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Regression tree for effects of temperature on the variable CyanoMax (seasonal maximum of cyanobacteria biomass). The numbers on the branches (18.85 and 20.2) show the significant breakpoints along temperature gradient. The bar plots in each resulting node show the probability distribution of CyanoMax across the three status classes: 1: High-Good (<10.5 g/L), 2: Moderate (10.5-20 g/L), Poor-Bad (≥20 g/L). n = number of observations in each node.

Regression tree for effects of temperature on the variable CyanoMax (seasonal maximum of cyanobacteria biomass). The numbers on the branches (18.85 and 20.2) show the significant breakpoints along temperature gradient. The bar plots in each resulting node show the probability distribution of CyanoMax across the three status classes: 1: High-Good (<10.5 g/L), 2: Moderate (10.5-20 g/L), Poor-Bad (≥20 g/L). n = number of observations in each node.

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Eutrophication of lakes and the risk of harmful cyanobacterial blooms due is a major challenge for management of aquatic ecosystems, and climate change is expected to reinforce these problems. Modelling of aquatic ecosystems has been widely used to predict effects of altered land use and climate change on water quality, assessed by chemistry and ph...

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... Bad). The corresponding indicator nodes in Module 3 (Monitoring data: Secchi, Total P, Chl-a and CyanoMax) were discretised into three intervals, with borders determined by the class boundaries of the national classification system (see Table A1a-d). Observed tem- perature was divided into two intervals, determined by a regression tree analysis (Fig. 3): A breakpoint in the effect of temperature on cyanobacteria was estimated at 19 • C (above which there was a higher probability of high cyanobacteria concentrations). For the corresponding variables predicted by MyLake (Module 2), the large amount of simulated data allowed discretisation with higher res- olution: predicted Total P, ...
Context 2
... seasonal variation in many of the variables, selecting only summer months would reduce the temporal vari- ation, and might therefore improve the precision of the model (i.e., result in narrower probability distributions of the indicators). We therefore compared the default model outcome (Fig. 5) with the corresponding results from summer months (Fig. B3). (To simplify the comparison we have displayed the result in terms of status classes, although it is not strictly correct to base the status assess- ment of summer values only). Lower probability of Moderate or better status can be seen for all indicators, except cyanobacteria; this is likely because Cyanobacteria status is based on ...
Context 3
... probability table for the two cyanobacteria nodes based on the alternative, larger dataset (EUREGI, see section 2.2.3): (a) Cyano, (b) CyanoMax (corresponding to Table 2c and d, respectively). For more information, see Table 2. Fig. B3. Effects of climate and management scenarios on the probability distribution of status classes for all indicator nodes, when the model is run only for the warmest months (July-August). For more details and for comparison with the default model, see Fig. 5. Fig. B4. Effects of high vs. low water temperature (above vs. below 19 • C, ...

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... Thus, water quality degradation affects the ecosystem, the water supply and recreation activities (Ansari et al., 2010;Wurtstbaugh et al., 2019). Furthermore, hydroclimatic variability associated with climate change, especially temperature, precipitation and evaporation, are important drivers that can act synergistically with the anthropic influence ruling changes in the environmental status of water bodies (O'Neil et al., 2012;Moe et al., 2016;Moorhouse et al., 2018, Nazari-Sharabian et al., 2018Huo et al., 2019). ...
... However, this theory has also met with strong opposition. erefore, there is a need to select appropriate fund performance evaluation models to further develop the fund evaluation system [7][8][9][10][11][12][13][14]. ...
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... Bayesian networks are commonly used in environmental modelling (Aguilera et al., 2011;Barton et al., 2012;Landuyt et al., 2013). For example, there are numerous BN models developed for assessment and management of water quality (Barton et al., 2014;Borsuk et al., 2004Borsuk et al., , 2012Moe et al., 2016Moe et al., , 2019. BN modelling has been applied more rarely within ecotoxicology and ecological risk assessment, but recent publications have demonstrated the applicability of BN modelling in these fields Landis et al., 2017Landis et al., , 2019Lehikoinen et al., 2015). ...
Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency, by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network modelling is explored as an alternative to traditional risk calculation. Bayesian networks can serve as meta-models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a Bayesian network has been developed and parameterised for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterisation using azoxystrobin as an example. We also demonstrate seasonal risk calculation for the three pesticides. This article is protected by copyright. All rights reserved.