Climatechange and nutrient fluctuations disrupt networks in lakes
and using the predictive skill rho as a proxy for how strongly a network node is affected and/or affects another node. Moreover, we used S-maps to explore the interactive effects of water temperature and nutrient levels on network propertiesCCM is a ‘nonlinear causality test’ that estimates the extent to which changes in one variable affect changes in another by measuring cross-prediction . Consider two variables, V1 and V2 .
Note that the time series require added placeholders for missing values to ensure having evenly spaced monthly data. As the time series used are on different scales , we rescale them using the function scale in the R package base .We use simplex from the R package rEDM to define the best embedding dimension for V1 using simplex projection . The embedding dimension was run over= 2:15. Time lag and prediction horizon were set to 1 month.
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