The stream evolution triangle: Integrating geology, hydrology, and biology

The foundations of river restoration science rest comfortably in the fields of geology, hydrology, and engineering, and yet, the impetus for many, if not most, stream restoration projects is biological recovery. Although Lane's stream balance equation from the mid‐1950s captured the dynamic equilibrium between the amount of stream flow, the slope of the channel, and the amount and calibre of sediment, it completely ignored biology. Similarly, most of the stream classification systems used in river restoration design today do not explicitly include biology as a primary driver of stream form and process. To address this omission, we cast biology as an equal partner with geology and hydrology, forming a triumvirate that governs stream morphology and evolution. To represent this, we have created the stream evolution triangle, a conceptual model that explicitly accounts for the influences of geology, hydrology, and biology. Recognition of biology as a driver leads to improved understanding of reachscale morphology and the dynamic response mechanisms responsible for stream evolution and adjustment following natural or anthropogenic disturbance, including stream restoration. Our aim in creating the stream evolution triangle is not to exclude or supersede existing stream classifications and evolutionary models but to provide a broader “thinking space” within which they can be framed and reconsidered, thus facilitating thought outside of the alluvial box

Modelling physical characteristics of river habitats

The physical characteristics of river habitats constitute the setting in which fluvial biota dwell and thrive. Determining the spatial and temporal patterns of physical habitat characteristics and the main factors that control them is extremely important to increase the efficiency of river management, conservation, and restoration. This study determined spatial patterns of physical habitat characteristics for Atlantic and Mediterranean rivers in northern Spain and developed a river classification based on hydromorphological characteristics. Data gathered from almost 600 sites following a modified version of the River Habitat Survey methodology were used. In addition to the usual River Habitat Survey variables, the sequence of hydromorphologic units (i.e., areas exhibiting similar hydraulic characteristics, in terms of water velocity and depth), water depths, and widths were recorded. Unmodified reaches were selected computing the Habitat Modification Score. Multiple Linear Regression models were employed to test relationships between Principal Component Analyses that summarized physical river habitat characteristics with ecological relevance and environmental variables (i.e., climate, topography, land cover, and geology) at different spatial scales and used to predict physical habitat attributes for all river reaches. The density of hydromorphologic units, flow turbulence, substrate size, and channel dimensions were able to discriminate river classes within the river network, with topography being the main environmental driver of habitat characteristics (although climate, geology, and land cover were also relevant). This classification scheme could constitute a useful tool to restore physical habitat conditions in modified river reaches.info:eu-repo/semantics/acceptedVersio

Physical Habitat and Fish Assemblage Relationships with Landscape Variables at Multiple Spatial Scales in Wadeable Iowa Streams

Landscapes in Iowa and other midwestern states have been profoundly altered by conversion of native prairies to agriculture. We analyzed landscape data collected at multiple spatial scales to explore relationships with reach-scale physical habitat and fish assemblage data from 93 randomly selected sites on second- through fifth-order wadeable Iowa streams. Ordination of sites by physical habitat showed significant gradients of channel shape, habitat complexity, substrate composition, and stream size. Several landscape variables were significantly associated with the physical habitat ordination. Row crop land use was associated with fine substrates and steep bank angles, whereas wetland land cover and greater sinuosity and catchment land area were associated with complex channel and bank morphology and greater residual pool volume, woody debris, and canopy cover. Thirteen landscape variables were significant predictors of physical habitat variables in multiple linear regressions, with adjusted R 2 values ranging from 0.07 to 0.74. Inclusion of landscape variables with physical habitat variables in multiple regression models predicting fish assemblage metrics and a fish index of biotic integrity resulted in negligible improvements over models based on only physical habitat variables. Physical habitat in wadeable Iowa streams is strongly associated with landscape characteristics. Results of this study and previous studies suggest that (1) landscape factors directly influence physical habitat, (2) physical habitat directly influences fish assemblages, and (3) the influence of landscape factors on fish assemblages is primarily indirect. Understanding how landscape factors, such as human land use, influence physical habitat and fish assemblages will help managers make more informed decisions for improving Iowa\u27s wadeable streams

Crowding Alone Cannot Account for Cosolute Effect on Amyloid Aggregation

Amyloid fiber formation is a specific form of protein aggregation, often resulting from the misfolding of native proteins. Aimed at modeling the crowded environment of the cell, recent experiments showed a reduction in fibrillation halftimes for amyloid-forming peptides in the presence of cosolutes that are preferentially excluded from proteins and peptides. The effect of excluded cosolutes has previously been attributed to the large volume excluded by such inert cellular solutes, sometimes termed “macromolecular crowding”. Here, we studied a model peptide that can fold to a stable monomeric β-hairpin conformation, but under certain solution conditions aggregates in the form of amyloid fibrils. Using Circular Dichroism spectroscopy (CD), we found that, in the presence of polyols and polyethylene glycols acting as excluded cosolutes, the monomeric β-hairpin conformation was stabilized with respect to the unfolded state. Stabilization free energy was linear with cosolute concentration, and grew with molecular volume, as would also be predicted by crowding models. After initiating the aggregation process with a pH jump, fibrillation in the presence and absence of cosolutes was followed by ThT fluorescence, transmission electron microscopy, and CD spectroscopy. Polyols (glycerol and sorbitol) increased the lag time for fibril formation and elevated the amount of aggregated peptide at equilibrium, in a cosolute size and concentration dependent manner. However, fibrillation rates remained almost unaffected by a wide range of molecular weights of soluble polyethylene glycols. Our results highlight the importance of other forces beyond the excluded volume interactions responsible for crowding that may contribute to the cosolute effects acting on amyloid formation

Quantum key distribution based on orthogonal states allows secure quantum bit commitment

For more than a decade, it was believed that unconditionally secure quantum bit commitment (QBC) is impossible. But basing on a previously proposed quantum key distribution scheme using orthogonal states, here we build a QBC protocol in which the density matrices of the quantum states encoding the commitment do not satisfy a crucial condition on which the no-go proofs of QBC are based. Thus the no-go proofs could be evaded. Our protocol is fault-tolerant and very feasible with currently available technology. It reopens the venue for other "post-cold-war" multi-party cryptographic protocols, e.g., quantum bit string commitment and quantum strong coin tossing with an arbitrarily small bias. This result also has a strong influence on the Clifton-Bub-Halvorson theorem which suggests that quantum theory could be characterized in terms of information-theoretic constraints.Comment: Published version plus an appendix showing how to defeat the counterfactual attack, more references [76,77,90,118-120] cited, and other minor change