Emotion and anticipation in an enactive framework for cognition (response to Andy Clark)

Undeniably, anticipation plays a crucial role in cognition. By what means, to what extent, and what it achieves remain open questions. In a recent BBS target article, Clark (in press) depicts an integrative model of the brain that builds on hierarchical Bayesian models of neural processing (Rao and Ballard, 1999; Friston, 2005; Brown et al., 2011), and their most recent formulation using the free-energy principle borrowed from thermodynamics (Feldman and Friston, 2010; Friston, 2010; Friston et al., 2010). Hierarchical generative models of cognition, such as those described by Clark, presuppose the manipulation of representations and internal models of the world, in as much detail as is perceptually available. Perhaps surprisingly, Clark acknowledges the existence of a “virtual version of the sensory data” (p. 4), but with no reference to some of the historical debates that shaped cognitive science, related to the storage, manipulation, and retrieval of representations in a cognitive system (Shanahan, 1997), or accounting for the emergence of intentionality within such a system (Searle, 1980; Preston and Bishop, 2002). Instead of demonstrating how this Bayesian framework responds to these foundational questions, Clark describes the structure and the functional properties of an action-oriented, multi-level system that is meant to combine perception, learning, and experience (Niedenthal, 2007).

The variation in transparency of amniotic membrane used in ocular surface regeneration

Background/aims: Scant consideration has been given to the variation in structure of the human amniotic membrane (AM) at source or to the significance such differences might have on its clinical transparency. Therefore, we applied our experience of quantifying corneal transparency to AM. Methods: Following elective caesarean, AM from areas of the fetal sac distal and proximal (ie, adjacent) to the placenta was compared with freeze-dried AM. The transmission of light through the AM samples was quantified spectrophotometrically; also, tissue thickness was measured by light microscopy and refractive index by refractometry. Results: Freeze-dried and freeze-thawed AM samples distal and proximal to the placenta differed significantly in thickness, percentage transmission of visible light and refractive index. The thinnest tissue (freeze-dried AM) had the highest transmission spectra. The thickest tissue (freeze-thawed AM proximal to the placenta) had the highest refractive index. Using the direct summation of fields method to predict transparency from an equivalent thickness of corneal tissue, AM was found to be up to 85% as transparent as human cornea. Conclusion: When preparing AM for ocular surface reconstruction within the visual field, consideration should be given to its original location from within the fetal sac and its method of preservation, as either can influence corneal transparency.

Methods and resources for climate impacts research: achieving synergy

The prediction of climate variability and change requires the use of a range of simulation models. Multiple climate model simulations are needed to sample the inherent uncertainties in seasonal to centennial prediction. Because climate models are computationally expensive, there is a tradeoff between complexity, spatial resolution, simulation length, and ensemble size. The methods used to assess climate impacts are examined in the context of this trade-off. An emphasis on complexity allows simulation of coupled mechanisms, such as the carbon cycle and feedbacks between agricultural land management and climate. In addition to improving skill, greater spatial resolution increases relevance to regional planning. Greater ensemble size improves the sampling of probabilities. Research from major international projects is used to show the importance of synergistic research efforts. The primary climate impact examined is crop yield, although many of the issues discussed are relevant to hydrology and health modeling. Methods used to bridge the scale gap between climate and crop models are reviewed. Recent advances include large-area crop modeling, quantification of uncertainty in crop yield, and fully integrated crop–climate modeling. The implications of trends in computer power, including supercomputers, are also discussed.

Estimating the spatial scales of regionalized variables by nested sampling, hierarchical analysis of variance and residual maximum likelihood

The variogram is essential for local estimation and mapping of any variable by kriging. The variogram itself must usually be estimated from sample data. The sampling density is a compromise between precision and cost, but it must be sufficiently dense to encompass the principal spatial sources of variance. A nested, multi-stage, sampling with separating distances increasing in geometric progression from stage to stage will do that. The data may then be analyzed by a hierarchical analysis of variance to estimate the components of variance for every stage, and hence lag. By accumulating the components starting from the shortest lag one obtains a rough variogram for modest effort. For balanced designs the analysis of variance is optimal; for unbalanced ones, however, these estimators are not necessarily the best, and the analysis by residual maximum likelihood (REML) will usually be preferable. The paper summarizes the underlying theory and illustrates its application with data from three surveys, one in which the design had four stages and was balanced and two implemented with unbalanced designs to economize when there were more stages. A Fortran program is available for the analysis of variance, and code for the REML analysis is listed in the paper. (c) 2005 Elsevier Ltd. All rights reserved.

Estimating the spatial scales of regionalized variables by nested sampling, hierarchical analysis of variance and residual maximum likelihood

The variogram is essential for local estimation and mapping of any variable by kriging. The variogram itself must usually be estimated from sample data. The sampling density is a compromise between precision and cost, but it must be sufficiently dense to encompass the principal spatial sources of variance. A nested, multi-stage, sampling with separating distances increasing in geometric progression from stage to stage will do that. The data may then be analyzed by a hierarchical analysis of variance to estimate the components of variance for every stage, and hence lag. By accumulating the components starting from the shortest lag one obtains a rough variogram for modest effort. For balanced designs the analysis of variance is optimal; for unbalanced ones, however, these estimators are not necessarily the best, and the analysis by residual maximum likelihood (REML) will usually be preferable. The paper summarizes the underlying theory and illustrates its application with data from three surveys, one in which the design had four stages and was balanced and two implemented with unbalanced designs to economize when there were more stages. A Fortran program is available for the analysis of variance, and code for the REML analysis is listed in the paper. (c) 2005 Elsevier Ltd. All rights reserved.

Using idealized snow forcing to test teleconnections with the Indian summer monsoon in the Hadley Centre GCM

Anomalous heavy snow during winter or spring has long been regarded as a possible precursor of deficient Indian monsoon rainfall during the subsequent summer. However previous work in this field is inconclusive, in terms of the mechanism that communicates snow anomalies to the monsoon summer, and even the region from which snow has the most impact. In this study we explore these issues in coupled and atmosphere-only versions of the Hadley Centre model. A 1050-year control integration of the HadCM3 coupled model, which well represents the seasonal cycle of snow cover over the Eurasian continent, is analysed and shows evidence for weakened monsoons being preceded by strong snow forcing (in the absence of ENSO) over either the Himalaya/Tibetan Plateau or north/west Eurasia regions. However, empirical orthogonal function (EOF) analysis of springtime interannual variability in snow depth shows the leading mode to have opposite signs between these two regions, suggesting that competing mechanisms may be possible. To determine the dominant region, ensemble integrations are carried out using HadAM3, the atmospheric component of HadCM3, and a variety of anomalous snow forcing initial conditions obtained from the control integration of the coupled model. Forcings are applied during spring in separate experiments over the Himalaya/Tibetan Plateau and north/west Eurasia regions, in conjunction with climatological SSTs in order to avoid the direct effects of ENSO. With the aid of idealized forcing conditions in sensitivity tests, we demonstrate that forcing from the Himalaya region is dominant in this model via a Blanford-type mechanism involving reduced surface sensible heat and longwave fluxes, reduced heating of the troposphere over the Tibetan Plateau and consequently a reduced meridional tropospheric temperature gradient which weakens the monsoon during early summer. Snow albedo is shown to be key to the mechanism, explaining around 50% of the perturbation in sensible heating over the Tibetan Plateau, and accounting for the majority of cooling through the troposphere.

Dispersal capacity and diet breadth modify the response of wild bees to habitat loss

Habitat loss poses a major threat to biodiversity, and species-specific extinction risks are inextricably linked to life-history characteristics. This relationship is still poorly documented for many functionally important taxa, and at larger continental scales. With data from five replicated field studies from three countries, we examined how species richness of wild bees varies with habitat patch size. We hypothesized that the form of this relationship is affected by body size, degree of host plant specialization and sociality. Across all species, we found a positive species–area slope (z ¼ 0.19), and species traits modified this relationship. Large-bodied generalists had a lower z value than small generalists. Contrary to predictions, small specialists had similar or slightly lower z value compared with large specialists, and small generalists also tended to be more strongly affected by habitat loss as compared with small specialists. Social bees were negatively affected by habitat loss (z ¼ 0.11) irrespective of body size. We conclude that habitat loss leads to clear shifts in the species composition of wild bee communities.

Impacts of a pesticide on pollinator species richness at different spatial scales

Pesticides are an important potential cause of biodiversity and pollinator decline. Little is known about the impacts of pesticides on wild pollinators in the field. Insect pollinators were sampled in an agricultural system in Italy with the aim of detecting the impacts of pesticide use. The insecticide fenitrothion was over 150 times greater in toxicity than other pesticides used in the area, so sampling was set up around its application. Species richness of wild bees, bumblebees and butterflies were sampled at three spatial scales to assess responses to pesticide application: (i) the ‘field’ scale along pesticide drift gradients; (ii) the ‘landscape’ scale sampling in different crops within the area and (iii) the ‘regional’ scale comparing two river basins with contrasting agricultural intensity. At the field scale, the interaction between the application regime of the insecticide and the point in the season was important for species richness. Wild bee species richness appeared to be unaffected by one insecticide application, but declined after two and three applications. At the landscape scale, the species richness of wild bees declined in vine fields where the insecticide was applied, but did not decline in maize or uncultivated fields. At the regional scale, lower bumblebee and butterfly species richness was found in the more intensively farmed basin with higher pesticide loads. Our results suggest that wild bees are an insect pollinator group at particular risk from pesticide use. Further investigation is needed on how the type, quantity and timing of pesticide application impacts pollinators.

Organic farming in isolated landscapes does not benefit flower-visiting insects and pollination

Organic farming has often been found to provide benefits for biodiversity, but the benefits can depend on the species considered and characteristics of the surrounding landscape. In an intensively farmed area of Northeast Italy we investigated whether isolated organic farms, in a conventionally farmed landscape, provided local benefits for insect pollinators and pollination services. We quantified the relative effects of local management (i.e. the farm system), landscape management (proportion of surrounding uncultivated land) and interactions between them. We compared six organic and six conventional vine fields. The proportion of surrounding uncultivated land was calculated for each site at radii of 200, 500, 1000 and 2000 m. The organic fields did not differ from the conventional in their floral resources or proportion of surrounding uncultivated land. Data were collected on pollinator abundance and species richness, visitation rates to, and pollination of experimental potted plants. None of these factors were significantly affected by the farming system. The abundance of visits to the potted plants in the conventional fields tended to be negatively affected by the proportion of surrounding uncultivated land. The proportion fruit set, weight of seeds per plant and seed weight in conventional and organic fields were all negatively affected by the proportion of surrounding uncultivated land. In vine fields the impact of the surrounding landscape was stronger than the local management. Enhancement of biodiversity through organic farming should not be assumed to be ubiquitous, as potential benefits may be offset by the crop type, organicmanagement practices and the specific habitat requirements in the surrounding landscape.

Effects of patch size and density on flower visitation and seed set of wild plants: a pan-European approach

1. Habitat fragmentation can affect pollinator and plant population structure in terms of species composition, abundance, area covered and density of flowering plants. This, in turn, may affect pollinator visitation frequency, pollen deposition, seed set and plant fitness. 2. A reduction in the quantity of flower visits can be coupled with a reduction in the quality of pollination service and hence the plants’ overall reproductive success and long-term survival. Understanding the relationship between plant population size and⁄ or isolation and pollination limitation is of fundamental importance for plant conservation. 3. Weexamined flower visitation and seed set of 10 different plant species fromfive European countries to investigate the general effects of plant populations size and density, both within (patch level) and between populations (population level), on seed set and pollination limitation. 4. Wefound evidence that the effects of area and density of flowering plant assemblages were generally more pronounced at the patch level than at the population level. We also found that patch and population level together influenced flower visitation and seed set, and the latter increased with increasing patch area and density, but this effect was only apparent in small populations. 5. Synthesis. By using an extensive pan-European data set on flower visitation and seed set we have identified a general pattern in the interplay between the attractiveness of flowering plant patches for pollinators and density dependence of flower visitation, and also a strong plant species-specific response to habitat fragmentation effects. This can guide efforts to conserve plant–pollinator interactions, ecosystem functioning and plant fitness in fragmented habitats.

Towards an assessment of multiple ecosystem processes and services via functional traits

Managing ecosystems to ensure the provision of multiple ecosystem services is a key challenge for applied ecology. Functional traits are receiving increasing attention as the main ecological attributes by which different organisms and biological communities influence ecosystem services through their effects on underlying ecosystem processes. Here we synthesize concepts and empirical evidence on linkages between functional traits and ecosystem services across different trophic levels. Most of the 247 studies reviewed considered plants and soil invertebrates, but quantitative trait–service associations have been documented for a range of organisms and ecosystems, illustrating the wide applicability of the trait approach. Within each trophic level, specific processes are affected by a combination of traits while particular key traits are simultaneously involved in the control of multiple processes. These multiple associations between traits and ecosystem processes can help to identify predictable trait–service clusters that depend on several trophic levels, such as clusters of traits of plants and soil organisms that underlie nutrient cycling, herbivory, and fodder and fibre production. We propose that the assessment of trait–service clusters will represent a crucial step in ecosystem service monitoring and in balancing the delivery of multiple, and sometimes conflicting, services in ecosystem management.

Ecological and life-history traits predict bee species responses to environmental disturbances

The ability to predict the responses of ecological communities and individual species to human-induced environmental change remains a key issue for ecologists and conservation managers alike. Responses are often variable among species within groups making general predictions difficult. One option is to include ecological trait information that might help to disentangle patterns of response and also provide greater understanding of how particular traits link whole clades to their environment. Although this ‘‘trait-guild” approach has been used for single disturbances, the importance of particular traits on general responses to multiple disturbances has not been explored. We used a mixed model analysis of 19 data sets from throughout the world to test the effect of ecological and life-history traits on the responses of bee species to different types of anthropogenic environmental change. These changes included habitat loss, fragmentation, agricultural intensification, pesticides and fire. Individual traits significantly affected bee species responses to different disturbances and several traits were broadly predictive among multiple disturbances. The location of nests – above vs. below ground – significantly affected response to habitat loss, agricultural intensification, tillage regime (within agriculture) and fire. Species that nested above ground were on average more negatively affected by isolation from natural habitat and intensive agricultural land use than were species nesting below ground. In contrast below-ground-nesting species were more negatively affected by tilling than were above-ground nesters. The response of different nesting guilds to fire depended on the time since the burn. Social bee species were more strongly affected by isolation from natural habitat and pesticides than were solitary bee species. Surprisingly, body size did not consistently affect species responses, despite its importance in determining many aspects of individuals’ interaction with their environment. Although synergistic interactions among traits remain to be explored, individual traits can be useful in predicting and understanding responses of related species to global change.