📄 earth_science

Modelling the potential carbon storage capacity of forests dominated by Pinus sylvestris across European ecological zones

This study develops a site-quality-dependent methodology to estimate the Potential Carbon Storage Capacity of European *Pinus sylvestris* forests, providing a standardized baseline for national greenhouse gas inventories and policy frameworks to evaluate carbon stock losses, gains, and mitigation benefits across diverse ecological zones.

Original authors: Juan Alberto Molina-Valero, Juan Gabriel Álvarez-González, César Pérez-Cruzado, Vítězslav Moudrý, Laura Ķēniņa, Daniel Moreno-Fernández, Amaël Le-Squin, César Alvites, Cornelia Roberge, Fernando Monte

Published 2026-07-08

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Juan Alberto Molina-Valero, Juan Gabriel Álvarez-González, César Pérez-Cruzado, Vítězslav Moudrý, Laura Ķēniņa, Daniel Moreno-Fernández, Amaël Le-Squin, César Alvites, Cornelia Roberge, Fernando Montes, Henri Cuny, Isabel Cañellas, Jeanne Portier, Klaus von Gadow, Marco Baldo, Roque Rodríguez-Soalleiro, Rubén Valbuena, Terje Gobakken, Vladimir Šebeň, Peter Surový, Róbert Marušak

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine a forest not just as a collection of trees, but as a giant, living warehouse. The goal of this study is to figure out the maximum amount of "carbon cargo" (stored in the wood and leaves) that a specific type of warehouse—dominated by the Scots Pine (Pinus sylvestris)—can possibly hold across Europe.

Here is the breakdown of their work, using simple analogies:

1. The Problem: Guessing the "Full" Tank

Forests are like carbon sinks; they soak up carbon dioxide from the air. To fight climate change, we need to know how much carbon a forest could hold if it were left alone or managed perfectly. This is called the Potential Carbon Storage Capacity (PCSC).

The problem is that forests are messy. Some are on rich soil, some on poor soil. Some are old and wild, others are young and managed by humans. If you just look at a forest today and say, "It holds 50 tons of carbon," you don't know if that's a lot or a little. Is it a half-full tank, or is it already full?

2. The Solution: The "Site Form" Ruler

To solve this, the researchers needed a way to measure the "potential" of the land itself, regardless of how old the trees are or how many there are.

They used a tool called the Site Form (SF) index.

The Analogy: Imagine you have a group of runners. To know how fast they could run, you don't just time them once. You look at their height and leg length (their physical potential) to predict their top speed.
In the Forest: The researchers looked at the height of the tallest trees compared to their trunk thickness (diameter). This ratio tells them how "fertile" or "productive" the soil is. A tree that is very tall for its thickness is growing on "rich soil" (high potential). A short, thick tree is on "poor soil" (lower potential).

This "Site Form" acts like a ruler for the land's potential. It doesn't care about the age of the forest; it only cares about the quality of the ground.

3. The Experiment: Mapping the "Ceiling"

The team gathered data from thousands of forest plots across nine European countries (from Spain to Sweden). They also looked at two special groups:

The "Wild" Group: Ancient, untouched forests in Latvia (old-growth).
The "Managed" Group: Forests in Spain where humans have been thinning trees to help them grow.

They plotted the data on a graph:

X-axis: The "Site Form" (Land Quality).
Y-axis: The amount of Carbon stored.

The Result: They found a clear "ceiling." For every level of land quality, there is a maximum line (the PCSC) that the carbon storage cannot easily cross.

High-quality land has a high ceiling (it can hold a lot of carbon).
Low-quality land has a lower ceiling.

4. What They Found in the Case Studies

The Wild Forests (Latvia): Even in these ancient, untouched forests, the trees didn't always reach the "ceiling." Sometimes, nature's own disturbances (like wind or disease) kept the forest below its maximum potential. This proves that just being "old" doesn't mean a forest is storing the maximum amount of carbon possible.
The Managed Forests (Spain): When humans cut down trees (thinning), the carbon stored in the standing trees dropped. The more intense the cutting, the lower the carbon stock. However, the researchers noted that if you wait until the trees are fully grown again, the total carbon (what's left in the trees + what was cut and used) might eventually catch up. But at any single moment, heavy management usually means the forest is "below the ceiling."

5. Why This Matters (The "Why Should You Care?")

The paper argues that we need a standardized benchmark to judge forests, similar to how a car manufacturer knows the maximum speed of a specific engine model.

For Policymakers: Instead of just counting how much carbon is in a forest right now, we can compare it to the "ceiling" for that specific type of land.
- If a forest is at 90% of its ceiling, it's doing great.
- If it's at 40%, we know there is room to grow more carbon, either by changing how we manage it or by letting it recover.
For Climate Agreements: This helps countries report more accurately on their progress toward climate goals (like the Paris Agreement). It helps answer: "Are we losing carbon due to bad management, or is this just the natural limit of this land?"

Summary

Think of this study as creating a map of "fullness" for European pine forests. By measuring the "shape" of the trees (height vs. width), the researchers can predict the maximum storage capacity of the soil beneath them. This allows us to see if a forest is operating at full potential or if it has room to grow more carbon, helping us make smarter decisions to fight climate change.

Technical Summary: Modelling the Potential Carbon Storage Capacity of Pinus sylvestris Forests

Problem Statement
Accurately assessing the maximum capacity of forests to store carbon in the absence of disturbances—defined as Potential Carbon Storage Capacity (PCSC)—is critical for National Greenhouse Gas Inventories (NGHGIs) and effective forest management. Current methodologies often rely on the Carbon Carrying Capacity (CCC), defined by primary forests, which can yield unrealistic comparisons when managed forests on fertile sites outperform intact forests due to historical site selection or disturbance suppression. Furthermore, existing biomass stock-change approaches used in NGHGIs require multiple measurements over time, limiting their utility when only single-timepoint data is available. There is a need for a site-quality-dependent reference framework that allows for comparable estimates of Aboveground Carbon (AGC) across diverse European ecological zones, enabling the evaluation of carbon stock losses, potential gains, and mitigation benefits relative to a theoretical maximum.

Methodology
The study focused on Pinus sylvestris L., the most widespread coniferous species in Europe, utilizing a dataset comprising 34,948 plot measurements from National Forest Inventories (NFIs) across nine countries (Czech Republic, France, Germany, Italy, Norway, Slovakia, Spain, Sweden, Switzerland) and two specific case studies: old-growth forests in Latvia and managed thinning trials in Spain.

Site Quality Quantification: The authors employed the Site Form (SF) index, defined as the dominant height ( $H_0$ ) at a reference dominant diameter ( $D_{0ref}$ ). Unlike the traditional Site Index (based on age), SF is age-independent, making it suitable for NFIs where age data is often missing or unreliable.
Model Fitting (SF): Using the Generalized Algebraic Difference Approach (GADA) with a Bertalanffy-Richards base equation, the authors fitted dynamic curves to model dominant height trajectories. The model incorporated dummy variables to account for differences across Global Ecological Zones (GEZs): Boreal, Subtropical, and Temperate. A reference dominant diameter of 32 cm was selected as the optimal predictor.
PCSC Estimation: The relationship between AGC (Mg C ha⁻¹) and SF (m) was modeled using a guide curve methodology. Separate asymptotic parameters were fitted for different GEZs to define the theoretical upper limit of carbon storage. The models were validated using cross-validation and residual analysis.
Case Studies: The framework was applied to:
- Latvia: 31 old-growth stands (86 plots) to assess if natural forests reach PCSC.
- Spain: 10 thinning trials with varying intensities (control, light, moderate, heavy) to evaluate the impact of management on AGC relative to PCSC.

Key Results

Site Form Modeling: The GADA model with dummy variables for GEZs demonstrated high robustness ( $R^2 = 0.999$ , RMSE = 0.193 m), confirming SF as an effective proxy for site productivity across the latitudinal gradient (37° N – 70° N).
PCSC Curves: Distinct PCSC curves were generated for each GEZ. The results indicate that PCSC increases with site quality (SF). Boreal coniferous forests exhibited lower PCSC values compared to temperate and subtropical zones, which showed similar potential carbon stocks.
Case Study Findings:
- Old-growth forests (Latvia): While some plots approached PCSC, significant variability was observed. The study concluded that the "old-growth" status alone does not guarantee a stand has reached its maximum carbon storage potential; PCSC serves better as a reference boundary to estimate the intensity of natural disturbance regimes.
- Managed forests (Spain): AGC decreased with increasing thinning intensity (Control > Light > Moderate > Heavy). However, the study noted that the "plateau rule" suggests that over a full rotation, the sum of standing stock and extracted wood in thinned plots may equal the standing stock of unmanaged plots, though the timing of maximum stock differs.
Model Performance: The PCSC model achieved an $R^2$ of 0.578 with an RMSE of 45.336 Mg C ha⁻¹. The authors acknowledge that the model focuses on aboveground carbon and excludes belowground and dead biomass, which can constitute a significant portion of total carbon, particularly in old-growth systems.

Significance and Claims
The paper posits that establishing a PCSC baseline based on site quality offers a complementary accounting solution to current NGHGI methods. The authors claim this framework is significant for three primary reasons:

Evaluation of Losses: It enables the consistent evaluation of past carbon stock losses due to human and natural disturbances by comparing current stocks against a site-specific theoretical maximum.
Prediction of Gains: It facilitates the prediction of potential carbon stock gains achievable through changes in forest management practices.
Assessment of Mitigation: It allows for the assessment of mitigation benefits lost when forests are managed with carbon reserves below their maximum capacity.

The study emphasizes that this approach is particularly relevant for continuous cover forestry (CCF) and unmanaged systems where maximum carbon potential is not defined by rotation age. It aligns with international frameworks like the Paris Agreement and the EU Forest Strategy by providing a mechanism to "conserve and enhance" carbon sinks. However, the authors remain cautious, noting that while promoting PCSC is a mitigation strategy, it may conflict with adaptation strategies (e.g., reducing density for drought resilience) and that the methodology requires careful application to avoid overestimating potential in disturbed or low-naturalness forests. The study concludes that while the methodology is developed for P. sylvestris, the framework has potential applicability to other forest types.