Local adaptation in migrated interior Douglas-fir seedlings is mediated by ectomycorrhizas and other soil factors

Separating edaphic impacts on tree distributions from those of climate and geography is notoriously difficult. Aboveground and belowground factors play important roles, and determining their relative contribution to tree success will greatly assist in refining predictive models and forestry strategies in a changing climate. In a common glasshouse, seedlings of interior Douglas-fir (Pseudotsuga menziesii var. glauca) from multiple populations were grown in multiple forest soils. Fungicide was applied to half of the seedlings to separate soil fungal and nonfungal impacts on seedling performance. Soils of varying geographic and climatic distance from seed origin were compared, using a transfer function approach. Seedling height and biomass were optimized following seed transfer into drier soils, whereas survival was optimized when elevation transfer was minimised. Fungicide application reduced ectomycorrhizal root colonization by c. 50%, with treated seedlings exhibiting greater survival but reduced biomass. Local adaptation of Douglas-fir populations to soils was mediated by soil fungi to some extent in 56% of soil origin by response variable combinations. Mediation by edaphic factors in general occurred in 81% of combinations. Soil biota, hitherto unaccounted for in climate models, interacts with biogeography to influence plant ranges in a changing climate.

Mesh adaptation on the sphere using optimal transport and the numerical solution of a Monge-Ampère type equation

An equation of Monge-Ampère type has, for the first time, been solved numerically on the surface of the sphere in order to generate optimally transported (OT) meshes, equidistributed with respect to a monitor function. Optimal transport generates meshes that keep the same connectivity as the original mesh, making them suitable for r-adaptive simulations, in which the equations of motion can be solved in a moving frame of reference in order to avoid mapping the solution between old and new meshes and to avoid load balancing problems on parallel computers. The semi-implicit solution of the Monge-Ampère type equation involves a new linearisation of the Hessian term, and exponential maps are used to map from old to new meshes on the sphere. The determinant of the Hessian is evaluated as the change in volume between old and new mesh cells, rather than using numerical approximations to the gradients. OT meshes are generated to compare with centroidal Voronoi tesselations on the sphere and are found to have advantages and disadvantages; OT equidistribution is more accurate, the number of iterations to convergence is independent of the mesh size, face skewness is reduced and the connectivity does not change. However anisotropy is higher and the OT meshes are non-orthogonal. It is shown that optimal transport on the sphere leads to meshes that do not tangle. However, tangling can be introduced by numerical errors in calculating the gradient of the mesh potential. Methods for alleviating this problem are explored. Finally, OT meshes are generated using observed precipitation as a monitor function, in order to demonstrate the potential power of the technique.

Rhodosporidium toruloides cultivated in NaCl-enriched glucose-based media: adaptation dynamics and lipid production

In the present report and for the first time in the international literature, the impact of the addition of NaCl upon growth and lipid production on the oleaginous yeast Rhodosporidium toruloides was studied. Moreover, equally for first time, lipid production by R. toruloides was performed under non-aseptic conditions. Therefore, the potentiality of R. toruloides DSM 4444 to produce lipid in media containing several initial concentrations of NaCl with glucose employed as carbon source was studied. Preliminary batch-flask trials with increasing amounts of NaCl revealed the tolerance of the strain against NaCl content up to 6.0% (w/v). However, 4.0% (w/v) of NaCl stimulated lipid accumulation for this strain, by enhancing lipid production up to 71.3% (w/w) per dry cell weight. The same amount of NaCl was employed in pasteurized batch-flask cultures in order to investigate the role of the salt as bacterial inhibiting agent. The combination of NaCl and high glucose concentrations was found to satisfactorily suppress bacterial contamination of R. toruloides cultures under these conditions. Batch-bioreactor trials of the yeast in the same media with high glucose content (up to 150 g/L) resulted in satisfactory substrate assimilation, with almost linear kinetic profile for lipid production, regardless of the initial glucose concentration imposed. Finally, fed-batch bioreactor cultures led to the production of 37.2 g/L of biomass, accompanied by 64.5% (w/w) of lipid yield. Lipid yield per unit of glucose consumed received the very satisfactory value of 0.21 g/g, a value amongst the highest ones in the literature. The yeast lipid produced contained mainly oleic acid and to lesser extent palmitic and stearic acids, thus constituting a perfect starting material for “second generation” biodiesel

Similarities in butterfly emergence dates among populations suggest local adaptation to climate

Phenology shifts are the most widely cited examples of the biological impact of climate change, yet there are few assessments of potential effects on the fitness of individual organisms or the persistence of populations. Despite extensive evidence of climate-driven advances in phenological events over recent decades, comparable patterns across species' geographic ranges have seldom been described. Even fewer studies have quantified concurrent spatial gradients and temporal trends between phenology and climate. Here we analyse a large data set (~129 000 phenology measures) over 37 years across the UK to provide the first phylogenetic comparative analysis of the relative roles of plasticity and local adaptation in generating spatial and temporal patterns in butterfly mean flight dates. Although populations of all species exhibit a plastic response to temperature, with adult emergence dates earlier in warmer years by an average of 6.4 days per °C, among-population differences are significantly lower on average, at 4.3 days per °C. Emergence dates of most species are more synchronised over their geographic range than is predicted by their relationship between mean flight date and temperature over time, suggesting local adaptation. Biological traits of species only weakly explained the variation in differences between space-temperature and time-temperature phenological responses, suggesting that multiple mechanisms may operate to maintain local adaptation. As niche models assume constant relationships between occurrence and environmental conditions across a species' entire range, an important implication of the temperature-mediated local adaptation detected here is that populations of insects are much more sensitive to future climate changes than current projections suggest.

Exploring smart grid possibilities: a complex systems modelling approach

Smart grid research has tended to be compartmentalised, with notable contributions from economics, electrical engineering and science and technology studies. However, there is an acknowledged and growing need for an integrated systems approach to the evaluation of smart grid initiatives. The capacity to simulate and explore smart grid possibilities on various scales is key to such an integrated approach but existing models – even if multidisciplinary – tend to have a limited focus. This paper describes an innovative and flexible framework that has been developed to facilitate the simulation of various smart grid scenarios and the interconnected social, technical and economic networks from a complex systems perspective. The architecture is described and related to realised examples of its use, both to model the electricity system as it is today and to model futures that have been envisioned in the literature. Potential future applications of the framework are explored, along with its utility as an analytic and decision support tool for smart grid stakeholders.

A decision framework for considering climate change adaptation in biodiversity conservation planning

General principles of climate change adaptation for biodiversity have been formulated, but do not help prioritize actions. This is inhibiting their integration into conservation planning. We address this need with a decision framework that identifies and prioritizes actions to increase the adaptive capacity of species. The framework classifies species according to their current distribution and projected future climate space, as a basis for selecting appropriate decision trees. Decisions rely primarily on expert opinion, with additional information from quantitative models, where data are available. The framework considers in-situ management, followed by interventions at the landscape scale and finally translocation or ex-situ conservation. Synthesis and applications: From eight case studies, the key interventions identified for integrating climate change adaptation into conservation planning were local management and expansion of sites. We anticipate that, in combination with consideration of socio-economic and local factors, the decision framework will be a useful tool for conservation and natural resource managers to integrate adaptation measures into conservation plans.

Participatory urban planning for climate change adaptation in coastal cities: lessons from a pilot experience in Maputo, Mozambique

This paper proposes a rights-based approach for participatory urban planning for climate change adaptation in urban areas. Participatory urban planning ties climate change adaptation to local development opportunities. Previous discussions suggest that participatory urban planning may help to understand structural inequalities, to gain, even if temporally, institutional support and to deliver a planning process in constant negotiation with local actors. Building upon an action research project which implemented a process of participatory urban planning for climate change in Maputo, Mozambique, this paper reflects upon the practical lessons that emerged from these experiences, in relation to the incorporation of climate change information, the difficulties to secure continued support from local governments and the opportunities for local impacts through the implementation of the proposals emerging from this process.

Bayesian analysis of uncertainty in the GlobCover 2009 land cover product at climate model grid scale

Land cover data derived from satellites are commonly used to prescribe inputs to models of the land surface. Since such data inevitably contains errors, quantifying how uncertainties in the data affect a model’s output is important. To do so, a spatial distribution of possible land cover values is required to propagate through the model’s simulation. However, at large scales, such as those required for climate models, such spatial modelling can be difficult. Also, computer models often require land cover proportions at sites larger than the original map scale as inputs, and it is the uncertainty in these proportions that this article discusses. This paper describes a Monte Carlo sampling scheme that generates realisations of land cover proportions from the posterior distribution as implied by a Bayesian analysis that combines spatial information in the land cover map and its associated confusion matrix. The technique is computationally simple and has been applied previously to the Land Cover Map 2000 for the region of England and Wales. This article demonstrates the ability of the technique to scale up to large (global) satellite derived land cover maps and reports its application to the GlobCover 2009 data product. The results show that, in general, the GlobCover data possesses only small biases, with the largest belonging to non–vegetated surfaces. In vegetated surfaces, the most prominent area of uncertainty is Southern Africa, which represents a complex heterogeneous landscape. It is also clear from this study that greater resources need to be devoted to the construction of comprehensive confusion matrices.

Dietary copper absorption and excretion in three semi-terrestrial grapsoid crabs with different levels of terrestrial adaptation

Three species of phylogenetically related semi-terrestrial crabs (Superfamily Grapsoidea - Sesarma rectum, Goniopsis cruentata and Neohelice granulata (formerly: Chasmagnathus granulatus) with different degrees of terrestriality were studied to quantify the accumulation of copper (Cu) in hemolymph, gills, hepatopancreas and antennal gland, and its excretion through the faeces. These crabs were fed for 15 days practical diets containing 0 (A), 0.5 (B), 1.0 (C), and 1.5% (D) of added CuCl2 (corresponding to 0, 0.2, 0.5 and 0.7% of Cu2+, respectively). The amount of food ingested was directly proportional to the degree of terrestriality: S. rectum, the most terrestrial species, ate around 2-3 times more than the other crabs, whereas G. cruentata ate 1.5-2 times more than N. granulata, the least terrestrial. The amount of Cu excreted in the feces was proportional to Cu ingestion, and was 76.8% and 64.2% higher for Sesarma fed diet D compared to G. cruentata and N. granulata, respectively. Sesarma also displayed higher Cu concentration in the haemolymph, gills and antennal glands, but not in the hepatopancreas. A detoxifying mechanism followed by elimination was probably present at this last organ, preventing Cu accumulation. More terrestrial crabs, such as Sesarma, may accumulate more Cu in hemolymph and tissues, showing a correlation between metal accumulation and increased terrestriality. In this aspect, contaminated feed sources with Cu may have more impact in conservation of terrestrial crabs. (C) 2008 Elsevier Inc. All rights reserved.

A hybrid case adaptation approach for case-based reasoning

Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.

Grid job scheduling using Route with Genetic Algorithm support

In 2006 the Route load balancing algorithm was proposed and compared to other techniques aiming at optimizing the process allocation in grid environments. This algorithm schedules tasks of parallel applications considering computer neighborhoods (where the distance is defined by the network latency). Route presents good results for large environments, although there are cases where neighbors do not have an enough computational capacity nor communication system capable of serving the application. In those situations the Route migrates tasks until they stabilize in a grid area with enough resources. This migration may take long time what reduces the overall performance. In order to improve such stabilization time, this paper proposes RouteGA (Route with Genetic Algorithm support) which considers historical information on parallel application behavior and also the computer capacities and load to optimize the scheduling. This information is extracted by using monitors and summarized in a knowledge base used to quantify the occupation of tasks. Afterwards, such information is used to parameterize a genetic algorithm responsible for optimizing the task allocation. Results confirm that RouteGA outperforms the load balancing carried out by the original Route, which had previously outperformed others scheduling algorithms from literature.