The results of the above analyses were compared to correlations between water chemistry parameters and catchment-scale land cover at both the watershed and the first-order streams scales.
Across all studied watersheds, riparian land cover was a significant predictor of among-site variation in water chemistry concentrations at the watershed and first-order streams scales, particularly for nutrients (Table 1).
Total P and NH4+ were significantly correlated with riparian land cover in all seasons except May (Fig. 4); in particular, riparian land cover at both the watershed and the first-order streams scales explained most variance in TP concentrations in January April compared to other seasons.
The extent and intensity of riparian urbanizations were high priority factors when performing water quality improvement, and increasing habitat complexity and heterogeneity at in-stream scale was crucial for increasing macroinvertebrates diversity.
Invertebrate richness was a good indicator of stream biophysical condition (e.g. nature of the substratum, riparian condition) at the stream scale irrespective of taxonomic resolution (family or higher) or sample size (down to 50 individuals per site), and was therefore a useful monitoring tool.
Based on the cooperative data stream scale, mapping scheme of data stream to users, and priority and redundancy of cooperative data packets, we defined the opportunistic cooperative platform.
First, opportunistic cooperative platform was defined according to the cooperative data stream scale, mapping algorithm, and priority and redundancy of cooperative data packets.
At hillslope plot and stream scales, the evolution in the values of HC was less evident, except the increment (by 5.4%) observed in the streams at K2 after the FMO.
On the top of the stack are many Spark wrappers such as Spark Streaming (Large Scale real-time stream processing), Blink DB (queries with bounded errors and bounded response times on very large data) [23], GraphX (Resilient distributed Graph System on Spark) [24] and MLBase (distributed machine learning library based on Spark) [25].
Google says the restructure is necessary to allow DeepMind's flagship health app, Streams, to scale up globally.
