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Conbio data analysis

Skill brycewang-stanford/Awesome-Journal-Skills/Conservation-Biology-Skills/skills/conbio-data-analysis

Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的 Claude Code/Codex 期刊技能包，从选题、识别策略到表格规范与审稿回复全流程，助你快速发论文。

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill conbio-data-analysis

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What its author says it does

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Use when executing and reporting the analysis for a Conservation Biology manuscript so it survives expert, double-blind review — appropriate ecological/statistical models, honest uncertainty, robustness, and reproducibility. Covers detection, hierarchical models, spatial structure, and effect sizes that matter for conservation. Guides analysis norms; it does not fabricate results.

SKILL.md

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Data Analysis (conbio-data-analysis)

Conservation Biology reviewers are methodologically sophisticated, and the journal expects a data-availability statement with data and code deposited at acceptance (see conbio-reporting-and-data-policy). Analyze as if your code will be re-run — because it may be. This skill covers execution and reporting norms; design decisions live in conbio-study-design.

When to trigger

  • Running main and supporting analyses; building the results section
  • A reviewer asked for robustness, alternative models, or uncertainty
  • Reconciling exploratory vs. confirmatory analyses
  • Making the analysis reproducible before deposit

Analysis norms Conservation Biology expects

  1. Report uncertainty honestly. Confidence/credible intervals, not just stars or p-values; report the magnitude and conservation meaning of the estimate, not only significance.
  2. Use the right model for the data. Hierarchical/mixed models for nested data; occupancy and N-mixture for detection; capture-recapture for survival/abundance; GLMs/GAMs for nonlinearity; account for spatial autocorrelation and zero-inflation where present.
  3. Robustness that probes, not decorates. Show specifications that could break the result (alternative predictors, samples, priors, estimators), and say what you learn.
  4. Right inference. Cluster/group at the correct level; avoid pseudoreplication in the analysis; correct for multiple comparisons when testing many implications.
  5. Confirmatory vs. exploratory. Separate preregistered/confirmatory tests from exploratory ones; do not mine for a significant interaction and theorize it post hoc.
  6. Model checking. Report convergence, residual diagnostics, validation/out-of-sample performance for predictive models; show the result is not an artifact of one modeling choice.

Conservation-specific reporting

  • Translate estimates into decision-relevant quantities (extinction risk, population trend, effect of a management action, area needed) with uncertainty.
  • For projections (PVA, SDM, climate), state the assumptions and the range of plausible outcomes — not a single point forecast.

Reproducibility while you work (not at the end)

  • One master script regenerates every table and figure from the (raw or constructed) data.
  • Set and report seeds for bootstrap, MCMC, simulation, and any stochastic step.
  • Pin software/package versions (renv.lock, requirements.txt, recorded installs).
  • Keep table/figure numbers matched to script outputs.

Anti-patterns

  • Stars/p-values with no effect sizes or intervals
  • Raw counts analyzed as abundance with detection ignored
  • "Robustness" that only reruns near-identical specs to manufacture stability
  • p-hacking / HARKing exploratory results into confirmatory claims
  • A single point projection presented as certain
  • A results section whose numbers the code cannot reproduce

Evidence pass for Conservation Biology

Use this as a second-pass capability check. First lock the species/system threat, conservation decision, and uncertainty relevant to action; then test whether the manuscript addresses conservation-science reviewers who ask whether evidence changes biodiversity, management, or policy action.

  • Primary move: Audit unit, comparison, uncertainty, missingness, sensitivity, and reproducibility before making any prose or submission recommendation.
  • Decision ledger: return claim / evidence / blocker / next edit rows so the next pass can patch the manuscript directly.
  • Neighbor test: compare against Biological Conservation for applied conservation breadth, Global Change Biology for climate/ecosystem process, Ecology Letters for theory-forward ecology; if the neighboring outlet has the stronger audience claim, recommend re-routing before polishing.
  • Submission-ready gate: before final advice, re-open resources/official-source-map.md for upload-week rules and name the one live-check item that could change the recommendation.

Output format

【Main estimate】magnitude + interval + conservation meaning
【Model】why this model fits the data (detection / hierarchy / spatial)
【Robustness】specs that could break it → what held
【Confirmatory vs exploratory】clearly separated?
【Uncertainty in projections】range stated, not a point?
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】conbio-figures-and-tables

Supplementary resources

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