Regression analyses are common when the response of a dependent variable is related to values of potential explanatory variables. However, in time series analyses the deficiency of regression analysis is evident due to serially correlated error terms, which results in an ineffectual or incorrect model. Autoregressive-integrated-moving average (ARIMA) models were introduced in order to account for the autocorrelated structure of time-series. The univariate ARIMA models are useful only for analysis of a single time-series, because the modeling is limited to the information contained in the series’ own past. In many cases, however, it may be possible to relate the response of one series not only to its own past values, but also to the past and present values of other, related time-series. This can be done with transfer function models (TF), merging the basic concepts of the general regression model with that of traditional ARIMA models. With TFs we discovered, that climatic changes in the Atlantic control the abundance of Temora longicornis, a dominant pelagic copepod of the Baltic Sea. Due to time lags between variables studied, we also are able to make predictions of changes expectable in early 2000's. We find TFs especially suitable to relate the physical and chemical environmental factors with zooplankton variability, but they may also be used down the line to model and predict subsequent changes in planktivores