A chrysophyte stomatocyst-based reconstruction of cold-season air temperature from Alpine Lake Silvaplana (AD 1500–2003); methods and concepts for quantitative inferences

Relatively little is known about past cold-season temperature variability in high-Alpine regions because of a lack of natural cold-season temperature proxies as well as under-representation of high-altitude sites in meteorological, early-instrumental and documentary data sources. Recent studies have shown that chrysophyte stomatocysts, or simply cysts (sub-fossil algal remains of Chrysophyceae and Synurophyceae), are among the very few natural proxies that can be used to reconstruct cold-season temperatures. This study presents a quantitative, high-resolution (5-year), cold-season (Oct–May) temperature reconstruction based on sub-fossil chrysophyte stomatocysts in the annually laminated (varved) sediments of high-Alpine Lake Silvaplana, SE Switzerland (1,789 m a.s.l.), since AD 1500. We first explore the method used to translate an ecologically meaningful variable based on a biological proxy into a simple climate variable. A transfer function was applied to reconstruct the ‘date of spring mixing’ from cyst assemblages. Next, statistical regression models were tested to convert the reconstructed ‘dates of spring mixing’ into cold-season surface air temperatures with associated errors. The strengths and weaknesses of this approach are thoroughly tested. One much-debated, basic assumption for reconstructions (‘stationarity’), which states that only the environmental variable of interest has influenced cyst assemblages and the influence of confounding variables is negligible over time, is addressed in detail. Our inferences show that past cold-season air-temperature fluctuations were substantial and larger than those of other temperature reconstructions for Europe and the Alpine region. Interestingly, in this study, recent cold-season temperatures only just exceed those of previous, multi-decadal warm phases since AD 1500. These findings highlight the importance of local studies to assess natural climate variability at high altitudes.

Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package

Kriging-based optimization relying on noisy evaluations of complex systems has recently motivated contributions from various research communities. Five strategies have been implemented in the DiceOptim package. The corresponding functions constitute a user-friendly tool for solving expensive noisy optimization problems in a sequential framework, while offering some flexibility for advanced users. Besides, the implementation is done in a unified environment, making this package a useful device for studying the relative performances of existing approaches depending on the experimental setup. An overview of the package structure and interface is provided, as well as a description of the strategies and some insight about the implementation challenges and the proposed solutions. The strategies are compared to some existing optimization packages on analytical test functions and show promising performances.

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas.

Associating optical measurements and estimating orbits of geocentric objects through population-based meta-heuristic methods

Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). In this context both, the correct associations among the observations and the orbits of the objects have to be determined. The complexity of the MTT problem is defined by its dimension S. The number S corresponds to the number of fences involved in the problem. Each fence consists of a set of observations where each observation belongs to a different object. The S ≥ 3 MTT problem is an NP-hard combinatorial optimization problem. There are two general ways to solve this. One way is to seek the optimum solution, this can be achieved by applying a branch-and- bound algorithm. When using these algorithms the problem has to be greatly simplified to keep the computational cost at a reasonable level. Another option is to approximate the solution by using meta-heuristic methods. These methods aim to efficiently explore the different possible combinations so that a reasonable result can be obtained with a reasonable computational effort. To this end several population-based meta-heuristic methods are implemented and tested on simulated optical measurements. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the correlation and orbit determination problems simultaneously, and is able to efficiently process large data sets with minimal manual intervention.