Heritability and correlations among learning and inhibitory control traits

To understand the evolution of cognitive abilities, we need to understand both how selection acts upon them and their genetic (co)variance structure. Recent work suggests that there are fitness consequences for free-living individuals with particular cognitive abilities. However, our current understanding of the heritability of these abilities is restricted to domesticated species subjected to artificial selection. We investigated genetic variance for, and genetic correlations among four cognitive abilities: inhibitory control, visual and spatial discrimination, and spatial ability, measured on >450 pheasants, Phasianus colchicus, over four generations. Pheasants were reared in captivity but bred from adults that lived in the wild and hence, were subject to selection on survival. Pheasant chicks are precocial and were reared without parents, enabling us to standardize environmental and parental care effects. We constructed a pedigree based on 15 microsatellite loci and implemented animal models to estimate heritability. We found moderate heritabilities for discrimination learning and inhibitory control (h2 = 0.17–0.23) but heritability for spatial ability was low (h2 = 0.09). Genetic correlations among-traits were largely positive but characterized by high uncertainty and were not statistically significant. Principle component analysis of the genetic correlation matrix estimate revealed a leading component that explained 69% of the variation, broadly in line with expectations under a general intelligence model of cognition. However, this pattern was not apparent in the phenotypic correlation structure which was more consistent with a modular view of animal cognition. Our findings highlight that the expression of cognitive traits is influenced by environmental factors which masks the underlying genetic structure

Phyllosticta citricarpa and sister species of global importance to Citrus.

Several Phyllosticta species are known as pathogens of Citrus spp., and are responsible for various disease symptoms including leaf and fruit spots. One of the most important species is P. citricarpa, which causes a foliar and fruit disease called citrus black spot. The Phyllosticta species occurring on citrus can most effectively be distinguished from P. citricarpa by means of multilocus DNA sequence data. Recent studies also demonstrated P. citricarpa to be heterothallic, and reported successful mating in the laboratory. Since the domestication of citrus, different clones of P. citricarpa have escaped Asia to other continents via trade routes, with obvious disease management consequences. This pathogen profile represents a comprehensive literature review of this pathogen and allied taxa associated with citrus, focusing on identification, distribution, genomics, epidemiology and disease management. This review also considers the knowledge emerging from seven genomes of Phyllosticta spp., demonstrating unknown aspects of these species, including their mating behaviour.TaxonomyPhyllosticta citricarpa (McAlpine) Aa, 1973. Kingdom Fungi, Phylum Ascomycota, Class Dothideomycetes, Order Botryosphaeriales, Family Phyllostictaceae, Genus Phyllosticta, Species citricarpa.Host rangeConfirmed on more than 12 Citrus species, Phyllosticta citricarpa has only been found on plant species in the Rutaceae.Disease symptomsP. citricarpa causes diverse symptoms such as hard spot, virulent spot, false melanose and freckle spot on fruit, and necrotic lesions on leaves and twigs.Useful websitesDOE Joint Genome Institute MycoCosm portals for the Phyllosticta capitalensis (https://genome.jgi.doe.gov/Phycap1), P. citriasiana (https://genome.jgi.doe.gov/Phycit1), P. citribraziliensis (https://genome.jgi.doe.gov/Phcit1), P. citrichinaensis (https://genome.jgi.doe.gov/Phcitr1), P. citricarpa (https://genome.jgi.doe.gov/Phycitr1, https://genome.jgi.doe.gov/Phycpc1), P. paracitricarpa (https://genome.jgi.doe.gov/Phy27169) genomes. All available Phyllosticta genomes on MycoCosm can be viewed at https://genome.jgi.doe.gov/Phyllosticta

Spatial memory predicts home range size and predation risk in pheasants

Most animals confine their activities to a discrete home range, long assumed to reflect the fitness benefits of obtaining spatial knowledge about the landscape. However, few empirical studies have linked spatial memory to home range development or determined how selection operates on spatial memory via the latter’s role in mediating space use. We assayed the cognitive ability of juvenile pheasants (Phasianus colchicus) reared under identical conditions before releasing them into the wild. Then, we used high-throughput tracking to record their movements as they developed their home ranges, and determined the location, timing and cause of mortality events. Individuals with greater spatial reference memory developed larger home ranges. Mortality risk from predators was highest at the periphery of an individual’s home range in areas where they had less experience and opportunity to obtain spatial information. Predation risk was lower in individuals with greater spatial memory and larger core home ranges, suggesting selection may operate on spatial memory by increasing the ability to learn about predation risk across the landscape. Our results reveal that spatial memory, determined from abstract cognitive assays, shapes home range development and variation, and suggests predation risk selects for spatial memory via experience-dependent spatial variation in mortality.</p

From prescriptive programming of solid-state devices to orchestrated self-organisation of informed matter

Achieving real-time response to complex, ambiguous, high-bandwidth data is impractical with conventional programming. Only the narrow class of compressible input-output maps can be specified with feasibly sized programs. Present computing concepts enforce formalisms that are arbitrary from the perspective of the physics underlying their implementation. Efficient physical realizations are embarrassed by the need to implement the rigidly specified instructions requisite for programmable systems. The conventional paradigm of erecting strong constraints and potential barriers that narrowly prescribe structure and precisely control system state needs to be complemented with a new approach that relinquishes detailed control and reckons with autonomous building blocks. Brittle prescriptive control will need to be replaced with resilient self-organisation to approach the robustness and efficiency afforded by natural systems. Structure-function self-consistency will be key to the spontaneous generation of functional architectures that can harness novel molecular and nano materials in an effective way for increased computational power