens. The aim of this study was to investigate, whether intercepted solar radiation received by tree crowns, modelled using drone-based Light Detection and Ranging (LiDAR) data, can be used as an indicator of competition in individual tree growth models instead of traditional competition variables such as basal area or basal area of larger trees. The analysis was based on repeated field measurements and a single acquisition of drone-based laser scanning data at the time of first field measurement. Nonlinear mixed-effects models were developed to predict individual tree stem volume growth. While crown metrics, such as crown volume and crown surface area, did not improve model performance compared to model based solely on field-measured variables, information about direct solar radiation based on ray tracing and tree crown information led to the best-fitting model. These results suggest that drone-based LiDAR measurements can provide such measurements of crown metrics and light availability that are both practically feasible and physiologically justified, and lead to better model fit than the traditional field measurement-based approaches.
The expansion of wind power plays a crucial role in the European Union's decarbonization strategy. However, the social acceptance of wind power projects, particularly among landowners and non-landowners, remains a significant concern. Drawing on behavioural insights of the social acceptance literature, this study examines the beliefs, attitudes, and behavioural intentions of these groups towards wind power. We focus on the national context of Finland, where wind energy development has accelerated in recent years. Utilizing survey data from 2,103 respondents, including forest owners and non-forest owners, the study employs Structural Equation Modelling (SEM) and regression models to explore how beliefs shape attitudes, policy support and forest owners' behavioural intentions.The acceptance measures indicate that non-forest owners held more favourable attitudes towards wind power, associating it with environmental benefits and economic opportunities, while forest owners expressed greater concerns about landscape changes, land ownership, and economic impacts. The SEM model reveals that attitudes toward wind power were strongly influenced by beliefs about energy production and local impacts, which, in turn, shaped policy support. Additionally, forest owners’ willingness to lease land for wind turbines was associated with higher income level, owned land area size, and positive attitudes towards wind power.These findings highlight the need for tailored landscape-orientated policies that address explicitly address landowners’ concerns while fostering broader societal support for wind power. Enhancing procedural and distributive justice, improving compensation mechanisms, and integrating participatory planning processes could help accommodate resistance and facilitate a just energy transition.
Agricultural soils are a major reservoir and conduit within the anthropogenic water cycle, receiving microplastics (MPs) through sewage-sludge/biosolid land application, reclaimed-water irrigation, plastic-mulch degradation and atmospheric deposition, and exporting dissolved nutrients to surface and ground waters via runoff and leaching. Phosphatase enzymes govern organic-phosphorus mineralization and therefore the size of the dissolved phosphorus (P) pool that drives aquatic eutrophication, yet how MPs alter soil acid (ACP) and alkaline (ALP) phosphatase activity, and which conditions create the greatest soil-to-water transfer risk, remains unresolved. We synthesized 171 global studies (866 ACP and 688 ALP observations) in a meta-analysis integrated with an interpretable machine-learning ensemble that stacked XGBoost, LightGBM and Random Forest, with SHAP and accumulated local effects (ALE) interpretation. MPs significantly increased ACP activity by 9.89%, whereas the overall ALP effect was non-significant (+1.40%) but strongly context-dependent, ranging from significant stimulation (e.g., biodegradable MPs: +15.15%; aged MPs: +25.60%) to significant decrease under certain conditions (e.g., with nitrogen addition:-9.45%). Soil pH was the dominant control on ACP responses (mean | SHAP| = 0.088) and clay content on ALP responses (0.062), each ranking first across all five best-performing XGBoost configurations, indicating divergent driver hierarchies for the two enzymes. In the machine-learning analysis, ALP suppression was concentrated in clay-rich soils, whereas low-clay soils showed neutral-to-positive responses. By identifying where MP contamination most strongly perturbs phosphatase-mediated P mobilization, our findings connect a terrestrial contaminant to diffuse nutrient pollution of receiving waters and provide a transferable framework for contaminant-water-quality synthesis.
Wildfires, insect outbreaks, and storms cause large pulses of tree mortality. Climate change amplifies these forest disturbances, yet their future magnitude and extent remain uncertain. Here, we simulated future forest disturbance regimes at 100-meter resolution across Europe using a deep learning-based simulation framework. Our results show that forest disturbances will continue to increase throughout the 21st century, with disturbed areas more than doubling relative to the recent past under an unabated continuation of climate change. Wildfires are the main agent driving future disturbance change. Changing disturbances result in an increase in young forests, substantially altering Europe's forest demography. Because of their profound implications for forest carbon storage and the habitat value of forest ecosystems, disturbances should be a priority of forest policy and management.
Spatial variables can be observed in many different forms, such as regularly sampled random fields (lattice data), point processes and randomly sampled spatial processes. Joint analysis of such collections of observations is clearly desirable, but complicated by the lack of an easily implementable analysis framework. We fill this gap by providing a multitaper analysis framework using coupled discrete and continuous data tapers, combined with the discrete Fourier transform for inference. Using this set of tools is important, as it forms the backbone for practical spectral analysis. In higher dimensions it is especially important not to be constrained to Cartesian product domains, and so we develop the methodology for spectral analysis using irregular domain data tapers and the tapered discrete Fourier transform. We discuss its fast implementation, as well as the asymptotic and large finite-domain properties. Estimators of partial association between different spatial processes are provided, as are principled methods to determine their significance, and we demonstrate their practical utility using a large-scale ecological dataset.
