The role of population origin and microenvironment in seedling emergence and early survival in Mediterranean maritime pine (Pinus pinaster Aiton).

Understanding tree recruitment is needed to forecast future forest distribution. Many studies have reported the relevant ecological factors that affect recruitment success in trees, but the potential for genetic-based differences in recruitment has often been neglected. In this study, we established a semi-natural reciprocal sowing experiment to test for local adaptation and microenvironment effects (evaluated here by canopy cover) in the emergence and early survival of maritime pine (Pinus pinaster Aiton), an emblematic Mediterranean forest tree. A novel application of molecular markers was also developed to test for family selection and, thus, for potential genetic change over generations. Overall, we did not find evidence to support local adaptation at the recruitment stage in our semi-natural experiment. Moreover, only weak family selection (if any) was found, suggesting that in stressful environments with low survival, stochastic processes and among-year climate variability may drive recruitment. Nevertheless, our study revealed that, at early stages of recruitment, microenvironments may favor the population with the best adapted life strategy, irrespectively of its (local or non-local) origin. We also found that emergence time is a key factor for seedling survival in stressful Mediterranean environments. Our study highlights the complexity of the factors influencing the early stages of establishment of maritime pine and provides insights into possible management actions aimed at environmental change impact mitigation. In particular, we found that the high stochasticity of the recruitment process in stressful environments and the differences in population-specific adaptive strategies may difficult assisted migration schemes.

Molecular Proxies for Climate Maladaptation in a Long-Lived Tree (Pinus pinaster Aiton, Pinaceae).

Understanding adaptive genetic responses to climate change is a main challenge for preserving biological diversity. Successful predictive models for climate-driven range shifts of species depend on the integration of information on adaptation, including that derived from genomic studies. Long-lived forest trees can experience substantial environmental change across generations, which results in a much more prominent adaptation lag than in annual species. Here, we show that candidate-gene SNPs (single nucleotide polymorphisms) can be used as predictors of maladaptation to climate in maritime pine (Pinus pinaster Aiton), an outcrossing long-lived keystone tree. A set of 18 SNPs potentially associated with climate, 5 of them involving amino acid-changing variants, were retained after performing logistic regression, latent factor mixed models, and Bayesian analyses of SNP-climate correlations. These relationships identified temperature as an important adaptive driver in maritime pine and highlighted that selective forces are operating differentially in geographically discrete gene pools. The frequency of the locally advantageous alleles at these selected loci was strongly correlated with survival in a common garden under extreme (hot and dry) climate conditions, which suggests that candidate-gene SNPs can be used to forecast the likely destiny of natural forest ecosystems under climate change scenarios. Differential levels of forest decline are anticipated for distinct maritime pine gene pools. Geographically defined molecular proxies for climate adaptation will thus critically enhance the predictive power of range-shift models and help establish mitigation measures for long-lived keystone forest trees in the face of impending climate change.

Stochastic Population Projection for Germany - based on the Quadratic Spline approach to modelling age specific fertility rates

This contribution builds upon a former paper by the authors (Lipps and Betz 2004), in which a stochastic population projection for East- and West Germany is performed. Aim was to forecast relevant population parameters and their distribution in a consistent way. We now present some modifications, which have been modelled since. First, population parameters for the entire German population are modelled. In order to overcome the modelling problem of the structural break in the East during reunification, we show that the adaptation process of the relevant figures by the East can be considered to be completed by now. As a consequence, German parameters can be modelled just by using the West German historic patterns, with the start-off population of entire Germany. Second, a new model to simulate age specific fertility rates is presented, based on a quadratic spline approach. This offers a higher flexibility to model various age specific fertility curves. The simulation results are compared with the scenario based official forecasts for Germany in 2050. Exemplary for some population parameters (e.g. dependency ratio), it can be shown that the range spanned by the medium and extreme variants correspond to the s-intervals in the stochastic framework. It seems therefore more appropriate to treat this range as a s-interval covering about two thirds of the true distribution.

Transition to parenthood: The role of social interaction and endogenous networks

Empirical studies indicate that the transition to parenthood is influenced by an individual's peer group. To study the mechanisms creating interdepen- dencies across individuals' transition to parenthood and its timing we apply an agent-based simulation model. We build a one-sex model and provide agents with three different characteristics regarding age, intended education and parity. Agents endogenously form their network based on social closeness. Network members then may influence the agents' transition to higher parity levels. Our numerical simulations indicate that accounting for social inter- actions can explain the shift of first-birth probabilities in Austria over the period 1984 to 2004. Moreover, we apply our model to forecast age-specific fertility rates up to 2016.

Extinction debt of high-mountain plants under 21st-century climate change

Quantitative estimates of the range loss of mountain plants under climate change have so far mostly relied on static geographical projections of species' habitat shifts(1-3). Here, we use a hybrid model(4) that combines such projections with simulations of demography and seed dispersal to forecast the climate-driven spatio-temporal dynamics of 150 high-mountain plant species across the European Alps. This model predicts average range size reductions of 44-50% by the end of the twenty-first century, which is similar to projections from the most 'optimistic' static model (49%). However, the hybrid model also indicates that population dynamics will lag behind climatic trends and that an average of 40% of the range still occupied at the end of the twenty-first century will have become climatically unsuitable for the respective species, creating an extinction debt(5,6). Alarmingly, species endemic to the Alps seem to face the highest range losses. These results caution against optimistic conclusions from moderate range size reductions observed during the twenty-first century as they are likely to belie more severe longer-term effects of climate warming on mountain plants.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Predicting current and future spatial community patterns of plant functional traits

Community-level patterns of functional traits relate to community assembly and ecosystem functioning. By modelling the changes of different indices describing such patterns - trait means, extremes and diversity in communities - as a function of abiotic gradients, we could understand their drivers and build projections of the impact of global change on the functional components of biodiversity. We used five plant functional traits (vegetative height, specific leaf area, leaf dry matter content, leaf nitrogen content and seed mass) and non-woody vegetation plots to model several indices depicting community-level patterns of functional traits from a set of abiotic environmental variables (topographic, climatic and edaphic) over contrasting environmental conditions in a mountainous landscape. We performed a variation partitioning analysis to assess the relative importance of these variables for predicting patterns of functional traits in communities, and projected the best models under several climate change scenarios to examine future potential changes in vegetation functional properties. Not all indices of trait patterns within communities could be modelled with the same level of accuracy: the models for mean and extreme values of functional traits provided substantially better predictive accuracy than the models calibrated for diversity indices. Topographic and climatic factors were more important predictors of functional trait patterns within communities than edaphic predictors. Overall, model projections forecast an increase in mean vegetation height and in mean specific leaf area following climate warming. This trend was important at mid elevation particularly between 1000 and 2000 m asl. With this study we showed that topographic, climatic and edaphic variables can successfully model descriptors of community-level patterns of plant functional traits such as mean and extreme trait values. However, which factors determine the diversity of functional traits in plant communities remains unclear and requires more investigations.

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Past and current climate change has already induced drastic biological changes. We need projections of how future climate change will further impact biological systems. Modeling is one approach to forecast future ecological impacts, but requires data for model parameterization. As collecting new data is costly, an alternative is to use the increasingly available georeferenced species occurrence and natural history databases. Here, we illustrate the use of such databases to assess climate change impacts on mountain flora. We show that these data can be used effectively to derive dynamic impact scenarios, suggesting upward migration of many species and possible extinctions when no suitable habitat is available at higher elevations. Systematically georeferencing all existing natural history collections data in mountain regions could allow a larger assessment of climate change impact on mountain ecosystems in Europe and elsewhere.

Structural macro factors and the affine term structure of interest rates

This work consists of three essays investigating the ability of structural macroeconomic models to price zero coupon U.S. government bonds. 1. A small scale 3 factor DSGE model implying constant term premium is able to provide reasonable a fit for the term structure only at the expense of the persistence parameters of the structural shocks. The test of the structural model against one that has constant but unrestricted prices of risk parameters shows that the exogenous prices of risk-model is only weakly preferred. We provide an MLE based variance-covariance matrix of the Metropolis Proposal Density that improves convergence speeds in MCMC chains. 2. Affine in observable macro-variables, prices of risk specification is excessively flexible and provides term-structure fit without significantly altering the structural parameters. The exogenous component of the SDF is separating the macro part of the model from the term structure and the good term structure fit has as a driving force an extremely volatile SDF and an implied average short rate that is inexplicable. We conclude that the no arbitrage restrictions do not suffice to temper the SDF, thus there is need for more restrictions. We introduce a penalty-function methodology that proves useful in showing that affine prices of risk specifications are able to reconcile stable macro-dynamics with good term structure fit and a plausible SDF. 3. The level factor is reproduced most importantly by the preference shock to which it is strongly and positively related but technology and monetary shocks, with negative loadings, are also contributing to its replication. The slope factor is only related to the monetary policy shocks and it is poorly explained. We find that there are gains in in- and out-of-sample forecast of consumption and inflation if term structure information is used in a time varying hybrid prices of risk setting. In-sample yield forecast are better in models with non-stationary shocks for the period 1982-1988. After this period, time varying market price of risk models provide better in-sample forecasts. For the period 2005-2008, out of sample forecast of consumption and inflation are better if term structure information is incorporated in the DSGE model but yields are better forecasted by a pure macro DSGE model.

Modeling and mitigation of supply-demand mismatches

In this thesis, I develop analytical models to price the value of supply chain investments under demand uncer¬tainty. This thesis includes three self-contained papers. In the first paper, we investigate the value of lead-time reduction under the risk of sudden and abnormal changes in demand forecasts. We first consider the risk of a complete and permanent loss of demand. We then provide a more general jump-diffusion model, where we add a compound Poisson process to a constant-volatility demand process to explore the impact of sudden changes in demand forecasts on the value of lead-time reduction. We use an Edgeworth series expansion to divide the lead-time cost into that arising from constant instantaneous volatility, and that arising from the risk of jumps. We show that the value of lead-time reduction increases substantially in the intensity and/or the magnitude of jumps. In the second paper, we analyze the value of quantity flexibility in the presence of supply-chain dis- intermediation problems. We use the multiplicative martingale model and the "contracts as reference points" theory to capture both positive and negative effects of quantity flexibility for the downstream level in a supply chain. We show that lead-time reduction reduces both supply-chain disintermediation problems and supply- demand mismatches. We furthermore analyze the impact of the supplier's cost structure on the profitability of quantity-flexibility contracts. When the supplier's initial investment cost is relatively low, supply-chain disin¬termediation risk becomes less important, and hence the contract becomes more profitable for the retailer. We also find that the supply-chain efficiency increases substantially with the supplier's ability to disintermediate the chain when the initial investment cost is relatively high. In the third paper, we investigate the value of dual sourcing for the products with heavy-tailed demand distributions. We apply extreme-value theory and analyze the effects of tail heaviness of demand distribution on the optimal dual-sourcing strategy. We find that the effects of tail heaviness depend on the characteristics of demand and profit parameters. When both the profit margin of the product and the cost differential between the suppliers are relatively high, it is optimal to buffer the mismatch risk by increasing both the inventory level and the responsive capacity as demand uncertainty increases. In that case, however, both the optimal inventory level and the optimal responsive capacity decrease as the tail of demand becomes heavier. When the profit margin of the product is relatively high, and the cost differential between the suppliers is relatively low, it is optimal to buffer the mismatch risk by increasing the responsive capacity and reducing the inventory level as the demand uncertainty increases. In that case, how¬ever, it is optimal to buffer with more inventory and less capacity as the tail of demand becomes heavier. We also show that the optimal responsive capacity is higher for the products with heavier tails when the fill rate is extremely high.

Spatial predictions of land-use transitions and associated threats to biodiversity: the case of forest regrowth in mountain grasslands

Question Can we predict where forest regrowth caused by abandonment of agricultural activities is likely to occur? Can we assess how it may conflict with grassland diversity hotspots? Location Western Swiss Alps (4003210m a.s.l.). Methods We used statistical models to predict the location of land abandonment by farmers that is followed by forest regrowth in semi-natural grasslands of the Western Swiss Alps. Six modelling methods (GAM, GBM, GLM, RF, MDA, MARS) allowing binomial distribution were tested on two successive transitions occurring between three time periods. Models were calibrated using data on land-use change occurring between 1979 and 1992 as response, and environmental, accessibility and socio-economic variables as predictors, and these were validated for their capacity to predict the changes observed from 1992 to 2004. Projected probabilities of land-use change from an ensemble forecast of the six models were combined with a model of plant species richness based on a field inventory, allowing identification of critical grassland areas for the preservation of biodiversity. Results Models calibrated over the first land-use transition period predicted the second transition with reasonable accuracy. Forest regrowth occurs where cultivation costs are high and yield potential is low, i.e. on steeper slopes and at higher elevations. Overlaying species richness with land-use change predictions, we identified priority areas for the management and conservation of biodiversity at intermediate elevations. Conclusions Combining land-use change and biodiversity projections, we propose applied management measures for targeted/identified locations to limit the loss of biodiversity that could otherwise occur through loss of open habitats. The same approach could be applied to other types of land-use changes occurring in other ecosystems.

Sterblichkeit während Grippeepidemien in der Schweiz 1969-1985. [Mortality in influenza epidemics in Switzerland 1969-1985].

In Switzerland from 1969-1985, 9 out of 11 influenza epidemics were associated with a statistically significant increase in mortality. A total of 12,202 excess deaths from all causes was identified. Expected deaths were forecast for each epidemic period separately for 4 age groups using Fourier and Arima modeling. 75.7% of all-cause excess deaths occurred in age group 70 to 89 and 5.1% in age group 1-59. In the 70-89 years old group the excess mortality risk during influenza epidemics was 271.6 per 100,000, whereas in age group 1-59 it was only 1.7 per 100,000. Only 40% of all excess deaths had been ascribed to acute respiratory conditions. Influenza viruses A H3N2 were the most frequently identified agents. In some instances mortality increased before the morbidity reports of the Swiss practitioners indicated the occurrence of an epidemic. Also, morbidity reporting decreased over successive years. A decrease in mortality following the epidemics was not observed. A more complete vaccination of high risk patients in Switzerland is desirable.

Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models

Aim: Modelling species at the assemblage level is required to make effective forecast of global change impacts on diversity and ecosystem functioning. Community predictions may be achieved using macroecological properties of communities (MEM), or by stacking of individual species distribution models (S-SDMs). To obtain more realistic predictions of species assemblages, the SESAM framework suggests applying successive filters to the initial species source pool, by combining different modelling approaches and rules. Here we provide a first test of this framework in mountain grassland communities. Location: The western Swiss Alps. Methods: Two implementations of the SESAM framework were tested: a "Probability ranking" rule based on species richness predictions and rough probabilities from SDMs, and a "Trait range" rule that uses the predicted upper and lower bound of community-level distribution of three different functional traits (vegetative height, specific leaf area and seed mass) to constraint a pool of environmentally filtered species from binary SDMs predictions. Results: We showed that all independent constraints expectedly contributed to reduce species richness overprediction. Only the "Probability ranking" rule allowed slightly but significantly improving predictions of community composition. Main conclusion: We tested various ways to implement the SESAM framework by integrating macroecological constraints into S-SDM predictions, and report one that is able to improve compositional predictions. We discuss possible improvements, such as further improving the causality and precision of environmental predictors, using other assembly rules and testing other types of ecological or functional constraints.

Overcoming limitations of modelling rare species by using ensembles of small models

1. Species distribution models (SDMs) have become a standard tool in ecology and applied conservation biology. Modelling rare and threatened species is particularly important for conservation purposes. However, modelling rare species is difficult because the combination of few occurrences and many predictor variables easily leads to model overfitting. A new strategy using ensembles of small models was recently developed in an attempt to overcome this limitation of rare species modelling and has been tested successfully for only a single species so far. Here, we aim to test the approach more comprehensively on a large number of species including a transferability assessment. 2. For each species numerous small (here bivariate) models were calibrated, evaluated and averaged to an ensemble weighted by AUC scores. These 'ensembles of small models' (ESMs) were compared to standard Species Distribution Models (SDMs) using three commonly used modelling techniques (GLM, GBM, Maxent) and their ensemble prediction. We tested 107 rare and under-sampled plant species of conservation concern in Switzerland. 3. We show that ESMs performed significantly better than standard SDMs. The rarer the species, the more pronounced the effects were. ESMs were also superior to standard SDMs and their ensemble when they were independently evaluated using a transferability assessment. 4. By averaging simple small models to an ensemble, ESMs avoid overfitting without losing explanatory power through reducing the number of predictor variables. They further improve the reliability of species distribution models, especially for rare species, and thus help to overcome limitations of modelling rare species.

The First International Workshop on Warning Criteria for Active Slides: technical issues, problems and solutions for managing early warning systems

Early warning systems (EWSs) rely on the capacity to forecast a dangerous event with a certain amount of advance by defining warning criteria on which the safety of the population will depend. Monitoring of landslides is facilitated by new technologies, decreasing prices and easier data processing. At the same time, predicting the onset of a rapid failure or the sudden transition from slow to rapid failure and subsequent collapse, and its consequences is challenging for scientists that must deal with uncertainties and have limited tools to do so. Furthermore, EWS and warning criteria are becoming more and more a subject of concern between technical experts, researchers, stakeholders and decision makers responsible for the activation, enforcement and approval of civil protection actions. EWSs imply also a sharing of responsibilities which is often averted by technical staff, managers of technical offices and governing institutions. We organized the First International Workshop on Warning Criteria for Active Slides (IWWCAS) to promote sharing and networking among members from specialized institutions and relevant experts of EWS. In this paper, we summarize the event to stimulate discussion and collaboration between organizations dealing with the complex task of managing hazard and risk related to active slides.