4.7 Article

The role of warm, dry summers and variation in snowpack on phytoplankton dynamics in mountain lakes

期刊

ECOLOGY
卷 101, 期 10, 页码 -

出版社

WILEY
DOI: 10.1002/ecy.3132

关键词

alpine; climate change; cryosphere; limnology; mountain lakes; nitrogen deposition; phytoplankton; snowmelt timing

类别

资金

  1. National Science Foundation (NSF) IGERT [DGE-0966346]
  2. Niwot Ridge Long Term Ecological Research Network site (LTER) (NSF) [DEB-1637686]

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Climate change is altering biogeochemical, metabolic, and ecological functions in lakes across the globe. Historically, mountain lakes in temperate regions have been unproductive because of brief ice-free seasons, a snowmelt-driven hydrograph, cold temperatures, and steep topography with low vegetation and soil cover. We tested the relative importance of winter and summer weather, watershed characteristics, and water chemistry as drivers of phytoplankton dynamics. Using boosted regression tree models for 28 mountain lakes in Colorado, we examined regional, intraseasonal, and interannual drivers of variability in chlorophyllaas a proxy for lake phytoplankton. Phytoplankton biomass was inversely related to the maximum snow water equivalent (SWE) of the previous winter, as others have found. However, even in years with average SWE, summer precipitation extremes and warming enhanced phytoplankton biomass. Peak seasonal phytoplankton biomass coincided with the warmest water temperatures and lowest nitrogen-to-phosphorus ratios. Although links between snowpack, lake temperature, nutrients, and organic-matter dynamics are increasingly recognized as critical drivers of change in high-elevation lakes, our results highlight the additional influence of summer conditions on lake productivity in response to ongoing changes in climate. Continued changes in the timing, type, and magnitude of precipitation in combination with other global-change drivers (e.g., nutrient deposition) will affect production in mountain lakes, potentially shifting these historically oligotrophic lakes toward new ecosystem states. Ultimately, a deeper understanding of these drivers and pattern at multiple scales will allow us to anticipate ecological consequences of global change better.

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