Phenotype-environment correlations in a putative whitefish adaptive radiation

1. The adaptive radiation of fishes into benthic (littoral) and pelagic (lentic) morphs in post-glaciallakes has become an important model system for speciation. Although these systems are well stud-ied, there is little evidence of the existence of morphs that have diverged to utilize resources in theremaining principal lake habitat, the profundal zone.
2. Here, we tested phenotype-environment correlations of three whitefish (Coregonus lavaretus)morphs that have radiated into littoral, pelagic and profundal niches in northern Scandinavianlakes. We hypothesized that morphs in such trimorphic systems would have a morphology adaptedto one of the principal lake habitats (littoral, pelagic or profundal niches). Most whitefish popula-tions in the study area are formed by a single (monomorphic) whitefish morph, and we furtherhypothesized that these populations should display intermediate morphotypes and niche utiliza-tion. We used a combination of traditional (stomach content, habitat use, gill raker counts) andmore recently developed (stable isotopes, geometric morphometrics) techniques to evaluate pheno-type-environment correlations in two lakes with trimorphic and two lakes with monomorphicwhitefish.
3. Distinct phenotype-environment correlations were evident for each principal niche in whitefishmorphs inhabiting trimorphic lakes. Monomorphic whitefish exploited multiple habitats, hadintermediate morphology, displayed increased variance in gillraker-counts, and relied significantlyon zooplankton, most likely due to relaxed resource competition.
4. We suggest that the ecological processes acting in the trimorphic lakes are similar to each other,and are driving the adaptive evolution of whitefish morphs, possibly leading to the formation ofnew species.

Moving window kernel PCA for adaptive monitoring of nonlinear processes

This paper discusses the monitoring of complex nonlinear and time-varying processes. Kernel principal component analysis (KPCA) has gained significant attention as a monitoring tool for nonlinear systems in recent years but relies on a fixed model that cannot be employed for time-varying systems. The contribution of this article is the development of a numerically efficient and memory saving moving window KPCA (MWKPCA) monitoring approach. The proposed technique incorporates an up- and downdating procedure to adapt (i) the data mean and covariance matrix in the feature space and (ii) approximates the eigenvalues and eigenvectors of the Gram matrix. The article shows that the proposed MWKPCA algorithm has a computation complexity of O(N2), whilst batch techniques, e.g. the Lanczos method, are of O(N3). Including the adaptation of the number of retained components and an l-step ahead application of the MWKPCA monitoring model, the paper finally demonstrates the utility of the proposed technique using a simulated nonlinear time-varying system and recorded data from an industrial distillation column.

Modelling attribute non-attendance in choice experiments for rural landscape valuation

Non-market effects of agriculture are often estimated using discrete choice models from stated preference surveys. In this context we propose two ways of modelling attribute non-attendance. The first involves constraining coefficients to zero in a latent class framework, whereas the second is based on stochastic attribute selection and grounded in Bayesian estimation. Their implications are explored in the context of a stated preference survey designed to value landscapes in Ireland. Taking account of attribute non-attendance with these data improves fit and tends to involve two attributes one of which is likely to be cost, thereby leading to substantive changes in derived welfare estimates.

China and the changing landscape of the world economy

The rapid development of emerging markets is changing the landscape of the world economy and may have profound implications for international relations. China is often regarded as the most influential emerging market economy because, during the last three decades, it has become increasingly integrated into the world economic system and its success and failure now affect the well-being of other nations in the world. As the financial crisis in the US and EU intensifies, the economic prosperity of the world depends to a large extent on the sustained development of the Chinese economy and other emerging markets, and vice versa.

Landscape effects on extremely fragmented populations of a rare solitary bee, Colletes floralis

Globally there is concern over the decline of bees, an ecologically important group of pollinating insects. Genetic studies provide insights into population structure that are crucial for conservation management but that would be impossible to obtain by conventional ecological methods. Yet conservation genetic studies of bees have primarily focussed on social species rather than the more species-rich solitary bees. Here we investigate the population structure of Colletes floralis, a rare and threatened solitary mining bee, in Ireland and Scotland using nine microsatellite loci. Genetic diversity was surprisingly as high in Scottish (Hebridean island) populations at the extreme northwestern edge of the species range as in mainland Irish populations further south. Extremely high genetic differentiation among populations was detected; multilocus FST was up to 0.53, and G’ST and Dest were even higher (maximum: 0.85 and 1.00 respectively). A pattern of isolation by distance was evident for sites separated by land. Water appears to act as a substantial barrier to gene flow yet sites separated by sea did not exhibit isolation by distance. Colletes floralis populations are extremely isolated and probably not in regional migration-drift equilibrium. GIS-based landscape genetic analysis reveals urban areas as a potential and substantial barrier to gene flow. Our results highlight the need for urgent site-specific management action to halt the decline of this and potentially other rare solitary bees.