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1.
Glob Chang Biol ; 26(12): 6831-6851, 2020 Dec.
Article in English | MEDLINE | ID: mdl-32893967

ABSTRACT

Submerged macrophytes are of key importance for the structure and functioning of shallow lakes and can be decisive for maintaining them in a clear water state. The ongoing climate change affects the macrophytes through changes in temperature and precipitation, causing variations in nutrient load, water level and light availability. To investigate how these factors jointly determine macrophyte dominance and growth, we conducted a highly standardized pan-European experiment involving the installation of mesocosms in lakes. The experimental design consisted of mesotrophic and eutrophic nutrient conditions at 1 m (shallow) and 2 m (deep) depth along a latitudinal temperature gradient with average water temperatures ranging from 14.9 to 23.9°C (Sweden to Greece) and a natural drop in water levels in the warmest countries (Greece and Turkey). We determined percent plant volume inhabited (PVI) of submerged macrophytes on a monthly basis for 5 months and dry weight at the end of the experiment. Over the temperature gradient, PVI was highest in the shallow mesotrophic mesocosms followed by intermediate levels in the shallow eutrophic and deep mesotrophic mesocosms, and lowest levels in the deep eutrophic mesocosms. We identified three pathways along which water temperature likely affected PVI, exhibiting (a) a direct positive effect if light was not limiting; (b) an indirect positive effect due to an evaporation-driven water level reduction, causing a nonlinear increase in mean available light; and (c) an indirect negative effect through algal growth and, thus, high light attenuation under eutrophic conditions. We conclude that high temperatures combined with a temperature-mediated water level decrease can counterbalance the negative effects of eutrophic conditions on macrophytes by enhancing the light availability. While a water level reduction can promote macrophyte dominance, an extreme reduction will likely decrease macrophyte biomass and, consequently, their capacity to function as a carbon store and food source.


Subject(s)
Lakes , Water , Nutrients , Sweden , Temperature
2.
Proc Natl Acad Sci U S A ; 113(50): E8089-E8095, 2016 12 13.
Article in English | MEDLINE | ID: mdl-27911776

ABSTRACT

Ecosystems can show sudden and persistent changes in state despite only incremental changes in drivers. Such critical transitions are difficult to predict, because the state of the system often shows little change before the transition. Early-warning indicators (EWIs) are hypothesized to signal the loss of system resilience and have been shown to precede critical transitions in theoretical models, paleo-climate time series, and in laboratory as well as whole lake experiments. The generalizability of EWIs for detecting critical transitions in empirical time series of natural aquatic ecosystems remains largely untested, however. Here we assessed four commonly used EWIs on long-term datasets of five freshwater ecosystems that have experienced sudden, persistent transitions and for which the relevant ecological mechanisms and drivers are well understood. These case studies were categorized by three mechanisms that can generate critical transitions between alternative states: competition, trophic cascade, and intraguild predation. Although EWIs could be detected in most of the case studies, agreement among the four indicators was low. In some cases, EWIs were detected considerably ahead of the transition. Nonetheless, our results show that at present, EWIs do not provide reliable and consistent signals of impending critical transitions despite using some of the best routinely monitored freshwater ecosystems. Our analysis strongly suggests that a priori knowledge of the underlying mechanisms driving ecosystem transitions is necessary to identify relevant state variables for successfully monitoring EWIs.


Subject(s)
Ecosystem , Fresh Water , Models, Biological , Animals , Biostatistics , Climate , Eutrophication , Food Chain , Lakes , Predatory Behavior , Time Factors
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