8 research outputs found

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

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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 chlorophyll a as 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 globalchange 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

    The Role of Warm, Dry Summers and Variation in Snowpack on Phytoplankton Dynamics in Mountain Lakes

    No full text
    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 chlorophyll a as 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 globalchange 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

    An integrated community and ecosystem-based approach to disaster risk reduction in mountain systems

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    The devastating 2015 earthquakes in Nepal highlighted the need for effective disaster risk reduction (DRR) in mountains, which are inherently subject to hazards and increasingly vulnerable to extreme events. As multiple UN policy frameworks stress, DRR is crucial to mitigate the mounting environmental and socioeconomic costs of disasters globally. However, specialized DRR guidelines are needed for biodiverse, multi-hazard regions like mountains. Ecosystem-based disaster risk reduction (Eco-DRR) emphasizes ecosystem conservation, restoration, and sustainable management as key elements for DRR. We propose that integrating the emerging field of EcoDRR with community-based DRR (CB-DRR) will help address the increasing vulnerabilities of mountain people and ecosystems. Drawing on a global mountain synthesis, we present paradoxes that create challenges for DRR in mountains and examine these paradoxes through examples from the 2015 Nepal earthquakes. We propose four principles for integrated CB- and Eco-DRR that address these challenges: (1) governance and institutional arrangements that fit local needs; (2) empowerment and capacity-building to strengthen community resilience; (3) discovery and sharing of constructive practices that combine local and scientific knowledge; and (4) approaches focused on well-being and equity. We illustrate the reinforcing relationship between integrated CB- and Eco-DRR principles with examples from other mountain systems worldwide. Coordinated community and ecosystem based actions offer a potential path to achieve DRR, climate adaptation, sustainable development, and biodiversity conservation for vulnerable ecosystems and communities worldwide

    Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?

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    © 2016 Elsevier B.V. Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms

    Female reproduction and type 1 diabetes: from mechanisms to clinical findings

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