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Our understanding of the Earth´s functional biodiversity and its imprint on ecosystem functioning, structure and resilience is still incomplete. Large-scale information on vegetation properties (‘plant traits’) is critical to assess functional diversity and its role in the Earth system. Such parameters are constantly changing due to variations in environmental conditions which makes extensive in-situ measurements in the field not logistically feasible. The advent of the upcoming space-borne hyperspectral missions will facilitate to map these properties. However, we are still lacking efficient and accurate methods to translate hyperspectral reflectance into large scale information on plant traits across biomes, land cover and sensor types. Yet, the absence of globally representative data sets on reflectance data and the corresponding in-situ measurements represents a bottleneck to develop empirical models for estimating plant traits from hyperspectral reflectance. Recent and ongoing initiatives (e.g. EcoSIS) provide a constantly growing source of hyperspectral data and plant trait observations from different vegetation types and sensors. In this study we integrated 29 data sets including four different ecosystem types spanning from Europe to north America. By combining these heterogeneous data sets, we propose multi-trait models based on Convolutional Neural Networks (CNNs) that simultaneously infer multiple plant traits from canopy spectra. We targeted a broad set of structural and chemical traits (n=20) related to light harvesting, growth, propagation and defense (e.g. leaf mass per area, leaf area index, pigments nitrogen, phosphorus). The performance of our multi-trait CNN models predicting these traits was compared to single-trait CNNs as well as single-trait partial least squares regression (PLSR) models. The results of the multi-trait models across a broad range of vegetation types (crops, forest, tundra, grassland, shrubland) and sensor types were promising and outcompeted state-of-the-art PLSR models. We found that the overall prediction performances significantly increased from single- to multi-trait CNN models and those of PLSR models. The key contribution of this study is to highlight the potential of weakly supervised approaches together with Deep Learning to overcome the scarcity of in-situ measurements and take a step forward in creating large-scale maps of Earth’s biophysical properties with the increase in availability of hyperspectral Earth observation data.

Antibiotic overuse has led to the emergence of resistant strains of bacteria, such as Methicillin-resistant Staphylococcus aureus (MRSA), which has become a global public health concern. The aim of this study was to investigate the genetic and microbiological characteristics of MRSA in hospitals in Crete, located in the southeastern Balkan region. S. Aureus samples were collected from hospitalized patients and tested for antibiotic susceptibility using the Kirby-Bauer disk diffusion method. The presence of mecA, enterotoxin genes (sem gene or seg genes), agr genes, and PVL genes was detected using PCR. Of the 141 S. Aureus strains tested, 102 (72.34%) were found to have the genes of interest, with 69 MRSA (48.9%) mecA+ strains and 33 methicillin-susceptible S. Aureus (MSSA) mecA negative strains. The most prevalent gene detected was the agr gene (48.93%), followed by the enterotoxin gene (44.68%). Four agr groups (I – IV) were detected in 69 MRSA isolates (48.9%). This study highlights the prevalence of MRSA in hospitals in Crete and emphasizes the importance of appropriate antibiotic use and infection control measures to prevent the spread of antibiotic-resistant bacteria. Although a correlation between the presence of the detected genes and certain resistance phenotypes was not observed, the findings indicate the necessity to trace these genes using PCR and involve them in clinical diseases. The circulation of sensitive strains to antibiotics and resistant strains to some antibiotics with different resistance phenotypes was also noted in the hospital. Overall, this study provides important insights into the genetic and microbiological characteristics of MRSA in the southeastern Balkan region, and the results can inform future efforts to control the spread of antibiotic-resistant bacteria.

In recent years, forest fires have become more frequent in central Europe. As the frequency and magnitude of future extreme weather events such as droughts are projected to increase, also the trend of increasing fire frequency in temperate forests is expected to continue. However, knowledge about fire behavior and spread dynamics in these forests is scarce. One of the key drivers of fire behavior is the availability of flammable vegetation, i.e. fuels. In the project ErWiN, we aim to describe the amount and distribution of fuels in different forest types in Southwestern Germany. Detailed field inventories of fuels in all vertical strata of the stands allow a first classification into different fuel types, which can be used in fire behavior simulations to obtain estimates of fire spread and intensity. In a further step, deep learning algorithms will be trained on recognizing these fuel types on GNSS located photos of forest stand situations to provide an efficient solution for mapping fuels in the field. By coupling field data with detailed remotely sensed information on forest structure obtained from airborne laserscanning, continuous fuel maps will be derived. Such fuel maps in turn allow landscape-scale analysis of fire behavior and can be useful in forest management decisions as well as in developing firefighting strategies. We thus hope to make a contribution to a better understanding of fuel-driven fire risk in central European forests and to facilitate the operational use of fire behavior models. In this contribution we present the concept developed in the ErWiN project and present first results obtained from the field survey of fuel types in Southwestern Germany.

Remote sensing is commonly used to map the presence of invasive alien plant species across large areas. However, remote sensing also has potential to predict fractional cover, which is more closely linked to the potential ecosystem impact of invasive alien plant species. Here, we mapped the fractional cover of the invasive bryophyte Campylopus introflexus using high resolution imaging spectroscopy (233 bands, 490–2430 nm, 3 m × 3 m pixel size) in different habitat types within a coastal dune ecosystem. Maps were predicted from generalized partial least squares (gPLS) regression models trained with cover information from 266 field plots (3 m × 3 m). Models were calibrated using different data subsets, following a spatially blocked subsampling design. In addition, the relationship between C. introflexus fractional cover and plant species richness was evaluated using a subset of plot data. Fractional cover estimates from gPLS models resulted in R2 values of 0.64 ± 0.17 and an RMSE of 0.14 ± 0.02 based on independent validation data. Field observations showed a negative relationship between C. introflexus fractional cover and local plant species richness for pixels with a fractional cover above 44%. This threshold was exceeded in 4.3–7.1% of the studied area depending on the habitat type. Our study demonstrates that remote sensing can be used to map the fractional cover of an invasive bryophyte in a coastal dune ecosystem. Combining cover maps with a threshold value derived from an abundance-impact relationship we were able to highlight areas with potential negative impact on local plant species richness. This method can be implemented on other species and other ecosystem properties affected by plant invasions, allowing to highlight potential invasion impacts in a spatially explicit manner. This is a contribution of the DIARS project funded by BiodivERsA. ispartof: Ecological Indicators vol:110 status: Published online

Imaging spectroscopy is a powerful tool for mapping chemical leaf traits at the canopy level. However, covariance with structural canopy properties is hampering the ability to predict leaf biochemical traits in structurally heterogeneous forests. Here, we used imaging spectroscopy data to map canopy level leaf nitrogen (Nₘₐₛₛ) and phosphorus concentrations (Pₘₐₛₛ) of a temperate mixed forest. By integrating predictor variables derived from airborne laser scanning (LiDAR), capturing the biophysical complexity of the canopy, we aimed at improving predictions of Nₘₐₛₛ and Pₘₐₛₛ. We used partial least squares regression (PLSR) models to link community weighted means of both leaf constituents with 245 hyperspectral bands (426–2425 nm) and 38 LiDAR-derived variables. LiDAR-derived variables improved the model's explained variances for Nₘₐₛₛ (R²cᵥ 0.31 vs. 0.41, % RSMEcᵥ 3.3 vs. 3.0) and Pₘₐₛₛ (R²cᵥ 0.45 vs. 0.63, % RSMEcᵥ 15.3 vs. 12.5). The predictive performances of Nₘₐₛₛ models using hyperspectral bands only, decreased with increasing structural heterogeneity included in the calibration dataset. To test the independent contribution of canopy structure we additionally fit the models using only LiDAR-derived variables as predictors. Resulting R²cᵥ values ranged from 0.26 for Nₘₐₛₛ to 0.54 for Pₘₐₛₛ indicating considerable covariation between biochemical traits and forest structural properties. Nₘₐₛₛ was negatively related to the spatial heterogeneity of canopy density, whereas Pₘₐₛₛ was negatively related to stand height and to the total cover of tree canopies. In the specific setting of this study, the importance of structural variables can be attributed to the presence of two tree species, featuring structural and biochemical properties different from co-occurring species. Still, existing functional linkages between structure and biochemistry at the leaf and canopy level suggest that canopy structure, used as proxy, can in general support the mapping of leaf biochemistry over broad spatial extents. ispartof: Remote Sensing of Environment vol:211 pages:13-25 status: published

© 2018 Elsevier B.V. Remote sensing is a promising tool for detecting invasive alien plant species. Mapping and monitoring those species requires accurate detection. So far, most studies relied on models that are locally calibrated and validated against available field data. Consequently, detecting invasive alien species at new study areas requires the acquisition of additional field data which can be expensive and time-consuming. Model transfer might thus provide a viable alternative. Here, we mapped the distribution of the invasive alien bryophyte Campylopus introflexus to i) assess the feasibility of spatially transferring locally calibrated models for species detection between four different heathland areas in Germany and Belgium and ii) test the potential of combining calibration data from different sites in one species distribution model (SDM). In a first step, four different SDMs were locally calibrated and validated by combining field data and airborne imaging spectroscopy data with a spatial resolution ranging from 1.8 m to 4 m and a spectral resolution of about 10 nm (244 bands). A one-class classifier, Maxent, which is based on the comparison of probability densities, was used to generate all SDMs. In a second step, each model was transferred to the three other study areas and the performance of the models for predicting C. introflexus occurrences was assessed. Finally, models combining calibration data from three study areas were built and tested on the remaining fourth site. In this step, different combinations of Maxent modelling parameters were tested. For the local models, the area under the curve for a test dataset (test AUC) was between 0.57–0.78, while the test AUC for the single transfer models ranged between 0.45–0.89. For the combined models the test AUC was between 0.54–0.9. The success of transferring models calibrated in one site to another site highly depended on the respective study site; the combined models provided higher test AUC values than the locally calibrated models for three out of four study sites. Furthermore, we also demonstrated the importance of optimizing the Maxent modelling parameters. Overall, our results indicate the potential of a combined model to map C. introflexus without the need for new calibration data. ispartof: International Journal of Applied Earth Observation and Geoinformation vol:68 pages:61-72 status: published

Habitat selection can be considered as a hierarchical process in which animals satisfy their habitat requirements at different ecological scales. Theory predicts that spatial and temporal scales should co-vary in most ecological processes and that the most limiting factors should drive habitat selection at coarse ecological scales, but be less influential at finer scales. Using detailed location data on roe deer (Capreolus capreolus) inhabiting the Bavarian Forest National Park, Germany, we investigated habitat selection at several spatial and temporal scales. We tested (i) whether time-varying patterns were governed by factors reported as having the largest effects on fitness, (ii) whether the trade-off between forage and predation risks differed among spatial and temporal scales and (iii) if spatial and temporal scales are positively associated. We analysed the variation in habitat selection within the landscape and within home ranges at monthly intervals, with respect to land-cover type and proxys of food and cover over seasonal and diurnal temporal scales. The fine-scale temporal variation follows a nycthemeral cycle linked to diurnal variation in human disturbance. The large-scale variation matches seasonal plant phenology, suggesting food resources being a greater limiting factor than lynx predation risk. The trade-off between selection for food and cover was similar on seasonal and diurnal scale. Habitat selection at the different scales may be the consequence of the temporal variation and predictability of the limiting factors as much as its association with fitness. The landscape of fear might have less importance at the studied scale of habitat selection than generally accepted because of the predator hunting strategy. Finally, seasonal variation in habitat selection was similar at the large and small spatial scales, which may arise because of the marked philopatry of roe deer. The difference is supposed to be greater for wider ranging herbivores. This article is protected by copyright. All rights reserved.

International audience; Non-native invasive plant species can influence ecosystem functioning over broad spatial scales, but most research on ecosystem impacts has focused on the plot level covering sampling units of only a few square meters or less. We used a multi-scale approach to analyze structural and leaf chemical vegetation traits depending on the presence of nonnative American black cherry (Prunus serotina) in a mixed deciduous forest at the plot level and at the forest stand level. Trait data were derived from remotely sensed maps of leaf area index (LAI), wood volume as well as canopy leaf nitrogen concentration (N-mass), phosphorous concentration (P-mass) and N:P ratio. Differences in these traits were compared between invaded and non-invaded areas at the plot level using 264 sampling units with a size of 25 m x 25 m and in 4119 forest management units (mean area: 7.6 +/- 5.1 ha). Observed patterns between invaded and non-invaded areas were similar at both spatial scales. Invaded areas were characterized by less wood volume, indicating that lower standing biomass promotes the occurrence of P. serotina. In contrast, LAI did not differ between invaded and non-invaded areas. Furthermore, the presence of P. serotina trees had an impact on the chemical composition of the forest canopy by decreasing leaf N:P. While for P-mass, we found no differences in between invaded and non-invaded areas, for N-mass we observed an invasion effect, though only at the plot level. Using remotely sensed trait data proved valuable to evaluate the spatial relevance of invasion impacts over large areas.

Alien invasive species can affect large areas, often with wide-ranging impacts on ecosystem structure, function and services. Prunus serotina is a widespread invader of European temperate forests, where it tends to form homogeneous stands and limits recruitment of indigenous trees. We hypotesized that invasion by P. serotina would be reflected in the nutrient contents of the native species’ leaves and in the respiration of invaded plots as efficient resource uptake and changes in nutrient cycling by P. serotina probably underly its aggressive invasiveness. We combined data from 48 field plots in the forest of Compiègne, France, and data from an experiment using 96 microcosms derived from those field plots. We used general linear models to separate effects of invasion by P. serotina on heterotrophic soil and litter respiration rates and on canopy foliar nutrient content from effects of soil chemical properties, litter quantity, litter species composition and tree species composition. In invaded stands, average respiration rates were 5.6% higher for soil (without litter) and 32% higher for soil and litter combined. Compared to indigenous tree species, P. serotina exhibited higher foliar N (+24.0%), foliar P (+50.7%) and lower foliar C:N (−22.4%) and N:P (−10.1%) ratios. Prunus serotina affected foliar nutrient contents of co-occuring indigenous tree species leading to decreased foliar N (−8.7 %) and increased C:N ratio (+9.5%) in Fagus sylvatica, decreased foliar N:P ratio in Carpinus betulus (−13.5%) and F. sylvatica (−11.8%), and increased foliar P in Pinus sylvestris (+12.3%) in invaded vs. uninvaded stands. Our results suggest that P. serotina is changing nitrogen, phosphorus and carbon cycles to its own advantage, hereby increasing carbon turnover via labile litter, affecting the relative nutrient contents in the overstory leaves, and potentially altering the photosynthetic capacity of the long-lived indigenous broadleaved species. Uncontrolled invasion of European temperate forests by P. serotina may affect the climate change mitigation potential of these forests in the long term, through additive effects on local nutrient cycles. To comply with Belgian and EU firearms legislation, RA obtained a Belgian weapon license Model 4 (Nr. 4/200014/15/15013010) to operate a firearm for scientific purposes and a European Firearms Pass (Nr. 20/21/15/14045), both from the Office of the Governor of the Province of Flemish Brabant. ispartof: Frontiers in Plant Science vol:8 pages:1-11 ispartof: location:Switzerland status: published

© 2017 John Wiley & Sons Ltd Aim: To propose a species distribution modelling framework and its companion “iSDM” R package for predicting the potential and realized distributions of invasive species within the invaded range. Location: Northern France. Methods: The non-equilibrium distribution of invasive species with the environment within the invaded range affects the environmental representativeness of species presence–absence data collected from the field and introduces uncertainty in observed absences as these may either reflect unsuitable sites or be incidental. To address these issues, we here propose an environmental systematic sampling design to collect presence–absence data from the field and a probability index to sort and subsequently separate environmental absences (EAs: reflecting environmentally unsuitable sites) from dispersal-limited absences (DLAs: reflecting sites out of dispersal reach). We first conducted a comprehensive test based on a virtual species to evaluate the performance of our framework. Then, we applied it on different life stages of a non-native tree species (Prunus serotina Ehrh.) invasive in Europe. Results: Regarding the potential distribution, we found higher model performances for both the virtual species (true skill statistics (TSS) > 0.75) and P. serotina (TSS ≥ 0.68) after carefully selecting absences with a low probability to be DLAs compared with classical models that incorporate both EAs and DLAs (e.g. TSS = 0.11 for P. serotina with 80% of DLAs). On the contrary, both EAs and DLAs as well as dispersal-related covariates were needed to capture the realized distribution of both the virtual species and P. serotina. Main Conclusions: Our framework helps overcoming the conceptual and methodological limitations of the disequilibrium in species’ distribution models inherent to invasive species and enables managers to robustly estimate both the realized and potential distributions of invasive species. Although more relevant for modelling the distribution of non-native species, this framework can also be applied to native species. ispartof: Diversity & Distributions vol:23 issue:7 pages:806-819 status: published

Reliable distribution maps are crucial for the management of invasive plant species. An alternative to traditional field surveys is the use of remote sensing data, which allows coverage of large areas. However, most remote sensing studies on invasive plant species focus on mapping large stands of easily detectable study species. In this study, we used hyperspectral remote sensing data in combination with field data to derive a distribution map of an invasive bryophyte species, Campylopus introflexus, on the island of Sylt in Northern Germany. We collected plant cover data on 57 plots to calibrate the model and presence/absence data of C. introflexus on another 150 plots for independent validation. We simultaneously acquired airborne hyperspectral (APEX) images during summer 2014, providing 285 spectral bands. We used a Maxent modelling approach to map the distribution of C. introflexus. Although C. introflexus is a small and inconspicuous species, we were able to map its distribution with an overall accuracy of 75 %. Reducing the sampling effort from 57 to 7 plots, our models performed fairly well until sampling effort dropped below 12 plots. The model predicts that C. introflexus is present in about one quarter of the pixels in our study area. The highest percentage of C. introflexus is predicted in the dune grassland. Our findings suggest that hyperspectral remote sensing data have the potential to provide reliable information about the degree of bryophyte invasion, and thus provide an alternative to traditional field mapping approaches over large areas.

International audience; Light detection and ranging (lidar) is a useful tool for measuring three-dimensional habitat structure; hence, its use in habitat suitability models has been explored, both as a single resource and in combination with other remote-sensing techniques. Here, we evaluated the suitability of airborne lidar data in comparison with aerial photographs and field surveys for modelling the distribution of an endangered and cryptic forest species, the hazel grouse (Bonasa bonasia). The study was conducted in the Bavarian Forest National Park of southeast Germany. Subsequently, a prediction map for conservation planning was generated for a large area, which encompassed the National Park. We examined the utility of lidar data for generating a hazel grouse distribution model by using machine learning (boosted regression trees), and then compared the results to variables derived from field surveys and aerial photographs, both separately and in combination. The cross-validated discrimination ability of the model was slightly higher when using lidar data (area under the receiver operator characteristic curve (AUC), 0.79) compared to models using aerial photographs (AUC, 0.75) or field survey data (AUC, 0.78). The predictive performance consistently increased when combining the predictors from different sources, with an AUC of 0.86 being produced in the model combining all three data sources. The three data sources complemented one another, with each data source probably having an advantage at deriving one of three key aspects of the hazel grouse habitat, namely, vertically well-structured forest stands, horizontally mixed successional vegetation stages, and certain deciduous trees as food resources such as mountain ash (Sorbus aucuparia). In addition, the diverse lidar metrics might be applied to simultaneously characterize vertically and horizontally well-structured forest stands. We conclude that public available airborne lidar data are a viable source for creating habitat suitability maps for large areas and may have increased utility for detecting forest characteristics and valuable wildlife habitats.

The synergies between remote sensing technologies and ecological research have opened new avenues for the study of alien plant invasions worldwide. Such scientific advances have greatly improved our capacity to issue warnings, develop early-response systems and assess the impacts of alien plant invasions on biodiversity and ecosystem functioning. Hitherto, practical applications of remote sensing approaches to support nature conservation actions are lagging far behind scientific advances. Yet, for some of these technologies, knowledge transfer is difficult due to the complexity of the different data handling procedures and the huge amounts of data it involves per spatial unit. In this context, the next logical step is to develop clear guidelines for the application of remote sensing data to monitor and assess the impacts of alien plant invasions, that enable scientists, landscape managers and policy makers to fully exploit the tools which are currently available. It is desirable to have such guidelines accompanied by freely available remote sensing data and generated in a free and open source environment that increases the availability and affordability of these new technologies. Here we present a toolbox that provides an easy-to-use, flexible, transparent and open source set of tools to sample, map, model and assess the impact of alien plant invasions using two high-resolution remote sensing products (hyperspectral and LiDAR images). This online toolbox includes a real case dataset designed to facilitate testing and training in any computer system and processing capacity.

SUMMARY Deterministic earthquake scenario simulations are playing an increasingly important role in seismic hazard and risk estimation. Our aim is to calculate a substantial number of different finite-source scenarios, embedded in a 3-D structure for a particular fault or fault system, by pre-calculating numerical Green’s functions (NGFs). A large seismic fault is divided into subfaults of appropriate size for which synthetic Green’s functions at the surface are calculated and stored. Consequently, ground motions from arbitrary kinematic sources can be simulated for the whole fault or parts of it, by superposition. To illustrate this approach and its functionalities we simulate M 7 (up to 0.5 Hz) scenario earthquakes for a simplified model of the Newport-Inglewood (NI) fault in the Los Angeles (LA) Basin. We quantify the variations of surface ground motion (e.g. peak ground velocity PGV and synthetic seismograms) due to source parameters (e.g. hypocentre location and corresponding slip history). The results show a complex behaviour, with dependence of absolute PGV and its variation on asperity location, source directionality and local structure and demonstrate the necessity to combine 3-D structural and finite-source effects to quantify ground motion characteristics and their variations.

For the insurance and reinsurance industries, earthquake loss estimation is crucial not only to adequately price its product but also to manage the accumulation risk in the face of the ever-increasing exposure in highly seismic regions. Changes in the built environment and a continuously evolving earthquake science make it a necessity for the industry to constantly refine earthquake loss estimation models. In particular, it has been recognized for a long time that the vulnerability of buildings to ground shaking is a key parameter in any earthquake risk model. Current methods tend either to rely on the limited historical damage and loss data or on the numerical simulation of the response of individual buildings to the ground-shaking produced by earthquakes. Although both methods have their advantages and pitfalls, we are proposing here a simple solution, using transparent input data, that can be realistically used for the needs of the insurance and reinsurance industry, whether detailed information about the insured structures is available or not. The resulting product is known as GEVES (Global Earthquake Vulnerability Estimation System). It is primarily intended for evaluating the mean damage ratio (MDR) suffered by a portfolio of buildings classified by use, under the action of a given earthquake scenario (i.e. an earthquake of given size at a given distance from the portfolio of buildings). A key assumption was that macroseismic intensity rather than spectral displacement would be the basis of loss estimation. The paper describes the model with emphasis on its structure and the justification for the assumptions made. In addition to a new set of earthquake vulnerability functions, the paper also provides recommendations on some aspects of the earthquake hazard, in particular about how to define macroseismic intensity at the site of interest, for a given earthquake scenario. This paper also discusses validation of the GEVES model against calculated vulnerability approaches, and the treatment of uncertainty within the model.

SUMMARY The deterministic calculation of earthquake scenarios using complete waveform modelling plays an increasingly important role in estimating shaking hazard in seismically active regions. Here we apply 3-D numerical modelling of seismic wave propagation to M 6+ earthquake scenarios in the area of the Lower Rhine Embayment, one of the seismically most active regions in central Europe. Using a 3-D basin model derived from geology, borehole information and seismic experiments, we aim at demonstrating the strong dependence of ground shaking on hypocentre location and basin structure. The simulations are carried out up to frequencies of ca. 1 Hz. As expected, the basin structure leads to strong lateral variations in peak ground motion, amplification and shaking duration. Depending on source–basin–receiver geometry, the effects correlate with basin depth and the slope of the basin flanks; yet, the basin also affects peak ground motion and estimated shaking hazard thereof outside the basin. Comparison with measured seismograms for one of the earthquakes shows that some of the main characteristics of the wave motion are reproduced. Cumulating the derived seismic intensities from the three modelled earthquake scenarios leads to a predominantly basin correlated intensity distribution for our study area.

The combination of GPS-Telemetry and resource selection functions is widely used to analyze animal habitat selection. Rapid large-scale assessment of vegetation structure allows bridging the requirements of habitat selection studies on grain size and extent, particularly in forest habitats. For roe deer, the cold period in winter forces individuals to optimize their trade off in searching for food and shelter. We analyzed the winter habitat selection of roe deer (Capreolus capreolus) in a montane forest landscape combining estimates of vegetation cover in three different height strata, derived from high resolution airborne Laser-scanning (LiDAR, Light detection and ranging), and activity data from GPS telemetry. Specifically, we tested the influence of temperature, snow height, and wind speed on site selection, differentiating between active and resting animals using mixed-effects conditional logistic regression models in a case-control design. Site selection was best explained by temperature deviations from hourly means, snow height, and activity status of the animals. Roe deer tended to use forests of high canopy cover more frequently with decreasing temperature, and when snow height exceeded 0.6 m. Active animals preferred lower canopy cover, but higher understory cover. Our approach demonstrates the potential of LiDAR measures for studying fine scale habitat selection in complex three-dimensional habitats, such as forests.

Finite-fault source inversions reveal the spatial complexity of earthquake slip over the fault plane. In this study, several possible earthquake scenarios of Mw7.0 are simulated with different quasi-dynamic finite source models for the Newport Inglewood (NI) fault system in the Los Angeles (LA) basin embedded in the 3-D SCEC community velocity model version 3. We make use of the recently developed Numerical Greens Functions method in (Wang, Strong Ground Motion Variations in Los Angeles Basin, 2007) and synthesize ground motions from a data base of 3-D Greens functions, calculated for a discretized model of the NI fault. This allows efficient simulation of arbitrary slip histories. We investigate the effects of the various slip histories on peak ground velocities and the related uncertainties in ground motion prediction for our study area. The results confirm that the fault perpendicular components of motion are dominated by directivity effects while the fault parallel component is influenced both by the slip distribution and the basin structure.

Numerical solutions to the problem of seismic wave propagation, that allow simulations of complete wave fields through 3D structures, are currently revolutionizing seismology and related fields. So far - in order to calculate theoretical seismograms in the observed frequency bands - one had to resort to solution methods with severe limitations (e.g., ray theoretical approximations, one-dimensional structures, perturbation theory, etc.). Only in the past few years, computational power has allowed us to simulate wave fields that can be directly compared to observations. Even though the computations still require substantial resources, the methodologies developed in the past decade are beginning to enter routine processing steps in all branches ranging from exploration seismics to global seismology. Here we present recent examples in global seismology (spectral element modeling of global wave propagation) and earthquake scenario simulations, their relation to shaking hazard estimation, and associated problems. The next decade will see fundamental changes in the way data fitting (inverse problem, parameter estimation) is done in seismology with the potential of advances in several fields of Earth Sciences.

We report advances in simulating wave propagation in the Earth's interior in 2D and 3D using several numerical methods. For the Earth's deep interior simulations are carried out on a global scale using axi-symmetric models and 3D spherical sections. In addition, we calculate earthquake scenarios on a regional scale for prediction of ground motion (e.g. peak motion amplitude, shaking duration), taking into account amplification effects of low velocity zones in active faults and basin structures, topography effects, shear wave splitting effects due to anisotropy and attenuation due to visco-elasticity. These predictions may be useful for risk evaluation and civil engineering purposes. We further simulate earthquake sources as dynamic fault ruptures in the context of typical fault-zone velocity structures and material interfaces. As observations of earthquake-induced ground rotations are becoming available we investigate systematically the effects of 3D heterogeneity on rotational motions.

For computational seismology the present years are extremely exciting. The reason is, that with the current supercomputer technology, the frequency band in which seismic waves are observed following regional or global earthquakes, can be simulated numerically for realistic 3D earth models for the first time. Depending on the spatial scales under consideration (whole planet, a sedimentary basin at risk from local earthquakes, a volcano with high risk for future eruptions) this will lead to considerable improvement (1) in the understanding of the structural properties (e.g. the Earth’s mantle, the inside of a sedimentary basin or a volcano) and (2) in forecasting strong ground motion for realistic earthquake scenarios. The latter point may have considerable long-term societal benefits, as the short-term prediction of large earthquakes seems out of sight. During the first phase of this project some of the highest-resolution simulations ever done were carried out with important implications for future directions in computational seismology. The most important scientific results can be summarized as: (1) 3D Simulations of several earthquakes in the Cologne Basin in Germany demonstrate that the main characteristics of ground motion (e.g. peak motion amplitude, shaking duration) are successfully predicted through numerical simulations; (2) The low seismic velocities inside active faults (e.g. San Andreas Fault, California) may act as an amplifier for ground motion. This has implications for buildings in the vicinity of faults; (3) Large scale simulations of strong earthquakes in subduction zones show that the local 3D structure at depth strongly influences the waves propagating to the surface. Ignoring this will lead to severe misinterpretations. (4) Including topography to understand wave propagation inside volcanoes is crucial. Our simulations demonstrate the scattering effects due to topography. If we want to understand the state of a volcanic system prior to eruptions from seismic waves these effects have to be taken into account.