How we know this.
Some values are recorded facts; others we work out from open data, using a method we publish. Either way the rule is the same: show where it came from, label it, and be straight about what it can and can’t tell you. Here are those rules, then the exact method behind each value we work out ourselves.
The rules we follow
A field with no openly-licensed value stays blank — never guessed, never filled with a made-up number. Coverage is uneven, and that’s fine: a blank is the honest answer.
Every value traces back to an open dataset, or to a published method built from open data. We check the licence at the original source, not a copy — and link it so you can check too.
We only work a value out when we have (1) a published, agreed-on method, (2) a clear “derived” label on the page, (3) a citation, and (4) an honest note on what it can’t tell you. A worked-out value is never shown as a direct observation.
You’re here to garden, not to read a journal. We lead with a label anyone understands (“Leaf build”, “Heat tolerance”) and keep the technical name, number, and method for the curious — here, and in an expandable on each plant page.
How each value is made
9 traits · 6 derived · the rest direct factsLeaf build
Direct factHow thick and tough a plant’s leaves are — thin, lightweight leaves grow fast and crave light, while thick, tough leaves last longer and shrug off shade, drought and grazing.
We read each species’ measured specific leaf area (leaf area per unit dry weight) from the Global Spectrum dataset and band it: thin & fast (high SLA), medium, or thick & tough (low SLA). The global median is about 10 mm²/mg.
- Built from
- Díaz et al. 2022 Global Spectrum (TRY-81) — species-mean SLA (from LMA), leaf area & leaf dry-matter content
- Coverage
- ~10k catalog species have specific leaf area; ~2k also have leaf dry-matter content
- Honest limits
- A species-mean from pooled measurements — individual plants vary with light and site. A broad strategy signal, not a precise per-plant figure.
- Technical
- Specific leaf area (SLA, mm²/mg) = 1000 / leaf mass per area (LMA, g/m²); leaf dry-matter content (LDMC, g/g).
Growth strategy
DerivedWhether a plant gets ahead mainly by out-competing its neighbours, by toughing out hard conditions (drought, shade, poor soil), or by colonising bare ground fast — or some blend of the three.
We place each species on Grime’s competitor–stress-tolerator–ruderal (C–S–R) triangle using the globally-calibrated “StrateFy” method: leaf size drives the competitor score, dense low-area leaves the stress-tolerator score, and thin high-area leaves the ruderal score. The result is a C/S/R percentage mix and one of 19 strategy classes; we show it in plain words and keep the percentages for the curious.
- Built from
- Leaf area, specific leaf area & leaf dry-matter content — Díaz et al. 2022 Global Spectrum (TRY-81), CC BY 4.0
- Coverage
- ~1,034 catalog species (limited by leaf dry-matter content coverage)
- Honest limits
- A species-mean strategy from pooled global leaf measurements — a broad ecological signal, not a precise per-plant or per-site value. Derived, never a measured fact.
- Technical
- Grime CSR via StrateFy (Pierce et al. 2017); C ← leaf area, S ← LDMC, R ← SLA; nearest of 19 reference strategy classes.
Photosynthesis
Direct factHow a plant fixes carbon — most plants use the standard pathway, but some have a heat- and water-efficient one (the kind prairie grasses and corn use), and succulents have a night-time water-saving one for extreme drought.
A recorded categorical fact: each species is tagged C3 (standard), C4 (heat/water-efficient) or CAM (succulent, night-time CO₂ uptake) — or a facultative combination. We only show a trait card for the noteworthy C4/CAM cases; C3 is the unremarkable majority, kept in the data but not surfaced as a card.
- Built from
- TRY Categorical Traits Lookup — PhotosyntheticPathway (CC BY 4.0)
- Coverage
- ~6,400 catalog species (C3 ~5,800 · C4 ~490 · CAM ~70 · facultative combos)
- Technical
- C3 / C4 / CAM photosynthetic pathway (+ facultative C3/C4, C3/CAM, C4/CAM).
Climate niche (heat tolerance & native rainfall)
DerivedThe warmth and rainfall of the places a plant actually grows in the U.S. — a guide to how much heat it can take and whether it’s used to dry or wet country.
We take the plant’s U.S. county range and look up each county’s long-term climate, then summarise: native rainfall = the median annual rainfall across its counties; heat tolerance = the warm end (90th percentile) of average temperature across its range (mirroring how our cold-hardiness floor uses an extreme). Counties + medians blunt the roadside/observer bias that makes point-level climate unreliable. Needs at least five placeable counties.
- Built from
- U.S. county occurrences — GBIF (county FIPS) · County climate normals 1991–2020 — NOAA NCEI nClimDiv (precipitation & average temperature), public domain
- Coverage
- ~11,200 species with ≥5 placeable counties
- Honest limits
- A realized, sampling-biased niche (where it has been recorded, not its physiological optimum), and county climate is coarse — large Western counties span deserts and mountains. Derived guide, never a measured fact.
- Technical
- Median annual precipitation (in) and p90 mean-annual-temperature (°F) over the species’ GBIF county set, banded.
Nutrition (edible part, per 100 g)
Direct factFor plants that are eaten, a rough nutrition label for the edible part — calories, protein, fat, carbs, fibre, and a few key minerals and vitamins per 100 grams.
A small, hand-checked crosswalk: each native edible is matched to its food in the USDA nutrient database (e.g. American persimmon → "Persimmons, native, raw", black walnut → "walnuts, black"), and we read that food’s per-100 g values. We only include matches that are unambiguous; a few genus-level commodities (e.g. mulberries) are marked as the closest food match. We do NOT auto-match by name (too error-prone) — so coverage is deliberately small.
- Built from
- USDA FoodData Central — SR Legacy nutrient values, public domain
- Coverage
- ~14 native edible species (curated; expands as matches are verified)
- Honest limits
- Composition of the edible part as a food (often the cultivated/commodity form), matched by common food name — a guide, not a measurement of the exact wild plant. Never an identification or edibility guarantee.
- Technical
- USDA FDC SR Legacy per-100 g: energy, protein, fat, carbohydrate, fibre, sugars, calcium, iron, vitamin C, potassium.
Mature width
DerivedRoughly how wide a tree or shrub grows when it has room — useful for spacing and picturing the mature garden.
For woody plants that have a height but no measured crown, we estimate width = height × a crown-to-height ratio fit for that plant’s form (conifers narrower than broadleaf trees, shrubs widest), calibrated on our measured open-grown crowns and capped at the largest one ever measured. A measured crown always wins; herbaceous plants get nothing (no anchor).
- Built from
- Measured open-grown crowns — USDA Urban Tree Database (public domain) · Measured wild crowns — Tallo (Jucker et al., CC BY) · Plant height — USDA / TRY
- Coverage
- ~599 woody species without a measured crown
- Honest limits
- A coarse class-median estimate for garden-scale spacing, not a measurement; woody single/multi-stem forms only.
- Technical
- Median crown-width/height ratio per leaf-type × form class; conifer/broadleaf split per Jucker 2022.
Garden height layers
DerivedWhich vertical layer a plant fills in a planting — from low groundcover at the front to the tree canopy at the back — so you can stack short in front of tall and give the bed structure.
We bucket each plant by its mature height (the top of its USDA height range) into four PlantKey layers: Canopy · tree (over 15 ft), Tall · shrub / border (4–15 ft), Mid (1.5–4 ft), and Front · low (under 1.5 ft). These thresholds are our own design scale (not a sourced botanical standard); the Garden Planner uses them to group the season grid, place plants in the bed cross-section, and flag when everything in bloom sits in one layer.
- Built from
- Mature height (max of the range) — USDA PLANTS characteristics / TRY
- Coverage
- any plant with a known mature height (USDA describes ~6% of taxa)
- Honest limits
- A coarse design bucket from the height range’s top, for garden layering — not a botanical category. Boundary plants (e.g. a 4 ft perennial) can fall either side of a line.
- Technical
- Layer = canopy >15 ft · tall 4–15 ft · mid 1.5–4 ft · front <1.5 ft, on max height (ft).
Fruit season
DerivedRoughly when a plant carries ripe fruit or seed, and what colour it is — so you can plan for berries and wildlife food after the flowers fade, not just bloom.
We take the plant’s USDA seed-ripe season (spring, summer, autumn or winter) and spread it across that season’s three calendar months — spring = March–May, summer = June–August, autumn = September–November, winter = December–February — pairing it with the plant’s recorded fruit/seed colour. The Garden Planner draws a berry in that colour across the fruiting months. Because USDA’s colour field mixes fleshy fruit with dry seed, we show this only for woody plants in families that bear a true fleshy fruit — a berry, drupe or pome (serviceberry, dogwood, holly, viburnum, blueberry…) — and we drop the colours that are almost always dry structures (brown seed/pods/samaras, green unripe). That keeps out conifer cones, sagebrush and dandelion seed heads, maple keys and legume pods, which USDA also records a “colour” for. We err toward leaving fruit blank rather than marking one that isn’t fleshy. And because fruit always follows flowering, we trim the season window to the months after the plant finishes blooming, so it never shows fruit before its own flowers.
- Built from
- Seed-ripe season — USDA PLANTS characteristics (Seed Period), public domain · Fruit / seed colour — USDA PLANTS characteristics (FruitSeedColor, PATO), public domain
- Coverage
- woody, fleshy-fruited species (berry / drupe / pome families) with a fruit colour and seed-ripe season
- Honest limits
- Season-level timing only — the real ripening window varies with latitude, weather and year, and is shown across the whole season as an approximation, not a calendar date.
- Technical
- Season→months: spring 3–5 · summer 6–8 · autumn 9–11 · winter 12–2; fruit colour bucketed from USDA FruitSeedColor.
Flower colour
DerivedThe dominant flower colour, read from community-science photos by an AI vision model — shown as an indicative tint, not a botanical measurement.
McKenzie et al. assigned each species a flower colour with a GPT-4V vision model over iNaturalist photos. We use the confident tier plus a separately-labelled lower-confidence top-up (mostly inconspicuous green/brown flowers), and render it as a small tint — never as an asserted fact, and kept out of the written synopsis.
- Built from
- McKenzie et al. 2025 — AI flower-colour over iNaturalist community-science photos
- Coverage
- thousands of species across two confidence tiers
- Honest limits
- AI image inference (~87% expert agreement on the confident tier); a decorative, confidence-tiered indicator, not a measured trait.
Deeper dives
The flagship scores and the full source list have their own detailed write-ups on the Data page.
Open & cited
Knowledge shouldn’t be gated. PlantKey’s compiled factual dataset is CC BY-SA 4.0 and the code is MIT. Every value on every page links to where it came from; derived values say so plainly and cite their method. If a method here is wrong or could be more honest, tell us on GitHub →