🔖 Poster DOI: http://dx.doi.org/10.13140/RG.2.2.24136.84486

📂 Repository DOI:

📜 Manuscript DOI: https://doi.org/10.1016/j.foreco.2025.122981

• A total of 1,512,721 observations representing 91 species were collected from the Spanish National Forest Inventory.
• The derived mixed-effects model provides unbiased species-specific predictions of total tree heights.
• The inclusion of site qualitative variables in the model plays an important role in improving the model predictability.
• Trees in plantations and pure stands tend to reach greater heights than those in natural and mixed stands.
• Regional variation depends on species, while the Atlantic region has larger trees than other regions.

Accurately characterizing tree allometry is essential for sustainable forest management to predict forest growth and yield, monitor long-term stand dynamics and assess the impacts of disturbances. Among different allometric attributes, past studies have focused on understanding the relationships between tree height and diameter at breast height (dbh), also known as height-diameter (h-d) relationships. Both variables are commonly used to estimate and predict tree and stand metrics (e.g., total volume, biomass and carbon) as well as to assess site productivity. Under limited budget and time constraints, h-d models, which predict tree heights using dbh measurements, provide a practical and cost-effective alternative. In this study, a mixed-effects h-d model was developed for making species-specific predictions for 91 tree species across broad geographic areas in Spain. A total of 1,512,721 observations were collected from the Spanish National Forest Inventory sample plots for analysis.

Results indicate that the final model, selected from a pool of 95 candidates, provides unbiased predictions of total tree height based on the 95% confidence interval of mean bias. In addition to dbh, the inclusion of site qualitative variables (stand origin, species mixture and biogeographic region) in the model plays an important role in improving the model predictability. For a given tree dbh, trees in plantations and pure stands tend to achieve greater heights than those in natural and mixed stands. Regional variation is species-dependent, while the Alpine region with a higher wind speed and cooler temperature tends to exhibit shorter trees compared to other regions. The proposed models are simple in structure and rely on easily-obtainable predictors, making them useful for field application and minimizing the need for complex measurements. This study provides an alternative quantitative tool for forest practitioners and managers when predicting total tree heights for diverse forest ecosystems across a wide range of geographic regions.

👉 As an outcome of this work, you can utilize the following resources available here:

• A new SIMANFOR model: SIMANFOR is a forest management simulator avaiable for free on its website (www.simanfor.es). The model developed in this study was implemented into the simulator. You can access it by selecting the Calcular alturas model during the scenario creation screen. Additionally, models without a specific height-diameter equation will use it when needed. Check the models documentation for more information
• 💻 🔢 Excel calculator: explore that Excel calculator file (both English and Spanish) to run the models developed in this study without effort

• 💻 🧑‍🔬 R functions: explore these R functions to run the models developed in this study without needing to code
• 💻 🐍 Python functions: explore these Python functions to run the models developed in this study without needing to code

• 📂 bibliography: compilation of all the literature cited or consulted during the creation of the document
• 📂 data: raw and processed data, check here for a detailed description
• 📂 output: figures, charts, tables and additional resources included in the document, check here for a detailed description
• 📂 scripts: compilation of the code used for data curation, analysis and outputs included in the document, check here for a detailed description
• 📂 tools: resources developed trough this study, check here for a detailed description

💫 To download the information of that repository, you can follow this guide.

♻️ To reproduce the analysis, users must:

• 💾 Data:

  • ☀️ WorldClim data required must be downloaded from its official website
  • 🌳🌲 Spanish Forest National Inventory (SFNI) data required must be downloaded from its official website, through the links above, or accessed via this GitHub repository

• 💻 Prerequisites: installation and code: R must be installed to run the code with the libraries used in each script. RStudio was used to develop the code. Some analyses may require high computational power, which could lead to out-of-memory issues on a standard computer. Access to high-performance computing services is strongly recommended in such cases.

• 📜 Usage: follow the numerical order of the scripts to reproduce each step correctly

To better understand how SIMANFOR works, you can explore its website, GitHub repository, manual, YouTube playlist or even the last paper.

The content of this repository is under the MIT license.