Introduction

This technical document provides the definition and operational estimation procedures of a number of forest ecosystem services to be evaluated for Catalonia (NE Spain) both in historical assessments (using forest inventory data) and future projections (using model simulations of forest function and dynamics).

The following table, modified from Roces-Díaz et al. (2018), provides a summary of definitions and indicators included:

The next sections detail the metrics and estimation procedures of each ecosystem service.

Provision services

Provision of materials (1)

Indicator: Potential timber and firewood supply

Definition: Potential timber and firewood harvested in forest stands per year, assessed as the annual increase in standing stocks

Metric and units: (m^3 \cdot ha^{-1} \cdot yr^{-1}) of timber and firewood

Estimation procedures: For historic periods, potential harvesting can be obtained comparing standing stock volumes between consecutive surveys of forest inventories (Roces-Díaz et al. 2021). This assessment should preferably exclude forest plots where management has occurred between surveys, because otherwise the potential supply may be underestimated. For future projections, simulated forest dynamics should not include forest management, either. A distinction is made between timber (wood for construction, furniture or other uses) and firewood (wood for burning), on the basis of tree diameter at breast height (DBH) and species:

• Timber: Changes in stem wood volume of trees from all species except firewood species (Quercus ilex, Q. pubescens, Q. faginea, Q. cerrioides, Arbutus unedo and Erica arborea), and DBH > 22.5
• Firewood: Changes in stem wood volume of firewood species (Quercus ilex, Q. pubescens, Q. faginea, Q. cerrioides, Arbutus unedo and Erica arborea), with DBH > 7.5, or stem wood volume of trees of other species and 7.5 < DBH < 22.5.

The standing volume ((m^3 \cdot ha^{-1})) of each tree is obtained applying allometric volume equations depending on species, DBH and height, which are provided by the Spanish National Forest Inventory for each Spanish province and survey (in the case of future projections, equations from IFN3 are used). Function IFNvolume() from the R package called IFNallometry (available at GitHub) is used for volume calculations. When not all trees in the forest plot have been measured, the standing volume of each measured tree is multiplied by its corresponding density factor in the plot to obtain the volume per hectare. Volumes can be aggregated across tree records per species, plot and/or diametric classes. The difference in standing volume for a given period (e.g. consecutive inventory surveys or model time steps) is divided by its duration in years.

Remarks and potential improvements: Estimation of potential timber and firewood supply assumes an extraction rate equal to 100% of growth, and should be estimated on forest stands where management operations have not been conducted, whereas actual extraction rates for a target region can be lower or higher than observed growth. The distinction among products could be made more sophisticated by considering a table indicating the proportion of different products for combinations of species and DBH class.

Provision of materials (2)

Indicator: Actual timber and firewood supply

Definition: Actual timber and firewood harvested in forest stands per year

Metric and units: (m^3 \cdot ha^{-1} \cdot yr^{-1}) of timber and firewood

Estimation procedures: For historic periods, actual (observed) harvested volumes can be obtained from forestry statistics of the target area (e.g. Roces-Díaz et al. (2018)). In addition, tree cuts between forest inventories in permanent plots can also be used to estimate actual harvested wood, if cut trees are identified. For future projections, simulations of forest dynamics including management scenarios can be used to estimate actual supply according to the scenario. As before, a distinction is made between timber (wood for construction, furniture or other uses) and firewood (wood for burning), on the basis of tree diameter at breast height (DBH) and species:

• Timber: Stem wood volume of cut trees from all species, except firewood species (Quercus ilex, Q. pubescens, Q. faginea, Q. cerrioides, Arbutus unedo and Erica arborea), and DBH > 22.5
• Firewood: Stem wood volume of cut firewood species (Quercus ilex, Q. pubescens, Q. faginea, Q. cerrioides, Arbutus unedo and Erica arborea), with DBH > 7.5, or stem wood volume of cut trees of other species and 7.5 < DBH < 22.5.

When necessary, the volume ((m^3 \cdot ha^{-1})) of each product is obtained applying allometric volume equations depending on species, DBH and height, which are provided by the Spanish National Forest Inventory for each Spanish province and survey (in the case of future projections, equations from IFN3 are used). The volume corresponding to a given cut tree is multiplied by its corresponding density factor to obtain the volume per hectare and values are aggregated across tree records. This aggregated value is divided by its duration (e.g. time interval between surveys or the model time step) to express it as a rate. Function IFNvolume() from the R package called IFNallometry (available at GitHub) is used for volume calculations.

Remarks and potential improvements: As before, the distinction among products could be made more sophisticated by considering a table indicating the proportion of different products for combinations of species and DBH class.

Food provision

Indicator: Mushroom production

Definition: Edible mushroom production for pine, oak and fir forests per year

Metric and units: Annual production of edible mushrooms, (kg^{-1} \cdot ha^{-1} \cdot yr^{-1})

Estimation procedures: Empirical models have been developed for edible mushrooms in Catalan pine forests by de Miguel et al (2014).

Remarks and potential improvements: Note that the current procedures are restricted to mushroom species that can be eaten. There is a need to include mushroom production models for other forest types such as oaks, fir, if equations are made available. Food provision should be complemented by other products, such as pinecones, for which production models have been published.

Water provision (1)

Indicator: Blue water

Definition: Amount of water exported by surface runoff or deep drainage into the water table per year

Metric and units: (mm \cdot yr^{-1} = l \cdot m^2\cdot yr^{-1})

Estimation procedures:

Blue water is defined as the sum of water leaving the forest stand via overland (surface) runoff and the water draining beyond the reach of plant roots (i.e. deep drainage). These two flows can be estimated on a daily scale using a water balance model, and then be aggregated for period of evaluation and expressed as (mm \cdot yr^{-1}). In our case, the MEDFATE model (De Cáceres et al. 2015) is used for performing water balance simulations over the evaluation period. See model details in medfate’s reference book.

Remarks and potential improvements: This indicator is appropriate to estimate the forest service on water supply, in absolute terms, but is very sensitive on the precipitation occurred during the evaluation period.

Water provision (2)

Indicator: Runoff ratio

Definition: Water exported in relative terms, as the ratio of blue water (i.e. the sum of surface runoff and deep drainage at the plot scale) over precipitation for an evaluation period

Metric and units: A coefficient ranging between 0 (no water is exported) and 1 (all water that falls in the stand becomes blue water)

Estimation procedures:

This indicator is estimated from the previous one, dividing the amount of blue water estimated for the evaluation period by the amount of precipitation in the same period (e.g. Roces-Díaz et al. (2021)).

Remarks and potential improvements: Even though this indicator is defined as a ratio, it is still dependent on the weather data used for evaluation. Generally speaking, the longer the period of evaluation (e.g. 5-10 years minimum) the more will the indicator reflect the role of forest structure and composition on provision, because variation in the run-off coefficient caused by inter-annual variation in precipitation will tend to be smoothed out.

Regulation services

Climate regulation

Indicator: Forest carbon sink

Definition: Forest carbon sink in above-ground and below-ground woody tissues of vegetation

Metric and units: (Mg,,CO_2 \cdot ha^{-1} \cdot yr^{-1}) or (Mg,,C \cdot ha^{-1} \cdot yr^{-1})

Estimation procedures: For historic periods, carbon sink can be obtained comparing wood biomass estimates from permanent plots of forest inventories. For future projections, simulations of forest dynamics are also possible. The sink rate is estimated subtracting the wood biomass at the beginning of the evaluation period from the wood biomass estimate at the end of the evaluation period, divided by period length. The tree biomass ((Mg CO_2^3 \cdot ha^{-1})) is obtained applying allometric biomass equations for the above-ground and below-ground components, which depend on DBH and height and come from different publications (Diéguez-Aranda et al. 2009; Ruiz-Peinado, Rio, and Montero 2011; Ruiz-Peinado, Montero, and Del Rio 2012). Function IFNbiomass() from the R package IFNallometry (available at GitHub) is used for biomass calculations. Forest carbon sink due to changes in shrub biomass can be also considered, using allometries presented in De Cáceres et al. (2019) and available in R package medfuels (also available at GitHub). Individual below-ground shrub biomass can be estimated from individual above-ground biomass using (B_{shrub, below} = 0.732 \cdot B_{shrub, above}^{0.9427}) (Silva and Rego 2004).

Remarks and potential improvements: Note that it would be possible to consider the biomass extracted in forest management as additional source of forest growth that is not observed when comparing the biomass estimates of standing trees. New allometries for shrub species in Spain have been presented in Montero et al. (2020).

Water regulation

Indicator: Vegetation and soil water storage capacity

Definition: Sum of vegetation water storage capacity and soil water holding capacity of the forest stand

Metric and units: mm = l/m2 of water that can be held in the canopy and soil

Estimation procedures:

The indicator for water regulation service of a forest stand has been defined as the sum of vegetation ((C_m)) and soil water storage capacity:

[WR = C_m + \sum_s{V_{fc,s}}]

Vegetation water holding capacity is estimated from the leaf area index of herbs and woody plant cohorts in the forest stand (Watanabe and Mizutani 1996): [C_m = LAI_{herb}\cdot S_{herb} + \sum_i{ LAI_i \cdot S_i}] where (LAI_{herb}) is the leaf area index of the herbaceous layer, (S_{herb} = 1) is the water holding capacity per LAI unit of the herbaceous layer, (LAI_i) is the leaf area index of plant woody cohort (i) and (S_i) is the water-holding capacity per LAI unit of woody cohort (i), which depends on species identity.

Soil water holding capacity is estimated as the sum, over all (s) soil layers, of the volume that can be held given soil layer depth (d_s), texture and percentage of rocks (P_{rocks,s}): [V_{fc,s} = d_s\cdot ((100-P_{rocks,s})/100)\cdot\theta_{fc,s}] where (\theta_{fc,s}) is the soil moisture content of the soil layer at field capacity (i.e. at -0.033 MPa), which depends on soil texture and organic matter content. (\theta_{fc,s}) is estimated from texture, bulk density and organic matter using Saxton & Rawls (2006)’s pedotransfer functions. Calculations are conducted using function soil_waterFC() from package medfate.

Remarks and potential improvements: Of the two components, only (C_m) is dynamic. Moreover, the estimation of (V_{fc,s}) is very uncertain unless reliable information is available on (P_{rocks,s}). The water regulation service could be estimated as the ratio between deep drainage flow and the overall water exported from the stand, assuming that sub-surface flows are better than surface flows from the perspective of hydrological regulation.

Erosion control

Indicator: Erosion control

Definition: Amount of rainfall erosive potential that is mitigated due to vegetation cover

Metric and units: (Mg \cdot ha^{-1} \cdot yr^{-1})

Estimation procedures: The estimation procedure is similar to Guerra et al. (2016), who re-interpreted the classical Revised Universal Soil Loss Equation (RUSLE), and Morán-Ordóñez et al. (2020). The RUSLE method estimates soil erosion as the product of (K \cdot LS \cdot R \cdot C \cdot P), where (K) is soil erodibility (an intrinsic characteristic of each soil type), (LS) is the topographic factor accounting for the slope length and steepness, (R) is a non-dimensional factor of physical erosivity of rainfall, (C) is related with the type and structure of the vegetation of each stand and finally (P) is the conservation practices factor (not considered here).

For our calculations, we first estimate for the evaluation period the structural impact ((\gamma)), i.e. potential soil erosion if vegetation were absent: [\gamma = K \cdot LS \cdot R] and the estimated actual soil loss (\beta) after vegetation cover is considered:

[ \beta = K \cdot LS \cdot R \cdot C ],

and finally calculated the erosion mitigation (mitigated impact) as their difference:

[EM = \gamma - \beta = K \cdot LS \cdot R \cdot (1 - C)] Erosion components are estimated as follows:

• (K) values were sourced from the data base by Panagos et al. (2014) at 500 m resolution, available at ESDAC;
• (LS) were obtained at 25 m of resolution from Panagos et al. (2015), available at ESDAC.
• (R) is calculated using the model by Diodato and Bellocchi (2010) and daily rainfall data (from “meteoland” package, De Caceres et al., 2018).
• (C) is the mitigation (reduction) effect of vegetation cover. Despite the (C) term promotes its interpretation as cover, a complete vegetation cover should have a maximum reduction of erosion, and hence (C = 0). In practice, we estimate (C) as the faction of photosynthetically active radiation (PAR) reaching the ground ((FPAR_{ground})), which can be estimated as a function of leaf area index and extinction coefficients: [C = FPAR_{ground}=e^{-0.5 \cdot LAI_{herb}} \cdot e^{-\sum_{i}{k_{i} \cdot LAI_{i}}}] where (LAI_{herb}) is the leaf area index of herbaceous vegetation (when present), (LAI_i) is the leaf area index of woody cohort (i), (k_i) is the corresponding extinction coefficient for PAR.

Remarks and potential improvements:

Cultural services

Recreational

Indicator: Potential recreational value

Definition: The perception of the value of the forest stand for recreational purposes as a function of its structure and composition

Metric and units: A value between 0 (no potential recreational value) and 1 (maximum potential recreational value).

Estimation procedures (yet to be implemented in a function):

A literature review allowed us to identify a number of key studies relating vegetation structure and composition to the potential recreational value of forests. From these papers, we extracted only those metrics that we believe can be estimated from standard dynamic model outputs. These are five stand-level metrics, i.e., tree size, variation in tree size, thickness of vegetation cover, density of ground vegetation, number of tree and shrub species, and two landscape-level metrics, i.e., variation between stands (landscape variability) and uniqueness (areas with the lowest density of plots have higher uniqueness).

A questionnaire is used to define the shape of the relationship between each metric and the potential recreational value. Then, the seven different functions are combined into an overall potential recreational value. The weight of the different metrics can be assumed equal or included in the previous questionnaire and adjusted accordingly in the calculation.

Remarks and potential improvements: The idea was to include also the amount of natural deadwood, but then the index becomes conceptually closer to the biodiversity potential (described below). In projections of forest dynamics, inclusion of species richness into this indicator requires that the model incorporates colonization processes. Otherwise, local extinction will always decrease species richness.

Supporting services

Biodiversity potential

Indicator: A simplified version of the Biodiversity potential index (BPI)

Definition: Potential of the forest stand to serve as habitat for biodiversity, on the basis of its diversity of woody species, vertical structure, presence of large trees and standing and downed dead wood.

Metric and units: A value between 0 (lowest biodiversity potential) and 5 (highest biodiversity potential).

Estimation procedures (yet to be implemented in a function):

The BPI is a simplified version of the Index de Biodiversitat Potencial (IBP) was adapted to Catalonia region by Centre de la Propietat Forestal (Baiges et al. 2019) from the work by Larrieu and Gonin (2008). IBP was designed to be applied in the field (including landscape features) and, therefore, not all its ten components can be evaluated from model simulation results. Here we adapted the Catalan IBP focusing on the following four components:

1. Diversity of native (or archaeophyte) species
2. Vertical structure of vegetation
3. The presence of dead wood
4. The presence of large trees

Moreover the current application of IBP in Catalonia (IBP ver. 3) is slightly different depending on two biogeographic domains: (D1) Mediterranean domain; (D2) Montane-subalpine domain. We addressed this level of complexity by calculating our BPI twice, once assuming one domain or the other, followed by averaging the two resulting values with weights depending on the ascription of the tree species occurring in the stand to the two domains. The following describes how the A-D components are estimated for each of the two domains and how the final BPI value is determined.

(BPI_A) - Native species

This component evaluates the diversity of native (or archaeophyte) tree species with individuals taller than 50 cm height in the stand. The list of native genus/species is the following: Quercus, Arbutus, Cercis, Acer, Corylus, Abies, Betula, Castanea, Prunus avium, Pinus, Fagus sylvatica, Fraxinus, Ceratonia, Celtis, Juglans regia, Olea europaea, Ulmus, Pyrus, Malus, Populus alba, Juniperus thurifera, Salix, Sorbus, Taxus, Tilia, Alnus, Cupressus

The following table describes how component (BPI_A) is defined depending on the number of genus of native species present in the stand and the domain of application:

For D2 we simplified the two tables given in BPI documentation. If the cover sum of native (or archaephyte) tree species in the stand is less than 50%, then the value of (BPI_A) cannot be larger than 2, even if the number of native genus is higher than 3.

(BPI_B) - Vertical structure of vegetation

Fine strata are considered: herbaceous vegetation, very low (< 1.5 m), low (1.5-7 m), intermediate (7-20 m) and high (> 20 m). Classify woody cohorts into strata according to their height. Calculate the cover of each woody cohort (for shrubs is directly in the shrub data, for trees it comes from an allometry based DBH and open-grown assumption). Sum the total cover of each strata. Count the number of strata that are more than 20% cover.

(BPI_C) - Dead wood

Conversely to the original BPI, we do not distinguishing between standing dead trees (i.e. snags) and dead trees in the ground (i.e. logs). Moreover, it is assumed that decay (decomposition) of dead wood is dealt externally, so that the index is calculated considering the actual current amount of dead wood in the stand.

First, dead trees are classified into medium or large depending on their size and biogeographic domain of application:

• For D1, classify dead trees as medium dead wood (MDW) if 17.5 < DBH < 27.5 or large dead wood (LDW) if DBH > 27.5
• For D2, classify dead trees as medium dead wood (MDW) if 17.5 < DBH < 37.5 or large dead wood (LDW) if DBH > 37.5
• Regardless of the domain, if species has a slow growth (Alnus, Arbutus, Acer, Pyrus, Malus, Sorbus) then if DBH > 17.5 cm is already considered as LDW

Then, the value of dead wood component is determined from the density of dead trees in MDW and LDW classes, using the following table:

(BPI_D) - Large trees alive

First, live trees are classified into large or very large depending on their size and biogeographic domain of application:

• For D1, classify trees as large trees (LT) if 37.5 cm < DBH < 57.5 cm or very large trees (VLT) if > 57.5 cm.
• For D2, classify trees as large trees (LT) if 47.5 cm < DBH < 67.5 cm or very large trees (VLT) if > 67.5 cm.
• Regardless of the domain, if species has a slow growth (Alnus, Arbutus, Acer, Pyrus, Malus, Sorbus) then if DBH > 37.5 cm is already considered as VLT.

Then, the value of large trees is determined from the density of trees in LT and VLT classes, using the following table:

Overall Biodiversity Potential Index

BPI is the average of the four metrics, each one going from 0 to 5. Double weight is given to dead wood, according to the original BPI definition of considering snags and logs separately.

[BPI_{domain} = (BPI_A + BPI_B + 2 \cdot BPI_C + BPI_D)/5] For a given plot, we calculate BPI corresponding to each of the two domains, i.e. (BPI_{D1}) and (BPI_{D2}). Then, we perform a weighted average of the two BPI values using the sum of basal area of species belonging to each of the domains, (BA_{D1}) and (BA_{D2}) as weights:

[BPI = \frac{BPI_{D1} \cdot BA_{D1} + BPI_{D2} \cdot BA_{D2}}{BA_{D1} + BA_{D2}}]

Remarks and potential improvements: In projections of forest dynamics, inclusion of species richness into this indicator requires that the model incorporates colonization processes. Otherwise, local extinction will always decrease species richness. A potential improvement would be to explicitly model dead woody decay using functions depending on climatic conditions.

References