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HeatMaps

Data-driven insights into recent changes
in European butterfly distributions

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HeatMaps

EuroLST

EuroLST is a gap-free database of daily land surface temperatures in Europe over the past 11 years. Accessible to everyone, EuroLST is powered by open source software and contains high spatial and temporal remote sensing data. The database helps us understand how Europe’s climate is changing and the impact this might have on flora and fauna.

Euro LST

Some Applications

BIO1: Annual mean temperature (°C*10)

BIO1: Annual mean temperature (°C*10)

BIO2: Mean diurnal range (Mean monthly (max - min tem))

BIO2: Mean diurnal range (Mean monthly (max - min tem))

BIO3: Isothermality ((bio2/bio7)*100)

BIO3: Isothermality ((bio2/bio7)*100)

BIO4: Temperature seasonality (standard deviation * 100)

BIO4: Temperature seasonality (standard deviation * 100)

BIO5: Maximum temperature of the warmest month (°C*10)

BIO5: Maximum temperature of the warmest month (°C*10)

BIO6: Minimum temperature of the coldest month (°C*10)

BIO6: Minimum temperature of the coldest month (°C*10)

BIO7: Temperature annual range (bio5 - bio6) (°C*10)

BIO7: Temperature annual range (bio5 - bio6) (°C*10)

BIO10: Mean temperature of the warmest quarter (°C*10)

BIO10: Mean temperature of the warmest quarter (°C*10)

BIO11: Mean temperature of the coldest quarter (°C*10)

BIO11: Mean temperature of the coldest quarter (°C*10)

The canary in the coalmine

Butterflies are sensitive to temperature changes and the loss or appearance of a species in any given area can be an early sign of climate change. Since butterflies are a popular group of species, they are well observed and thousands of reported sightings across Europe have been recorded over decades of time. This means the butterfly data could reveal much about potential changes in species distributions across Europe during this time of critical change.

How have the species I see in my local area changed?

The EuroLST dataset provides a high spatio-temporal resolution record of temperatures across Europe for a time period in which we also have good butterfly observation data. Further research could reveal how these temperature patterns are connected to the distribution of certain butterfly species and predict what might happen in the future. For example, the chart below tracks the abundance of a selection of butterfly species over time and you can see that whilst some species have become more abundant, others have been reduced. How these changes are connected to temperature has yet to be explored but could help us understand the impact of climate change.

Conclusion

Further research that combines data from EuroLST and species databases such as the Global Biodiversity Information Facility (GBIF) could improve our understanding of how European species are responding to our changing climate. Which species will thrive and which ones won’t? Will pest species increase in number and threaten our agricultural security? Knowing the answers to these kinds of questions could help us adapt to the challenges of the future.

Contribute

Some countries have more butterfly data than others. If you’re a keen citizen scientist, why don’t you look for butterflies and contribute your sightings to a scientific database?

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