StatrivaEcology and biodiversity statistics that connect community patterns to ecological questions.
Analysis for ecological, environmental, microbiome, and biodiversity datasets, with emphasis on multivariate structure, diversity metrics, ordination, and clear interpretation of ecological results.
From species tables to interpretable community patterns
Ecological datasets often combine many taxa, environmental variables, groups, and sampling factors. The analysis needs to respect that structure.
Observed richness, Chao1, Shannon, Simpson, and related indices when appropriate.
Distance-based approaches for comparing multivariate community structure.
NMDS, PCA, RDA, and related approaches for visualizing and explaining multivariate patterns.
Relate community patterns to soil, climate, habitat, management, or other measured variables.
Ecology & biodiversity toolkit
A significant p-value is not the whole ecological story
A useful analysis connects statistical evidence to effect size, explained variation, community structure, study design, and ecological meaning.
Question first
Define what differs, what predicts what, and which component of community structure is actually being tested.
Distance & design
Choose an appropriate dissimilarity and account for the sampling design rather than treating every observation as independent by default.
Interpretation
Report the statistical result together with the information needed to understand the magnitude and structure of the pattern.
Typical ecological analysis workflow
Audit the community table and metadata
Check sample identifiers, taxonomy or feature structure, environmental variables, missingness, and grouping factors.
Define diversity and multivariate questions
Choose metrics and ordination or hypothesis-testing approaches that correspond to the biological question.
Run, diagnose, visualize, explain
Produce publication-ready figures and explain what the analysis supports, and what it does not.
Working with an ecology dataset?
Send the research question, a sample of the data or metadata, and any analysis already attempted. The first step is to determine the right analytical scope.
View the ecology gig