SimA: Forest Ecosystem Model

• SimA is a process-based, gap-type forest ecosystem model that simulates individual-tree responses, carbon and nitrogen cycles, and forest dynamics under various management and climate scenarios.
• Integrated with SIMA-Play, it translates in-game decisions into quantitative estimates, enabling multi-objective trade-off analysis through intuitive visualization outputs.
• SimA supports forestry education and stakeholder discussions by linking ecological, economic, and sustainability outcomes, facilitating systems thinking in forest management.

SimA is presented as a process-based, gap-type forest ecosystem model developed in Finland and used in the serious game SIMA-Play to project forest development under alternative management and climate scenarios (Majhi et al., 6 Apr 2026). In the paper, the acronym itself is not expanded. Functionally, SimA models individual-tree responses, the carbon and nitrogen cycles, and forest dynamics, and in the SIMA-Play framework it serves as the computational layer that converts gameplay decisions into quantitative estimates for economic and sustainability outcomes. This positioning makes SimA less a standalone pedagogical interface than a scientific simulation backend whose outputs are translated into comparative visual feedback for discussion, systems thinking, and forest management reflection.

1. Model identity and scope

In the paper, SimA is introduced as a process-based, gap-type forest ecosystem model developed in Finland (Majhi et al., 6 Apr 2026). Its stated functionality is to model individual-tree responses, the carbon and nitrogen cycles, and forest dynamics under alternative management and climate scenarios. It has been used in Finland for regional predictions and scenario analysis under evolving climate conditions.

Within the scope described there, SimA is not exposed as a user-facing modeling environment with equations or parameter editing by players. Instead, it operates as the analytical engine behind a serious game. This distinction is important. The article does not present SimA as a simplified classroom simulator; rather, it presents it as a scientifically grounded ecosystem model whose outputs are mediated through gameplay and information visualization. A plausible implication is that the pedagogical contribution lies in translation and interface design, not in reducing the model itself to a toy abstraction.

The paper also makes clear what is not specified. Although net present value and multi-criteria evaluation are referenced, it does not provide explicit mathematical formulations or algorithms for growth, carbon accounting, biodiversity indexing, discounting, or multi-objective scoring (Majhi et al., 6 Apr 2026). Formal expressions such as net present value or Pareto front computations are therefore not reproduced.

2. Integration with SIMA-Play

SimA is embedded in a three-part system that combines gameplay mechanics, post-game forest-growth simulation, and information visualization (Majhi et al., 6 Apr 2026). The analogue game presents a 60-year forest management sequence in four decision phases: Year 0 regeneration or planting, Year 30 first commercial thinning, Year 45 second commercial thinning, and Year 60 final harvest. After play, the decisions made during these phases are encoded as management scenarios and fed into SimA.

The gameplay layer is intentionally concrete. The board has 40 square parcels, each representing one hectare of forested land. Each participant starts with ten parcels and an initial budget of €8,000. Players choose Scots pine, Norway spruce, or silver birch per parcel, and use color-coded pins to represent trees, with each pin standing for 400 trees and a maximum initial density of 2,000 trees per hectare (Majhi et al., 6 Apr 2026). These choices, together with later thinning, leasing, and insurance decisions, form the scenario specification later interpreted by SimA.

This coupling gives SimA a specific epistemic role. Rather than driving the game turn by turn, the model is invoked after the session to show how decisions ripple through decades of forest growth, economics, and ecosystem services. The paper frames this as a way to support systems thinking by connecting simplified play decisions to modelled long-term outcomes. A common misunderstanding would be to treat SIMA-Play as a pure board game; the paper instead presents it as a board game whose reflective phase depends on SimA-generated simulation outputs.

3. Inputs, scenarios, and indicator outputs

In SIMA-Play, the data sources for the simulation component are explicitly identified as player decision logs from the analogue game and SimA-generated outputs (Majhi et al., 6 Apr 2026). The decision logs include species choices, planting densities, thinnings, insurance purchases, leasing decisions, and disturbance or market card outcomes. This means the model is not operating on abstract management categories alone; it is parameterized by a structured record of in-game actions across the four decision phases.

The game includes environmental and market dynamics through “multi-risk cards.” These represent disturbances such as mammal grazing of saplings, bark beetle infestations, storms, and fungal root rot, as well as timber price changes. Insurance is parcel-specific to final harvest, and market fluctuation cards adjust timber prices by ±€10/m³ for all players (Majhi et al., 6 Apr 2026). These mechanisms do not replace SimA’s ecosystem modeling, but they shape the management scenarios later submitted to it.

For those scenarios, SimA produces quantitative estimates for tree biomass carbon, total soil carbon, ecosystem carbon, wood products carbon, timber outputs, deadwood, soil water, and net present value (Majhi et al., 6 Apr 2026). The framework is therefore multi-objective by design. The paper does not define composite scores or formal Pareto comparisons; instead, it emphasizes side-by-side comparison across a dashboard of indicators. This suggests that SimA’s role is not to collapse forestry decisions into a single optimum, but to preserve the visibility of competing objectives.

The representative scenario included in the paper clarifies how this works in practice. A spruce- and birch-heavy strategy, combined with leasing and selective insurance, can lead to comparatively higher timber yields but lower ecosystem carbon relative to a peer who diversified species and insured more parcels (Majhi et al., 6 Apr 2026). The article uses this example to surface trade-offs among species choice, disturbance exposure, insurance, and long-term outcomes rather than to present a formally solved optimization problem.

4. Decision support, visualization, and trade-off analysis

The post-game review stage uses SimA-generated visualization outputs to compare players or strategies across multiple indicators (Majhi et al., 6 Apr 2026). The mockup described in the paper uses an interactive data visualization scaled 1–100 and presents outcomes for carbon pools, timber, deadwood, soil water, and net present value. The stated design principles are clarity, comparative context, and feedback that aids sense-making and systems thinking.

Here SimA functions as a decision-support model rather than only a simulator of stand development. The paper repeatedly links the model outputs to trade-off analysis. Players can examine how prioritizing spruce-heavy planting for sawwood may expose stands to beetle risk and volatility, how pine stands may face higher mammal browsing losses at early stages, and how diversified species choices can stabilize outcomes across disturbance regimes (Majhi et al., 6 Apr 2026). These are not framed as algorithmically ranked prescriptions; they are comparative outcomes intended to support discussion.

The paper’s emphasis on visualization also matters methodologically. Abstract ecosystem services are translated into intuitive visual cues so that learners and stakeholders can connect actions to long-term forest dynamics. This is particularly significant in the Finnish context of privately-owned forests and multi-actor governance, where the intended users include students, private forest owners, and professionals (Majhi et al., 6 Apr 2026). A plausible implication is that SimA’s scientific credibility is being operationalized through interface design rather than through direct exposure of model internals.

5. Novelty, reproducibility, and methodological limits

The paper positions the integration of board gameplay with process-based forest simulators and modern interactive visualization as rare and underdeveloped in forestry education (Majhi et al., 6 Apr 2026). In that positioning, SimA provides the model realism that distinguishes SIMA-Play from more generic policy simulators or “SimCity-style experiences.” The stated novelty lies in the direct coupling of a proven forest ecosystem simulator with tactile gameplay and in the use of information visualization to surface multi-objective trade-offs immediately after play.

At the same time, the methodological limits are explicit. The study is described as a design and demonstration study rather than a summative evaluation (Majhi et al., 6 Apr 2026). Formal experiments, learning outcome measures, or engagement analytics are not yet reported. Likewise, the paper does not detail calibration or validation procedures for SimA within this study, nor does it provide parameter tables or algorithmic specifications. These omissions are important for interpreting the system. They limit direct reproducibility of the simulation layer even though the analogue component is supported by an appendix containing the gameboard, play objects, and rulebook.

The reproducibility claim therefore has asymmetry. The analogue game is relatively transparent, while the computational layer is described mainly through its input categories and output indicators. The paper does note that descriptive and inferential statistical analyses can be used to compare strategies and identify decision patterns (Majhi et al., 6 Apr 2026). However, this is a statement about possible analysis rather than a report of a completed empirical evaluation.

6. Practical uses and future research

In practice, SimA is presented as a model that can support forestry education, stakeholder communication, and management reflection when coupled to SIMA-Play (Majhi et al., 6 Apr 2026). Educators can use the system in courses on forest management, sustainability, and systems thinking; policymakers and extension professionals can use it to communicate with forest owners and community stakeholders; and forest managers can use it to explore how risk mitigation, diversification, market volatility, and disturbance interact over decades.

The future research agenda attached to SimA in this framework is extensive. The paper proposes longitudinal studies over 3–6 months with repeated sessions, delayed post-tests at 1, 3, and 6 months, and semi-structured interviews to evaluate systems-level understanding and transfer to real-world scenario planning (Majhi et al., 6 Apr 2026). It also proposes mixed-methods analysis combining quantitative metrics with qualitative coding, digital enhancements such as tablet-based ecological dashboards and continuous feedback overlays, and iterative co-design workshops with end-users.

Further planned work includes controlled trials comparing analogue-only, post-game visualization, and continuous digital-feedback variants; testing multi-session progression systems and different feedback modalities; examining how different player profiles evolve over time; and documenting and sharing simulation parameterizations, scenario libraries, and open materials to strengthen reproducibility and facilitate adaptation to other regions and species mixes (Majhi et al., 6 Apr 2026). Taken together, this roadmap suggests that the present contribution is not a final account of SimA as an educational platform, but a demonstration of how a process-based forest ecosystem model can be inserted into a serious-game workflow to make long-term forest management trade-offs easier to understand, discuss, and teach.