ClimateFish: A database to track the abundance of selected coastal fish species as candidate indicators of climate change

The ClimateFish database collates abundance data of 15 fish species proposed as candidate indicators of climate change in the Mediterranean Sea. Data were collected according to a simplified visual census methodology (Garrabou et al. 2019) along standard transects of five minutes performed at a constant speed of 10m/min, corresponding approximately to an area of 50x5m. Four different depth layers were
surveyed:  0-3m, 5-10 m, 11-20 m, 21-30 m. So far, the ClimateFish database includes fish counts collected along 3142 transects carried out in seven Mediterranean countries between 2009 and 2021, for a total number of 101'771 observed individuals belonging to the 15 fish species.
An initial group of eight Mediterranean indigenous species (Epinephelus marginatus, Thalassoma pavo, Sparisoma cretense, Coris julis, Sarpa salpa, Serranus scriba, Serranus cabrilla and Caranx crysos) with wide distribution, responsiveness to temperature conditions and easy identification were selected by a network of Mediterranean scientists joined under the CIESM programme ‘Tropical Signals’ (https://www.ciesm.org/marine/programs/tropicalization.htm; Azzurro et al. 2010). Soon after, and thanks to the discussion with other expert groups and projects, C. crysos was no longer considered, and Lessepsian fishes (Red Sea species entering the Mediterranean through the Suez Canal) were included, namely: Fistularia commersonii, Siganus luridus, Siganus rivulatus, Pterois miles, Stephanolopis diaspros, Parupeneus forskali, Pempheris rhomboidea and Torquigener flavimaculosus. 
Data were collected by a large team of researchers which joined in a common monitoring strategy supported by different international projects, which are acknowledged below. This database, when associated with climate data, offers new opportunities to investigate spatio-temporal effects of climate change in the Mediterranean Sea and test the effectiveness of each species as a possible climate change indicator. Contacts: ernesto.azzurro(at)cnr.it  References: Azzurro E., Maynou F., Moschella P. (2010). A simplified visual census
methodology to detect variability trends of coastal mediterranean fishes under climate change scenarios. Rapp. Comm. int. Mer Médit., 39. D’Amen, M. and Azzurro, E. (2020). Lessepsian fish invasion in
Mediterranean marine protected areas: a risk assessment under climate change scenarios. ICES Journal of Marine Science, 77(1), pp.388-397. Garrabou, J., Bensoussan, N., Azzurro, E. (2019). Monitoring
climate-related responses in Mediterranean marine protected areas and beyond: five standard protocols. Golani D.,  Azzurro E.,  Dulčić J., Massutí E., Orsi-Relini L.  (2021).  Atlas of Exotic Fishes in the
Mediterranean Sea.  2nd edition  [F. Briand, Ed.]  365 pages. CIESM Publishers, Paris, Monaco. ISBN number  978-92-990003-5-9. Considering the trend of increase of these species in the Mediterranean Sea (Golani et al. 2021) and their projected distribution according to climate change scenarios (D’Amen and Azzurro, 2020), more data on these tropical invaders are expected to come in the future implementation of
the study. 
The Mediterranean ClimateFish initiative was initially Tropical Signals (funded by the Albert II of Monaco Foundation) and subsequently supported by the Interreg Med Programme (Projects: MPA Adapt, grant number  1MED15_3.2_M2_337 and MPA Engage, grant number MED18_3.2_M23_007), 85% co funded by the European Regional Development Fund.The Interreg Med project MPA Engage (grant number: 5216 | 5MED18_3.2_M23_007) partially supported this work.
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Processing methodology: first of all, training sessions/materials were delivered to both MPA staff and collaborating researchers; data were collected by trained people according to the for error and underwent the technical validation process; data were same protocol and checked by the same people before submission; formatted data were then examined gathered and formatted to meet Darwin Core archive standards where reports which did not meet inclusion criteria were removed; the data were manually examined again for quality assessment and to detect any remaining error.