Urbanization, environmental stabilization and temporal persistence of bird species: A view from Latin America

Background. A scarcely studied consequence of urbanization is the effect of temporal stabilization of the environment on bird communities. This alteration is thought to dampen environmental variations between day and night, seasons and years, promoting a temporal persistence of bird composition in urban areas. The aim of this study was to review current evidence of temporal stabilization of biotic and abiotic factors in urban environments and the potential effects of such stabilization on temporal variation of bird species presence at different temporal scales. Methods. I selected the literature by searching published articles and book chapters using Scopus and Google scholar. I only included articles that compared the temporal variation of bird composition or resources between different levels of urbanization. Results. In general, there is evidence of temporal stabilization of abiotic and biotic factors at the three time scales considered. At the diurnal scale, the main factor considered was artificial light in the context of light pollution. At the seasonal and interannual scales, several case studies found a smaller temporal variation of primary productivity in urban than in natural and rural areas. Bird species composition showed more stabilization in urban environments at the three temporal scales: (1) several case studies reported bird activity at night, associated with artificial light; (2) studies in urban parks and along urbanization gradients showed smaller seasonal variation of bird composition in the more urbanized areas; and (3) in general, case studies along urbanization gradients showed smaller interannual variation of bird composition in the more urbanized areas, although some studies showed no relationships or opposite trends than expected. Discussion. The published evidence suggests that urban areas dampen the natural cycles at several temporal scales. The stabilization of biotic and abiotic factors, such as light, temperature, food and habitat structure, is desynchronized from natural diurnal, seasonal and interannual cycles. However, there is a dearth of long-term comparisons of bird composition and studies that simultaneously analyze the relationship between resources and bird composition stabilization at the seasonal and interannual scales. More research is needed in the Southern hemisphere, where there is a lack of studies dealing with the seasonal and interannual variations of primary productivity along urbanization gradients and nocturnal activity of bird species. A future research agenda should include differentiation of spatial and temporal homogenization of avifaunas.Fil: Leveau, Lucas Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Ecología, Genética y Evolución de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecología, Genética y Evolución de Buenos Aires; Argentin

Seven properties of self-organization in the human brain

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward

Epigenetic regulation of adaptive responses of forest tree species to the environment

Epigenetic variation is likely to contribute to the phenotypic plasticity and adaptative capacity of plant species, and may be especially important for long-lived organisms with complex life cycles, including forest trees. Diverse environmental stresses and hybridization/polyploidization events can create reversible heritable epigenetic marks that can be transmitted to subsequent generations as a form of molecular “memory”. Epigenetic changes might also contribute to the ability of plants to colonize or persist in variable environments. In this review, we provide an overview of recent data on epigenetic mechanisms involved in developmental processes and responses to environmental cues in plant, with a focus on forest tree species. We consider the possible role of forest tree epigenetics as a new source of adaptive traits in plant breeding, biotechnology, and ecosystem conservation under rapid climate chang

Evolutionary processes from the perspective of flowering time diversity.

Although it is well appreciated that genetic studies of flowering time regulation have led to fundamental advances in the fields of molecular and developmental biology, the ways in which genetic studies of flowering time diversity have enriched the field of evolutionary biology have received less attention despite often being equally profound. Because flowering time is a complex, environmentally responsive trait that has critical impacts on plant fitness, crop yield, and reproductive isolation, research into the genetic architecture and molecular basis of its evolution continues to yield novel insights into our understanding of domestication, adaptation, and speciation. For instance, recent studies of flowering time variation have reconstructed how, when, and where polygenic evolution of phenotypic plasticity proceeded from standing variation and de novo mutations; shown how antagonistic pleiotropy and temporally varying selection maintain polymorphisms in natural populations; and provided important case studies of how assortative mating can evolve and facilitate speciation with gene flow. In addition, functional studies have built detailed regulatory networks for this trait in diverse taxa, leading to new knowledge about how and why developmental pathways are rewired and elaborated through evolutionary time

Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios