Multiple processes regulate long-term population dynamics of sea urchins on Mediterranean rocky reefs

We annually monitored the abundance and size structure of herbivorous sea urchin populations (Paracentrotus lividus and Arbacia lixula) inside and outside a marine reserve in the Northwestern Mediterranean on two distinct habitats (boulders and vertical walls) over a period of 20 years, with the aim of analyzing changes at different temporal scales in relation to biotic and abiotic drivers. P. lividus exhibited significant variability in density over time on boulder bottoms but not on vertical walls, and temporal trends were not significantly different between the protection levels. Differences in densities were caused primarily by variance in recruitment, which was less pronounced inside the MPA and was correlated with adult density, indicating density-dependent recruitment under high predation pressure, as well as some positive feedback mechanisms that may facilitate higher urchin abundances despite higher predator abundance. Populations within the reserve were less variable in abundance and did not exhibit the hyper-abundances observed outside the reserve, suggesting that predation effects maybe more subtle than simply lowering the numbers of urchins in reserves. A. lixula densities were an order of magnitude lower than P. lividus densities and varied within sites and over time on boulder bottoms but did not differ between protection levels. In December 2008, an exceptionally violent storm reduced sea urchin densities drastically (by 50% to 80%) on boulder substrates, resulting in the lowest values observed over the entire study period, which remained at that level for at least two years (up to the present). Our results also showed great variability in the biological and physical processes acting at different temporal scales. This study highlights the need for appropriate temporal scales for studies to fully understand ecosystem functioning, the concepts of which are fundamental to successful conservation and management.

Characterization of cerebral glucose dynamics in vivo with a four-state conformational model of transport at the blood-brain barrier.

Determination of brain glucose transport kinetics in vivo at steady-state typically does not allow distinguishing apparent maximum transport rate (T(max)) from cerebral consumption rate. Using a four-state conformational model of glucose transport, we show that simultaneous dynamic measurement of brain and plasma glucose concentrations provide enough information for independent and reliable determination of the two rates. In addition, although dynamic glucose homeostasis can be described with a reversible Michaelis-Menten model, which is implicit to the large iso-inhibition constant (K(ii)) relative to physiological brain glucose content, we found that the apparent affinity constant (K(t)) was better determined with the four-state conformational model of glucose transport than with any of the other models tested. Furthermore, we confirmed the utility of the present method to determine glucose transport and consumption by analysing the modulation of both glucose transport and consumption by anaesthesia conditions that modify cerebral activity. In particular, deep thiopental anaesthesia caused a significant reduction of both T(max) and cerebral metabolic rate for glucose consumption. In conclusion, dynamic measurement of brain glucose in vivo in function of plasma glucose allows robust determination of both glucose uptake and consumption kinetics.

Stand dynamics modelling approaches for multicohort management of eastern Canadian boreal forests

Abstract

Dynamics and vertical distribution of a Hrabeiella periglandulata (Annelida) population in South Moravia, Czech Republic

The objective of this work was to evaluate the density, dynamics and vertical distribution of a Hrabeiella periglandulata population in a forest soil at Brno, Czech Republic. From December 2003 to November 2004, two plots covered by mixed stands and two covered by coniferous stands were sampled monthly. Six soil cores per plot were taken down to 15 cm and subdivided into layers, which were subjected to wet funnel extraction. Missing in one of the coniferous stands H. periglandulata was abundant in the mixed stand with the highest soil pH. In this stand, monthly sampling continued until November 2005, with three additional samplings up to January 2007. Mean annual density was 2,672±1,534 individuals m-2. Population dynamics differed from those reported from Germany. Highest densities were reached in early summer, lowest between August and December. Due to aggregated horizontal distribution, differences between monthly values were often nonsignificant. No significant correlation with climatic data was found. Nevertheless, the observed dynamics corresponded to the climatic conditions, showing particularly the negative effect of drought. The population was evenly distributed in the sampled soil profile, only avoiding the organic layer. Except for a locality in Poland, this is the easternmost record of the species.

Spatiotemporal dynamics of soil rotifers in a South-Bohemian beech forest

The objective of this work was to determine seasonal variation and vertical distribution of the soil rotifer assemblage in a climax beech forest in South Bohemia. During 2005, soil rotifer was investigated to the species level. Soil samples of 10 cm² and 10 cm in depth were divided into five layers, which were processed separately. Thirty one rotifer species were identified during the investigation. Dominant species significantly changed throughout the seasons. The most abundant species were Encentrum arvicola and Wierzejskiella vagneri among the monogononts, and Adineta steineri, Ceratotrocha cornigera, Habrotrocha filum, Habrotrocha ligula, Macrotrachela plicata, Mniobia tentans, Mniobia incrassata and Mniobia granulosa among the bdelloids. Mean Shannon diversity index varied from 1.99 to 2.63. Total rotifer abundance varied from 212±63 to 513±127 10³ individuals m-2 along the year, with the highest numbers found in May, and the lowest in July. The great part of the community was concentrated in the upper (fresh litter) and second (partially decomposed litter) layers and significantly decreased in the soil vertical profile on all sampling dates. The highest rotifer density of 43 individuals g-1 was found in the upper layer in May.

Diffusion dynamics on multiplex networks

We study the time scales associated with diffusion processes that take place on multiplex networks, i.e., on a set of networks linked through interconnected layers. To this end, we propose the construction of a supra-Laplacian matrix, which consists of a dimensional lifting of the Laplacian matrix of each layer of the multiplex network. We use perturbative analysis to reveal analytically the structure of eigenvectors and eigenvalues of the complete network in terms of the spectral properties of the individual layers. The spectrum of the supra-Laplacian allows us to understand the physics of diffusionlike processes on top of multiplex networks.

On learning dynamics underlying the evolution of learning rules.

In order to understand the development of non-genetically encoded actions during an animal's lifespan, it is necessary to analyze the dynamics and evolution of learning rules producing behavior. Owing to the intrinsic stochastic and frequency-dependent nature of learning dynamics, these rules are often studied in evolutionary biology via agent-based computer simulations. In this paper, we show that stochastic approximation theory can help to qualitatively understand learning dynamics and formulate analytical models for the evolution of learning rules. We consider a population of individuals repeatedly interacting during their lifespan, and where the stage game faced by the individuals fluctuates according to an environmental stochastic process. Individuals adjust their behavioral actions according to learning rules belonging to the class of experience-weighted attraction learning mechanisms, which includes standard reinforcement and Bayesian learning as special cases. We use stochastic approximation theory in order to derive differential equations governing action play probabilities, which turn out to have qualitative features of mutator-selection equations. We then perform agent-based simulations to find the conditions where the deterministic approximation is closest to the original stochastic learning process for standard 2-action 2-player fluctuating games, where interaction between learning rules and preference reversal may occur. Finally, we analyze a simplified model for the evolution of learning in a producer-scrounger game, which shows that the exploration rate can interact in a non-intuitive way with other features of co-evolving learning rules. Overall, our analyses illustrate the usefulness of applying stochastic approximation theory in the study of animal learning.