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Environmental factors shape the spatial distribution and dynamics of populations. Understanding how these factors interact with movement behavior is critical for efficient conservation, in particular for migratory species. Adult female green sea turtles, Chelonia mydas, migrate between foraging and nesting sites that are generally separated by thousands of kilometers. As an emblematic endangered species, green turtles have been intensively studied, with a focus on nesting, migration, and foraging. Nevertheless, few attempts integrated these behaviors and their trade‐offs by considering the spatial configurations of foraging and nesting grounds as well as environmental heterogeneity like oceanic currents and food distribution. We developed an individual‐based model to investigate the impact of local environmental conditions on emerging migratory corridors and reproductive output and to thereby identify conservation priority sites. The model integrates movement, nesting, and foraging behavior. Despite being largely conceptual, the model captured realistic movement patterns which confirm field studies. The spatial distribution of migratory corridors and foraging hot spots was mostly constrained by features of the regional landscape, such as nesting site locations, distribution of feeding patches, and oceanic currents. These constraints also explained the mixing patterns in regional forager communities. By implementing alternative decision strategies of the turtles, we found that foraging site fidelity and nesting investment, two characteristics of green turtles' biology, are favorable strategies under unpredictable environmental conditions affecting their habitats. Based on our results, we propose specific guidelines for the regional conservation of green turtles as well as future research suggestions advancing spatial ecology of sea turtles. Being implemented in an easy to learn open‐source software, our model can coevolve with the collection and analysis of new data on energy budget and movement into a generic tool for sea turtle research and conservation. Our modeling approach could also be useful for supporting the conservation of other migratory marine animals.
This study analyses some hydrological driving forces and their interrelation with surface-flow initiation in a semiarid Caatinga basin (12km(2)), Northeastern Brazil. During the analysis period (2005 - 2014), 118 events with precipitation higher than 10mm were monitored, providing 45 events with runoff, 25 with negligible runoff and 49 without runoff. To verify the dominant processes, 179 on-site measurements of saturated hydraulic conductivity (Ksat) were conducted. The results showed that annual runoff coefficient lay below 0.5% and discharge at the outlet has only occurred four days per annum on average, providing an insight to the surface-water scarcity of the Caatinga biome. The most relevant variables to explain runoff initiation were total precipitation and maximum 60-min rainfall intensity (I-60). Runoff always occurred when rainfall surpassed 31mm, but it never occurred for rainfall below 14mm or for I-60 below 12mmh(-1). The fact that the duration of the critical intensity is similar to the basin concentration time (65min) and that the infiltration threshold value approaches the river-bank saturated hydraulic conductivity support the assumption that Hortonian runoff prevails. However, none of the analysed variables (total or precedent precipitation, soil moisture content, rainfall intensities or rainfall duration) has been able to explain the runoff initiation in all monitored events: the best criteria, e.g. failed to explain 27% of the events. It is possible that surface-flow initiation in the Caatinga biome is strongly influenced by the root-system dynamics, which changes macro-porosity status and, therefore, initial abstraction. Copyright (c) 2016 John Wiley & Sons, Ltd.
Internet connectivity of cloud services is of exceptional importance for both their providers and consumers. This article demonstrates the outlines of a method for measuring cloud-service connectivity at the internet protocol level from a client's perspective. For this, we actively collect connectivity data via traceroute measurements from PlanetLab to several major cloud services. Furthermore, we construct graph models from the collected data, and analyse the connectivity of the services based on important graph-based measures. Then, random and targeted node removal attacks are simulated, and the corresponding vulnerability of cloud services is evaluated. Our results indicate that cloud service hosts are, on average, much better connected than average hosts. However, when interconnecting nodes are removed in a targeted manner, cloud connectivity is dramatically reduced.
The Internet can be considered as the most important infrastructure for modern society and businesses. A loss of Internet connectivity has strong negative financial impacts for businesses and economies. Therefore, assessing Internet connectivity, in particular beyond their own premises and area of direct control, is of growing importance in the face of potential failures, accidents, and malicious attacks. This paper presents CORIA, a software framework for an easy analysis of connectivity risks based on large network graphs. It provides researchers, risk analysts, network managers and security consultants with a tool to assess an organization's connectivity and paths options through the Internet backbone, including a user-friendly and insightful visual representation of results. CORIA is flexibly extensible in terms of novel data sets, graph metrics, and risk scores that enable further use cases. The performance of CORIA is evaluated by several experiments on the Internet graph and further randomly generated networks.
A comprehensive hydro-sedimentological dataset for the Isábena catchment, northeastern (NE) Spain, for the period 2010–2018 is presented to analyse water and sediment fluxes in a Mediterranean mesoscale catchment. The dataset includes rainfall data from 12 rain gauges distributed within the study area complemented by meteorological data of 12 official meteo-stations. It comprises discharge data derived from water stage measurements as well as suspended sediment concentrations (SSCs) at six gauging stations of the River Isábena and its sub-catchments. Soil spectroscopic data from 351 suspended sediment samples and 152 soil samples were collected to characterize sediment source regions and sediment properties via fingerprinting analyses. The Isábena catchment (445 km 2 ) is located in the southern central Pyrenees ranging from 450 m to 2720 m a.s.l.; together with a pronounced topography, this leads to distinct temperature and precipitation gradients. The River Isábena shows marked discharge variations and high sediment yields causing severe siltation problems in the downstream Barasona Reservoir. The main sediment source is badland areas located on Eocene marls that are well connected to the river network. The dataset features a comprehensive set of variables in a high spatial and temporal resolution suitable for the advanced process understanding of water and sediment fluxes, their origin and connectivity and sediment budgeting and for the evaluation and further development of hydro-sedimentological models in
Mediterranean mesoscale mountainous catchments.
We present a novel approach for recovery of the directional connectivity of a small oscillator network by means of the phase dynamics reconstruction from multivariate time series data. The main idea is to use a triplet analysis instead of the traditional pairwise one. Our technique reveals an effective phase connectivity which is generally not equivalent to a structural one. We demonstrate that by comparing the coupling functions from all possible triplets of oscillators, we are able to achieve in the reconstruction a good separation between existing and non-existing connections, and thus reliably reproduce the network structure.
Processing of reward is the basis of adaptive behavior of the human being. Neural correlates of reward processing seem to be influenced by developmental changes from adolescence to late adulthood. The aim of this study is to uncover these neural correlates during a slot machine gambling task across the lifespan. Therefore, we used functional magnetic resonance imaging to investigate 102 volunteers in three different age groups: 34 adolescents, 34 younger adults, and 34 older adults. We focused on the core reward areas ventral striatum (VS) and ventromedial prefrontal cortex (VMPFC), the valence processing associated areas, anterior cingulate cortex (ACC) and insula, as well as information integration associated areas, dorsolateral prefrontal cortex (DLPFC), and inferior parietal lobule (IPL). Results showed that VS and VMPFC were characterized by a hyperactivation in adolescents compared with younger adults. Furthermore, the ACC and insula were characterized by a U-shape pattern (hypoactivation in younger adults compared with adolescents and older adults), whereas the DLPFC and IPL were characterized by a J-shaped form (hyperactivation in older adults compared with younger groups). Furthermore, a functional connectivity analysis revealed an elevated negative functional coupling between the inhibition-related area rIFG and VS in younger adults compared with adolescents. Results indicate that lifespan-related changes during reward anticipation are characterized by different trajectories in different reward network modules and support the hypothesis of an imbalance in maturation of striatal and prefrontal cortex in adolescents. Furthermore, these results suggest compensatory age-specific effects in fronto-parietal regions. Hum Brain Mapp 35:5153-5165, 2014. (c) 2014 Wiley Periodicals, Inc.
Species assembly from a regional pool into local metacommunities and how they colonize and coexist over time and space is essential to understand how communities response to their environment including abiotic and biotic factors. In highly disturbed landscapes, connectivity of isolated habitat patches is essential to maintain biodiversity and the entire ecosystem functioning. In northeast Germany, a high density of the small water bodies called kettle holes, are good systems to study metacommunities due to their condition as “aquatic islands” suitable for hygrophilous species that are surrounded by in unsuitable matrix of crop fields. The main objective of this thesis was to infer the main ecological processes shaping plant communities and their response to the environment, from biodiversity patterns and key life-history traits involved in connectivity using ecological and genetic approaches; and to provide first insights of the role of kettle holes harboring wild-bee species as important mobile linkers connecting plant communities in this insular system.
t a community level, I compared plant diversity patterns and trait composition in ephemeral vs. permanent kettle holes). My results showed that types of kettle holes act as environmental filers shaping plant diversity, community-composition and trait-distribution, suggesting species sorting and niche processes in both types of kettle holes. At a population level, I further analyzed the role of dispersal and reproductive strategies of four selected species occurring in permanent kettle holes. Using microsatellites, I found that breeding system (degree of clonality), is the main factor shaping genetic diversity and genetic divergence. Although, higher gene flow and lower genetic differentiation among populations in wind vs. insect pollinated species was also found, suggesting that dispersal mechanisms played a role related to gene flow and connectivity. For most flowering plants, pollinators play an important role connecting communities. Therefore, as a first insight of the potential mobile linkers of these plant communities, I investigated the diversity wild-bees occurring in these kettle holes. My main results showed that local habitat quality (flower resources) had a positive effect on bee diversity, while habitat heterogeneity (number of natural landscape elements surrounding kettle holes 100–300m), was negatively correlated.
This thesis covers from genetic flow at individual and population level to plant community assembly. My results showed how patterns of biodiversity, dispersal and reproduction strategies in plant population and communities can be used to infer ecological processes. In addition, I showed the importance of life-history traits and the relationship between species and their abiotic and biotic interactions. Furthermore, I included a different level of mobile linkers (pollinators) for a better understanding of another level of the system. This integration is essential to understand how communities respond to their surrounding environment and how disturbances such as agriculture, land-use and climate change might affect them. I highlight the need to integrate many scientific areas covering from genes to ecosystems at different spatiotemporal scales for a better understanding, management and conservation of our ecosystems.
Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.
Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.