Navigating through space and time: A methodological approach to quantify spatiotemporal connectivity using stream flow data as a case study

The growing interest in combining spatial and temporal patterns in nature has been fostered by the current availability of high-frequency measurements. However, we still lack a methodological framework to process and interpret spatiotemporal datasets into meaningful values, adaptable to different time windows and/or responding to different spatial structures. Here, we developed and tested a framework to evaluate spatiotemporal connectivity using two new measures: the spatiotemporal connectivity (STcon) and the spatiotemporal connectivity matrix (STconmat). To obtain these measures, we consider a set of spatially connected sites within a temporally dynamic network. These measures are calculated from a spatiotemporal matrix where spatial and temporal connections across sites are captured. These connections respond to a determined network structure, assign different values to these connections and generate different scenarios from which we obtain the spatiotemporal connectivity. We developed these measures by using a dataset of stream flow state spanning a 513-day period obtained from data loggers installed in seven temporary streams. These measures allowed us to characterise connectivity among stream reaches and relate spatiotemporal patterns with macroinvertebrate community structure and composition. Spatiotemporal connectivity differed within and among streams, with STcon and STconmat capturing different hydrological patterns. Macroinvertebrate richness and diversity were higher in more spatiotemporally connected sites. Community dissimilarity was related to STconmat showing that more spatiotemporally connected sites had similar communities for active and passive dispersers. Interestingly, both groups were related to spatiotemporal connectivity patterns for some of the analysed scenarios, highlighting the relevance of spatiotemporal connectivity in dynamic systems. As we exemplified, the proposed framework can help to disentangle and quantify spatiotemporal dynamics