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Macrophytes are one of the main components of the aquatic ecosystem. They are used in several countries as metrics for the ecological assessment of hydrosystems. The objective of our study was to evaluate the suitability of the Macrophyte Biological Index for Rivers (IBMR) to determine a trophic level in the upper Oum Er Rbia basin (Morocco) and to understand physicochemical parameters of water that govern the distribution of macrophyte species. CCA analysis was used to relate the distribution of macrophytes to hydrochemical parameters of water. The CCA analysis shows that the distribution of macrophytes was more correlated with abiotic parameters (EC, WT and DO) than nutrient parameters (PO4-P, NO3-N, NO2-N, NH3-N and CODMn). The recorded values of IBMR in the upper Oum Er Rbia basin indicate that the trophic level of the studied rivers ranged from "moderate" to "very high". Pearson's correlation coefficient showed that the IBMR is more correlated with the abiotic parameters such as WT and EC and does not show any significant correlation with the content of PO4-P and NH3-N in water, which makes the IBMR index unreliable for assessing the trophic status related to phosphate and ammonia concentrations in our lotic waters. [ABSTRACT FROM AUTHOR]

Behavior-interaction-priority (BIP) is a component-based framework supporting rigorous design of embedded systems. BIP supports incremental design of large systems from atomic components that communicate via connectors and whose interactions can be described with a powerful algebra. This paper presents SBIP, an extension of BIP for stochastic systems. SBIP offers the possibility to add stochastic information to atomic component's behaviors, and hence to the entire system. Atomic component's semantics in SBIP is described by Markov Chains. We show that the semantics of the entire system is described by a Markov chain, showing that the non-determinism arising from system interactions is automatically eliminated by BIP. This allows us to verify systems described in SBIP with Statistical Model Checking. This paper introduces SBIP and illustrates its usability on several industrial case studies. [ABSTRACT FROM AUTHOR]

Altitude is one of the determinant factors that influence the spatial distribution of birds and their species richness. However, few investigations studied its influence on African bird communities over a wide gradient in North Africa. Considering this knowledge gap, 194 sampling points were carried out along altitudinal gradient (773 to 2595 m) in two adjacent mountainous valleys in the Central High Atlas of Beni Mellal in Morocco, Aït Bouguemmaz (AB), and Ahançal (OA). Each valley was divided into three altitudinal intervals (low, medium, and high zones). For each spatial subdivision, we studied the phenological cycle, in the sense that each valley underwent a four-season monitoring, during 2018 and 2019. The results showed 131 species in the study area in 43 families; both valleys had similar community richness, but they differed by the phenological status of their species. Indeed, the increase in altitude led to a decrease in bird species richness, while it does not show any correlation with abundance. In addition, richness and abundance increased during the high-productivity seasons (i.e., spring and summer). Although these results were expected, they provide an opportunity to compare these results with higher latitude communities in Europe and to better focus future large-scale investigations on the combined influence of latitude and altitude on birds. [ABSTRACT FROM AUTHOR]