Colonization history of the Swiss Rhine basin by the bullhead (Cottus gobio): inference under a Bayesian spatially explicit framework

The present distribution of freshwater fish in the Alpine region has been strongly affected by colonization events occurring after the last glacial maximum (LGM), some 20,000 years ago. We use here a spatially explicit simulation framework to model and better understand their colonization dynamics in the Swiss Rhine basin. This approach is applied to the European bullhead (Cottus gobio), which is an ideal model organism to study fish past demographic processes since it has not been managed by humans. The molecular diversity of eight sampled populations is simulated and compared to observed data at six microsatellite loci under an approximate Bayesian computation framework to estimate the parameters of the colonization process. Our demographic estimates fit well with current knowledge about the biology of this species, but they suggest that the Swiss Rhine basin was colonized very recently, after the Younger Dryas some 6600 years ago. We discuss the implication of this result, as well as the strengths and limits of the spatially explicit approach coupled to the approximate Bayesian computation framework.

Evaluating the Quality and Accuracy of TanDEM-X Digital Elevation Models at Archaeological Sites in the Cilician Plain, Turkey

Satellite remote sensing provides a powerful instrument for mapping and monitoring traces of historical settlements and infrastructure, not only in distant areas and crisis regions. It helps archaeologists to embed their findings from field surveys into the broader context of the landscape. With the start of the TanDEM-X mission, spatially explicit 3D-information is available to researchers at an unprecedented resolution worldwide. We examined different experimental TanDEM-X digital elevation models (DEM) that were processed from two different imaging modes (Stripmap/High Resolution Spotlight) using the operational alternating bistatic acquisition mode. The quality and accuracy of the experimental DEM products was compared to other available DEM products and a high precision archaeological field survey. The results indicate the potential of TanDEM-X Stripmap (SM) data for mapping surface elements at regional scale. For the alluvial plain of Cilicia, a suspected palaeochannel could be reconstructed. At the local scale, DEM products from TanDEM-X High Resolution Spotlight (HS) mode were processed at 2 m spatial resolution using a merge of two monostatic/bistatic interferograms. The absolute and relative vertical accuracy of the outcome meet the specification of high resolution elevation data (HRE) standards from the National System for Geospatial Intelligence (NSG) at the HRE20 level.

Potential drought stress in a Swiss mountain catchment-Ensemble forecasting of high mountain soil moisture reveals a drastic decrease, despite major uncertainties

Climate change is expected to profoundly influence the hydrosphere of mountain ecosystems. The focus of current process-based research is centered on the reaction of glaciers and runoff to climate change; spatially explicit impacts on soil moisture remain widely neglected. We spatio-temporally analyzed the impact of the climate on soil moisture in a mesoscale high mountain catchment to facilitate the development of mitigation and adaptation strategies at the level of vegetation patterns. Two regional climate models were downscaled using three different approaches (statistical downscaling, delta change, and direct use) to drive a hydrological model (WaSiM-ETH) for reference and scenario period (1960–1990 and 2070–2100), resulting in an ensemble forecast of six members. For all ensembles members we found large changes in temperature, resulting in decreasing snow and ice storage and earlier runoff, but only small changes in evapotranspiration. The occurrence of downscaled dry spells was found to fluctuate greatly, causing soil moisture depletion and drought stress potential to show high variability in both space and time. In general, the choice of the downscaling approach had a stronger influence on the results than the applied regional climate model. All of the results indicate that summer soil moisture decreases, which leads to more frequent declines below a critical soil moisture level and an advanced evapotranspiration deficit. Forests up to an elevation of 1800 m a.s.l. are likely to be threatened the most, while alpine areas and most pastures remain nearly unaffected. Nevertheless, the ensemble variability was found to be extremely high and should be interpreted as a bandwidth of possible future drought stress situations.

Spatial Assessment of Erosion and its Impact on Soil Fertility in the Tajik Foothills

Efficient planning of soil conservation measures requires, first, to understand the impact of soil erosion on soil fertility with regard to local land cover classes; and second, to identify hot spots of soil erosion and bright spots of soil conservation in a spatially explicit manner. Soil organic carbon (SOC) is an important indicator of soil fertility. The aim of this study was to conduct a spatial assessment of erosion and its impact on SOC for specific land cover classes. Input data consisted of extensive ground truth, a digital elevation model and Landsat 7 imagery from two different seasons. Soil spectral reflectance readings were taken from soil samples in the laboratory and calibrated with results of SOC chemical analysis using regression tree modelling. The resulting model statistics for soil degradation assessments are promising (R2=0.71, RMSEV=0.32). Since the area includes rugged terrain and small agricultural plots, the decision tree models allowed mapping of land cover classes, soil erosion incidence and SOC content classes at an acceptable level of accuracy for preliminary studies. The various datasets were linked in the hot-bright spot matrix, which was developed to combine soil erosion incidence information and SOC content levels (for uniform land cover classes) in a scatter plot. The quarters of the plot show different stages of degradation, from well conserved land to hot spots of soil degradation. The approach helps to gain a better understanding of the impact of soil erosion on soil fertility and to identify hot and bright spots in a spatially explicit manner. The results show distinctly lower SOC content levels on large parts of the test areas, where annual crop cultivation was dominant in the 1990s and where cultivation has now been abandoned. On the other hand, there are strong indications that afforestations and fruit orchards established in the 1980s have been successful in conserving soil resources.

Simulating the evolution of a clonal trait in plants with sexual and vegetative reproduction

Aims Phenotypic optimality models neglect genetics. However, especially when heterozygous genotypes ire fittest, evolving allele, genotype and phenotype frequencies may not correspond to predicted optima. This was not previously addressed for organisms with complex life histories. Methods Therefore, we modelled the evolution of a fitness-relevant trait of clonal plants, stolon internode length. We explored the likely case of air asymmetric unimodal fitness profile with three model types. In constant selection models (CSMs), which are gametic, but not spatially explicit, evolving allele frequencies in the one-locus and five-loci cases did not correspond to optimum stolon internode length predicted by the spatially explicit, but not gametic, phenotypic model. This deviation was due to the asymmetry of the fitness profile. Gametic, spatially explicit individual-based (SEIB) modeling allowed us relaxing the CSM assumptions of constant selection with exclusively sexual reproduction. Important findings For entirely vegetative or sexual reproduction, predictions. of the gametic SEIB model were close to the ones of spatially explicit CSMs gametic phenotypic models, hut for mixed modes of reproduction they appoximated those of gametic, not spatially explicit CSMs. Thus, in contrast to gametic SEIB models, phenotypic models and, especially for few loci, also CSMs can be very misleading. We conclude that the evolution of trails governed by few quantitative trait loci appears hardly predictable by simple models, that genetic algorithms aiming at technical optimization may actually, miss the optimum and that selection may lead to loci with smaller effects, in derived compared with ancestral lines.

Water quality and health in a Sahelian semi-arid urban context: an integrated geographical approach in Nouakchott, Mauritania

Access to sufficient quantities of safe drinking water is a human right. Moreover, access to clean water is of public health relevance, particularly in semi-arid and Sahelian cities due to the risks of water contamination and transmission of water-borne diseases. We conducted a study in Nouakchott, the capital of Mauritania, to deepen the understanding of diarrhoeal incidence in space and time. We used an integrated geographical approach, combining socio-environmental, microbiological and epidemiological data from various sources, including spatially explicit surveys, laboratory analysis of water samples and reported diarrhoeal episodes. A geospatial technique was applied to determine the environmental and microbiological risk factors that govern diarrhoeal transmission. Statistical and cartographic analyses revealed concentration of unimproved sources of drinking water in the most densely populated areas of the city, coupled with a daily water allocation below the recommended standard of 20 l per person. Bacteriological analysis indicated that 93% of the non-piped water sources supplied at water points were contaminated with 10-80 coliform bacteria per 100 ml. Diarrhoea was the second most important disease reported at health centres, accounting for 12.8% of health care service consultations on average. Diarrhoeal episodes were concentrated in municipalities with the largest number of contaminated water sources. Environmental factors (e.g. lack of improved water sources) and bacteriological aspects (e.g. water contamination with coliform bacteria) are the main drivers explaining the spatio-temporal distribution of diarrhoea. We conclude that integrating environmental, microbiological and epidemiological variables with statistical regression models facilitates risk profiling of diarrhoeal diseases. Modes of water supply and water contamination were the main drivers of diarrhoea in this semi-arid urban context of Nouakchott, and hence require a strategy to improve water quality at the various levels of the supply chain.

Methane emissions from floodplains in the Amazon Basin: challenges in developing a process-based model for global applications

Tropical wetlands are estimated to represent about 50% of the natural wetland methane (CH4) emissions and explain a large fraction of the observed CH4 variability on timescales ranging from glacial–interglacial cycles to the currently observed year-to-year variability. Despite their importance, however, tropical wetlands are poorly represented in global models aiming to predict global CH4 emissions. This publication documents a first step in the development of a process-based model of CH4 emissions from tropical floodplains for global applications. For this purpose, the LPX-Bern Dynamic Global Vegetation Model (LPX hereafter) was slightly modified to represent floodplain hydrology, vegetation and associated CH4 emissions. The extent of tropical floodplains was prescribed using output from the spatially explicit hydrology model PCR-GLOBWB. We introduced new plant functional types (PFTs) that explicitly represent floodplain vegetation. The PFT parameterizations were evaluated against available remote-sensing data sets (GLC2000 land cover and MODIS Net Primary Productivity). Simulated CH4 flux densities were evaluated against field observations and regional flux inventories. Simulated CH4 emissions at Amazon Basin scale were compared to model simulations performed in the WETCHIMP intercomparison project. We found that LPX reproduces the average magnitude of observed net CH4 flux densities for the Amazon Basin. However, the model does not reproduce the variability between sites or between years within a site. Unfortunately, site information is too limited to attest or disprove some model features. At the Amazon Basin scale, our results underline the large uncertainty in the magnitude of wetland CH4 emissions. Sensitivity analyses gave insights into the main drivers of floodplain CH4 emission and their associated uncertainties. In particular, uncertainties in floodplain extent (i.e., difference between GLC2000 and PCR-GLOBWB output) modulate the simulated emissions by a factor of about 2. Our best estimates, using PCR-GLOBWB in combination with GLC2000, lead to simulated Amazon-integrated emissions of 44.4 ± 4.8 Tg yr−1. Additionally, the LPX emissions are highly sensitive to vegetation distribution. Two simulations with the same mean PFT cover, but different spatial distributions of grasslands within the basin, modulated emissions by about 20%. Correcting the LPX-simulated NPP using MODIS reduces the Amazon emissions by 11.3%. Finally, due to an intrinsic limitation of LPX to account for seasonality in floodplain extent, the model failed to reproduce the full dynamics in CH4 emissions but we proposed solutions to this issue. The interannual variability (IAV) of the emissions increases by 90% if the IAV in floodplain extent is accounted for, but still remains lower than in most of the WETCHIMP models. While our model includes more mechanisms specific to tropical floodplains, we were unable to reduce the uncertainty in the magnitude of wetland CH4 emissions of the Amazon Basin. Our results helped identify and prioritize directions towards more accurate estimates of tropical CH4 emissions, and they stress the need for more research to constrain floodplain CH4 emissions and their temporal variability, even before including other fundamental mechanisms such as floating macrophytes or lateral water fluxes.

A systematized workflow for assessing land resources using readily available spatial datasets

Land degradation as well as land conservation maps at a (sub-) national scale are critical for pro-ject planning for sustainable land management. It has long been recognized that online accessible and low-cost raster data sets (e.g. Landsat imagery, SRTM-DEM’s) provide a readily available basis for land resource assessments for developing countries. However, choice of spatial, tempo-ral and spectral resolution of such data is often limited. Furthermore, while local expert knowl-edge on land degradation processes is abundant, difficulties are often encountered when linking existing knowledge with modern approaches including GIS and RS. The aim of this study was to develop an easily applicable, standardized workflow for preliminary spatial assessments of land degradation and conservation, which also allows the integration of existing expert knowledge. The core of the developed method consists of a workflow for rule-based land resource assess-ment. In a systematic way, this workflow leads from predefined land degradation and conserva-tion classes to field indicators, to suitable spatial proxy data, and finally to a set of rules for clas-sification of spatial datasets. Pre-conditions are used to narrow the area of interest. Decision tree models are used for integrating the different rules. It can be concluded that the workflow presented assists experts from different disciplines in col-laboration GIS/RS specialists in establishing a preliminary model for assessing land degradation and conservation in a spatially explicit manner. The workflow provides support when linking field indicators and spatial datasets, and when determining field indicators for groundtruthing.

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20–30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow–Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

Marginal lands or marginal people? Analysing key processes determining the outcomes of large-scale land acquisitions in Lao PDR and Cambodia

This chapter aims to overcome the gap existing between case study research, which typically provides qualitative and process-based insights, and national or global inventories that typically offer spatially explicit and quantitative analysis of broader patterns, and thus to present adequate evidence for policymaking regarding large-scale land acquisitions. Therefore, the chapter links spatial patterns of land acquisitions to underlying implementation processes of land allocation. Methodologically linking the described patterns and processes proved difficult, but we have identified indicators that could be added to inventories and monitoring systems to make linkage possible. Combining complementary approaches in this way may help to determine where policy space exists for more sustainable governance of land acquisitions, both geographically and with regard to processes of agrarian transitions. Our spatial analysis revealed two general patterns: (i) relatively large forestry-related acquisitions that target forested landscapes and often interfere with semi-subsistence farming systems; and (ii) smaller agriculture-related acquisitions that often target existing cropland and also interfere with semi-subsistence systems. Furthermore, our meta-analysis of land acquisition implementation processes shows that authoritarian, top-down processes dominate. Initially, the demands of powerful regional and domestic investors tend to override socio-ecological variables, local actors’ interests, and land governance mechanisms. As available land grows scarce, however, and local actors gain experience dealing with land acquisitions, it appears that land investments begin to fail or give way to more inclusive, bottom-up investment models.