Ecosystem services and adaptation for agriculture and food security: analysis of two decades (1991–2011) of financing by the Global Environment Facility

Investing in global environmental and adaptation benefits in the context of agriculture and food security initiatives can play an important role in promoting sustainable intensification. This is a priority for the Global Environment Facility (GEF), created in 1992 with a mandate to serve as financial mechanism of several multilateral environmental agreements. To demonstrate the nature and extent of GEF financing, we conducted an assessment of the entire portfolio over a period of two decades (1991–2011) to identify projects with direct links to agriculture and food security. A cohort of 192 projects and programs were identified and used as a basis for analyzing trends in GEF financing. The projects and programs together accounted for a total GEF financing of US$1,086.8 million, and attracted an additional US$6,343.5 million from other sources. The value-added of GEF financing for ecosystem services and resilience in production systems was demonstrated through a diversity of interventions in the projects and programs that utilized US$810.6 million of the total financing. The interventions fall into the following four main categories in accordance with priorities of the GEF: sustainable land management (US$179.3 million), management of agrobiodiversity (US$113.4 million), sustainable fisheries and water resource management (US$379.8 million), and climate change adaptation (US$138.1 million). By aligning GEF priorities with global aspirations for sustainable intensification of production systems, the study shows that it is possible to help developing countries tackle food insecurity while generating global environmental benefits for a healthy and resilient planet.

Extensión de la especificación IMS Learning Design desde la adaptación e integración de unidades de aprendizaje. `Extension of the IMS Learnin Design specification based on adaptation and integration of units of learning´.

La tesis se centra en el aprendizaje personalizado y en la integración de Unidades de Aprendizaje. El objetivo fundamental es mejorar la expresividad pedagógica de IMS-LD, refinando estructuras de modelado y arquitectura existentes y desarrollando estructuras complementarias que permitan una formalización más precisa, versátil y sencilla de procesos de aprendizaje adaptativo y de mecanismos y procesos de integración con sistemas de aprendizaje y otras especificaciones. En esta tesis se estudia la especificación desde la base, analizando su modelo de información y cómo se construyen Unidades de Aprendizaje. Se analiza la estructura de la especificación, basándose en un estudio teórico y una investigación práctica fruto del modelado de Unidades de Aprendizaje reales y ejecutables que proporcionan una información útil de base. A partir de este estudio, se analiza la integración de Unidades de Aprendizaje con otros sistemas y especificaciones, abarcando desde la integración mínima mediante un enlace directo hasta compartir variables y estados que permiten una comunicación en tiempo real de ambas partes. La conclusión es que IMS-LD necesita una reestructuración y modificación de ciertos elementos, así como la incorporación de otros nuevos, para mejorar una expresividad pedagógica y una capacidad de integración con otros sistemas de aprendizaje y estándares eLearning, si se pretenden alcanzar: la personalización del proceso de aprendizaje y la interoperabilidad real. La implantación de la especificación se vería mejorada si existieran unas herramientas de más alto nivel, preferiblemente con planteamiento visual, que permitieran un modelado sencillo por parte de los usuarios finales reales de este tipo de especificaciones, como son los profesores, los creadores de contenido y los pedagogos-didactas que diseñan la experiencia de aprendizaje.

Adaptation of the Evidence-Based Practices Attitude Scale in Spanish child welfare professionals

Resumen tomado de la publicaci??n

Functional identity and diversity of animals predict ecosystem functioning better than species-based indices

Drastic biodiversity declines have raised concerns about the deterioration of ecosystem functions and have motivated much recent research on the relationship between species diversity and ecosystem functioning. A functional trait framework has been proposed to improve the mechanistic understanding of this relationship, but this has rarely been tested for organisms other than plants. We analysed eight datasets, including five animal groups, to examine how well a trait-based approach, compared with a more traditional taxonomic approach, predicts seven ecosystem functions below- and above-ground. Trait-based indices consistently provided greater explanatory power than species richness or abundance. The frequency distributions of single or multiple traits in the community were the best predictors of ecosystem functioning. This implies that the ecosystem functions we investigated were underpinned by the combination of trait identities (i.e. single-trait indices) and trait complementarity (i.e. multi-trait indices) in the communities. Our study provides new insights into the general mechanisms that link biodiversity to ecosystem functioning in natural animal communities and suggests that the observed responses were due to the identity and dominance patterns of the trait composition rather than the number or abundance of species per se.

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

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.

New genes of Xanthomonas citri subsp citri involved in pathogenesis and adaptation revealed by a transposon-based mutant library

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Adaptation and penetration of resin-based root canal sealers in root canals irradiated with high-intensity lasers

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Analysis and development of ecologically based approaches to coastal defense

Climate-change related impacts, notably coastal erosion, inundation and flooding from sea level rise and storms, will increase in the coming decades enhancing the risks for coastal populations. Further recourse to coastal armoring and other engineered defenses to address risk reduction will exacerbate threats to coastal ecosystems. Alternatively, protection services provided by healthy ecosystems is emerging as a key element in climate adaptation and disaster risk management. I examined two distinct approaches to coastal defense on the base of their ecological and ecosystem conservation values. First, I analyzed the role of coastal ecosystems in providing services for hazard risk reduction. The value in wave attenuation of coral reefs was quantitatively demonstrated using a meta-analysis approach. Results indicate that coral reefs can provide wave attenuation comparable to hard engineering artificial defenses and at lower costs. Conservation and restoration of existing coral reefs are cost-effective management options for disaster risk reduction. Second, I evaluated the possibility to enhance the ecological value of artificial coastal defense structures (CDS) as habitats for marine communities. I documented the suitability of CDS to support native, ecologically relevant, habitat-forming canopy algae exploring the feasibility of enhancing CDS ecological value by promoting the growth of desired species. Juveniles of Cystoseira barbata can be successfully transplanted at both natural and artificial habitats and not affected by lack of surrounding adult algal individuals nor by substratum orientation. Transplantation success was limited by biotic disturbance from macrograzers on CDS compared to natural habitats. Future work should explore the reasons behind the different ecological functioning of artificial and natural habitats unraveling the factors and mechanisms that cause it. The comprehension of the functioning of systems associated with artificial habitats is the key to allow environmental managers to identify proper mitigation options and to forecast the impact of alternative coastal development plans.

Tree phenology and carbon dioxide fluxes - use of digital photography for process-based interpretation at the ecosystem scale

Vegetation phenology is an important indicator of climate change and climate variability and it is strongly connected to biospheric–atmospheric gas exchange. We aimed to evaluate the applicability of phenological information derived from digital imagery for the interpretation of CO2 exchange measurements. For the years 2005–2007 we analyzed seasonal phenological development of 2 temperate mixed forests using tower-based imagery from standard RGB cameras. Phenological information was jointly analyzed with gross primary productivity (GPP) derived from net ecosystem exchange data. Automated image analysis provided reliable information on vegetation developmental stages of beech and ash trees covering all seasons. A phenological index derived from image color values was strongly correlated with GPP, with a significant mean time lag of several days for ash trees and several weeks for beech trees in early summer (May to mid-July). Leaf emergence dates for the dominant tree species partly explained temporal behaviour of spring GPP but were also masked by local meteorological conditions. We conclude that digital cameras at flux measurement sites not only provide an objective measure of the physiological state of a forest canopy at high temporal and spatial resolutions, but also complement CO2 and water exchange measurements, improving our knowledge of ecosystem processes.

Desertification mitigation and water harvesting based on sustainable land management

Traditionally, desertification research has focused on degradation assessments, whereas prevention and mitigation strategies have not sufficiently been emphasised, although the concept of sustainable land management (SLM) is increasingly being acknowledged. SLM strategies are interventions at the local to regional scale aiming at increasing productivity, protecting the natural resource base, and improving livelihoods. The global WOCAT initiative and its partners have developed harmonized frameworks to compile, evaluate and analyse the impact of SLM practices around the globe. Recent studies within the EU research project DESIRE developed a methodological framework that combines a collective learning and decision-making approach with use of best practices from the WOCAT database. In-depth assessment of 30 technologies and 8 approaches from 17 desertification sites enabled an evaluation of how SLM addresses prevalent dryland threats such as water scarcity, soil and vegetation degradation, low production, climate change, resource use conflicts and migration. Among the impacts attributed to the documented technologies, those mentioned most were diversified and enhanced production and better management of water and soil degradation, whether through water harvesting, improving soil moisture, or reducing runoff. Water harvesting offers under-exploited opportunities for the drylands and the predominantly rainfed farming systems of the developing world. Recently compiled guidelines introduce the concepts behind water harvesting and propose a harmonised classification system, followed by an assessment of suitability, adoption and up-scaling of practices. Case studies go from large-scale floodwater spreading that make alluvial plains cultivable, to systems that boost cereal production in small farms, as well as practices that collect and store water from household compounds. Once contextualized and set in appropriate institutional frameworks, they can form part of an overall adaptation strategy for land users. More field research is needed to reinforce expert assessments of SLM impacts and provide the necessary evidence-based rationale for investing in SLM. This includes developing methods to quantify and value ecosystem services, both on-site and off-site, and assess the resilience of SLM practices, as currently aimed at within the new EU CASCADE project.

Combining analytical frameworks to assess livelihood vulnerability to climate change and analyse adaptation options

Experts working on behalf of international development organisations need better tools to assist land managers in developing countriesmaintain their livelihoods, as climate change puts pressure on the ecosystemservices that they depend upon. However, current understanding of livelihood vulnerability to climate change is based on a fractured and disparate set of theories andmethods. This reviewtherefore combines theoretical insights from sustainable livelihoods analysis with other analytical frameworks (including the ecosystem services framework, diffusion theory, social learning, adaptive management and transitions management) to assess the vulnerability of rural livelihoods to climate change. This integrated analytical framework helps diagnose vulnerability to climate change,whilst identifying and comparing adaptation options that could reduce vulnerability, following four broad steps: i) determine likely level of exposure to climate change, and how climate change might interact with existing stresses and other future drivers of change; ii) determine the sensitivity of stocks of capital assets and flows of ecosystem services to climate change; iii) identify factors influencing decisions to develop and/or adopt different adaptation strategies, based on innovation or the use/substitution of existing assets; and iv) identify and evaluate potential trade-offs between adaptation options. The paper concludes by identifying interdisciplinary research needs for assessing the vulnerability of livelihoods to climate change.

Mutual Information and Perplexity based Clustering of Dialogue Information for Dynamic Adaptation of Language Models

We present two approaches to cluster dialogue-based information obtained by the speech understanding module and the dialogue manager of a spoken dialogue system. The purpose is to estimate a language model related to each cluster, and use them to dynamically modify the model of the speech recognizer at each dialogue turn. In the first approach we build the cluster tree using local decisions based on a Maximum Normalized Mutual Information criterion. In the second one we take global decisions, based on the optimization of the global perplexity of the combination of the cluster-related LMs. Our experiments show a relative reduction of the word error rate of 15.17%, which helps to improve the performance of the understanding and the dialogue manager modules.