Genetic variants of HvCbf14 are statistically associated with frost tolerance in a European germplasm collection of Hordeum vulgare

Two quantitative trait loci (Fr-H1 and Fr-H2) for frost tolerance (FT) have been discovered on the long arm of chromosome 5H in barley. Two tightly linked groups of CBF genes, known to play a key role in the FT regulatory network in A. thaliana, have been found to co-segregate with Fr-H2. Here, we investigate the allelic variations of four barley CBF genes (HvCbf3, HvCbf6, HvCbf9 and HvCbf14) in a panel of European cultivars, landraces and H. spontaneum accessions. In the cultivars a reduction of nucleotide and haplotype diversities in CBFs compared with the landraces and the wild ancestor H. spontaneum, was evident. In particular, in cultivars the loss of HvCbf9 genetic variants was higher compared to other sequences. In order to verify if the pattern of CBF genetic variants correlated with the level of FT, an association procedure was adopted. The pairwise analysis of linkage disequilibrium (LD) among the genetic variants in four CBF genes was computed to evaluate the resolution of the association procedure. The pairwise plotting revealed a low level of LD in cultivated varieties, despite the tight physical linkage of CBF genes analysed. A structured association procedure based on a general liner model was implemented, including the variants in CBFs, of Vrn-H1, and of two reference genes not involved in FT (α-Amy1 and Gapdh) and considering the phenotypic data for FT. Association analysis recovered two nucleotide variants of HvCbf14 and one nucleotide variant of Vrn-H1 as statistically associated to FT

Capturing Ecosystem Services, Stakeholders' Preferences and Trade-Offs in Coastal Aquaculture Decisions : A Bayesian Belief Network Application

Aquaculture activities are embedded in complex social-ecological systems. However, aquaculture development decisions have tended to be driven by revenue generation, failing to account for interactions with the environment and the full value of the benefits derived from services provided by local ecosystems. Trade-offs resulting from changes in ecosystem services provision and associated impacts on livelihoods are also often overlooked. This paper proposes an innovative application of Bayesian belief networks - influence diagrams - as a decision support system for mediating trade-offs arising from the development of shrimp aquaculture in Thailand. Senior experts were consulted (n = 12) and primary farm data on the economics of shrimp farming (n = 20) were collected alongside secondary information on ecosystem services, in order to construct and populate the network. Trade-offs were quantitatively assessed through the generation of a probabilistic impact matrix. This matrix captures nonlinearity and uncertainty and describes the relative performance and impacts of shrimp farming management scenarios on local livelihoods. It also incorporates export revenues and provision and value of ecosystem services such as coastal protection and biodiversity. This research shows that Bayesian belief modeling can support complex decision-making on pathways for sustainable coastal aquaculture development and thus contributes to the debate on the role of aquaculture in social-ecological resilience and economic development

Standardizing the assessment of citizen scientists’ motivations: a motivational goal-based approach

Science Communication and Societ

Adaptation options for marine industries and coastal communities using community structure and dynamics

Identifying effective adaptation strategies for coastal communities dependent on marine resources and impacted by climate change can be difficult due to the dynamic nature of marine ecosystems. The task is more difficult if current and predicted shifts in social and economic trends are considered. Information about social and economic change is often limited to qualitative data. A combination of qualitative and quantitative models provide the flexibility to allow the assessment of current and future ecological and socio-economic risks and can provide information on alternative adaptations. Here, we demonstrate how stakeholder input, qualitative models and Bayesian belief networks (BBNs) can provide semi-quantitative predictions, including uncertainty levels, for the assessment of climate and non-climate-driven change in a case study community. Issues are identified, including the need to increase the capacity of the community to cope with change. Adaptation strategies are identified that alter positive feedback cycles contributing to a continued decline in population, local employment and retail spending. For instance, the diversification of employment opportunities and the attraction of new residents of different ages would be beneficial in preventing further population decline. Some impacts of climate change can be combated through recreational bag or size limits and monitoring of popular range-shifted species that are currently unmanaged, to reduce the potential for excessive removal. Our results also demonstrate that combining BBNs and qualitative models can assist with the effective communication of information between stakeholders and researchers. Furthermore, the combination of techniques provides a dynamic, learning-based, semi-quantitative approach for the assessment of climate and socio-economic impacts and the identification of potential adaptation strategies