# Multi-criteria analysis to compare multiple risks associated with management alternatives in planted forests

Margot Régolini, Céline Meredieu, Hervé Jactel, Ander Arias-González, Manuela Branco, Alejandro Cantero, Amélie Castro, Jean-Yves Fraysse, Barry Gardiner, Andrea Hevia, et al.

► To cite this version:

Margot Régolini, Céline Meredieu, Hervé Jactel, Ander Arias-González, Manuela Branco, et al. Multi-criteria analysis to compare multiple risks associated with management alternatives in planted forests. FOREST SYSTEMS, 2020, 29 (2), pp.1-17. 10.5424/fs/2020292-15660. hal-03179331

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RESEARCH ARTICLE

OPEN ACCESS

# Multi-criteria analysis to compare multiple risks associated with management alternatives in planted forests

Margot Régolini (Régolini, M.)¹,², Céline Meredieu (Meredieu, C.)³,*, Hervé Jactel (Jactel, H.)¹, Ander Arias-González (Arias-González, A.)⁴, Manuela Branco (Branco, M.)⁵, Alejandro Cantero (Cantero, A.)⁶, Amélie Castro (Castro, A.)⁷, Jean-Yves Fraysse (Fraysse, J.-Y.)⁸, Barry Gardiner (Gardiner, B.)²,⁹, Andrea Hevia (Hevia, A.)¹⁰, Francisco-José Lario (Lario, F.-J.)¹¹, Catherine Steffy-Pater (Steffy-Pater, C.)², Roque Rodríguez-Soalleiro (Rodríguez-Soalleiro, R.)¹², Paula Soares (Soares, P.)⁵, Christophe Orazio (Orazio, C.)²,⁹

¹INRAE, Univ. Bordeaux, BIOGECO, F-33610 Cestas, France ²European Forest Institute, F-33610 Cestas, France ³INRAE, UEPF, F-33610 Cestas, France ⁴NEIKER-Tecnalia, Department of Forest Sciences, 48160 Derio, Spain ⁵Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal ⁶HAZI Foundation. Granja Modelo de Arkaute. 01192 Arkaute-Vitoria, Spain ⁷CRPF Nouvelle-Aquitaine, F-33000 Bordeaux, France ⁸FCBA, F-33610 Cestas, France ⁹IEFC, F-33610 Cestas, France ¹⁰Dept. de Ciencias Agroforestales, Escuela Técnica Superior de Ingeniería Universidad de Huelva, Palos de la Frontera 21819, Spain ¹¹TRAGSA, Ctra. Maceda-Baldrei km 2, 32708 Maceda, Ourense, Spain ¹²Departamento de Producción Vegetal Escuela Politécnica Superior. Campus Universitario, 27002-Lugo, Spain

## Abstract

**Aim of study:** Adaptation of silviculture in planted forest may help to mitigate damage due to biotic and abiotic hazards. However, compromises have to be found because it is not possible to minimize the risk from all hazards through application of a single forest management approach. The objective of this study was to improve a multi-criteria risk analysis (MCRA) method that makes it possible to rank forest management alternatives (FMAs) according to multiple risks.

**Material and methods:** We defined eight FMAs for maritime pine forests in France, Spain and Portugal. We used as the definition of risk the combination of hazard, susceptibility and exposure. Hazard level was estimated using archive data on occurrence and severity of damaging agents over the last few decades. Forest susceptibility to hazards was evaluated by experts who scored the effect on stand resistance of eleven silvicultural operations characterizing each FMA. Exposure was estimated as value at stake, which combined forest standing volume, simulated with forest growth models, and wood prices.

**Main results:** Using the PROMETHEE algorithm, we found that the overall ranking of FMAs was consistent across all countries, with short rotation plantations to produce pulpwood or energy wood were the least at risk. The ranking was mainly driven by forest values at stake. We found that by improving the accuracy of forest values exposed to damage, based on growth models and representative wood prices, the MCRA outcomes were more useful and realistic.

**Research highlights:** Our methodology provides a relevant framework to design FMAs that would minimize risks while maintaining income.

**Keywords:** Pinus pinaster; vulnerability; hazards; growth modelling; expert assessment; wood price; southwestern Europe.

**Authors' contributions:** CM, MR and HJ conceived the idea and structure of the article and wrote the final version of the manuscript; CO coordinated the research project; MR and HJ designed the study; MR analyzed data with advices from HJ and CM; all other co-authors compiled and prepared information, and revised the manuscript drafts.

**Citation:** Régolini, M., Meredieu, C., Jactel, H., Arias-González, A., Branco, M., Cantero, A., Castro, A., Fraysse, J.-Y., Gardiner, B., Hevia, A., Lario, F.-J., Steffy-Pater, C., Rodríguez-Soalleiro, R., Soares, P., Orazio, C.. (2020). Multi-criteria analysis to compare multiple risks associated with management alternatives in planted forests. Forest Systems, Volume 29, Issue 2, e004. https://doi.org/10.5424/fs/2020292-15660

**Supplementary material:** Appendices A1 to A6 accompany the paper on FS website.

**Received:** 28 AUG 2019 **Accepted:** 08 Jul 2020

**Copyright © 2020 INIA.** This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC-by 4.0) License.

<table><thead><tr><td>Funding agencies/institutions</td><td>Project / Grant</td></tr></thead><tbody><tr><td></td><td>INTERREG IV B SUDOE 2007-2013</td></tr><tr><td>The French Ministry of Agriculture, Agri-Food and Forest</td><td></td></tr><tr><td>The Regional Council of Aquitaine</td><td></td></tr><tr><td>The Department of Economic Development and Competitiveness of the Basque Government</td><td></td></tr><tr><td>Forest Research Centre (CEF) funded by Fundação para a Ciência e a Tecnologia IP (FCT)</td><td>UID/AGR/00239/2019</td></tr><tr><td>A. Hevia is supported by PinCaR project</td><td>UHU-1266324, FEDER Funds, Andalusia Regional Government, Consejería de Economía, Conocimiento, Empresas y Universidad 2014-2020</td></tr></tbody></table>

**Competing interests:** The authors have declared that no competing interests exist.

**Correspondence** should be addressed to Céline Meredieu: Celine.Meredieu@inrae.fr
Introduction

Planted forests represent 7% of the global forest area and their surface is increasing worldwide (Payn et al., 2015). They are likely to play a significant role in the near future to meet the increasing demand for wood products and therefore decrease the logging pressure on natural forests (ICPF, 2013; Pirard et al., 2016). Meanwhile, planted forests are also expected to provide many other ecosystem services such as regulating services (e.g. carbon sequestration, soil conservation and watershed resources) but also biodiversity conservation at the landscape scale (Brockerhoff et al., 2013; Thompson et al., 2014). However, forests are increasingly affected by biotic and abiotic hazards (Lindner et al., 2010; Seidl et al., 2011a; Albert et al., 2015). In many parts of the world, pest and diseases are expanding their range and increasingly causing damage thus compromising the delivery of forest ecosystem services (Schelhaas et al., 2003; Boyd et al., 2013; Seidl et al., 2014). In recent decades, forest disturbance regimes have intensified markedly in Europe, resulting in much higher levels of damage from hazards such as windstorms, insect outbreaks and wildfires (Lindner et al., 2010; van Lierop et al., 2015). Moreover, climatic changes were identified as key drivers of this increase (Seidl et al., 2011c; Boyd et al., 2013) mainly through change in hazard occurrence, duration, severity and intensity (e.g. extreme climatic events such as drought, Allen et al., 2010) and forest susceptibility (e.g. water stress reducing tree resistance to pest, Jactel et al., 2012b). However, changes in forest management can also increase the level of damage (Jactel et al., 2009). For example, increasing both the age of the final cut and the standing volume increase storm vulnerability (Gardiner & Birot, 2013). Besides, reduced tree diversity can result in higher susceptibility to biotic and abiotic disturbances (Jactel et al., 2017). Because 99.9% of planted forests are monoculture plantations (Nichols et al., 2006), producing ca. 50% of industrial round wood (Payn et al., 2015), it is essential that new methods for evaluating the risk to planted forests are developed and implemented.

Risk depends on the combination of three components (Kron, 2005; Jactel et al., 2009): 1) the occurrence and severity of the hazard (e.g. wind, fire, pathogen, pest) which is the cause of damage, 2) the susceptibility of the system (e.g. trees and forest stand) to the hazard which determines the amount of damage when it occurs and, 3) the value exposed to damage, i.e. the socio-economic impact that depends on the value at stake. There is a multitude of biotic (e.g. insect pests, fungal diseases, pathogenic nematodes, mammal grazers) and abiotic (e.g. drought, frost, storm, fire) hazards in forests. In addition, they do not operate independently but often interact or generate cascade effects, leading to synergistic adverse effects on tree growth or survival. For example, drought often increases

tree sensitivity to infection by pest and diseases (Jactel et al., 2012b) and windstorm triggers bark beetle outbreaks (Temperli et al., 2013). There is thus a need to consider forest risks in a holistic perspective, i.e. to operate a páradigmatic shift from individual to multiple risk analyses.

While several reviews demonstrated the possibility to reduce stand susceptibility through changes in silvicultural treatments (e.g. Fettig et al., 2007; Jactel et al., 2009; Klapwijk et al., 2016; Jactel et al., 2017), they also pointed out that a given particular operation (i.e. a cutting or any tending operation such as pruning or bush cleaning...) can have multiple, and sometimes, contradictory effects on stand susceptibility to different damaging agents. For example, thinning can improve individual tree vigor, through reduced competition, thus increasing individual tree resistance to bark beetles (Fettig et al., 2007), whereas thinned stands can be more heavily attacked by tree defoliators (e.g. pine processionary moth, Régolini et al., 2014). It follows that no single forest management alternative (FMA) can simultaneously minimize all risks of damage and that compromises have to be made.

Several empirical or process-based models have been developed to link forest stand management with susceptibility to specific damaging agents and infer on associated risks but only a few can deal with two combined hazards, e.g. wind and bark beetles (Temperli et al., 2013), drought and bark beetles (Lexer et al., 2002) or fire and bark beetles (Simard et al., 2011). However, none, to our knowledge, are generic enough to incorporate multiple hazards in many different forest conditions. There is thus a need for a flexible and robust methodological framework enabling to rank different forest management alternatives according to their relative exposure to multiple hazards.

Multi-criteria decision analysis (MCDA) methods provide a suitable framework and effective techniques for finding compromise solutions (Ishizaka & Nemery, 2013). They are flexible with input data (able to combine qualitative and quantitative information), propose mathematical algorithms to solve complex questions of data aggregation while being user friendly, thus enabling decisions makers to participate in the process (Ishizaka & Nemery, 2013). One of the most widely used MCDA method is PROMETHEE (Preference Ranking Organisation Method for Enrichment Evaluation). It is an outranking method, typical of the European MCDA school (Brans et al., 1986). It has been successfully used to address decision making problems for risk management (e.g. Linkov et al., 2006; Ruzante et al., 2010; Su & Tung, 2013; Liu et al., 2016; Doyi et al., 2018) and also forest planning (e.g. Huth et al., 2005; Ananda & Herath, 2009; Ghaffariyan et al., 2013; De Barros et al., 2014; Segura et al., 2014; Schuler et al., 2017) or both (Seidl et al., 2011b). In 2012, the method was adapted to forest risk assessment and the term MCRA was coined for "Multi-criteria Risk Analysis", which enables ranking forest management
alternatives according to their vulnerability to multiple hazards (Jactel et al., 2012a).

We focused on forest risks for maritime pine (Pinus pinaster Ait), a native and major timber tree species of southern Europe, to exemplify our approach (Abad Viñas et al., 2016). The study area encompassed three regions: Aquitaine (France), North Portugal and Galicia (Spain), where maritime pine is the main tree species in productive forests and accounts for ca. 47, 45 and 44% of the forested area, respectively (Sanz et al., 2006; AFN, 2010; IFN, 2010). Maritime pine is also one of the main harvested wood material in the three studied regions (MAGRAMA, 2011; Dias & Arroja, 2012; DRAAF Aquitaine, 2015). Maritime pine forests thus play a major role in the regional economies. Recently, large disturbances occurred in the three regions: in 2009, in Aquitaine, the storm Klaus blew down approximately 43 million m³ of wood, which represented an economic damage of approx. 2 billion Euros (Hanewinkel & Peyron, 2013); wildfires that occurred in Portugal during 2003 (312,430 ha damaged area) and 2005 (274,783 ha) were responsible for economic losses of over 3 billion Euros (ICNF, 2013); in 1999 the pine wood nematode was discovered in Portugal and five million trees had been felled by 2008 (Rodrigues, 2008). In 2006, forest fires affected an area in Galicia of 86,000 ha (around 60% covered by trees), being responsible for economic losses of 248-336 million Euros (Loureiro & Barrio, 2009). In 2017, major fires burnt another 46,000 ha in Portugal (with 65 fatalities) (Nunes et al., 2019). These large-scale events raised a lot of concern among foresters about the sustainability of wood production in cultivated maritime pine forests. Such a context therefore offered a good opportunity to engage with forest stakeholders in comparing forest management alternatives (FMAs) regarding multiple forest risks.

The main objective of this paper is to move a step forward and present an enhanced version of MCRA that addresses previous limitations. In particular, the study aimed at improving two key elements in the multi-criteria risk analysis framework, i.e. at proposing and testing: i) more accurate estimates of hazards levels, combining occurrence and severity and based on archive data instead of expert opinion, ii) more meaningful values at stake, based on economic valuation of forests using forest growth models and real wood prices, again instead of a simple score provided by experts.

## Methods

We first present the Forest Management Alternatives (FMAs) designed by forest experts according to local silvicultural practices in the three selected regions. Then we assess the three components of the risk used in this paper as “hazard level”, “forest susceptibility” and

“wood value at stake” (Fig. 1). They were combined to rank the forest management alternatives (FMAs) according to associated multiple forest risks in a given region, using the outranking method PROMETHEE (Mareschal, 2013). PROMETHEE was developed by Brans (1982), further extended by Brans & Vincke (1985) and Brans & Mareschal (1994).

## Forest management alternatives (FMAs)

Eleven silvicultural treatments were defined: site preparation, fertilisation, regeneration type, tree genetic material, stand structure, stand composition, cleaning of understory vegetation, stand clearing, thinning, pruning and final harvesting (Table 1). For each treatment, several options were considered. The combination of these options enabled the construction of eight contrasting FMAs exploring a wide range of possibilities (e.g. with large variation in rotation length, site management techniques and tree genetic materials) and with diverse objectives in terms of quantity of timber and forest biomass production. The FMAs are explained in the next paragraph and described in Table 1 under the following simplified names: M1- High quality, M2- Standard classic, M3- Low investment, M4- Short-term with subsidies, M5- Low density without thinning, M6- Half-dedicated to biomass, M7- Biomass, M8- No management.

Some FMAs are currently applied in the case studies regions: for example, the “Standard classic” M2 is very common in Aquitaine and in Galicia and aims at producing timber in the medium-term. “High quality” M1 is representative of the objective of producing high quality timber on the long-term. The “Low investment” M3 is designed to consider stands in which forest owners invest as little money as possible before getting a return on investment with the first thinning. The “Short-term with subsidies” M4 is designed considering subsidies for planting, leading to active silviculture until the first thinning and then applying no other treatment until the final cut at 25 years, which is close to the official Galician Regional Government models PP2 (DOG Diario Oficial de Galicia, 2014). The “Low density without thinning” M5 is aimed at limiting thinnings and associated costs by planting at final density and harvest earlier. Others FMAs are more innovative, like the “Biomass” M7 aiming at producing energy wood and pulp-wood rapidly and the “Half-dedicated to biomass” M6 in which half of the stand is early thinned for energy wood and the other half is kept longer for timber production. They are considered possible options to restore quickly wood resources for the local forest-based sector in Aquitaine (Lesgourgues & Drouineau, 2009) and Galicia following the Klaus storm in 2009. Finally, “No management” M8 is a FMA with natural regeneration and no further management. It has no wood
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Conceptual flow diagram of multi-criteria risk analysis (MCRA) methodology for a given regional case study.</figcaption></figure>

production objective. However, harvesting of high-grade timber may occur in small private forest properties.

## Hazard level assessment

We let the list of hazards vary with regions for the sake of realism but we set as constraint to comprise both abiotic and biotic hazards. Therefore, at least two abiotic and two biotic hazards were chosen in each region according to their local relevance based on local occurrence data for the last 10 to 40 years (Table 2 and see Appendix A1 [suppl.]). Hazards included storm, fire, torrential rain, bark beetles (*Ips sexdentatus* Börner, *Orthotomicus erosus* Wollaston, *Tomicus piniperda* Linnaeus and *Tomicus desruens* Wollaston), game (*Capreolus capreolus* Linnaeus and *Cervus elaphus* Linnaeus), insect defoliator (*Thaumetopoea pityocampa* Denis and Schiffermüller), root rot fungus (*Heterobasidion annosum* Brefeld) and pitch canker (*Fusarium circinatum* Nirenberg & O'Donnell). The invasive pine wood nematode (PWN, *Bursaphelenchus xylophilus* Steiner and Buhrern) and drought, even though locally relevant in the studied area (southwestern

Europe) were not included in the analysis. PWN, a quarantine organism within the European Union, was not retained because it requires specific phytosanitary measures (mainly sanitary cuts) that were not included in the FMAs analysed here. Drought symptoms have been shown lagging one or several years behind the climatic event (Bréda & Badeau, 2008) and cannot be easily disentangled from damage caused by biotic hazards, making drought severity difficult to estimate properly.

After, hazard selection, hazard level was defined as a combination of mean occurrence and severity; it was defined for each region based on archive data (Fig. 1). We estimated hazard occurrence as the mean percent of forest area or volume damaged each year in the region ($P_{1H}$). It was averaged across the longest possible period with reliable data, which was often of several decades. However, hazard occurrence is not enough to account for the relevance of damage. For example in Aquitaine, 16% of the surface of maritime pine forest have been impacted per year by the pine processionary moth since 1981 (Data from the French Forest Health Department) whereas only 1.5% of the volume has been impacted per year by storms since 1976 (communication from CNPF,
<table><caption>**Table 1.** Maritime pine forest management alternatives (FMAs) numbered from M1 to M8 with corresponding silvicultural treatments and options per treatment in all regions.</caption><thead><tr><th>Name of FMA</th><th>M1- High quality</th><th>M2- Standard classic</th><th>M3- Low investment</th><th>M4- Short-term with subsidies</th><th>M5- Low density without thinning</th><th>M6- Half-dedicated to biomass</th><th>M7- Biomass</th><th>M8- No management</th></tr></thead><tbody><tr><td>Objective</td><td>High quality<br>timber > 1.5 m³</td><td>Timber 1 to<br>1.2 m³</td><td>Timber 1 m³</td><td>Small timber 0.3<br>to 0.4 m³</td><td>Small timber 0.3<br>to 0.4 m³</td><td>Biomass 30t/ha<br>and timber 1 m³</td><td>Biomass 70 t/ha</td><td>No objective</td></tr><tr><td colspan="9" style="text-align: center; font-weight: bold;">Silvicultural treatments</td></tr><tr><td>1-Site<br>preparation</td><td>Full ploughing<br>or double<br>ripping,<br>broadleaf trees<br>preservation</td><td>Strip ploughing<br>or single ripping</td><td>Smashing<br>roller or rotary<br>chipping</td><td>Full ploughing<br>or double<br>ripping</td><td>Full ploughing<br>or double<br>ripping</td><td>Stump removal,<br>full ploughing<br>or double<br>ripping</td><td>Stump removal,<br>full ploughing<br>or double<br>ripping</td><td>No</td></tr><tr><td>2-Fertilisation</td><td>Yes</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>3-Regeneration<br>type</td><td>Plantation<br>1250 stems/ha</td><td>Plantation<br>1250 stems/ha</td><td>Natural regene-<br>ration</td><td>Plantation<br>1250 stems/ha</td><td>Plantation<br>800 stems/ha</td><td>Plantation<br>2500 stems/ha</td><td>Sowing to<br>obtain around<br>3000 stems/ha</td><td>Natural<br>regeneration</td></tr><tr><td>4-Genetic<br>material</td><td>Genetically<br>improved plants</td><td>Genetically<br>improved plants</td><td>No specific<br>material</td><td>Genetically<br>improved plants</td><td>Genetically<br>improved plants</td><td>Genetically<br>improved plants</td><td>Seeds from<br>graded stands</td><td>No specific<br>material</td></tr><tr><td>5-Stand<br>structure</td><td>Even-aged</td><td>Even-aged</td><td>Even-aged</td><td>Even-aged</td><td>Even-aged</td><td>Even-aged</td><td>Even-aged</td><td>Uneven-aged</td></tr><tr><td>6-Stand<br>composition</td><td>Maritime pine,<br>broadleaf<br>preservation, di-<br>versified wooded<br>undergrowth</td><td>Single species<br>(maritime pine)</td><td>Single species<br>(maritime pine)</td><td>Single species<br>(maritime pine)</td><td>Single species<br>(maritime pine)</td><td>Single species<br>(maritime pine)</td><td>Single species<br>(maritime pine)</td><td>Multiple species<br>(Mixed maritime<br>pine forests)</td></tr><tr><td>7-Cleaning of<br>the vegetation</td><td>Full cleaning at<br>5 years and then<br>1 cleaning with<br>each thinning</td><td>Full cleaning at<br>5 years old and<br>then 1 cleaning<br>with each<br>thinning</td><td>No</td><td>Full cleaning at<br>5 years old</td><td>2 cleanings</td><td>First cleaning<br>at 9 years old<br>(with biomass<br>thinning) then<br>1 cleaning with<br>each thinning</td><td>No</td><td>No</td></tr><tr><td>8-Stand<br>clearing</td><td>No</td><td>No</td><td>Rack creation<br>and then<br>clearing</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>9-Thinnings</td><td>5 thinnings</td><td>3 thinnings</td><td>4 thinnings</td><td>1 thinning</td><td>No</td><td>1 biomass<br>thinning (1250<br>stems/ha), then<br>3 thinnings</td><td>No</td><td>Yes, partial<br>harvests</td></tr><tr><td>10-Pruning</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>11-Final<br>harvesting</td><td>60 years,<br>250 stems/ha</td><td>40 years,<br>300 stems/ha</td><td>55 years;<br>300 stems/ha</td><td>25 years,<br>700 stems/ha</td><td>25 years,<br>700 stems/ha</td><td>35 years,<br>300 stems/ha</td><td>8-12 years</td><td>No clear cut</td></tr></tbody></table>

2014). On the other hand, damage caused by the Klaus storm (2009) had much larger economic consequences on the forestry sector than chronic defoliation by pine processionary moth. To account for this issue, we estimated hazard severity as the percent of losses induces per area or volume affected (P2<sub>Hi</sub>) (see more details in Table 2). We used various estimates depending on hazards: losses due to occasional damage such as those produced by storms, fires or bark beetles are usually well

assessed and a direct loss on wood price is generally observed, e.g. 86.5% for storms and fire on average (Table 2). On the contrary, for some other hazards the consequences are often not discernible on the short-term, but become visible after a certain amount of time or a certain threshold of damage. They operate in a similar way as the creeping environmental problems defined by Glantz (1994) as “incremental, low-grade, cumulative and long-term biospheric changes induced by humans”. In this
<table><caption>**Table 2.** Hazard assessment for maritime pine in Aquitaine.</caption><thead><tr><th>Hazards (H)</th><th>Type and source of information for impacted volume or surface each year</th><th>P1<sub>H</sub> (%)</th><th>Type of damage</th><th>Type and source of information for hazard severity</th><th>P2<sub>H</sub> (%)</th><th>Relative weight (W<sub>H</sub>)</th></tr></thead><tbody><tr><td>Storm</td><td>Data compilation from CNPF/ IDF (communication from CNPF, 2014) and NFI (Colin et al. 2010)</td><td>0.0150</td><td>Mortality</td><td>Huge loss on wood prices (Lesgourgues and Chantre 2009)</td><td>0.8650</td><td><strong>0.1741</strong></td></tr><tr><td>Fire</td><td>Data compilation from burnt surfaces 1980-2006 (Ministère de l'Agriculture et al. 2008)</td><td>0.002</td><td>Mortality</td><td>Huge loss on wood prices (similar to loss from storm, source CRPF Aquitaine)</td><td>0.8650</td><td><strong>0.0232</strong></td></tr><tr><td>Defoliator</td><td>Calculation on Forest Health Data monitoring (Régolini et al. 2014)</td><td>0.1600</td><td>Growth loss</td><td>Financial analysis (Gatto et al. 2009): difference in revenues =17.3%</td><td>0.1730</td><td><strong>0.3713</strong></td></tr><tr><td>Bark beetle</td><td>Book from Forest Health Department (Nageleisen et al. 2010) and data produced after Klaus storm (Colin et al. 2010)</td><td>0.0040</td><td>Mortality</td><td>Data from last storm (similar to loss from storm, source CRPF Aquitaine)</td><td>0.9400</td><td><strong>0.0504</strong></td></tr><tr><td>Game</td><td>Report from Irstea (Ballon and Hamard 2003)</td><td>0.0100</td><td>Browsing</td><td>Report from Irstea (Ballon and Hamard 2003)</td><td>0.0200</td><td><strong>0.0027</strong></td></tr><tr><td>Root rot fungus</td><td>Expert knowledge based on the proportion of downgraded wood because of the fungus during the harvest</td><td>0.0300</td><td>Mortality</td><td>Data from expert knowledge based on the fact that the wood sored because of the fungus is sold at prices similar to wood caught in a fire or wood damaged after storms</td><td>0.9400</td><td><strong>0.3783</strong></td></tr></tbody></table>

The defoliator is the pine processionary moth *Thaumetopoea pityocampa*, the root rot fungus is *Heterobasidion annosum*. “Bark beetle” refers to *Ips sexdentatus*. P1<sub>H</sub> is hazard occurrence estimated as mean area or volume affected by a hazard each year. P2<sub>H</sub> is % of losses induced per area or volume affected. The W<sub>H</sub> is the product of P1<sub>H</sub> by P2<sub>H</sub> divided by the sum of all hazard levels in a given region giving the relative losses per year induced by a given hazard.

category, we included herbivore damage (game) (Landmann *et al.*, 2015) and insect defoliators like the pine processionary moth. It was more difficult to estimate hazard severity for these problems and we mainly relied on relative growth loss estimates (Ballon & Hamard, 2003; Gatto *et al.*, 2009). To estimate P1<sub>Hi</sub> and P2<sub>Hi</sub>, we used scientific or grey literature, including regional reports or studies in local languages (Appendix A1 [suppl.]). We multiplied the two values to provide an estimate of absolute hazard level in each region. It was further divided by the sum of all hazard levels in a given region in order to produce ‘relative level’ per hazard and region (eq. 1). These values were used to weight criteria in the MCRA (Fig. 1).

$$W_{Hi} = \frac{P_{1Hi} \times P_{2Hi}}{\sum_{i=1}^{n} P_{1Hi} \times P_{2Hi}} \quad (\text{eq. 1})$$

Where W<sub>Hi</sub> is the relative weight of hazard Hi; P1<sub>Hi</sub> is the mean percent of forest surface or volume damaged by

hazard Hi each year, P2<sub>Hi</sub> is the loss in wood biomass or price in a given region due to hazard Hi (n=6 for Aquitaine, n=6 for Portugal and n=4 for Galicia).

For example (see in Table 2), the average volume affected by storms per year was 1.5% in Aquitaine (P1<sub>storm</sub>=0.015). Then, the loss due to storms was estimated to be 86.5% of wood price (P2<sub>storm</sub>=0.865), using the example of what happened after the Klaus storm (Lesgourgues & Chantre, 2009). Therefore storm level in Aquitaine was estimated as P1<sub>storm</sub>×P2<sub>storm</sub>=0.0130. The sum of P1<sub>Hi</sub>×P2<sub>Hi</sub> for all hazards in Aquitaine was 0.0746. The relative weight of storm in Aquitaine was thus 0.013/0.0746=0.174 (17.4%). The calculation of hazard relative weights in Aquitaine is fully documented in Tables 2 and 3 and details for Portugal and Galicia are provided in Table 3 and Appendix A1 [suppl.].

Last, to account for the fact that the number of hazards varied between regions and might bias the comparison of MCRA outcomes across regions, we re-did the analyses with a selected set of four hazards by region, each of them
<table><caption>**Table 3.** Hazard assessment for maritime pine in Aquitaine.</caption><thead><tr><th rowspan="2">Hazards</th><th colspan="3">Relative weight</th></tr><tr><th>Aquitaine</th><th>Portugal</th><th>Galicia</th></tr></thead><tbody><tr><td><strong>Storm</strong></td><td>0.1741</td><td>0.0168</td><td>0.4714</td></tr><tr><td><strong>Fire</strong></td><td>0.0232</td><td>0.4804</td><td>0.4598</td></tr><tr><td><strong>Torrential rain</strong></td><td></td><td>0.0151</td><td></td></tr><tr><td><strong>Defoliator</strong></td><td>0.3713</td><td>0.4077</td><td></td></tr><tr><td><strong>Bark beetles</strong></td><td>0.0504</td><td>0.0471</td><td></td></tr><tr><td><strong>Game</strong></td><td>0.0027</td><td></td><td>0.0007</td></tr><tr><td><strong>Root rot fungus</strong></td><td>0.3783</td><td></td><td></td></tr><tr><td><strong>Pitch canker</strong></td><td></td><td>0.0330</td><td>0.0681</td></tr></tbody></table>

The defoliator is the pine processionary moth *Thaumetopoea pityocampa*, the root rot fungus is *Heterobasidion annosum*. “Bark beetle” refers to *Ips sexdentatus*. P1<sub>H</sub> is hazard occurrence estimated as mean area or volume affected by a hazard each year. P2<sub>H</sub> is % of losses induced per area or volume affected. The W<sub>H</sub> is the product of P1<sub>H</sub> by P2<sub>H</sub> divided by the sum of all hazard levels in a given region giving the relative losses per year induced by a given hazard.

being present in at least two regions (Table 4). This change had no major impact on the conclusions.

## Forest susceptibility assessment

Following the method developed by Jactel *et al.* (2012a), we used expert panels to evaluate maritime pine stand susceptibility to multiple hazards.

*Panels:* In each region, a panel of experts was gathered during a one-day workshop. It was highly recommended to have several experts per hazard but one expert could be consulted about different hazards. Experts were highly qualified researchers in forest ecology or forest managers with expertise in targeted hazards and maritime pine silviculture, belonging to the leading universities, research institutes, advisory companies or risk management authorities of each region. The number of experts per panel was between 10 and 15 people, because bigger groups are difficult to moderate and the number of available experts

in each region was anyway limited. The number of experts participating in the panels and the expertise covered are detailed in Appendix A2 [suppl.].

*Scoring method:* Regional experts had to reach a consensus before giving a score from 0 to 1 for the effect of each silvicultural treatment (Table 1) on stand susceptibility to individual hazards. For each silvicultural treatment, they identified the "reference" option triggering the lowest amount of damage for a given hazard and gave it a score of zero. Then the experts gave a score of 0.25, 0.50, 0.75, or 1.0 respectively, to any other option that would increase a little amount, a moderate amount, a large amount or an extreme amount the damage due to a given hazard as compared to that of the reference option. We note that this was a semi-quantitative approach by which experts produced scores according to a relative scale from non-susceptible (0) to most susceptible (1) to a given hazard. Still, in multi-criteria decision analysis it is not the absolute numbers (net flows) that matters to qualify a given option (here a FMA) but its ranking, i.e. relative position with

<table><caption>**Table 4.** Relative weights of four hazards common to at least two regions.</caption><thead><tr><th rowspan="2">Hazards</th><th colspan="3">Relative weight</th></tr><tr><th>Aquitaine</th><th>Portugal</th><th>Galicia</th></tr></thead><tbody><tr><td><strong>Storm</strong></td><td>0.6952</td><td>0.0290</td><td>0.4714</td></tr><tr><td><strong>Fire</strong></td><td>0.0927</td><td>0.8323</td><td>0.4598</td></tr><tr><td><strong>Bark beetles</strong></td><td>0.2014</td><td>0.0815</td><td></td></tr><tr><td><strong>Game</strong></td><td>0.0107</td><td></td><td>0.0007</td></tr><tr><td><strong>Pitch canker</strong></td><td></td><td>0.0572</td><td>0.0681</td></tr></tbody></table>

*Fusarium circinatum* is responsible for the pitch canker. The mention “Bark beetles” refers to *Ips sexdentatus* only in Aquitaine, and to the four main species, e.g. *Ips sexdentatus*, *Orthotomicus erosus*, *Tomicus piniperda* and *Tomicus destruens* in Portugal.
respect to the others (i.e. whether it should be preferred or not to the others).

At the end, for each of the eight FMAs, scores were summed across the eleven silvicultural treatments (Tk) that characterized a FMA (Mj), for each individual hazard considered (Hi), in order to get a general score of susceptibility (S) (Fig. 1) for the given hazard and a given FMA (eq. 2):

$$S_{MjHi} = \sum_{k=1}^{11} S_{MjHiTk} \quad (\text{eq. 2})$$

Susceptibility scores are given in Appendix A3 [suppl.]

## Assessment of wood value at stake

The score of forest value at stake, that was usually provided by experts in Jactel et al. (2012a), was here replaced by a quantitative estimate of the wood standing value of a virtual forest that is composed of N stands of one hectare corresponding to each year of a rotation cycle of N years. The wood standing value was estimated for each stand (i.e., the entire virtual forest) and then divided by the number of stands (equal to the number of years in the rotation cycle). This provided the value at stake of a one hectare of this “virtual forest” where all ages and corresponding wood values would be represented, irrespective of the rotation length (age of trees at final harvesting time). This method of calculation enabled testing for the impact of all hazards on all possible ages of a stand managed with a given FMA. It should be noted that age effects were taken into account when assessing the susceptibility of stands to different hazards.

To evaluate the forest growing stock, each FMA, except M8, was simulated using a maritime pine growth model adapted to regional conditions. The models used were PBRAVO (Páscoa, 1987) in Portugal, GesMO (González González et al., 2012) in Galicia and the Lemoine model in Aquitaine (Lemoine, 1995; Meredieu & Labbé, 2006). Each model had its own equations (see the FORMODELS database http://www.iefc.net/formodels_database_forest_modeles_liste/ for details). To start a simulation, all the models need the values of initial tree density, stand basal area at an initial age of 10 – 12 years old and a site index (dominant height at a reference age; one average value per region). Fertilisation and improved genetic materials were taken into account by using higher site indices. The estimation of wood volume produced in M8 (No management) was based on data from the study of Sánchez-Orois & Rodríguez-Soalleiro (2002) for Galicia and from selected national forest inventory plots of maritime pine stands with irregular structure in Aquitaine (IGN, 2012). As we missed similar data for Portugal, we used the Aquitaine

data to simulate M8 in Portugal after checking the plausibility of productivity figures.

Outputs of growth models were stand characteristics at every age including tree density, basal area, mean height, mean diameter, and total volume. Therefore growing stock in cubic meters was available for all stands composing the “virtual forest” for each FMA. Then current wood prices (S4) were used to convert the cubic meters into Euros and these values were averaged across stands to provide a wood standing value ($E_M$) for the “virtual forest” corresponding to each tested FMA. Wood prices were extracted from regional data based on wood sales in Aquitaine (2014), information from the Centro PINUS in Portugal (2016) and information from the “Asociación Forestal de Galicia” and auctions of the Galician Regional Forest Administration for Galicia (2011-2016).

We defined vulnerability as the product of stand susceptibility to a particular hazard by its exposed value (Jactel et al., 2012a) (Fig. 1). To estimate the vulnerability of a FMA to a particular hazard ($V_{M,H}$), we multiplied the wood standing value in Euros ($E_M$) by the score of forest susceptibility ($S_{M,H}$) (eq. 3)

$$V_{MjHi} = S_{MjHi} \times E_{Mj} \quad (\text{eq. 3})$$

## Data integration into MCRA tool and sensitivity tests

We used Visual PROMETHEE ©, a multi-criteria decision analysis software (Mareschal, 2013) to process the data. The software can deal with quantitative and qualitative input data and implements the PROMETHEE algorithm and GAIA multi-criteria decision tools (Mareschal, 2013).

We ranked the FMAs (Mj) according to multiple criteria, using as criterion the maritime pine forest vulnerability to a given hazard ($V_{MjHi}$). We used the relative level of hazard to weight the criteria ($W_{Hi}$). One multi-criteria decision matrix was thus built per region and the 3 matrices were incorporated in Visual PROMETHEE © (Appendix A5 [suppl.]).

We performed PROMETHEE II analyses (Brans et al., 1984; Brans et al., 1986) to make a complete ranking of FMAs according to multiple risks (see Jactel et al., 2012a for details). As main output, we used the net flow $\Phi$, which is the difference between outgoing $\Phi+$ and incoming $\Phi-$ flows. The outgoing flow $\Phi+$ estimates how far a given FMA outranks other FMAs and the incoming flow $\Phi-$, estimates how far it is outranked by other FMAs.

The calculation of $\Phi$ needs the choice of a preference function, which translates the difference in criterion value between two FMAs to a preference value ranging from 0 to 1, thus enabling the combination of several criteria.
Each preference function is associated with a preference threshold (which is the smallest difference that is considered sufficient to generate a true preference). As in the Jactel *et al.* (2012a) study, we chose the V-shape preference function for all criteria because preference was likely to decrease proportionally to both susceptibility and value at stake. The distribution of vulnerability per FMA and per hazard, which led us to choose the V-shape model, is available in Appendix A6 [suppl.]. For each hazard and region, we sorted the vulnerability values in ascending order, calculated increment in vulnerability values between two successive FMAs and averaged increment values. Then we averaged the mean increment values across all hazards by region, which we used as preference threshold. The preference thresholds were 4238 for Aquitaine, 2987 for Portugal and 1419 for Galicia.

Because the main objective of the present study was to identify which FMAs were more or less at risk compared to others, we decided to set the decision rule to “minimize” all criteria, i.e. to minimize the product of exposure, vulnerability and hazard for any damaging agent considered. The classification would then rank first the FMAs least at risk. We also used the multiple scenarios tool in Visual PROMETHEE© to combine the three multi-criteria regional decision matrices and then provide an overall complete ranking of the FMAs irrespective of the regional case studies.

We then performed several types of sensitivity tests to assess the relative importance of the three risk components, hazard level, forest susceptibility and wood value at stake, in the ranking of FMAs. To rank FMAs irrespective of hazard agents, we set all weights to be equal. Then to rank FMAs irrespective of wood values at stake, we used only the susceptibility scores (S) instead of vulne-

rability (V) in the multi-criteria decision matrix. We also combined the two by setting all weights equal and ranking FMAs only according to their susceptibility. Finally, we tested the impact of the number of hazards taken into account by doing the analysis only with the four hazards selected in Table 4.

# Results

## Wood values at stake

The forest standing values (E) greatly varied between FMAs and to a lesser extent between regions for a given alternative (Fig. 2). Standing values increased with the duration of forest rotation (e.g. from M4-M7 to M1-M3) with the exception of M8 where the low quality of forest products explained a low value at stake. On average, standing values were generally higher in Aquitaine than in the other two regions due to higher wood prices in this region (Appendix A4 [suppl.]).

## Complete ranking of FMAs according to associated risks

The ranking of FMAs was remarkably consistent across regions (Fig. 3) except for the “No management” (M8) and the “Low density without thinning” (M5) alternatives. The ranking of these two FMAs greatly varied between regions, being better ranked in Aquitaine than in Galicia. This is likely due to difference in hazard relative weights and susceptibility scores, as values at stake were quite similar (Fig. 2). The most preferred FMA was

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Forest standing value per forest management alternative (FMA) for each region (€/ha).</figcaption></figure>
always the “Biomass” (M7), followed by the “Short rota-
tion” (M4). These two FMAs had in common short rota-
tions (from 9 to 25 years) and produce small diameter tim-
ber or wood biomass. The least preferred FMAs (at higher
risk) were the “High quality” (M1) and “Low investment”
(M3), which were characterized by long rotations (ca. 55-
60 years) and high expectation in terms of forest products
(large stem wood) (Fig. 3 and Fig. 4a). “Standard” (M2)
and “Half dedicated” (M6) were intermediate, being of
medium rotation length (35-40 years) with an objective of
stem wood production.

Using only four common hazards agents (Fig. 3) did
not change the ranking of FMAs, except for M8 in Aqui-
taine and M8 and M3 in Portugal, which were ranked
lower than in the analysis with all hazards.

Ranking of FMAs to assess the relative
importance of the three risk components

Setting all weights to equal, *i.e.* considering all hazards to be of equal damaging effect within a given region, hardly changed the ranking of FMAs (Fig. 4b).

By contrast, considering only the susceptibility of
FMAs (*i.e.* value at stake not taken into account), dras-
tically changed their ranking (Fig. 4c and Fig. 4d). The
most preferred FMAs (least susceptible) were the stan-
dard alternatives (M1, M2 and to a certain extent M4)
which were characterized by the use of genetically impro-
ved material, planted at low density and with several thin-
ning operations. The least preferred were the biomass al-
ternatives (M6 and M7) which were characterized by high
density planting and no or few thinning operations. Using

hazard levels to weight susceptibility criteria hardly chan-
ged the ranking of FMAs (Fig. 4c vs. Fig. 4d).

Discussion

The methodological framework that we presented
in this study represents a step forward for multiple risk
analyses in forests. In particular, we improved the MCRA
proposed by Jactel *et al.* (2012a) by providing more ob-
jective and accurate estimate of two components of risk,
namely hazard level and the value at stake of forest (ex-
posure value to damage).

The relevance of the improved hazards level evalua-
tion appears more clearly when comparing the relative
importance of the four main hazards agents shared by the
three regions. Table 4 shows that storm importance was
considered higher than fire in Aquitaine where landscape
and climate is more favourable to fight against fires, whe-
reas it is the other way round in Portugal. In Galicia, both
storm and fire are the major concerns. This is consistent
with what was recently observed in the regions: Aqui-
taine faced two major storms in the past 15 years (Colin
et al., 2010), whereas Portugal and Spain are among the
Southern European countries with the highest percentage
of burnt forest every year since 1980 (San-Miguel-Ayanz
et al., 2016). Moreover, Galicia was one of the two Spa-
nish regions most affected by the Klaus storm (see the
FORESTORMS database http://www.iefc.net/storm/ for
details) and it ranks first in Spain for the surfaces affec-
ted by fires during the period 2001-2010 (ADCIF, 2012).
Bark beetles were considered more important in Aqui-
taine than in Portugal, probably because past important

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Outcomes of PROMETHEE complete ranking for maritime pine forest management alternatives (FMAs) in 3 regions, Aquitaine, Portugal and Galicia and all regions together. FMAs with a net flow of -1 would be the most at risk and those at +1 would be the least at risk. For each region, analysis with all hazards is presented on the left (called “All H”) and analysis with 4 hazards is presented on the right (called “4 H”).</figcaption></figure>
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

**Figure 4.** Ranking of the 8 forest management alternatives (FMAs) for all three regions together (Aquitaine, Portugal and Galicia): a) based on forest vulnerability as criteria and hazard level as weight of criteria; b) based on forest vulnerability as criteria and hazard level set equal (equal weight); c) based on susceptibility values as criteria and hazard level as weight of criteria; d) based on susceptibility values as criteria and hazard level set equal (equal weight). FMAs with higher net flow are preferred to those with lower net flow because they are less at risk
storms in Aquitaine triggered two major bark beetles out-
breaks (Aumonier & Maugard, 2006; DSF, 2010). Game
and *Fusarium circinatum* are not of major concern in any
of the regions.

Sensitivity tests with the entire dataset showed that ha-
zard levels (used as weights in the MCRA) had a negligi-
ble effect on the ranking of FMAs in terms of associated
risks. This may be because we considered a high number
of hazards agents together, which may have levelled out
their individual effect, resulting in something similar to
the equal weight option. This was confirmed by the ran-
king of FMAs based on their susceptibility to a number
of hazards reduced to four (Fig. 3). In such situation, two
FMAs were ranked worse, M8 in Aquitaine and M3 and
M8 in Portugal. They have in common low susceptibili-
ty scores for the two hazards removed from the analysis
(the insect defoliator and the root rot fungus for Aquitaine
and the insect defoliator and the torrential rain for Portu-
gal, respectively; see S3). This suggests that the number
and the identity of hazards retained for the multi-criteria
analysis might influence the overall risk ranking and that
forest managers have to be consulted to set priorities ba-
sed on their risk perception.

The sensitivity tests showed that when setting weights
equal, i.e. considering equal hazard levels, the ranking of
FMAs hardly changed (Fig. 4b) whereas when risk was
reduced to susceptibility (i.e. without taking into account
value at stake, Fig. 4c and Fig. 4d) the ranking of FMAs
completely changed (Fig. 4c). Wood values at stake thus
emerged as main drivers of risk ranking. The FMAs most
at risk were those with highest values at stake, e.g. M1,
M2 and M3 (Fig. 2). They were also the management al-
ternatives with the longest rotation length (Table 1). This
could be explained as the result of a longer period of time
during which forest damage can occur, such as those due
to occasional hazards (e.g. storms: Gardiner & Birot,
2013) or those that accumulate such as creeping hazards
(e.g. insect defoliation or drought). Reciprocally, forest
damage can be avoided if clearcutting was applied in the
stand before the hazard occurs (Gardiner & Quine, 2000;
Dhôte, 2005; Jactel et al., 2012b). Forest growing stocks
are also higher in longer rotations, thus increasing the vo-
lume of damage (e.g. volume of windthrow) if affected by
hazards (e.g. storms, Seidl et al., 2011a). Because forest
values at stake were calculated by combining wood vo-
lume and wood price, the quality of products (timber vs.
biomass energy wood) had also a great impact on standing
value (Fig. 2). One might argue that when experts scored
the silvicultural treatments for their susceptibility to ha-
zards, they may have unconsciously overestimated those
associated with FMAs designed to produce high value
products. This was not the case, as demonstrated by our
sensitivity analysis where values at stake were set equal:
the FMAs most at risk (e.g. M1 and M3, Fig. 4a) were
ranked as least susceptible (Fig. 4c).

Because new hazards may emerge in the future such
as invasive pest species (Kenis et al., 2008; Seebens
et al., 2017), or the occurrence of existing ones might
change due to landscape or climate evolution, it remains
important to adapt forest management in order to reduc-
ce stand susceptibility *per se*. The sensitivity test with
no value at stake and hazard weights set equal revealed
that the least resistant maritime pine plantations to a lar-
ge array of hazards would be those produced by M6 or
M7 (Fig. 4d). Their main differences with other FMAs
are their high stand density, absence of thinning and their
short rotation length. These silvicultural treatments were
considered by experts to sharply increase stand suscep-
tibility to fire due to their effects on fuel accumulation
(Fernandes & Rigolot, 2007). High stand density is also
known to reduce individual tree vigour, which may re-
sult in higher sensitivity to bark beetle attacks (Fettig et
al., 2007). Reduced distance between neighbouring trees
in dense pine plantations can also facilitate the transmis-
sion of root rot fungi through root contact (Garbelotto
& Gonthier, 2013).

There are four main limitations to our study. First,
the combination of heterogeneous sources and the lack
of accurate and direct figures may have limited the ac-
curacy of hazard assessment. It remains very difficult to
obtain quantitative estimates of hazards occurrence due to
the lack of a European-scale monitoring network where
causes of damage are identified as well as their econo-
mic consequences. Still, since MCRA uses weights (re-
lative levels of hazards) for criteria (FMA vulnerabilities
to hazards), the use of absolute quantitative data is not
imperative and qualitative data are as relevant as quan-
titative data, as long as scores are properly used to pro-
duce consistent relative ranking positions. Second, FMA
susceptibility was estimated with the help of experts due
to the lack of experimental studies on this topic, and yet
only one panel per region could be gathered. As risk as-
sessment also depends on risk aversion (Hanewinkel et
al., 2011), it is difficult for experts to provide purely ob-
jective estimates. It would be advisable in the future to
organize several panels of experts (Jactel et al., 2012a)
and use structured forecasting approaches (e.g. the Del-
phi method: MacMillan & Marshall, 2006; Salas-Garita
& Soliño, 2019) to elicit expert opinions so as to come
to a consensus on risk rating. We can also use quantitati-
ve technics such as Saaty's pairwise comparison (see in
Diaz-Balteiro et al., 2016) provided that the number of
experts are large enough to do so. Third, we could only
estimate stand value at stake for even-aged stands using
available growth models for maritime pine, preventing us
from testing other FMAs based on uneven-aged forestry
even though we assume that this type of structure would
perform better in case of hazard. Fourth, the susceptibility
of a given stand might depend on that of neighbouring
stands, particularly for hazards with contagion dynamics,
like storms or fire. It would be then advisable to upscale forest risk analysis to the landscape level.

## Conclusions

Our MRCA methodology provides a solid starting point for more detailed studies incorporating the concept of multiple risks in forest planning when no modelling tools are available.

First, we highlighted the great importance of forest value at stake for ranking FMAs according to vulnerability to multiple hazards. We showed that short rotation forestry is less prone to risk due to a reduced probability of being hit by large disturbances with long return periods (e.g. storm, fire, bark beetles) but, above all, to the lower economic value of standing growing stock (and also the lower operational costs for reforestation). However, wood biomass production is also probably less profitable for forest owners due to reduced revenues (Schwarzbauer & Stern, 2010) so forest owners have to combine risk aversion and gain expectation. In addition, managers may consider paying insurance to cover losses due to forest damage. The next step would be thus to combine risk and profitability as criteria to rank FMAs and thus propose more realistic advice to forest managers.

Second, we restricted our analysis to pure stands, focused on wood production and without any interaction with the other components of the landscape (hazard propagation, benefits of surrounding patches of diverse forests...). However there are other silvicultural options that could reduce forest risks while maintaining high productivity, in particular the use of tree species mixtures (Zhang et al., 2012; Gamfeldt et al., 2013; Jactel et al., 2017; Jactel et al., 2018). Finally, to compare more complete sets of forest management alternatives, more criteria related to forest value could be included in the decision-making process such as the provision of ecosystem goods (de-Miguel et al., 2014) and services (Gamfeldt et al., 2013; Branco et al., 2015), which are increasingly taken into account when assessing forest profitability (Deal et al., 2012). Forest price volatility could be also considered as additional hazard.

A way forward to test more combinations of silvicultural treatments and to identify FMAs reducing risks while increasing the provision of services will be to design forest growth models (Seidl et al., 2010) able to model stand evolution under several types of forest management and hazard dynamics. This will require knowing trends in hazard occurrence in a given region and identifying linking functions between stand features (including species choice and composition), resistance to hazards (Pukkala et al., 2005; Seidl et al., 2007) and delivery of services (Lindroth et al., 2009). Model outcomes could be then used

as criteria in multi-criteria decision analyses where forest stakeholders would be invited to weight the criteria according to the ecosystem services they expect from forests, with the objective to identify tradeoffs and synergies.

## Acknowledgments

We gratefully acknowledge the members of the panels and Dominique Merzeau (IDF-CPFA), Cécile Maris (CRPF Nouvelle-Aquitaine), Sarah Yoga (IEFC), Juan Pedro Majada Guijo and Paloma Barrero (CETEMAS), João Gonçalves (Centro PINUS, Viana do Castelo, Portugal) for supplying information on Pinus pinaster and also the Provincial Councils and Forest Owners Associations from Araba, Bizkaia and Gipuzkoa, the Escuela Politécnica de Mieres (University of Oviedo, Department of Organisms and Systems Biology (University of Oviedo), ENCE (Navia, Asturias), and the Forest Policy Services and Plant Health Laboratory (LSV) of the Government of the Principality of Asturias.

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