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Phytoplankton and cyanobacteria abundances in mid‐21st century lakes depend strongly on future land use and climate projections

Authors: Benjamin M. Kraemer; Heidrun Feuchtmayr; Karan Kakouei; Scott N. Higgins; Francesco Pomati; Jason D. Stockwell; Jennifer L. Graham; +7 Authors

Phytoplankton and cyanobacteria abundances in mid‐21st century lakes depend strongly on future land use and climate projections

Abstract

AbstractLand use and climate change are anticipated to affect phytoplankton of lakes worldwide. The effects will depend on the magnitude of projected land use and climate changes and lake sensitivity to these factors. We used random forests fit with long‐term (1971–2016) phytoplankton and cyanobacteria abundance time series, climate observations (1971–2016), and upstream catchment land use (global Clumondo models for the year 2000) data from 14 European and 15 North American lakes basins. We projected future phytoplankton and cyanobacteria abundance in the 29 focal lake basins and 1567 lakes across focal regions based on three land use (sustainability, middle of the road, and regional rivalry) and two climate (RCP 2.6 and 8.5) scenarios to mid‐21st century. On average, lakes are expected to have higher phytoplankton and cyanobacteria due to increases in both urban land use and temperature, and decreases in forest habitat. However, the relative importance of land use and climate effects varied substantially among regions and lakes. Accounting for land use and climate changes in a combined way based on extensive data allowed us to identify urbanization as the major driver of phytoplankton development in lakes located in urban areas, and climate as major driver in lakes located in remote areas where past and future land use changes were minimal. For approximately one‐third of the studied lakes, both drivers were relatively important. The results of this large scale study suggest the best approaches for mitigating the effects of human activity on lake phytoplankton and cyanobacteria will depend strongly on lake sensitivity to long‐term change and the magnitude of projected land use and climate changes at a given location. Our quantitative analyses suggest local management measures should focus on retaining nutrients in urban landscapes to prevent nutrient pollution from exacerbating ongoing changes to lake ecosystems from climate change.

Countries
France, United Kingdom, Switzerland
Keywords

[SDE] Environmental Sciences, land use change, 570, 550, Climate Change, forecast, climate change; forecast; freshwater lakes; land use change; machine learning; phytoplankton; cyanobacteria, Cyanobacteria, cyanobacteria, 333, Ecology and Environment, Humans, Ecosystem, [SDE.BE] Environmental Sciences/Biodiversity and Ecology, Lakes, climate change, machine learning, [SDE]Environmental Sciences, Phytoplankton, phytoplankton, freshwater lakes, [SDE.BE]Environmental Sciences/Biodiversity and Ecology

  • BIP!
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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    43
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
43
Top 10%
Top 10%
Top 1%
Green
hybrid