environment-life-review-forge

name

environment-life-review-forge

description

Adapts evidence synthesis workflows for environmental, ecological, biomedical, and life-science questions. Use for PECO/PICO frameworks, exposure-outcome reviews, ecological heterogeneity, dose-response evidence, risk-of-bias planning, environmental indicators, NDVI or vegetation-index models, partial least squares regression, PLS VIP audits, ecosystem-service relationships, ESR synergy/trade-off mapping, interpretable machine learning, GWR/XGBoost spatial modeling, threshold-oriented ecological management, optimal interval identification, air pollution crop-yield models, ozone/aerosol food-security co-benefits, SIF-based crop productivity, soil biodiversity, aridity gradients, ecosystem stability, climate-stress moderation, soil fauna meta-analysis, ant-mediated carbon cycling, SOC and CO2 dual-outcome synthesis, organism/tissue/time-scale coding, wetland methane scaling, small-patch geospatial upscaling, cryosphere or permafrost evidence products, near-surface ground ice mapping, geospatial environmental maps, spatial autocorrelation audits, map uncertainty audits, food-system environmental nexus reviews, food-waste geospatial ML forecasting, agroecosystem nutrient meta-analysis, crop-yield machine-learning prediction, environmental causal machine learning, double machine learning, spatial DML, urban heat causal inference, interpretable exposure-response modeling, genotype-environment modeling, agricultural irrigation optimization, brackish-water irrigation, GAM or NSGA-II trade-off modeling, environmental scenario modeling, land-use optimization, Pareto-frontier trade-off synthesis, policy trade-off synthesis, system-hub environmental variables, nitrogen-SDG coupling, safe-boundary synthesis, hotspot-layer identification, and domain-specific systematic review protocols.

Environment Life Review Forge

Use this skill for environmental, ecological, biomedical, and life-science systematic reviews where exposure, organism/population, outcome, context, and study design need careful domain adaptation.

Core Principle

Domain structure matters. The same effect-size workflow may be misleading if exposure windows, species, tissues, endpoints, geography, or measurement platforms are not comparable.

Intake

Identify:

• domain: environment, ecology, toxicology, epidemiology, life science, molecular biology, public health;
• framework: PECO or PICO;
• population or organism;
• exposure or intervention;
• comparator;
• outcomes/endpoints;
• study design;
• spatial and temporal scale;
• ecosystem-service set, pairwise relationship definition, synergy/trade-off coding, and threshold-management target, if ecosystem-service relationships are in scope;
• pollutant exposure, crop outcome, food-security endpoint, counterfactual air-quality target, and crop-calorie translation, if air-quality food-security modeling is in scope;
• biodiversity dimension, stability metric, climate-stress gradient, and moderation/interaction target, if biodiversity-stability evidence is in scope;
• spatial unit and aggregation boundary, if geospatial prediction is in scope;
• minimum mapping unit and detection threshold, if small-patch systems are in scope;
• target map variable, spatial resolution, observation inventory, predictor stack, spatial autocorrelation plan, and uncertainty layer, if an environmental map product is in scope;
• measurement method;
• bidirectional pathways, if impacts and feedbacks are both in scope;
• expected heterogeneity.

Load:

• references/environmental-life-science.md for domain heterogeneity.
• references/cee-alignment.md for environmental evidence standards.
• references/pls-vip-environmental-indicators.md for NDVI, vegetation, soil, climate, ecological indicator, PLS regression, and VIP interpretation audits.
• references/ecosystem-service-threshold-ml.md for ecosystem-service relationship mapping, GWR-plus-ML workflows, nonlinear driver interpretation, threshold/optimal-interval identification, and spatial management translation.
• references/air-quality-food-security.md for ozone, aerosol, SIF, crop yield, crop-calorie, counterfactual air-quality targets, and food-security co-benefit modeling.
• references/soil-biodiversity-aridity-stability.md for soil biodiversity, aridity gradients, ecosystem stability, climate-stress moderation, and biodiversity-function buffering claims.
• references/ant-soil-carbon-meta.md for soil-fauna meta-analysis, ecosystem-engineer effects on SOC stock and CO2 flux, trait-mediated moderators, and climate-context extraction.
• references/small-wetland-methane-scaling.md for wetland methane, small water bodies, fine-resolution remote sensing, and scale-sensitive upscaling.
• references/cryosphere-ground-ice-mapping.md for permafrost, near-surface ground ice, borehole observations, geospatial predictors, ensemble machine learning, spatial autocorrelation, prediction intervals, and public map-data audits.
• references/agroecosystem-nutrient-meta-analysis.md for crop yield, soil organic carbon, fertilizer, amendment, and nutrient-management meta-analyses.
• references/agricultural-ml-yield-prediction.md for crop-yield prediction studies integrating meteorological, breeding, genomic, remote-sensing, or field-trial data.
• references/agricultural-irrigation-optimization.md for brackish-water irrigation, water-salt-yield-emission trade-offs, GAM nonlinear response modeling, NSGA-II optimization, and decision ranges such as ECw management windows.
• references/environmental-causal-ml.md for environmental causal machine learning studies using DML, CATE, AutoML, SHAP/PDP-style interpretation, high-dimensional pollutant exposure data, socioeconomic covariates, ARGs, drinking-water safety, or One Health outcomes.
• references/food-system-bidirectional-nexus.md for food-system reviews linking environmental pressures, feedbacks, trade, diets, crops, livestock, and aquatic foods.
• references/food-waste-geospatial-ml.md for county, city, supply-chain, or market-level food-waste forecasting with geospatial analytics and machine learning.
• references/environmental-scenario-synthesis.md when a review builds a literature-derived database, machine-learning/spatial model, or policy scenario simulation.
• references/land-use-optimization-tradeoffs.md when a study uses multiobjective optimization, Pareto frontiers, land-use allocation, or food-water-carbon trade-off modeling.
• references/system-hub-policy-synthesis.md when a paper uses one focal variable, such as nitrogen, carbon, water, phosphorus, air pollution, or biodiversity pressure, to connect multiple environmental, production, health, or policy outcomes under a boundary or scenario framework.

Workflow

1. Build PECO/PICO.
2. Define exposure or intervention precisely.
3. Define outcome families and measurement units.
4. Specify eligible designs.
5. Identify heterogeneity sources.
6. Plan risk-of-bias or study quality appraisal.
7. Plan grey-literature and supplementary search if relevant.
8. Decide narrative, evidence map, or meta-analysis.
9. Build domain-specific extraction table.

Use templates/peco-framework.md. Use templates/pls-vip-environmental-audit.md for PLS/VIP environmental indicator studies. Use templates/ecosystem-service-threshold-audit.md and templates/ecosystem-service-threshold-schema.csv for ecosystem-service relationship threshold-management studies. Use templates/air-quality-food-security-audit.md and templates/air-quality-food-security-schema.csv for air-pollution, crop-yield, SIF, and food-security co-benefit studies. Use templates/biodiversity-stability-climate-stress-audit.md and templates/biodiversity-stability-climate-stress-schema.csv for soil biodiversity, aridity, ecosystem-stability, and climate-stress moderation studies. Use templates/soil-fauna-carbon-meta-audit.md and templates/soil-fauna-carbon-schema.csv for ant, termite, earthworm, or other soil-fauna meta-analyses that separate SOC stock, CO2 flux, and organic-matter stability outcomes. Use templates/wetland-methane-scale-audit.md and templates/wetland-methane-geospatial-schema.csv for small-wetland methane and scale-sensitive upscaling studies. Use templates/cryosphere-ground-ice-map-audit.md and templates/cryosphere-map-validation-schema.csv for permafrost, near-surface ground ice, and other cryosphere map products. Use templates/food-environment-bidirectional-audit.md and templates/food-environment-pressure-schema.csv for food-system nexus reviews. Use templates/food-waste-forecast-audit.md and templates/food-waste-geospatial-feature-schema.csv for geospatial food-waste forecasting. Use templates/dual-outcome-meta-audit.md and templates/nutrient-meta-extraction-schema.csv for agroecosystem nutrient meta-analysis. Use templates/nutrient-meta-reproducibility-ledger.csv when a nutrient meta-analysis provides Zenodo/OSF/GitHub data and code. Use templates/nutrient-meta-dataset-schema.csv and templates/nutrient-meta-r-workflow-blueprint.csv when designing data tables and R scripts for nutrient meta-analysis. Use templates/ml-yield-prediction-audit.md and templates/ml-yield-feature-schema.csv for agricultural ML yield-prediction studies. Use templates/environmental-causal-ml-audit.md and templates/environmental-causal-ml-feature-schema.csv for environmental causal ML studies. Use templates/irrigation-optimization-audit.md and templates/irrigation-optimization-schema.csv for brackish-water irrigation and water-salt-yield-emission optimization studies. Use templates/scenario-model-audit.md and templates/policy-scenario-matrix.csv for scenario-model evidence synthesis. Use templates/pareto-frontier-audit.md and templates/multi-objective-tradeoff-schema.csv for multiobjective optimization and land-use trade-off studies. Use templates/system-hub-variable-audit.md and templates/system-hub-variable-schema.csv for papers that organize evidence around a system hub variable, safe boundary, hotspot layer, co-benefit structure, or policy-portfolio translation.

Output Modes

PECO Protocol Memo

Population:
Exposure:
Comparator:
Outcome families:
Eligible designs:
Heterogeneity:
Risk of bias:
Synthesis plan:

Domain Extraction Plan

Include:

• species/population;
• site/geography;
• exposure dose/intensity;
• duration/window;
• endpoint;
• assay/measurement method;
• confounders;
• study quality.

PLS/VIP Environmental Indicator Audit

Use this mode when a study links NDVI, vegetation productivity, soil quality, biodiversity, ecosystem-service, pollutant, climate, or hydrological indicators to multiple correlated predictors using partial least squares regression and VIP rankings.

Include:

• outcome indicator and unit;
• predictor families and expected ecological meaning;
• sample size, time span, site count, and clustering;
• missing-data and scaling decisions;
• component-count selection rule;
• cross-validation design and whether it respects time, site, or spatial grouping;
• RMSEP, R2/Q2, residual checks, and influential observations;
• VIP table, VIP threshold, and coefficient direction;
• whether VIP is interpreted as predictive importance, mechanism, or causal effect;
• robustness checks such as alternative component count, leave-one-year/site-out validation, or baseline regression comparison.

Ecosystem-Service Threshold ML Audit

Use this mode when a study maps ecosystem services or ecosystem-service relationships, then uses interpretable machine learning, GWR, GAM, XGBoost, GBDT, SHAP, PDP, ICE, ALE, or response-curve overlays to identify management thresholds or optimal driver intervals.

Include:

• ecosystem services and pairwise ESR definitions;
• spatial unit, resolution, years, and service-assessment models;
• relationship coding: synergy, trade-off, co-benefit, competition, or probability of comprehensive synergy;
• driver families: climate, topography, landscape, land use, human pressure, accessibility, and policy;
• spatial heterogeneity model such as GWR and its bandwidth/kernel choices;
• ML model set, tuning, validation, and spatial leakage checks;
• interpretability method and whether thresholds come from PDP, SHAP dependence, ALE, response-curve superposition, or another method;
• optimal interval definition, probability target, and uncertainty;
• zoning or management translation and whether it is descriptive, predictive, or causal;
• robustness checks against alternative service models, spatial resolution, model family, and threshold rule.

Air Quality Food-Security Audit

Use this mode when a study estimates how ozone, PM2.5, aerosol optical depth, diffuse radiation, SIF, temperature, or related atmospheric conditions affect crop productivity, crop yield, calories, self-sufficiency, or food-security indicators.

Include:

• crops, regions, years, and spatial unit;
• pollutant exposure metrics, such as AOT40, peak-season ozone, PM2.5, AOD, or aerosol loading;
• crop outcome: yield, SIF, productivity proxy, calorie output, or supply-demand balance;
• statistical model, flexible functional form, crop fixed effects, spatial effects, weather controls, and trend controls;
• counterfactual air-quality targets and whether targets are policy-based;
• nonlinear and synergistic pollutant-response claims;
• validation against observed yields or independent crop productivity data;
• translation from yield to calories, self-sufficiency, imports, or food security;
• data/code availability and uncertainty in source data, crop area, crop-calorie conversion, and counterfactual scenarios.

Biodiversity Stability Climate-Stress Audit

Use this mode when a study evaluates whether biodiversity, soil biodiversity, microbial diversity, functional diversity, or community composition supports ecosystem stability under aridity, drought, warming, land-use stress, or other climate gradients.

Include:

• biodiversity dimension and measurement platform;
• stability endpoint: temporal stability, resistance, resilience, multifunctionality, productivity variability, or service stability;
• climate-stress gradient and exposure window;
• ecosystem type, spatial domain, sampling design, and temporal depth;
• interaction or moderation model: whether aridity weakens, strengthens, or changes the biodiversity-stability relationship;
• controls for productivity, soil, climate, land use, management, and spatial dependence;
• mechanism evidence from microbes, soil nutrients, plant traits, or food-web structure;
• uncertainty, threshold, nonlinear, and subgroup evidence;
• whether claims are observational associations, experiments, manipulations, or causal estimates.

Soil Fauna Carbon Meta-Analysis Audit

Use this mode when a study synthesizes how ants or other soil fauna affect soil carbon storage, carbon fluxes, organic matter turnover, or stability across ecosystems.

Include:

• focal fauna and functional traits;
• stock versus flux versus stability outcome separation;
• effect-size family and percent-change interpretation;
• climate, latitude, baseline SOC, or ecosystem moderators;
• dependence plan for multiple endpoints per study;
• whether "more storage" and "more emissions" are both present and how the paper interprets that pattern.

Small-Wetland Methane Scaling Audit

Use this mode when a study estimates emissions from small wetlands, ponds, small water bodies, or patchy ecosystems where spatial resolution and minimum mapping unit change the global or regional budget.

Include:

• wetland or water-body size class;
• mapping resolution and minimum detectable area;
• forested/non-forested domain boundary;
• wetland inventory or remote-sensing product;
• flux model or emission-factor source;
• annual trend window;
• contribution to total emissions;
• uncertainty, double-counting, and omission risks;
• implications for methane budgets and restoration policy.

Cryosphere Ground-Ice Map Audit

Use this mode when a study creates or reuses a spatial map product for permafrost, near-surface ground ice, volumetric ice content, active-layer properties, thermokarst susceptibility, or related cryosphere hazards.

Include:

• target map variable and depth convention;
• spatial domain, permafrost mask, map year/window, resolution, and grid definition;
• field observation type, count, spatial distribution, and measurement comparability;
• predictor stack: substrate, hydrology, topography, geology, paleoclimate, modern climate, remote sensing, and vegetation;
• model families, ensemble strategy, calibration data, and simulation count;
• validation design, including independent or spatially blocked validation;
• spatial autocorrelation, sampling bias, and extrapolation checks;
• accuracy, bias, RMSE, R-squared, prediction interval, and uncertainty maps;
• storage, extent, hazard, infrastructure, climate, hydrology, and ecosystem interpretations;
• data DOI, code availability, and versioning of map products.

Agroecosystem Nutrient Meta-Analysis

Use this mode for fertilizer, manure, compost, liming, biochar, and nutrient-management reviews with crop, soil, emission, or microbial outcomes.

Include:

• intervention nutrient form and rate;
• comparator nutrient background;
• crop or ecosystem;
• soil baseline status;
• climate and geography;
• experiment duration;
• yield endpoint;
• soil-carbon or soil-health endpoint;
• response-ratio or percent-change metric;
• moderator plan;
• dual-outcome trade-off interpretation.

Food-System Bidirectional Nexus Review

Use this mode when food production affects environmental pressures and those environmental changes feed back onto food production.

Include:

• food-system scope and commodity groups;
• pressure domains: nutrients, water, biodiversity, climate, land use, soil, pollutants;
• feedback receptors: crops, livestock, blue foods, food security;
• trade or displacement pathways;
• supply-side strategies;
• demand-side strategies;
• circularity or redesign strategies;
• regional vulnerability and equity concerns;
• evidence certainty and data-source limits.

System-Hub Variable Audit

Use this mode when a paper treats nitrogen, carbon, water, phosphorus, potassium, air pollution, biodiversity pressure, or another focal variable as a systems connector linking environmental burdens, productivity, health, food security, ecosystem function, or policy goals.

Include:

• focal system variable and why it is treated as a hub;
• coupled outcome families and which are measured versus translated;
• boundary, threshold, or target definition;
• sectors, regions, or layers included;
• hotspot or hidden-layer logic;
• co-benefit and trade-off pattern;
• scenario or policy-portfolio structure;
• uncertainty in the translation from biophysical result to policy claim;
• cross-domain lesson that could transfer to other nexus variables.

Food-Waste Geospatial ML Forecast

Use this mode when a study predicts food waste or organic waste across counties, cities, markets, facilities, or supply-chain regions.

Include:

• waste stream and supply-chain boundary;
• spatial unit and resolution;
• target estimate source and measurement limits;
• demographic, retail, expenditure, income, policy, and access predictors;
• model families and hyperparameter tuning;
• spatial validation and leakage checks;
• feature importance and uncertainty;
• logistics, facility siting, circular bioeconomy, or waste-to-resource interpretation;
• limits on translating predicted waste into collectible feedstock.

Crop-Yield ML Prediction Audit

Use this mode when a study predicts crop yield from weather, genotype, breeding value, trial, remote-sensing, soil, or management inputs.

Include:

• crop, region, years, sites, genotypes, and trial design;
• input data families and temporal aggregation;
• target yield definition and unit;
• model families compared;
• hyperparameter tuning and validation split;
• leakage risks across site, year, genotype, and repeated trials;
• performance metrics;
• interpretability method;
• extrapolation and future-climate assumptions;
• reproducibility and data-access status.

Environmental Causal ML Audit

Use this mode when a study claims causal effects from high-dimensional environmental, pollutant, socioeconomic, microbial, omics, health, or infrastructure data using DML, causal forests, meta-learners, AutoML-plus-causal-inference, or interpretable ML.

Include:

• unit of observation and sampling frame;
• treatment/exposure definitions;
• outcome endpoint and measurement platform;
• covariate timing and confounder set;
• predictive model and causal estimator;
• nuisance-model, cross-fitting, and overlap checks;
• interpretability method and whether it is causal or predictive;
• sensitivity to hidden confounding, spatial dependence, and multiple testing;
• policy or risk-assessment claim and limits.

For urban heat DML, also include:

• treatment or exposure such as heatwave event, green space, impervious surface, shade, cool roof, ventilation, or adaptation policy;
• outcome such as LST, SUHI, air temperature, heat exposure, nighttime lights, activity, energy use, or health;
• remote-sensing, morphology, meteorology, population, income, activity, and infrastructure confounders;
• spatial autocorrelation, spillover, clustered inference, and panel/event-study structure;
• whether the claim is average effect, heterogeneous effect, or mediation.

Irrigation Salinity Optimization Audit

Use this mode when a study combines field trials, statistical response curves, and multiobjective optimization to choose irrigation or nutrient-management windows.

Include:

• crop, region, irrigation method, salinity or nutrient treatments;
• response variables: yield, quality, soil properties, GHG emissions, water use, salt balance;
• model used to estimate nonlinear responses, such as GAM;
• optimizer used to define compromise ranges, such as NSGA-II;
• objectives, constraints, units, and direction of improvement;
• decision range and whether it is site-specific;
• risks from short time windows, salinity accumulation, unmeasured costs, and extrapolation.

Environmental Scenario Audit

Use this mode when papers combine study extraction, model prediction, spatial extrapolation, and policy scenarios.

Include:

• evidence database scope;
• target variable and units;
• model family and validation plan;
• spatial/temporal extrapolation assumptions;
• scenario levers;
• management-quality assumptions;
• uncertainty and sensitivity checks;
• policy claim and limits.

Pareto-Frontier Trade-Off Audit

Use this mode when a study optimizes across multiple environmental or ecosystem-service objectives.

Include:

• decision unit;
• land-use or management options;
• objectives and units;
• baseline/reference;
• constraints and excluded areas;
• model used to generate objective values;
• optimization algorithm;
• frontier interpretation;
• priority maps and consensus;
• transition costs, emissions, biodiversity, governance, and realism limits.

Guardrails

• Do not pool across species, exposure windows, or outcome constructs without a biological or environmental rationale.
• Do not collapse mechanistic, observational, and experimental evidence without design labels.
• Do not hide geography, climate zone, tissue type, or measurement platform when they drive heterogeneity.
• Do not treat PLS VIP rankings as causal drivers of an environmental outcome; VIP is a model-dependent predictive summary under a chosen component count and scaling.
• Do not interpret PLS/VIP without component-selection, cross-validation, coefficient direction, and residual checks.
• Do not treat ESR thresholds from PDP, SHAP, ALE, or response curves as causal ecological tipping points without experimental, quasi-experimental, mechanistic, or temporal evidence.
• Do not translate model-derived optimal intervals into zoning rules without auditing spatial leakage, resolution, service-model uncertainty, threshold uncertainty, and local feasibility.
• Do not treat air-quality food-security counterfactuals as realized production gains without checking crop area, weather, management, prices, imports, storage, and policy implementation.
• Do not treat SIF or remote-sensing productivity proxies as yield unless crop-specific validation and conversion logic are explicit.
• Do not claim soil biodiversity stabilizes ecosystems under aridity without defining the stability metric, stress gradient, temporal scale, spatial dependence, and possible productivity confounding.
• Do not interpret biodiversity-stability moderation as causal unless the design supports causal identification or experimental manipulation.
• Do not flatten soil-fauna effects into one "carbon benefit" or "carbon cost" label when stock and flux outcomes move in different directions.
• Do not treat coarse-resolution wetland products as scale neutral; audit which small features are omitted.
• Do not treat a geospatial environmental map as ground truth; audit observation density, spatial autocorrelation, permafrost or domain masks, predictor time windows, uncertainty layers, and extrapolation zones.
• Do not compare cryosphere maps across products or periods without checking target definition, depth convention, permafrost boundary, resolution, map vintage, and uncertainty.
• Do not compare methane budgets without checking double-counting between wetlands, lakes, rivers, reservoirs, and inundated vegetation.
• Do not turn methane emissions alone into a wetland management recommendation without carbon storage, biodiversity, hydrology, and ecosystem-service trade-offs.
• Do not use vote-counting as evidence of effect direction or strength.
• Do not treat SHAP, feature importance, PDP, or AutoML performance as causal evidence without a separate identification strategy.
• Do not call DML or CATE estimates causal unless treatment timing, confounder adjustment, overlap, and hidden-confounding risks are explicit.
• Do not treat DML as a generic prediction method; it estimates causal parameters only under stated identification assumptions.
• Do not treat spatial DML as solved by adding coordinates; audit spatial dependence, spillovers, clustered inference, and validation boundaries.
• Do not interpret short-term yield gains as soil-carbon sequestration evidence without duration, baseline SOC, depth, and measurement-method checks.
• Do not collapse fertilizer form, nutrient rate, baseline nutrient limitation, crop type, and climate into one pooled effect without moderator or subgroup logic.
• Do not describe food-system impacts in only one direction when the review question is bidirectional; map both pressure and feedback pathways.
• Do not treat global average food footprints as local risk without tracing trade, geography, vulnerability, and production-system context.
• Do not treat county-level predicted food waste as directly collectible waste without collection participation, contamination, hauling cost, facility capacity, and policy constraints.
• Do not ignore spatial autocorrelation, adjacency leakage, urban-rural imbalance, or high-waste outliers when auditing geospatial ML forecasts.
• Do not interpret store counts, restaurant counts, population, income, or social-program access as causal drivers unless the design supports causal inference.
• Do not treat random train/test performance in crop trials as proof of generalization to new years, sites, or genotypes; require grouped or out-of-domain validation where the claim needs it.
• Do not interpret variable importance, PDPs, or SHAP values causally unless the design supports causal interpretation.
• Do not present future yield forecasts without explicit weather/climate inputs, scenario assumptions, and extrapolation checks.
• Do not treat a machine-learning scenario projection as causal proof unless the design supports causal interpretation.
• Do not present a policy lever, such as recycling rate or intervention coverage, as sufficient without auditing implementation quality and compensating inputs.
• Do not treat a Pareto-efficient frontier as a politically feasible pathway without social, economic, governance, biodiversity, and transition-cost constraints.
• Do not collapse global objective gains into local welfare claims without regional supply, trade, livelihood, water-access, and justice checks.
• Do not generalize an irrigation salinity decision range across regions without soil texture, groundwater depth, climate, crop variety, irrigation method, and long-term salt-balance checks.