Assembly processes of gastropod community change with horizontal and vertical zonation in ancient Lake Ohrid: a metacommunity speciation perspective

The Balkan Lake Ohrid is the oldest and most diverse freshwater lacustrine system in Europe. However, it remains unclear whether species community composition, as well as the diversification of its endemic taxa, is mainly driven by dispersal limitation, environmental filtering, or species interaction. This calls for a holistic perspective involving both evolutionary processes and ecological dynamics, as provided by the unifying framework of the “metacommunity speciation model”. The current study used the species-rich model taxon Gastropoda to assess how extant communities in Lake Ohrid are structured by performing process-based metacommunity analyses. Specifically, the study aimed (1) to identifying the relative importance of the three community assembly processes and (2) to test whether the importance of these individual processes changes gradually with lake depth or discontinuously with eco-zone shifts. Based on automated eco-zone detection and process-specific simulation steps, we demonstrated that dispersal limitation had the strongest influence on gastropod community composition. However, it was not the exclusive assembly process, but acted together with the other two processes – environmental filtering and species interaction. The relative importance of the community assembly processes varied both with lake depth and eco-zones, though the processes were better predicted by the latter. This suggests that environmental characteristics have a pronounced effect on shaping gastropod communities via assembly processes. Moreover, the study corroborated the high importance of dispersal limitation for both maintaining species richness in Lake Ohrid (through its impact on community composition) and generating endemic biodiversity (via its influence on diversification processes). However, according to the metacommunity speciation model, the inferred importance of environmental filtering and biotic interaction also suggests a small but significant influence of ecological speciation. These findings contribute to the main goal of the Scientific Collaboration on Past Speciation Conditions in Lake Ohrid (SCOPSCO) deep drilling initiative – inferring the drivers of biotic evolution – and might provide an integrative perspective on biological and limnological dynamics in ancient Lake Ohrid

A topology-oblivious routing protocol for NDN-VANETs

Vehicular Ad Hoc Networks (VANETs) are characterized by intermittent connectivity, which leads to failures of end-to-end paths between nodes. Named Data Networking (NDN) is a network paradigm that deals with such problems, since information is forwarded based on content and not on the location of the hosts. In this work, we propose an enhanced routing protocol of our previous topology-oblivious Multihop, Multipath, and Multichannel NDN for VANETs (MMM-VNDN) routing strategy that exploits several paths to achieve more efficient content retrieval. Our new enhanced protocol, i mproved MMM-VNDN (iMMM-VNDN), creates paths between a requester node and a provider by broadcasting Interest messages. When a provider responds with a Data message to a broadcast Interest message, we create unicast routes between nodes, by using the MAC address(es) as the distinct address(es) of each node. iMMM-VNDN extracts and thus creates routes based on the MAC addresses from the strategy layer of an NDN node. Simulation results show that our routing strategy performs better than other state of the art strategies in terms of Interest Satisfaction Rate, while keeping the latency and jitter of messages low

Variable Selection and Model Choice in Geoadditive Regression Models

Model choice and variable selection are issues of major concern in practical regression analyses. We propose a boosting procedure that facilitates both tasks in a class of complex geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, random effects, and varying coefficient terms. The major modelling component are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a remaining smooth component with one degree of freedom to obtain a fair comparison between all model terms. A generic representation of the geoadditive model allows to devise a general boosting algorithm that implements automatic model choice and variable selection. We demonstrate the versatility of our approach with two examples: a geoadditive Poisson regression model for species counts in habitat suitability analyses and a geoadditive logit model for the analysis of forest health

Working towards an Improved Monitoring Infrastructure to support Disaster Management, Humanitarian Relief and Civil Security

Within this paper experiences and results from the work in the context of the European Initiative on Global Monitoring for Environment and Security (GMES) as they were gathered within the German Remote Sensing Data Center (DFD) are reported. It is described how data flows, analysis methods and information networks can be improved to allow better and faster access to remote sensing data and information in order to support the management of crisis situations. This refers to all phases of a crisis or disaster situation, including preparedness, response and recovery. Above the infrastructure and information flow elements, example cases of different crisis situations in the context of natural disasters, humanitarian relief activities and civil security are discussed. This builds on the experiences gained during the very active participation in the network of Excellence on Global Monitoring for Stability and Security (GMOSS), the GMES Service Element RESPOND, focussing on Humanitarian Relief Support and supporting the International Charter on Space and Major Disasters as well as while linking closely to national, European and international entities related to civil human security. It is suggested to further improve the network of national and regional centres of excellence in this context in order to improve local, regional and global monitoring capacities. Only when optimum interoperability and information flow can be achieved among systems and data providers on one hand side and the decision makers on the other, efficient monitoring and analysis capacities can be established successfully

SurfelMeshing: Online Surfel-Based Mesh Reconstruction

We address the problem of mesh reconstruction from live RGB-D video, assuming a calibrated camera and poses provided externally (e.g., by a SLAM system). In contrast to most existing approaches, we do not fuse depth measurements in a volume but in a dense surfel cloud. We asynchronously (re)triangulate the smoothed surfels to reconstruct a surface mesh. This novel approach enables to maintain a dense surface representation of the scene during SLAM which can quickly adapt to loop closures. This is possible by deforming the surfel cloud and asynchronously remeshing the surface where necessary. The surfel-based representation also naturally supports strongly varying scan resolution. In particular, it reconstructs colors at the input camera's resolution. Moreover, in contrast to many volumetric approaches, ours can reconstruct thin objects since objects do not need to enclose a volume. We demonstrate our approach in a number of experiments, showing that it produces reconstructions that are competitive with the state-of-the-art, and we discuss its advantages and limitations. The algorithm (excluding loop closure functionality) is available as open source at https://github.com/puzzlepaint/surfelmeshing .Comment: Version accepted to IEEE Transactions on Pattern Analysis and Machine Intelligenc

Spatial Smoothing Techniques for the Assessment of Habitat Suitability

Precise knowledge about factors influencing the habitat suitability of a certain species forms the basis for the implementation of effective programs to conserve biological diversity. Such knowledge is frequently gathered from studies relating abundance data to a set of influential variables in a regression setup. In particular, generalised linear models are used to analyse binary presence/absence data or counts of a certain species at locations within an observation area. However, one of the key assumptions of generalised linear models, the independence of the observations is often violated in practice since the points at which the observations are collected are spatially aligned. While several approaches have been developed to analyse and account for spatial correlation in regression models with normally distributed responses, far less work has been done in the context of generalised linear models. In this paper, we describe a general framework for semiparametric spatial generalised linear models that allows for the routine analysis of non-normal spatially aligned regression data. The approach is utilised for the analysis of a data set of synthetic bird species in beech forests, revealing that ignorance of spatial dependence actually may lead to false conclusions in a number of situations