1. Clarifying the question
We start by understanding the biological and operational questions, not just the dataset. What decisions are you trying to make? What would “signal” look like in vegetation, soil, or climate space?
- Define hypotheses and contrasts that matter to your team
- Align experimental design and sampling with those questions
- Identify how vegetation, soil, and climate data should be integrated
2. Cleaning and structuring messy data
Real-world datasets are rarely tidy. Gradient focuses on reproducible workflows that make complex, multi-source data usable and auditable.
- Data cleaning, joining, and QA/QC across trials and platforms
- Transparent scripts and documentation for future reuse
- Structures that support both exploratory and confirmatory analyses
3. Modeling across scales
Vegetation responses are shaped by chemistry, soil, microbiome, weather, and history. Our analyses aim to reflect that complexity while remaining interpretable.
- Classical statistics (ANOVA, mixed models, regression) for clear contrasts
- Effect sizes and response ratios to compare chemistries and practices
- Structural equation models to separate direct and indirect pathways
- Climate and spatial data to connect plot outcomes with broader patterns
4. Communicating results for action and publication
We present results in a way that supports both internal decisions and external communication. That means figures, tables, and narratives that make the vegetation, soil, and climate story clear to technical and non-technical audiences.
- Executive summaries for decision-makers
- Technical reports for R&D and stewardship teams
- Manuscript-ready figures and methods sections
- Collaboration with your internal teams on next study iterations