Streams and rivers are examples of vital ecosystems that frequently undergo various environmental and anthropogenic stresses. A core question in population ecology is whether a given population will persist under changing ecological conditions. This thesis consists of three papers and is devoted to the mathematical analysis of responses of river-dwelling species to population persistence threats. The first paper presents a stochastic approach to the 'drift paradox' problem, where the classical reaction-advection-diffusion model is replaced by a birth-death-emigration process. We explore the effects of temporally varying flow on the persistence probability and highlight the importance of the benthic stage for the persistence of stream organisms. The second paper addresses the problem of river network fragmentation through disconnecting structures such as dams. We construct a population matrix model that incorporates the spatial structure of the studied river network and compare structural connectivity to an indicator of population persistence. The third paper adapts the same basic matrix model to examine fish response to disturbances travelling downstream from upstream sites. The study of these three aspects of persistence challenges for river populations contributes to the cumulative effects assessment on river networks.

This study analyzes the available radar evidence in order to describe the morphology of channel networks around the north polar seas of Titan. Critical flow depths necessary to entrain water-ice grains, and denudation rates for a north polar channel network are discussed. The results indicate that channel networks on Titan have similar morphologies to channel networks cut by water on Earth. We also find that water-ice sediment should be readily entrained in the headwaters and downstream sections of the analyzed Titanian basin, given sufficient flow depths of liquid hydrocarbons. Also, the importance of slope and the elevated topography of the highlands surrounding the polar lakes are considered, as well as potential formation theories for the elevated highlands and low-lying maria that dominate the north polar region.

A new paradigm of electronic devices with bio-inspired features is aiming to mimic the brain’s fundamental mechanisms to achieve recognition of very complex patterns and more efficient computational tasks. Networks of electropolymerized dendritic fibers are attracting much interest because of their ability to achieve advanced learning capabilities, form neural networks, and emulate synaptic and plastic processes typical of human neurons. Despite their potential for braininspired computation, the roles of the single parameters associated with the growth of the fiber are still unclear, and the intrinsic randomness governing the growth of the dendrites prevents the development of devices with stable and reproducible properties. In this manuscript, we provide a systematic study on the physical parameters influencing the growth, defining cause-effect relationships for direction, symmetry, thickness, and branching of the fibers. We build an electrochemical model of the phenomenon and we validate it in silico using Montecarlo simulations. This work shows the possibility of designing dendritic polymer fibers with controllable physical properties, providing a tool to engineer polymeric networks with desired neuromorphic features.