A Bayesian Belief Network Approach to Predict Damages Caused by Disturbance Agents

Mountain forests provide essential ecosystem services, including protection against natural hazards. However, climate change and the predominance of structurally homogeneous Norway spruce stands increase their predisposition to disturbances such as windthrow, bark beetle infestation, and snow breakage. Given limited resources for proactive and reactive forest management, prioritizing silvicultural interventions is crucial. We present a framework for the spatially explicit assessment of forest predisposition to snow breakage, windthrow, and bark beetle infestation at 10 m resolution across regional scales. The approach integrates high-resolution forest structural and site factor attributes primarily derived from nationally available airborne laser scanning (ALS) data. These attributes are weighted using an expert-based model according to their influence on disturbance predisposition. Weighted values are aggregated and classified into four predisposition classes, from low (< 50th) to extreme (> 95th percentile). Validation of local foresters at 142 reference sites showed significant correlations between forester-rated and modeled predisposition for windthrow and bark beetle, but not for snow breakage. New bark beetle infestations were strongly associated with increasing predisposition classes. Forest structural parameters such as forest mixture and spruce share, development stage, and canopy layering were key predictors and could be determined with high accuracy, enabling detailed and accurate predisposition mapping even in steep terrain. Assessing predisposition to snow breakage remains challenging due to the high stochastic variability in the location, characteristics, and amount of snow fall. As the main predictors are derived from nationwide ALS data, the methodology is transferable to other mountain regions. Developed in close collaboration with practitioners, the framework supports operational forest management by improving prioritization of interventions. • Framework for spatially explicit predisposition mapping in mountain forests. • Use of detailed forest structure parameters based on nationwide airborne laser scanning. • High correlation between predisposition mapping and on-site forester assessment. • Increase of detected bark beetle nests with increasing predisposition class. • Support of spatial prioritization of forest management interventions.

The cumulative impacts of human and natural activity on forest landscapes in Alberta are clear. Human activity, such as forestry and oil and gas development, and natural processes such as wildfire leave distinctive marks on the composition, age class structure and spatial configuration of the forest. Also, other processes such as climate change may be slowly and subtly modifying forest dynamics and may lead to important changes over time. Given the importance and ubiquitous nature of these cumulative impacts, a forest management plan that does not adequately take such impacts into account cannot be expected to adequately manage the forest, neither its components nor its processes. In order to address the question of cumulative impacts in the context of forestry, a landscape model was designed and built in order to simulate forestry, oil and gas, climate change, wildfire, and demographic change for the Whitecourt forest management area over a long time horizon. This paper presents the model and the forest landscape states it forecasted with cumulative impacts, and evaluates the fate of some key indicators of biodiversity and forest productivity. Simulations of harvesting as the only disturbance, the nearest analogue to the current approach to forest management planning, yield results that differ greatly, in every respect, from the results of simulations of harvesting combined with other disturbance agents. The simulation of multiple disturbance agents together allows for the detection of interactions among disturbance agents, and indeed, there are important interactions between the processes of fire and oil and gas. Results also show that climate and demographic change will intensify the impact of fire on the supply of timber and other values. Also, the continued development of petroleum resources will lead to an important erosion of the forest landbase. Overall, this paper makes a strong case for cumulative impacts assessment and the use of spatial and temporal stochastic modelling in forest management. Key words: cumulative impacts, forest management, climate change, landscape modelling, APLM

Updating conditional probabilities of Bayesian belief networks by merging expert knowledge and system monitoring data

Forest change detection and monitoring is a key part of the Copernicus Land Monitoring Service (CLMS) (https://land.copernicus.eu/). Various methodologies already implement near real-time (NRT) forest monitoring in tropical regions (e.g. Reiche et al., 2021) with the focus on timely detection of deforestation activities. However, there is not yet an operational pan-European product tracking forest dynamics at such temporal frequency, which has moreover the capability to separate also subtle disturbances of the tree canopy from signal noise. This kind of product is under demand by the user community, hence a new CLMS prototype on &amp;#8220;Continuous Forest Monitoring&amp;#8221;, with the goal to capture natural and human-induced forest disturbances by detecting tree cover vitality loss on a monthly basis is tested and implemented within the Horizon Europe project Evolution of the Copernicus Land Service portfolio (EvoLand). In a second instance, the feasibility to detect disturbance agents, i.e., (i) windthrow/storm damage, (ii) wildfire, (iii) insect infestations, as well as (iv) human-induced disturbances (e.g., forest clearing, clear-cutting, and thinning activities) is tested.Dense time series from Sentinel-2 serve as main input for both prototypes, supported by forest masks from the CLMS High Resolution Vegetated Land Cover Characteristics (HRL VLCC) and ancillary data on forest disturbance locations and agents. From a benchmarking of various tools, the Exponentially Weighted Moving Average (EWMA) &amp;#8211; proposed by Brooks et al. (2014) for Landsat time series data and implemented as part of the JRC-NRT tool (https://github.com/ec-jrc/nrt) &amp;#8211; yielded the most promising results, especially considering the balance between accuracy, NRT capability, and computational effort. It is an unsupervised data-driven approach using univariate input indices to detect location and timing of disturbances. A supervised classification to derive the disturbance agents is added on top.This study describes the implementation and results of this prototype and compares the detected forest disturbance locations and dates to the radar-based Tree Cover Disturbance Monitoring (TCDM) product and the 3-yearly VLCC forest change product. Two large EvoLand European sites were chosen for a first phase implementation: one in Germany (analysis years 2019-2021) and another in Spain (analysis years 2020-2022). The evaluation is carried by disturbance agent, concluding to different effects on either the physical structure of the trees and/or the spectral signal of the canopy, and hence also on the suitability of a method of detection. Products are delivered at pixel level (10m spatial resolution), improving the 20m resolution of the currently available CLMS forest change products, while increasing the change frequency from 3-yearly or yearly to monthly.The resulting information can be utilized to enhance forest management and planning, aid forest-related decision-making or contribute to reporting on forest-related EU policies. These two prototypes are proposed within EvoLand to enhance the CLMS forest portfolio and to meet or go beyond users' requirements and demands.&amp;#160;

Uncertainty, complexity, and dynamic changes present challenges for conservation and natural resource management. Evidence-based approaches grounded in reliable information and rigorous analysis can enhance the navigation of the uncertainties and trade-offs inherent in conservation problems. This study highlights the importance of collaborative efforts and evidence-based decision-making, specifically implementing the Resilience-Experimentalist school of adaptive management (RE-ARM), which emphasizes stakeholder involvement, shared understanding, and experimentation. Our goal was to develop an adaptive management framework to reduce the uncertainty around the use of prescribed fire to manage the habitat for eastern black rails (Laterallus jamaicensis jamaicensis) and mottled ducks (Anas fulvigula) in saltmarshes of the Gulf of Mexico. Supported by discussions at a series of workshops, we used a value of information analysis to select a fire management hypothesis to test, developed an influence diagram to represent the system under fire management, used the influence diagram to develop a Bayesian decision network (BDN), and conducted a power analysis to guide management experiments and monitoring. Value of information analysis identified fire return interval as the critical uncertainty. Our BDN provided valuable insight into how managers believe prescribed fire influences vegetation characteristics and how vegetation influences both eastern black rail occupancy and mottled duck abundance. The results of the power analysis indicated that a standard occupancy modeling framework was more useful to compare 2- and 5-year fire return intervals for black rails than two alternative designs (removal and conditional). Our BDN can be used to predict the probability of achieving the desirable vegetative response to increase the occupancy probability of black rails and abundance of mottled ducks, and monitoring data can be used to update the BDN (learn) and improve best management practices for prescribed burns (adapt). Linking the value of information, BDNs, and power analysis enhances our understanding of the system, improves management decision-making, and builds trust among scientists, interested parties, and decision-makers. This approach lays the groundwork for knowledge co-production and adaptive management.

Comparative study of fuzzy-AHP and BBN for spatially-explicit prediction of bark beetle predisposition

Boreal forests play an important role in climate change mitigation, biodiversity conservation and the provision of vital ecosystem services. Changing climate is likely to increase the frequency and the severity of forest disturbances. Hence, increasing disturbances may offset the past and ongoing efforts to increase forest-based mitigation and halt biodiversity loss. Therefore, understanding the dynamics of forest ecosystems and predicting their responses to management, changing climate and disturbance regimes is vital. &amp;#160;While forest disturbance risk prevention measures i.e., adaptive management, offer solutions to safeguard future timber yields, the effects of adaptive management on biodiversity, climate change mitigation potential of forests and other ecosystem services have received little attention. In addition, it remains unknown whether climate change alters disturbance regimes in a way that cancels out efforts to increase and preserve carbon stocks and protect forest biodiversity. In this study we contrast the effects of mitigation versus adaptation forest management on the resilience of boreal forest ecosystems in a changing climate. We address the following questions: i) How timber harvests, forest carbon stocks and disturbed volumes develop in different forest management and land-use options that emphasize either adaptation or mitigation under different climate scenarios? ii) What are the synergies and trade-offs in ecosystem service and biodiversity indicators in adaptation and mitigation options? To address these questions, we used the process-based forest landscape and disturbance model iLand to dynamically simulate interactions of forest management, climate change and disturbances. We simulated combinations of seven forest management scenarios and three climate scenarios with ten replicate runs for 80 years in Finland. The forest management scenarios included a business-as-usual scenario and mitigation and adaptation scenarios with changes in rotation lengths and in the shares of deciduous trees in regeneration. Mitigation managements resulted in on average 6 to 15% higher carbon stocks over the simulation period compared to business-as-usual even when disturbances were accounted for but even halved the annual harvests. Mitigation management generally increased the amount of deadwood (3-21%) and large diameter trees (10-52%) compared to business-as-usual management but the severity of climate change reduced the positive trend on large diameter trees. Adaptive management reduced especially the bark beetle disturbances but, in some cases, the disturbed volumes were even higher than business-as-usual management because of increased wind damages. Generally, over the simulation period, adaptive management had a small positive impact on deadwood and mixed effects on large diameter trees. &amp;#160;Scenic beauty was impacted very little by climate change or management. Our findings highlight the complex interactions between disturbance risk prevention, biodiversity, carbon storage and sequestration and other ecosystem services. The results help to guide forest managers and policymakers in planning conservation and mitigation efforts, maximizing multiple benefits and enhancing forest resilience under a changing climate.

Reducing uncertainty and risk through forest management planning in British Columbia

Temperate forests are crucial in global carbon dynamics, acting as significant carbon pools through biomass accumulation and soil organic matter storage. This study investigates the dynamics of the forest floor, soil organic matter, and carbon stock estimation on species type, age and forest management. The research focuses on Romania's temperate forests, on broadleaf, coniferous and mixed stands in the Southern Carpathians. The measurements were conducted across a systematic grid network of 450 plots, each combining two circular plots of 500 m&amp;#178; in size. These plots span an altitudinal gradient from 500 to 1650 meters above sea level (ASL), providing a robust dataset for analyzing carbon dynamics. Our research evaluates the contribution of forest floor components - litter, coarse woody debris and fine roots - to total carbon stocks, alongside soil organic matter dynamics at 30 cm depth. The study also integrates tree biomass data for major species such as beech (Fagus sylvatica L.), silver fir (Abies alba Mill. ), and Norway spruce (Picea abies L.), providing a comprehensive assessment of carbon sequestration potential.Our preliminary results reveal significant differences in both forest floor weight and bulk density between management types. Plots in conservation areas had a significantly higher mean forest floor weight (21.1 tonnes/ha) than production plots (17.9 tonnes/ha, p &lt; 0.01), while their mean bulk density was significantly lower (0.84 Mg/m&amp;#179; vs. 0.96 Mg/m&amp;#179;, p &lt; 0.01). These findings, combined with higher forest floor weight in conservation plots, underline the critical role of management strategies on soil carbon storage. The results also highlight the influence of forest type, stand age, and management practices on carbon storage, emphasizing their importance in climate change mitigation and sustainable forest management. This study provides valuable insights for optimizing forest management strategies to enhance carbon sequestration and improve understanding of carbon fluxes in temperate ecosystems. The findings are particularly relevant for aligning forest policies with national and international climate goals.

Abstract. Estonian forest management is based traditionally on long rotations. Recent trends in wood industry and wood consumption promote also production of small-size timber for bioenergy and timber products. This opens a discussion about shorter rotations and intensive forest management again in Estonia. In current study, profitability of Scots pine, Norway spruce and silver birch stand management on fertile soils was analyzed according to three different rotation lengths: traditional long rotation, short rotation and economically optimal rotation according Finnish TAPIO rules. Stand simulation software MOTTI was used to simulate and optimize forest growth and management. Simulation results were compared with actual data on forest growth and yield in Estonia. Economically optimal rotations were for Scots pine stand 58 years, Norway spruce stand 53 years and silver birch stand 41 years. Short and optimal rotation length enable 5.9-6.5% profitability for forest owner, traditional long rotation length enables 4.8-5.4% profitability. Rotation forestry is challenging task. Based on our analysis, it is likely that applied practices can intensify wood production and reduce forest management risks. However, theoretical approach is not enough for assessing practical feasibility of short rotations. Thus further studies and experiments are needed for careful analysis and recommendations

A method for modelling operational risk with fuzzy cognitive maps and Bayesian belief networks

Wildfire activity in the western U.S. has highlighted the importance of effective management to address this growing threat. The Lookout Mountain Thinning and Fuels Reduction Study (LMS) is an operational-scale, long-term study of the effects of forest restoration and fuel reduction treatments in ponderosa pine (Pinus ponderosa Dougl. ex Laws.) and mixed-conifer forests in central Oregon, U.S. The broad objectives of the LMS are to examine the effectiveness and longevity of treatments on wildfire risk and to assess the collateral effects. Treatments include four levels of overstory thinning followed by mastication of the understory vegetation and prescribed burning. Stands were thinned to residual densities of 50, 75, or 100% of the upper management zone (UMZ), which accounts for site differences as reflected by stand density relationships for specific plant communities. A fourth treatment combines the 75 UMZ with small gaps (~0.1 ha) to facilitate regeneration (75 UMZ + Gaps). A fifth treatment comprises an untreated control (UC). We examined the causes and levels of tree mortality that occurred 2–9 years after treatments. A total of 391,292 trees was inventoried, of which 2.3% (9084) died. Higher levels of tree mortality (all causes) occurred on the UC (7.1 ± 1.9%, mean ± SEM) than on the 50 UMZ (0.7 ± 0.1%). Mortality was attributed to several bark beetle species (Coleoptera: Curculionidae) (4002 trees), unknown factors (2682 trees), wind (1958 trees), suppression (327 trees), snow breakage (61 trees), prescribed fire (19 trees), western gall rust (15 trees), cankers (8 trees), mechanical damage (5 trees), dwarf mistletoe (4 trees), and woodborers (3 trees). Among bark beetles, tree mortality was attributed to western pine beetle (Dendroctonus brevicomis LeConte) (1631 trees), fir engraver (Scolytus ventralis LeConte) (1580 trees), mountain pine beetle (Dendroctonus ponderosae Hopkins) (526 trees), engraver beetles (Ips spp.) (169 trees), hemlock engraver (Scolytus tsugae (Swaine)) (77 trees), and Pityogenes spp. (19 trees). Higher levels of bark beetle-caused tree mortality occurred on the UC (2.9 ± 0.7%) than on the 50 UMZ (0.3 ± 0.1%) which, in general, was the relationship observed for individual bark beetle species. Higher levels of tree mortality were attributed to wind on the 100 UMZ (1.0 ± 0.2%) and UC (1.2 ± 1.5%) than on the 50 UMZ (0.2 ± 0.02%) and 75 UMZ (0.4 ± 0.1%). Higher levels of tree mortality were attributed to suppression on the UC (0.5 ± 0.3%) than on the 50 UMZ (0.003 ± 0.002%) and 75 UMZ + Gaps (0.0 ± 0.0%). Significant positive correlations were observed between measures of stand density and levels of tree mortality for most causal agents. Tree size (diameter at 1.37 m) frequently had a significant effect on tree mortality, but relationships varied by causal agent. The forest restoration and fuels reduction treatments implemented on the LMS increased resistance to multiple disturbances. The implications of these and other results to the management of fire-adapted forests are discussed.

Purpose of ReviewTheoretical frameworks for adaptive natural resource management are quite common, whereas documented examples showing successful implementation of adaptive management and learning through multiple time intervals have remained uncommon. Measures of quality of adaptive natural resource management processes are needed to examine potential factors driving the successful implementation. To address this gap, we developed a multimetric index composed of 22 metrics to assess quality of case studies using quantitative decision support (QDS) to inform adaptive forest management (AFM). Metrics represented three main tasks, including conceptual setup, modeling, and application. We further distinguished these into subtasks: definition of objectives and management options (setup); specifying uncertainty, prediction, and optimization (modeling); and stakeholder involvement along with practice and learning (application). We used a multimetric index to examine temporal and geographic variation in quality of reviewed case studies using QDS to inform AFM. We then conducted a structured literature review of 179 articles, wherein 34 case studies met a priori criteria.Recent FindingsWhen applying the multimetric index to these case studies, we found that over the past decade the index has been intermediate and annual average scores declined by 33% from 4.5 to 3.0 of 10 (where 10 is the highest possible quality score). Aligning with reviews of adaptive natural resource management, reported on-ground application of QDS to inform AFM was rare (n = 2). We also confirmed the expectation that there has been a substantial lack of stakeholder engagement during QDS development tasks.SummaryOur multimetric index provides a novel tool to examine gaps in the use of QDS for adaptive management in diverse domains including but not limited to forests.

The ability to predict spatial variation in biodiversity is a long-standing but elusive objective of landscape ecology. It depends on a detailed understanding of relationships between landscape and patch structure and taxonomic richness, and accurate spatial modelling. Complex heterogeneous environments such as cities pose particular challenges, as well as heightened relevance, given the increasing rate of urbanisation globally. Here we use a GIS-linked Bayesian Belief Network approach to test whether landscape and patch structural characteristics (including vegetation height, green-space patch size and their connectivity) drive measured taxonomic richness of numerous invertebrate, plant, and avian groups. We find that modelled richness is typically higher in larger and better-connected green-spaces with taller vegetation, indicative of more complex vegetation structure and consistent with the principle of ‘bigger, better, and more joined up’. Assessing the relative importance of these variables indicates that vegetation height is the most influential in determining richness for a majority of taxa. There is variation, however, between taxonomic groups in the relationships between richness and landscape structural characteristics, and the sensitivity of these relationships to particular predictors. Consequently, despite some broad commonalities, there will be trade-offs between different taxonomic groups when designing urban landscapes to maximise biodiversity. This research demonstrates the feasibility of using a GIS-coupled Bayesian Belief Network approach to model biodiversity at fine spatial scales in complex landscapes where current data and appropriate modelling approaches are lacking, and our findings have important implications for ecologists, conservationists and planners.

In 2016, in response to forest loss, the Myanmar government banned logging operations for 1 year throughout the entire country and for 10 years in specific regions. However, it is unclear whether this measure will effectively reduce forest loss, because disturbance agents other than logging may have substantial effects on forest loss. In this study, we investigated an approach to attribute disturbance agents to forest loss, and we characterized the attribution of disturbance agents, as well as the areas affected by these agents, in tropical seasonal forests in the Bago Mountains, Myanmar. A trajectory-based analysis using a Landsat time series was performed to detect change pixels. After the aggregation process that grouped adjacent change pixels in the same year as patches, a change attribution was implemented using the spectral, geometric, and topographic information of each patch via random forest modeling. The attributed agents of change include “logging”, “plantation”, “shifting cultivation”, “urban expansion”, “water invasion”, “recovery”, “other change”, and “no change”. The overall accuracy of the attribution model at the patch and area levels was 84.7% and 96.0%, respectively. The estimated disturbance area from the attribution model accounted for 10.0% of the study area. The largest disturbance agent was found to be logging (59.8%), followed by water invasion (14.6%). This approach quantifies disturbance agents at both spatial and temporal scales in tropical seasonal forests, where limited information is available for forest management, thereby providing crucial information for assessing forest conditions in such environments.