Defensible models.Confident decisions.
We make technical diligence easier by turning ecological data and model assumptions into clear, defensible analysis that helps projects secure funding and investors assess delivery risk.
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Ex-ante carbon modeling
Build robust, investor-ready project models that stand up to diligence.
Growth & survival
Species-, site-, and ecosystem-specific trajectories, mortality, and uncertainty.
MRV & technical diligence
Pressure-test assumptions, monitoring strategies, and projected delivery risk.
Ecological rigor for better carbon decisions.
Dendro Analytics is an independent technical consulting practice focused on making nature-based carbon models more credible, transparent, and useful for decision-making.
We work at the intersection of forest ecology, applied statistics, and carbon-market diligence. Our focus is the part of a carbon project where biological reality meets a spreadsheet: how trees grow, survive, die, accumulate biomass, respond to climate, and translate into expected credit delivery over time.
For developers, that means building ex-ante models and ecological assumptions that can stand up to investor, buyer, registry, and third-party review. For investors and buyers, it means independently pressure-testing project models to understand where delivery risk actually comes from and which assumptions matter most.
Our approach is grounded in first-principles ecology rather than treating carbon trajectories as purely financial forecasts. We combine empirical data, peer-reviewed literature, uncertainty analysis, spatial and climate information, and transparent modeling to make diligence faster and easier to defend.
Dendro Analytics is led by R. Alex Thompson, PhD, a forest ecologist with formal training in applied statistics and experience evaluating nature-based carbon projects from the investment side. That combination gives us a practical view of both how projects are built and how technical claims are challenged during diligence.
Carbon modeling across diverse ecological systems.
Representative regions where we have built or evaluated nature-based carbon models. Locations are intentionally generalized and do not identify specific projects, clients, boundaries, or investment decisions.