How Sporalo calculates its scores

1Weather growth

For each municipality and each day, the engine assesses four factors between 0 (unfavourable) and 1 (ideal), then multiplies them.

A multiplication rather than an addition: a single unfavourable factor is enough to make the score drop. A heatwave on waterlogged soil, or an ideal temperature after three weeks without rain, gives a low score, just as in the field. This is the principle of the limiting factor.

Favourable temperature by species

Each curve shows the effect of the week's temperature, from nil (bottom) to ideal (top), and the lowland season.

2The habitat

Ideal weather is useless without the right trees. Most of Sporalo's mushrooms are mycorrhizal: they live in association with the roots of certain tree species and do not grow without them. The habitat therefore follows the same logic as the weather: the factors multiply, and a blocking factor makes the score drop whatever the others are.

On the map, the habitat is calculated pixel by pixel (about 800 m), from the forest, soils and land cover at that very spot; the weather, for its part, is that of the nearest municipality, brought down to the altitude of the pixel (see “The daily calculation”). The edge of a wood in a landscape of fields therefore appears on the map, whereas the weather varies more gently.

Check against published observation sites (GBIF): for forest species, the areas judged blocking cover a third to more than half of the municipalities but contain only 3 to 17% of the observations. The habitat therefore separates “forest” from “non-forest” well. However, the fine composition (tree species, pH, aspect) hardly improves the distinction between two wooded areas, for lack of sufficiently precise data (tree species are only known to within 1.4 km).

3The published score

The score normally keeps a share of the weather potential (the “floor”, of the order of a third), because habitat data remain imperfect: copses, hedgerows and woodland edges are not always mapped. When the habitat is blocking, this floor fades gradually and the score becomes very low. The same calculation is done on a spot's page and to paint the map.

• 75 – 100 Very favourable: conditions are right.
• 55 – 74 Favourable.
• 35 – 54 Possible: one or two limiting factors.
• 15 – 34 Unlikely.
• 0 – 14 Unfavourable or out of season.

4Checked against real observations

The settings were calibrated on public observations of these species in France (GBIF, 2010-2025), matched with the historical weather of each place (ERA5 reanalysis). The criterion: a day on which the species was found must get a better score than another day of the same season, in the same place. The settings are adjusted on even-numbered years and checked on odd-numbered years, which are never used during the adjustment.

How to read it: for ceps, the day they were found gets a better score than another day of the same season in the same place in 76% of cases. Chance would give 50%; the earlier version of the calculation got 65% (on the 5 species followed at the time). Morels rest on very few observations: their figure is fragile. Updated: 4 novembre 2026.

What this test says, and does not say. A score of about 70% means a real ability to spot the good days, not a reliable forecast for every outing: in about three comparisons out of ten, an ordinary day is scored higher than a day on which the species was found. The test uses observed weather (ERA5 reanalysis), not forecasts: beyond two or three days, the uncertainty of the weather forecast adds to it. Finally, GBIF observations come mostly from naturalists and walkers: they show where people look as much as where they find.

Habitat check. Separately, the habitat was tested on observation sites compared with randomly drawn municipalities: the correct ranking is obtained in 72 to 84% of cases depending on the species (excluding morels, which no available data predicts well). Compared this time with places where other species grow, that is to say other forests, the result falls to around 50% for most: the habitat says “there is a forest”, not “it is the right forest”.

Belgium, Switzerland and Luxembourg. The same model is applied there, with the same settings, calibrated on France. The habitat was checked against the GBIF observations of each country: the correct ranking against randomly drawn places is obtained in 60 to 82% of cases in Belgium, and only 53 to 65% in Switzerland, where relief and often rounded observation positions make the exercise harder. In Luxembourg, only 363 observations exist: for the three sufficiently observed species (ceps, chanterelles, parasol mushrooms), the correct ranking against randomly drawn places is obtained in 72 to 78% of cases, but the other species do not have enough observations to be tested. The weather part has not yet been checked separately there.

Germany. Same model, same settings. On German GBIF observations (18,587), the habitat correctly ranks the observation site against a randomly drawn place in 73 to 86% of cases depending on the species (excluding morels, 49%); against places where other species grow, the result is 41 to 65%. The east and the north of the country have no tree species map: the habitat there rests on forest, land cover, soils (OpenLandMap) and aspect.

Austria. Same model, same settings. On Austrian GBIF observations, the habitat correctly ranks the observation site against a randomly drawn place in 63 to 72% of cases (morels excepted). The weather part has not yet been checked separately there.

Czechia. Same model, same settings. On Czech GBIF observations (1,382 observations, seven sufficiently observed species): the habitat correctly ranks the observation site against a randomly drawn place in 77 to 86% of cases, except for morels (57%), but only 43 to 61% against places where other species were seen. The weather part has not yet been checked separately there.

Italy. Same model, same settings calibrated on France. The season is not always right there: in a Mediterranean climate, the growth of ceps and chanterelles does not stop in summer in the mountains, and that of saffron milk caps and parasol mushrooms slides towards late autumn and winter in the south. The weather part has not yet been checked separately there. On Italian GBIF observations, the habitat correctly ranks the observation site against a randomly drawn place in 70 to 83% of cases depending on the species (morels: 55%); wood blewits, too little observed, are not tested.

United Kingdom. A single map for the United Kingdom and Ireland. Same model, same settings calibrated on France; in the United Kingdom, the “municipalities” are the 361 districts (including the very large Scottish councils), grouped into 12 regions. On British GBIF observations (19,914 observations), the habitat correctly ranks the observation site against a randomly drawn place in 65 to 80% of cases (morels: 56%, wood blewits: 62%), but only 40 to 64% against places where other species were seen. The season, calibrated on France, is close to the observations (chanterelles and wood blewits about two weeks later). The weather part has not yet been checked separately there.

Ireland. On the same map, with 166 local electoral areas grouped into 26 counties (same settings). Irish GBIF observations are rare (202): only four species can be tested; the habitat correctly ranks the observation site against a randomly drawn place in 85 to 89% of cases for chanterelles, hedgehog mushrooms and saffron milk caps (wood blewits: 54%), and 57 to 65% against places where other species were seen. The weather part has not yet been checked separately there.

Spain. Same model, same settings calibrated on France, applied to the mainland and the Balearic Islands (the Canary Islands, Ceuta and Melilla are not covered: different climate, different weather). On Spanish GBIF observations, the habitat correctly ranks the site of an observation against a randomly drawn place in 78 to 89% of cases for seven species out of nine (65% for wood blewits, 58% for morels), and the weather part, tested on a sample, gives 70 to 90% depending on the species. A known gap: the season calibrated on France is too early for the Mediterranean and Atlantic climate of Spain. The saffron milk cap (níscalo) is mainly observed there from late October to November, and hedgehog mushrooms, black trumpets, wood blewits and funnel chanterelles from November to December, that is after the model's peak season: their scores there are underestimated in late autumn.

5The daily calculation

Every morning, Sporalo retrieves from Open-Meteo 14 days of history and 8 days of forecast for each point. Each municipality blends the weather of the four nearest points (the nearest weigh more), corrected for its own altitude: this avoids the “squares” that a single point would produce.

A map finer than the weather. The weather is only known every ~20 km, but the map is drawn pixel by pixel (about 800 m). Each pixel takes the growth of the nearest municipality, brought down to the real altitude of the pixel (growth is also calculated every morning 400 and 800 m higher and lower, then interpolated), and multiplies it by the pixel's habitat: woods, towns, fields and valley bottoms therefore stand out clearly, whereas the weather varies slowly. Without this correction, two neighbouring points on either side of a municipal boundary, in gorges for example, would take the altitude of two different villages. A spot's page shows the municipality used and the exact coordinates of the clicked point; rankings and municipality pages use the score of the whole municipality.

6Limits

• Not a guarantee. The model ignores picking pressure, the history of each plot and the vigour of the mycelium.
• Imperfect observations. Naturalist data also reflect the activity of observers (weekends, mycological outings).
• What science does not yet know. No published soil moisture or temperature threshold triggers growth; little-studied species (funnel chanterelle, wood blewit, milk caps) are tuned mainly from observations.
• Not yet taken into account. The effect of the previous year's conditions, the age of stands and forest management (thinning, for example, changes production). The often-cited “cold snap” after rain was tested: it does not improve forecasts on French observations.
• Data resolution. Tree species are only known to about 1.4 km, soils to a few hundred metres, the weather to ~20 km: a small isolated wood or a hedgerow may be missing, and the edge of a blocking area is never exact to the pixel.
• Germany. Beyond about 9.9° E and 52.3° N (most of Bavaria, Brandenburg, Saxony, Thuringia and the north), host tree species are unknown: a neutral profile is used. Soils come from OpenLandMap. The forest map is deduced from satellite images as in Belgium.
• Belgium, Switzerland and Luxembourg. The forest map there is deduced from satellite images (ESA WorldCover, recalibrated on the French BD Forêt), soils come from OpenLandMap, and the tree species of the far east of the Grisons are taken from the neighbouring covered municipality. In the mountains, the weather is corrected for the altitude of the point, but not for valley effects (temperature inversions, shade).
• Austria. The tree species atlas only covers the far west: elsewhere, tree species are estimated from altitude and region. Soils come from OpenLandMap. The altitude of an Alpine municipality is that of its forests, not that of its glaciers.
• Czechia. The JRC atlas and SoilGrids could not be used: tree species come from OpenGeoHub's species maps (sessile oak treated as pedunculate oak, birch and downy oak ignored) and soils from OpenLandMap. The forest map is deduced from satellite images as in Belgium.
• Italy. Tree species (JRC atlas) are only available for the north and west of the country: elsewhere, each municipality takes those of the nearest covered municipality at a comparable altitude (a rough approximation, especially in the south and in Sicily); soils come from OpenLandMap. The season calibration comes from France; the Mediterranean climate (dry summer, autumn growth shifted in the south) is not finely reproduced.
• Spain. Tree species come from the maps of Bonannella et al. (2022) and not from the JRC atlas (unreachable at the time of generation): sessile oak, downy oak and birch are absent, maritime pine is replaced by Aleppo pine and stone pine, and holm oaks and cork oaks (hosts of many Mediterranean ceps) are not taken into account. The forest map is deduced from satellite images recalibrated on the French BD Forêt, so it is less reliable for matorrals and dehesas; soils come from OpenLandMap.
• United Kingdom and Ireland. The JRC atlas and SoilGrids could not be used: tree species come from OpenGeoHub's species maps (birch, sessile and downy oak ignored, Sitka spruce and larch not represented) and soils from OpenLandMap; three British districts have no mapped soil (average values). The units are large (districts, electoral areas): the map is coarser than elsewhere, especially in the Highlands. The forest map is deduced from satellite images as in Belgium.
• Forecasts. The coming days rest on a weather forecast whose reliability falls with lead time, especially for rain.
• Morels. Their host tree species (ash, elm, poplar, fruit trees) and burnt areas are not mapped: their habitat score is less reliable.

7Data used

All source data are open. The attribution notices requested by the producers appear when you hover over the “?”; the full licences can be consulted by following the links. Sporalo transforms them (aggregation, interpolation, cross-referencing): the scores are a derived work and are not the original data.

8Scientific bibliography

Peer-reviewed publications on which the choice of factors, their delays and the season windows rest.

What triggers flushes: temperature and rain

1. Kauserud H. et al. (2008). PNAS 105(10):3811-3814 — Mushroom fruiting and climate change. doi.org/10.1073/pnas.070903710534,500 Norwegian records: late-summer rain and temperature drive the timing of the autumn flushes.
2. Kauserud H. et al. (2010). Proc. R. Soc. B 277:1169-1177 — Climate change and spring-fruiting fungi. doi.org/10.1098/rspb.2009.1537Spring mushrooms: warming is bringing their fruiting date forward.
3. Kauserud H. et al. (2012). PNAS 109(36):14488-14493 — Warming-induced shift in European mushroom fruiting phenology. doi.org/10.1073/pnas.1200789109European scale: different responses of mycorrhizal and saprotrophic mushrooms.
4. Gange A.C. et al. (2007). Science 316:71 — Rapid and recent changes in fungal fruiting patterns. doi.org/10.1126/science.1137489England: the fruiting season has lengthened markedly over half a century of warming.
5. Mihail J.D., Bruhn J.N., Bonello P. (2007). Mycological Research 111:339-346 — Spatial and temporal patterns of morel fruiting. doi.org/10.1016/j.mycres.2007.01.007Morels: spatial and temporal fruiting patterns, in relation to temperature and rain.
6. Salerni E., Paoli L., Perini C. (2023). Italian Journal of Mycology 52:76-88 — Boletus edulis, gestion forestière et extrêmes climatiques. doi.org/10.6092/issn.2531-7342/16464Ceps in fir forest: production monitored over the 21 days following an episode of heavy rain or a heat peak.
7. Salerni E. et al. (2002). Israel Journal of Plant Sciences 50:189-198 — Effects of temperature and rainfall on fruiting of macrofungi in oak forests of the Mediterranean area. doi.org/10.1560/GV8J-VPKL-UV98-WVU1Tuscan oak woods: rainfall over the previous 30 days explains the number of fruiting bodies.

Soil moisture and drought

1. Karavani A. et al. (2018). Agricultural and Forest Meteorology 248:432-440 — Effect of climatic and soil moisture conditions on mushroom productivity. doi.org/10.1016/j.agrformet.2017.10.024Soil moisture is the best predictor of mushroom production in Catalonia.
2. Olano J.M. et al. (2020). Agricultural and Forest Meteorology 288-289:108015 — Primary productivity and climate control mushroom yields in Mediterranean pine forests. doi.org/10.1016/j.agrformet.2020.108015Mediterranean pine forests: forest productivity and climate control mushroom production.
3. Ágreda T. et al. (2015). Global Change Biology 21:3499-3510 — Increased evapotranspiration demand might cause a decline in fungal yields. doi.org/10.1111/gcb.12960Mediterranean climate: higher evapotranspiration demand could reduce mushroom production.
4. Ágreda T. et al. (2016). Agricultural and Forest Meteorology 223:39-47 — Long-term monitoring of climatic control of sporocarp production. doi.org/10.1016/j.agrformet.2016.03.015Long-term monitoring: the climate control of production varies strongly from one species to another.
5. Büntgen U. et al. (2015). Fungal Ecology 16:6-18 — Drought-induced changes in the phenology, productivity and diversity of Spanish fungi. doi.org/10.1016/j.funeco.2015.03.008Spain: drought shifts the season and reduces mushroom production and diversity.
6. Büntgen U., Kauserud H., Egli S. (2012). Frontiers in Ecology and the Environment 10(1):14-19 — Linking climate variability to mushroom productivity and phenology. doi.org/10.1890/110064Long Swiss time series: climate and mushroom productivity.

Phenology, latitude and altitude

1. Andrew C. et al. (2018). Ecology 99(6):1306-1315 — Explaining European fungal fruiting phenology with climate variability. doi.org/10.1002/ecy.2237Europe: the growth date varies by about 25 days with latitude and by up to 30 days with altitude; temperature is the main driver.
2. Krah F.S. et al. (2023). Ecology Letters 26(9):1572-1583 — Temperature affects the timing and duration of fungal fruiting patterns. doi.org/10.1111/ele.14275Large scale: temperature influences the date and duration of fruiting.
3. Sato H. et al. (2012). PLoS ONE 7:e49777 — A thirty-year survey reveals that ecosystem function of fungi predicts phenology of mushroom fruiting. doi.org/10.1371/journal.pone.004977730-year monitoring in Japan: the growth date depends on the lifestyle (mycorrhizal or decomposer).
4. Boddy L. et al. (2014). Fungal Ecology 10:20-33 — Climate variation effects on fungal fruiting. doi.org/10.1016/j.funeco.2013.10.006Review: effects of climate variability on fruiting.

Forecasting models and productive forests

1. Bonet J.A. et al. (2010). Canadian Journal of Forest Research 40:347-356 — Production and species richness of wild mushrooms in pine forests of the Central Pyrenees. doi.org/10.1139/X09-198Central Pyrenees: production models according to climate and stand.
2. Martínez-Peña F. et al. (2012). Forest Ecology and Management 282:63-69 — Yield models for ectomycorrhizal mushrooms in Pinus sylvestris forests. doi.org/10.1016/j.foreco.2012.06.034Scots pine forests: production models, including ceps (Boletus edulis) and saffron milk caps.
3. Parladé J., Martínez-Peña F., Pera J. (2017). Forest Ecology and Management 390:73-79 — Effects of forest management and climatic variables on mycelium dynamics and sporocarp production of Boletus edulis. doi.org/10.1016/j.foreco.2017.01.025Ceps: effects of forest management and climate on the mycelium and on production.
4. Collado E. et al. (2019). Science of the Total Environment 689:602-615 — Mushroom productivity trends in relation to tree growth and climate across European forest biomes. doi.org/10.1016/j.scitotenv.2019.06.471European forest biomes: production trends linked to tree growth and climate.
5. Tahvanainen V. et al. (2016). Forest Ecology and Management 362:79-88 — Modelling the yields of marketed mushrooms in Picea abies stands. doi.org/10.1016/j.foreco.2015.11.040Spruce forests of eastern Finland: yield models for commercially harvested mushrooms.
6. Morera A. et al. (2024). Ecological Informatics 81:102557 — Climate change impacts on productivity of edible fungi in Mediterranean forests. doi.org/10.1016/j.ecoinf.2024.102557Impact of climate change on edible mushroom production in Mediterranean forest.
7. Pilz D., Molina R., Mayo J. (2006). Journal of Forestry 104(1):9-14 — Effects of thinning young forests on chanterelle production. doi.org/10.1093/jof/104.1.9Chanterelles: effect of thinning young stands on production.

Species pages (habitat, host trees, soils)

1. Pilz D., Norvell L., Danell E., Molina R. (2003). USDA Forest Service, PNW-GTR-576 — Ecology and management of commercially harvested chanterelle mushrooms. www.fs.usda.gov/pnw/pubs/gtr576.pdfEcology and management of chanterelles.
2. Pilz D. et al. (2007). USDA Forest Service, PNW-GTR-710 — Ecology and management of morels harvested from the forests of western North America. research.fs.usda.gov/treesearch/26906Ecology and management of morels.
3. Santolamazza-Carbone S. et al. (2025). Microorganisms 13(9):2196 — Metabarcoding unveils seasonal soil microbiota shifts and their influence on Boletus edulis. doi.org/10.3390/microorganisms13092196Ceps: seasonality of the soil microflora and its influence on fruiting.
4. Xu et al. (2026). IMA Fungus 17:e172544 — Multi-gene phylogeny and taxonomy of Hydnum. doi.org/10.3897/imafungus.17.172544Taxonomy of Hydnum (hedgehog mushrooms).
5. Svanberg I., Løvaas E., Ståhlberg S. (2025). Journal of Ethnobiology and Ethnomedicine 21:37 — How funnel chanterelle (Craterellus tubaeformis) became an urban forager favorite in Scandinavia. doi.org/10.1186/s13002-025-00789-xFunnel chanterelle: uses and popularity of the species in Scandinavia.