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Historical citizen science to understand and predict climate-driven trout decline
Author(s) -
Miguel Clavero,
Miquel Ninyerola,
Virgilio Hermoso,
Ana Filipa Filipe,
Magda Pla,
Dani Villero,
Lluı́s Brotons,
Miguel Delibes
Publication year - 2017
Publication title -
proceedings of the royal society b biological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.342
H-Index - 253
eISSN - 1471-2954
pISSN - 0962-8452
DOI - 10.1098/rspb.2016.1979
Subject(s) - brown trout , salmo , trout , climate change , citizen science , historical ecology , range (aeronautics) , fish <actinopterygii> , historical record , geography , biodiversity , ecology , species distribution , physical geography , fishery , history , biology , habitat , botany , materials science , composite material , memoir , art history
Historical species records offer an excellent opportunity to test the predictive ability of range forecasts under climate change, but researchers often consider that historical records are scarce and unreliable, besides the datasets collected by renowned naturalists. Here, we demonstrate the relevance of biodiversity records developed through citizen-science initiatives generated outside the natural sciences academia. We used a Spanish geographical dictionary from the mid-nineteenth century to compile over 10 000 freshwater fish records, including almost 4 000 brown trout (Salmo trutta) citations, and constructed a historical presence-absence dataset covering over 2 000 10 × 10 km cells, which is comparable to present-day data. There has been a clear reduction in trout range in the past 150 years, coinciding with a generalized warming. We show that current trout distribution can be accurately predicted based on historical records and past and present values of three air temperature variables. The models indicate a consistent decline of average suitability of around 25% between 1850s and 2000s, which is expected to surpass 40% by the 2050s. We stress the largely unexplored potential of historical species records from non-academic sources to open new pathways for long-term global change science.

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