Deliverable 3.1. Monitoring protocol

CREAF. Campus UAB. Edifici C 08193 Cerdanyola del Vallès (Barcelona) Tel. + 34 93 581 13 12 contact[email protected] www.creaf.cat | blog.creaf.cat MONIMED - Monitoring Mediterranean climate-smart forestry practices for climate resilience and ecosystem service provision This research was supported by the project MONIMED (G-04-2024), funded by the European Union through FORWARDS (Horizon Europe Project No. 101084481) grants to third parties managed by European Forest Institute Deliverable 3.1. Monitoring protocol Due date of deliverable: 22/02/2025 Actual submission date: 21/02/2025 Deliverable 3.1. Monitoring protocol 2 Authors Diana Pascual Sánchez, Eduard Pla Ferrer, Mireia Banqué i Casanovas, Agustí Escobar Rúbies, Matias Pons, Xavier Domene. Abstract This deliverable gives an in deep description of the monitoring tasks and network developed to assess and compare the evolution of the field trials in comparison with the initial conditions, with the control plots and with the experiences in other areas of the project. The first section is a short introduction to the deliverable, with a briefly description of the background and the main objectives of this deliverable. The second section describes the location of the field trials. The third and fourth sections detail the monitoring network, focusing on the installation of the monitoring subplots (section third) and the monitoring protocol (section fourth). The monitoring protocol details the variables that will be measured in each trial, the frequency of the monitoring and the methodology employed to measure the variables. Citation Pascual D, Pla E, Banqué M, Escobar A, Pons M, Domene X (2025). Deliverable 3.1 Monitoring protocol. MONIMED Project Revision Name of the version Writer/ Reviewer Data Description D3.1_Monitoring_Protocol_M9 .docx Diana Pascual et al. 22/02/2025 First version of the DL delivered to EFIMED MONIMED_D3.1.Monitoring Protocol2_20250221 Diana Pascual et al. 21/11/2025 Second version of the DL included in an Open Science repository, mention to funding and Creative Commons license. Deliverable 3.1. Monitoring protocol 3 Index 1. Introduction ............................................................................................................ 4 2. Location of the field trials ...................................................................................... 5 2.1. Holm oak forest in La Albera protected area (Girona) ........................................ 5 2.2. Mixed forest of Pinus sylvestris and oaks in the Montesquiu natural park (Barcelona) ............................................................................................................... 6 2.3. Holm oak forest in the Montnegre-Corredor natural park (Barcelona) ................ 8 3. Monitoring network: Installation of monitoring subplots .................................. 10 3.1. Installation of monitoring subplots ................................................................... 10 4. Monitoring network: Monitoring protocol ........................................................... 12 4.1. Variables for measuring the mitigation capacity ............................................... 13 4.1.1. Carbon storage in soils ........................................................................... 13 4.1.2. Carbon storage in forests........................................................................ 14 4.2. Variables for measuring the resistance to droughts ......................................... 15 4.2.1. Forest health status ................................................................................ 15 4.2.2. Soil moisture ........................................................................................... 16 4.3. Variables for measuring the restauration of other ecosystem services ............ 17 4.3.1. Biodiversity ............................................................................................. 17 4.3.2. Wood provision ....................................................................................... 19 4.4. Variables for measuring the reduction of fire risk ............................................. 20 4.4.1. Forest structure ...................................................................................... 20 4.4.2. Forest fuel continuity ............................................................................... 21 5. References ............................................................................................................ 23 Deliverable 3.1. Monitoring protocol 4 1. Introduction Rural abandonment in recent decades, combined with climate change, has rendered Euro-Mediterranean Forest areas highly vulnerable to the impacts of climate change, thereby threatening the provision of ecosystem services. Historically, forest management in Mediterranean regions was characterised by high intensity, system simplification, and a single-product focus. Contemporary forest structures are the outcome of these historical trends, leading to management abandonment in highly vulnerable stands prone to drought, pest outbreaks, and wildfires. Over the past decades, several demonstrative projects have assessed the impact of forest management on the adaptive capacity of Mediterranean forests. These studies have focused on monitoring key indicators such as forest growth, tree vitality, and fire risk reduction following different thinning intensities and understory clearing treatments. This project seeks to advance adaptive forest management by integrating closerto-nature management strategies and incorporating novel parameters related to multifunctional management, biodiversity conservation, carbon sequestration potential, and climate resilience. The project encompasses the long-term monitoring of ongoing pilot sites under conventional management (9 and 4 years of monitoring) alongside newly established adjacent pilots implementing Climate-Smart Forestry (CSF) practices: (1) integrative multifunctional management and (2) management geared towards fostering natural development processes. All management approaches will be assessed using newly defined indicators reflecting the forest’s capacity to support biodiversity, its carbon sequestration potential, and its resilience to climatic stressors. Monitoring of the ongoing and new CSF pilots is the central objective of the MONIMED project, with the aim of making the monitoring data collected available to the FORWARDS project, as part of the future ForestWard Observatory. This is the framework of this Deliverable 3.1 Monitoring protocol, an in deep description of the monitoring tasks and network developed to assess and compare the evolution of the different practices in comparison with their initial conditions, with the control plots and with the experiences in other areas of the project. The ultimate aim of the monitoring is to evaluate the efficiency of the practices to improve the adaptation capacity of forest to face climate change threatens, through the analysis of the effects of the practices on growth and forest structure (diameter distribution, basal area, tree cover, understorey cover); the potential forest capacity to host biodiversity (using the Potential Biodiversity Index, IBP); the ecosystem services provision (water status, protection against wildfires, health status); and mitigation capacity (carbon sink capacity, carbon storage in forest compartments (soil, biomass, dead wood and litter)). This report presents the monitoring action, a description of the monitoring variables that will be measured in each plot, the frequency of the monitoring and the methodology employed to measure the variables. Deliverable 3.1. Monitoring protocol 5 2. Location of the field trials Before elaborating further on the monitoring protocol, a short description of the location and design of the practices is included, more detailed in the previously presented Deliverable 3.3 Database (month 6). Besides, a more detailed description of the implementation of the practices will be given on Deliverable 3.2. Description of the applied CSF practices, foreseen on month 10. The project is developed in the Mediterranean area of Catalonia (NE Spain), taking advantage of the existing network of CREAF pilots on adaptive forest management, including the most representative Mediterranean forest types (dominated by Quercus ilex, Quercus humilis and Pinus sylvestris). The field trials are established in three sites: - A Holm oak forest in La Albera protected area (Girona). - A mixed forest of Pinus sylvestris and oaks in the Montesquiu natural park (Barcelona). - A Holm oak forest in the Montnegre-Corredor natural park (Barcelona). 2.1. Holm oak forest in La Albera protected area (Girona) The Holm oak (Quercus ilex) forest is in the protected area of La Albera (Girona), in a private state. Holm oak is the dominant species (80-90% of the basal area), in a very dense forest (more than 2,000 ft/ha), with a basal area of 30 m2/ha and an irregular structure. According to the estate owner, the forest has not been managed for the last 80 years (approximately). Conventional silvicultural practices applied in the site had the objective to reduce the fire risk through the reduction of tree density and the promotion of mature structures with bigger trees and fuel discontinuity. In this site, three plots of about 1 ha were implemented in 2015 (Figure 1, red polygons): - Plot of low intensity treatment: Application of a low thinning clearing with the objective to adapt the forest to a regular structure. Regular forests are, in general, more efficient in the water use and in fire prevention. - Plot of high intensity treatment: Application of a selection treatment and intense understory clearing to adapt forest to an irregular structure and to stimulate forest regeneration. Irregular forests are supposed to preserve better the sol, its quality and nutrients. - Control plot, with no intervention. The existing practices are complemented with two more field trials in the framework of the MONIMED project. Currently, two new plots of 1 hectare have been defined in the field, where CSF practices are being implemented along February 2025: - Plot of closer-to-nature silviculture: Application of different treatments to increase tree species biodiversity, promoting mixed structures and functional Deliverable 3.1. Monitoring protocol 6 diversity, and to regulate competition, favouring the vitality of the best stands, stimulating fruiting and reducing competition for water. - Plot of preparation to natural dynamics in non-productive stands: Application of different actuation to promote maturity or restore complex ecological processes in forests, interrupting the human interventions. Figure 1. Location of the treatment plots (red polygons) and monitoring subplots (green circles) in Requesens. 2.2. Mixed forest of Pinus sylvestris and oaks in the Montesquiu natural park (Barcelona) The mixed forest of Pinus sylvestris and Quercus humilis is in Montesquiu natural park (Barcelona). Scots pine is the dominant species in the area (68% of the basal area) in a moderately dense forest (more than 1,000 ft/ha) and a basal area of 21 m2/ha of pine, with a regular structure and a 65% canopy cover. According to the park managers, the forest has not been managed for the last 40 years (approximately). Conventional silvicultural practices applied in the site had the objective to improve the tree health, containing tree mortality and to evaluate the potential of oak replacement of Scots pine under conditions of climate change. In this site, four plots of about 1 ha were implemented in 2015 (Figure 2, red polygons): - Plot of low intensity treatment: Application of understory clearing with the objective to reduce resources competition. Deliverable 3.1. Monitoring protocol 7 - Plot of high intensity treatment: Application of low thinning and intense understory clearing with the objective to reduce tree competition. - Plot of pine logging. Elimination of Scots pines to accelerate the replacement of pines by oaks and evaluate the oaks’ future development. - Control plot, with no intervention. The existing practices are complemented with two more field trials in the framework of the MONIMED project. Currently, two new plots of 1 hectare have been defined in the field, together with a new control area (C2). The CSF practices have been implemented along January 2025: - Plot of closer-to-nature silviculture: The same treatments as Requesens forest will be applied. - Plot of preparation to natural dynamics in non-productive stands: The same treatments as in Requesens forest will be applied. - Control plot 2: A newly designated non-intervention area has been established to serve as a reference for comparing the two new CSF practices with an unmanaged site. This additional control area was necessary because Control Plot 1 exhibits significantly different forest structure and age, making it unsuitable for direct comparison with the newly defined areas. As a result, two additional monitoring subplots have been introduced, which were not initially planned. Figure 2. Location of the treatment plots (red polygons) and monitoring subplots (green circles) in Montesquiu. Deliverable 3.1. Monitoring protocol 8 2.3. Holm oak forest in the Montnegre-Corredor natural park (Barcelona) The Holm oak (Quercus ilex) forest is in Montnegre-Corredor natural park (Barcelona), in a private estate. Holm oak is the dominant species, in a moderately dense forest (more than 1,122 ft/ha), with a basal area of 25 m2/ha and an irregular structure. According to the estate owner, the forest has not been managed for the last 20 years (approximately). Conventional silvicultural practices applied in the site had the objective to reduce the forest stand’s vulnerability to fire based on improving its resistance and resilience to disturbances, increasing the complexity of its structure and composition. In this site, two plots were implemented in 2020 (Figure 3, red polygons): - Plot of high intensity treatment (5.4 ha): Application of selective thinning (ensuring a selection of trees of all ages and fostering those that are better formed, more robust and healthier), coppice management (selection of young sprouts of tree species such as holm oak and oak), and selective clearing of shrub species (using the coppice-with-standards system for any tree-like species. The species most vulnerable to fire are removed and those of interest in terms of biodiversity (proportion of fruit or shelter) are maintained/encouraged). - Control plot (1.9 ha), with no intervention. The existing practices are complemented with two more field trials in the framework of the MONIMED project. The CSF practices have been implemented along December 2014: - Plot of closer-to-nature silviculture: The same treatments as Requesens forest will be applied. - Plot of preparation to natural dynamics in non-productive stands: The same treatments as in Requesens forest will be applied. Deliverable 3.1. Monitoring protocol 9 Figure 3. Location of the treatment plots (red polygons) and monitoring subplots (green circles) in Montnegre. Deliverable 3.1. Monitoring protocol 16 discolouration (leaves not green compared to green leaves on a healthy tree). Forest decline is assessed for 10 trees per monitoring subplot. The trees are marked and identified with a number tag so that their evolution can be followed throughout the study. This field identification method is based on the DEBOSCAT project (Banqué, Vayreda, & Martínez-Vilalta, 2013) and the Spanish Forest Monitoring Network (Level II, www.magrama.gob.es). Figure 6. Representation of the three estimations used to determine the forest decline (Source: DEBOSCAT). 4.2.2. Soil moisture The soil moisture is an indicator of water availability for the vegetation and recovery of soil functioning. Humidity sensors are used to monitor the evolution of the water in the first centimetres of the soil. One sensor is installed in each monitoring subplot and formatted to provide a soil water content value each 6 hours (4 measured per day). The following sensors are used: - TMS-4 Standard dataloggers, from Tomst company (https://tomst.com/web/en/). These devices allow to measure air and soil temperature as well as soil moisture thanks to three temperature sensors and one soil moisture sensor. Standard unit is measuring temperature in 3 different levels and measuring the soil moisture as well. The unit is installed into the ground and the reading probe of the unit is above the ground level. It is mainly used for measuring soil moisture and temperature in a depth of -6, +2 and +15cm. The TMS-4 are installed in the field, one per monitoring subplot, in a representative point of the subplots. Mortality Defoliation Discolouration Deliverable 3.1. Monitoring protocol 17 Figure 7. TMS-4 Standard dataloggers to be installed. 4.3. Variables for measuring the restauration of other ecosystem services 4.3.1. Biodiversity Biodiversity is estimated with the Potential Biodiversity Index (IBP) (Baiges, Cervera, Palero, Gonin, & Larrieu, 2022). The IBP is a support tool for forest planning and management, primarily designed to facilitate the integration of biodiversity conservation criteria into multifunctional management. This approach combines various objectives, including the production of goods and/or fire prevention. The version used in this project is an adaptation of the index defined in France, but for Mediterranean forests in Catalonia. This index is estimated three times: before the implementation of the practices (October-December 2024), at the end of the project only in the CSF plots (SeptemberOctober 2025) and three years after the end of the project (September-October 2028). For forest areas under 2 hectares, IBP data collection involves a full-coverage walkthrough using a regular transect pattern. The surveyor visually assesses the factors, where observations are estimated rather than measured, ensuring efficiency. IBP assessments include the monitoring of ten factors, grouped into two categories: 1. Management-modifiable elements: These factors can be influenced through forest management practices to enhance biodiversity: • Tree Species Diversity (A): The presence of native tree species is a crucial indicator of biodiversity. The IBP assigns scores based on the number of different genera rather than species, as similar genera often support comparable ecological communities. Exotic species are not counted, and stands dominated by non-native trees receive lower scores Deliverable 3.1. Monitoring protocol 18 • Vertical Vegetation Structure (B): A forest's structural complexity supports a variety of species. IBP classifies vegetation into five layers: herbaceous, low shrubs, medium-height vegetation, tree canopy, and emergent trees. The more layers present, the higher the score, as vertical diversity increases habitat opportunities • Standing Deadwood (C) and Fallen Deadwood (D): Deadwood is a key habitat for saproxylic species, fungi, and insects. IBP differentiates between standing and fallen deadwood, prioritizing larger-diameter logs as they host more specialized organisms. Scores increase with higher volumes of deadwood • Large Living Trees (E): Old, large-diameter trees provide habitats for many species. They feature thick bark, cavities, and microhabitats that are crucial for birds, insects, and fungi. IBP scores increase as the number of large trees per hectare rises • Trees with Dendromicrohabitats (F): These are specific features like cavities, peeling bark, epiphytic plants, or fungal fruiting bodies. IBP considers trees with multiple microhabitats to be particularly valuable for biodiversity • Open Spaces with Flowering Vegetation (G): Small clearings or gaps in the canopy provide light and resources for pollinators and herbivores. IBP assigns higher scores to forests with 1–5% open spaces, striking a balance between open and closed habitats. 2. Fixed Contextual Factors. These factors depend on natural and historical site conditions and cannot be directly modified through management: • Forest Continuity (H): Stands that have remained continuously forested since at least 1945 receive higher scores, as they support stable, mature ecosystems with species that are sensitive to disturbance • Presence of Water Bodies (I): Natural or artificial aquatic habitats (e.g., streams, ponds, wetlands) increase overall biodiversity. The IBP assigns higher scores to stands with multiple water sources • Rocky Features (J): Rocky habitats, such as cliffs, caves, or boulder fields, provide specialized niches for various species. Forests with multiple types of rock formations receive higher IBP scores. Each factor is scored from 0 to 5, based on field observations. The sum of these scores provides a total IBP score, which helps forest managers assess biodiversity potential and make informed conservation decisions. Deliverable 3.1. Monitoring protocol 19 Figure 8. Different elements that are considered in the estimation of the biodiversity index. 4.3.2. Wood provision Wood provision refers to the volume of wood present in the forest stands, in m3/ha. Wood volume is estimated from the basal area and the height of the trees and considering a form factor. Wood volume is monitored through the variable diameter at breast height (DBH). The variables are measured three times: before the implementation of the practices (October-December 2024), at the end of the project only in the closer-to-nature silviculture plots (September-October 2025) and three years after the end of the project (September-October 2028). Wood provision is monitored through a forest inventory, with the following methodology: - Diametric distribution: In each subplot, we measure the diameter at breast height (DBH) of all trees with a DBH higher of 7.5 cm, using a diametric tape. To remeasure the diameters of the trees in the future, a line is painted with striking paint, at chest height (1.30 m) of all inventoried trees (with DBH greater than 7.5cm). Wood provision is estimated using species-specific allometry equations developed by (Gracia, Burriel, Ibàñez, Mata, & Vayreda, 2004) and (Montero, Ruíz-Peinado, & Muñoz, 2005) for Catalonia and Spain, using the equation: 𝐵 =𝑎 × 𝐷𝐵𝐻𝑏 where a and b are allometric coefficients for the allometric relation biomass vs tree diameter. Figure 9. Measurement of the diameter at breast height in Requesens, subplot RC1. Deliverable 3.1. Monitoring protocol 20 4.4. Variables for measuring the reduction of fire risk 4.4.1. Forest structure Forest structure refers to the distribution and characteristics of the individual trees within the subplot. Forest structure is monitored through different variables, such as tree density, resprouting or canopy cover. The variables are measured three times: before the implementation of the practices (October-December 2024), at the end of the project only in the closer-to-nature silviculture plots (September-October 2025), and three years after the end of the project (September-October 2028). Forest structure is monitored through a forest inventory, with the following methodologies: - Tree density (number of trees / ha): In each subplot, we count the number of trees or resprouts that are within the 10 m radius circumference. The perimeter of the parcel is determined by reduced distance to the centre, and a tree is considered within the parcel if the horizontal projection of the centre of the trunk at the base is only 10 meters or less in the centre of the parcel. Only the inventoriable trees, with a diameter at breast height (DBH or Dn) greater than 7.5 cm, are counted. - Regeneration: In each subplot, two smaller rectangles are selected (200*200 cm), close to the subplot centre and oriented east and west. In each rectangle, the number of individuals is counted, including the identification of the species, and distributed in the following classes: o Number of individuals with height lower than 0.3 m. o Number of individuals with height between 0.3 m and 1.30 m. o Number of individuals with height lower than 1.3 and DBH lower than 2.5 cm. o Number of individuals with height higher than 1.3 and DBH lower than 7.5 cm. - Canopy cover FCC (%): The fraction of canopy cover, considering all strata (arboreal, shrub and herbaceous) is estimated for the whole subplot, measured visually as the mean cover percentage. Figure 10. Left: All trees in the subplot (DBH>7.5 cm) are marked with a pinkpainted line at chest height (1.30 m) and counted, subplot MT4-1 in Montesquiu. Right: The yellow pols mark the extreme of the regeneration rectangles, subplot RT2-2 in Requesens. Deliverable 3.1. Monitoring protocol 21 4.4.2. Forest fuel continuity Forest fuel continuity refers to the spatial distribution and height of the different strata of the fuel (aerial, ladder or surface cover), which has a direct effect in the vulnerability of the forest to fire risk due to fire propagation. Forest fuel continuity is monitored through the crown fire hazard and the understorey biovolume. The variables are measured two times: before the implementation of the practices (October-December 2024) and three years after the end of the project (September-October 2028). The crown fire hazards is also measured at the end of the project (September-October 2025). Forest fuel continuity is monitored through a forest inventory, with the following methodologies: - Crown fire hazard: In each inventory subplot, the risk of crown fire hazard is determined following the methodology of the CVFoC Manual (Piqué, et al., 2011). For the estimation of this variable, the following measures are taken: • Fuel aerial cover (FCC, %) • Fuel surface cover (RCS, %) • Fuel ladder cover (RCE, %) • Height of surface fuel (m) • Distance between surface and ladder fuel (if RCE> 25%) (m) • Distance between surface and aerial fuel (if RCE <25%) (m) • Distance between ladder and aerial fuel (if RCE> 25%) (m) From these data and using a set of keys included in (Piqué, et al., 2011), the fuel continuity model of the forest stand (Figure 11) and the crown fire hazard are obtained. Figure 11. Graph of what the fuel continuity models look like according to the methodology in the Crown Fire Hazard Manual (Piqué et al. 2011). - Understorey biovolume. We measure fuel coverage making two 10m long strip biomass transects in the direction of maximum slope. The start of the transects is located 2m from the wooden stake that identifies the centre of the plot and following the contour line. The initial and final point of the transects is marked with a wooden stake and painted with permanent spray that allows its later identification. From the upper point of the transect, square polygons of 0.5 x 0.5 m are created towards the outside of the transect where the following information is identified and recorded: Deliverable 3.1. Monitoring protocol 22 • Identification of the species of scrub and / or regenerated trees that cannot be inventoried (DBH less than 7.5 cm) present in each square. Square occupancy percentage and average height (m). • Percentage (%) of covering of logging residues or dead trees. • Percentage (%) of moss cover. • Percentage (%) of herbaceous cover. • Percentage (%) of litter cover. • Percentage (%) of stone covering. • Diagram of the transects that allows reconstructing them in the annual inventories. From these data, the understory biovolume is estimated in m3/ha as the relation between the surface occupied by the understory and the average height, related to the surface of the subplot. Figure 12. Two examples of the 10m long strip biomass transects. Left: Subplot CC1 in Montnegre-Corridor. Right: Subplot CC3 in Montnegre-Corridor. Deliverable 3.1. Monitoring protocol 23 5. References Armand , D., Etienne , M., Legrand, C., Marechal, J., & Valette, J. (1993). Phytovolume, phytomasse et relations structurales chez quelques arbustes méditerranéens. Annales des Sciences Forestières, 50(7989). Baiges, T., Cervera, T., Palero, N., Gonin, P., & Larrieu, L. (2022). El Índice de Biodiversidad Potencial (IBP) como herramienta de apoyo a la gestión forestal: fundamentos y aplicaciones en Cataluña. 8º Congreso Forestal Español. Sociedad Española de Ciencias Forestales. . ISBN 978-84-941695-6-4. 8CFE646. Banqué, M., Vayreda, J., & Martínez-Vilalta, J. (2013). Monitoreo del decaimiento de los bosques de Cataluña: Proyecto DEBOSCAT. Proceeding of 6º Congreso Forestal Español. 6CFE01-438. FAO. (2020). GSOC MRV Protocol: A protocol for measurement, monitoring, reporting and verification of soil organic carbon in agricultural landscapes. Gracia, C., Burriel, J., Ibàñez, J., Mata, T., & Vayreda, J. (2004). Inventari Ecològic i Forestal de Catalunya. Mètodes. Bellaterra (Barcelona): CREAF, 112p. Montero, G., Ruíz-Peinado, R., & Muñoz, M. (2005). Producción de biomasa y fijación de CO2 por los bosques españoles. Madrid: Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA). 270p. Piqué, M., Castellnou, M., Valor, T., Pagés, J., Larrañaga, A., Miralles, M., & Cervera, T. (2011). Integració del risc de gran incendis forestals (GIF) en la gestió forestal: Incendis tipus i vulnerabilitat de les estructures forestals al foc de capçades. Sèrie: Orientacions de gestió forestal sostenible per a Catalunya (ORGEST). Centre de la Propietat Forestal, Departament d’Agricultura, Ramaderia, Pesca, Alimentació I Medi Natural, Generalitat de Catalunya. Walkley, A., & Black, I. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science 37, 29-38. WRB, I. W. (2022). World Reference Base for Soil Resources. International soil classification system for naming soils and creating legends for soil maps. Vienna, Austria: 4th edition, International Union of Soil Sciences (IUSS).