Designing effective reserve networks for nonequilibrium metacommunities

The proliferation of efficient fishing practices has promoted the depletion of commercial stocks around the world and caused significant collateral damage to marine habitats. Recent empirical studies have shown that marine reserves can play an important role in reversing these effects. Equilibrium metapopulation models predict that networks of marine reserves can provide similar benefits so long as individual reserves are sufficiently large to achieve self-sustainability, or spaced based on the extent of dispersal of the target species in order to maintain connectivity between neighboring reserves. However, these guidelines have not been tested in nonequilibrium metacommunity models that exhibit the kinds of complex spatiotemporal dynamics typically seen in natural marine communities. Here, we used a spatially explicit predator–prey model whose predictions have been validated in a marine system to show that current guidelines are not optimal for metacommunities. In equilibrium metacommunities, there is a community-level trade-off for designing effective reserves: Networks whose size and spacing are smaller than the extent of dispersal maximize global predator abundance but minimize global prey abundance because of trophic cascades, whereas the converse is true for reserve networks whose size and spacing are larger than the extent of dispersal. In nonequilibrium metacommunities, reserves whose size and spacing match the extent of spatial autocorrelation in adult abundance (i.e., the extent of patchiness) escape this community-level trade-off by maximizing global abundance and persistence of both the prey and the predator. Overall, these results suggest that using the extent of adult patchiness instead of the extent of larval dispersal as the size and spacing of reserve networks is critical for designing community-based management strategies. By emphasizing patchiness over dispersal distance, our results show how the apparent complexity of nonequilibrium communities can actually simplify management guidelines and reduce uncertainty associated with the assessment of dispersal in marine environments.Keywords: metacommunities, dynamic resources, spatial management, patchiness, reserve networks, nonequilibrium, trophic cascade

Two-dimensional gravitactic bioconvection in a protozoan (tetrahymena pyriformis) culture

Gravitactic bioconvective patterns created by Tetrahymena pyriformis in a Hele-Shaw apparatus were realized and compared with theoretical results. There were found to be two thresholds for bioconvection development: the first indicates the transition from the diffusion to the steady convection state; the second corresponds to the transition from the steady to the unsteady convection state. The results showed that the Hele-Shaw apparatus may be used as a physical analogy of porous media to study 2D bioconvection, with possible extensions to larger scale biological systems where population growth and distribution are driven by similar bio-physical interactions.TNQ thanks Prof. N. Lima (Centre for Biological Engineering, University of Minho, Braga, Portugal) for his precious support and Prof. M. P. Sauvant Rochat (Cellular Biology Lab., Universite d'Auvergne, France) for her collaboration. We especially thank Mrs. Laviolette (Ecole Polytechnique de Montreal, Canada), and Mr. Morency and Mrs. Phoenix (University of Montreal, Canada), for their kind assistance. FG acknowledges support from the James S. McDonnell Foundation through a 21st Century Science Initiative award. We thank an anonymous reviewer for his interesting comments

Causes of maladaptation

Evolutionary biologists tend to approach the study of the natural world within a framework of adaptation, inspired perhaps by the power of natural selection to produce fitness advantages that drive population persistence and biological diversity. In contrast, evolution has rarely been studied through the lens of adaptation's complement, maladaptation. This contrast is surprising because maladaptation is a prevalent feature of evolution: population trait values are rarely distributed optimally; local populations often have lower fitness than imported ones; populations decline; and local and global extinctions are common. Yet we lack a general framework for understanding maladaptation; for instance in terms of distribution, severity, and dynamics. Similar uncertainties apply to the causes of maladaptation. We suggest that incorporating maladaptation-based perspectives into evolutionary biology would facilitate better understanding of the natural world. Approaches within a maladaptation framework might be especially profitable in applied evolution contexts – where reductions in fitness are common. Toward advancing a more balanced study of evolution, here we present a conceptual framework describing causes of maladaptation. As the introductory article for a Special Feature on maladaptation, we also summarize the studies in this Issue, highlighting the causes of maladaptation in each study. We hope that our framework and the papers in this Special Issue will help catalyze the study of maladaptation in applied evolution, supporting greater understanding of evolutionary dynamics in our rapidly changing world

FredericGuichard/Aramugam_EWS: rate-dependentEWS

<p>Code used in Arumugam et al, "Early warning indicators capture catastrophic transitions driven by explicit rates of environmental change", to appear in Ecology</p&gt

Application of Particle Filtering to Image Enhancement

In this report we propose a novel - assumption-free on the noise model - technique based on random walks for image enhancement. Our method explores multiple neighbors sets (or hypotheses) that can be used for pixel denoising, through a particle filtering approach. This approach associates weights for each hypotheses according to its relevance and its contribution in the denoising process. Toward accounting for the image structure, we introduce perturbations based on local statistical properties of the image. In other words, particle evolution are controlled by the image structure leading to a filtering window adapted to image contents. Promising experimental results and comparison with the state-of-the-art methods demonstrate the potential of such an approac

Curve Finder Combining Perceptual Grouping and a Kalman Like Fitting

We present an algorithm that extracts curves from a set of edgels within a specific class in a decreasing order of their "length". The algorithm inherits the perceptual grouping approaches. But, instead of using only local cues, a global constraint is imposed to each extracted subset of edgels, that the underlying curve belongs to a specific class. In order to reduce the complexity of the solution, we work with a linearly parameterized class of curves, function of one image coordinate. This allows, first, to use a recursive Kalman based fitting and, second, to cast the problem as an optimal path search in an directed graph. Experiments on finding lane-markings on roads demonstrate that real-time processing is achievable

Choosing fitness-enhancing innovations can be detrimental under fluctuating environments.

The ability to predict the consequences of one's behavior in a particular environment is a mechanism for adaptation. In the absence of any cost to this activity, we might expect agents to choose behaviors that maximize their fitness, an example of directed innovation. This is in contrast to blind mutation, where the probability of becoming a new genotype is independent of the fitness of the new genotypes. Here, we show that under environments punctuated by rapid reversals, a system with both genetic and cultural inheritance should not always maximize fitness through directed innovation. This is because populations highly accurate at selecting the fittest innovations tend to over-fit the environment during its stable phase, to the point that a rapid environmental reversal can cause extinction. A less accurate population, on the other hand, can track long term trends in environmental change, keeping closer to the time-average of the environment. We use both analytical and agent-based models to explore when this mechanism is expected to occur