Implications of movement for species distribution models - rethinking environmental data tools

Movement is considered an essential process in shaping the distributions of species. Nevertheless, most species distribution models (SDMs) still focus solely on environment-species relationships to predict the occurrence of species. Furthermore, the currently used indirect estimates of movement allow to assess habitat accessibility, but do not provide an accurate description of movement. Better proxies of movement are needed to assess the dispersal potential of individual species and to gain a more practical insight in the interconnectivity of communities. Telemetry techniques are rapidly evolving and highly capable to provide explicit descriptions of movement, but their usefulness for SDMs will mainly depend on the ability of these models to deal with hitherto unconsidered ecological processes. More specifically, the integration of movement is likely to affect the environmental data requirements as the connection between environmental and biological data is crucial to provide reliable results. Mobility implies the occupancy of a continuum of space, hence an adequate representation of both geographical and environmental space is paramount to study mobile species distributions. In this context, environmental models, remote sensing techniques and animal-borne environmental sensors are discussed as potential techniques to obtain suitable environmental data. In order to provide an in-depth review of the aforementioned methods, we have chosen to use the modelling of fish distributions as a case study. The high mobility of fish and the often highly variable nature of the aquatic environment generally complicate model development, making it an adequate subject for research. Furthermore, insight into the distribution of fish is of great interest for fish stock assessments and water management worldwide, underlining its practical relevance

A Flexible Privacy-preserving Framework for Singular Value Decomposition under Internet of Things Environment

The singular value decomposition (SVD) is a widely used matrix factorization tool which underlies plenty of useful applications, e.g. recommendation system, abnormal detection and data compression. Under the environment of emerging Internet of Things (IoT), there would be an increasing demand for data analysis to better human's lives and create new economic growth points. Moreover, due to the large scope of IoT, most of the data analysis work should be done in the network edge, i.e. handled by fog computing. However, the devices which provide fog computing may not be trustable while the data privacy is often the significant concern of the IoT application users. Thus, when performing SVD for data analysis purpose, the privacy of user data should be preserved. Based on the above reasons, in this paper, we propose a privacy-preserving fog computing framework for SVD computation. The security and performance analysis shows the practicability of the proposed framework. Furthermore, since different applications may utilize the result of SVD operation in different ways, three applications with different objectives are introduced to show how the framework could flexibly achieve the purposes of different applications, which indicates the flexibility of the design.Comment: 24 pages, 4 figure

Estuarine behaviour of European silver eel (<i>Anguilla anguilla</i>) in the Scheldt estuary

Estuaries are among the most productive ecosystems in the world and are characterised by high habitat diversity. As transition areas between inland rivers and the open sea, they function as transport zones for diadromous species like the European eel (Anguilla anguilla), a catadromous fish species that migrates to the Sargasso Sea for spawning. However, information on the migratory behaviour of eel in estuaries is scarce. Therefore, more insight is needed to efficiently restore and conserve the species. We tracked 47 eels with acoustic telemetry between July 2012 and October 2015 and analysed their behaviour from the Braakman creek into the Scheldt Estuary, separated by a tidal barrier. Eels arrived in the Braakman between mid-summer and early winter and stayed there on average 44 days (0 - 578 days). As such, arrival in the Scheldt Estuary was much later: between early autumn and early winter. The average residence time in the Scheldt Estuary was considerably shorter than in the Braakman, and was only five days (0 - 64 days). The long residence time in the Braakman was probably due to the discontinuous operation of the tidal barrier, which is used to control the water level in the upstream wetland area. This resulted in a discontinuous flow conditions, leading to searching behaviour in eels. Eventually 37 eels did pass the sluice and reached the Scheldt Estuary; the 10 eels which did not pass the sluice were probably caught by a commercial eel fisherman in the Braakman creek. In the Scheldt Estuary, 26 eels migrated towards the sea, whereas eight took the opposite direction and three were only detected at the first receivers downstream of the sluice. The eight eels that did not migrate towards the sea showed estuarine retention behaviour. They could have been injured by the tidal barrier or missed the right moment to migrate, and could be waiting in the estuary until favourable conditions are met to proceed their journey. Our results indicate that eel migration is obstructed by a tidal barrier, which resulted in delayed eel migration. As the migratory period occurred from mid-summer to early winter, this information can be implemented in management plans such as environmental windows to open the sluice during eel migration if circumstances allow such measurements

Predictability of marine nematode biodiversity

In this paper, we investigated: (1) the predictability of different aspects of biodiversity, (2) the effect of spatial autocorrelation on the predictability and (3) the environmental variables affecting the biodiversity of free-living marine nematodes on the Belgian Continental Shelf. An extensive historical database of free-living marine nematodes was employed to model different aspects of biodiversity: species richness, evenness, and taxonomic diversity. Artificial neural networks (ANNs), often considered as “black boxes”, were applied as a modeling tool. Three methods were used to reveal these “black boxes” and to identify the contributions of each environmental variable to the diversity indices. Since spatial autocorrelation is known to introduce bias in spatial analyses, Moran's I was used to test the spatial dependency of the diversity indices and the residuals of the model. The best predictions were made for evenness. Although species richness was quite accurately predicted as well, the residuals indicated a lack of performance of the model. Pure taxonomic diversity shows high spatial variability and is difficult to model. The biodiversity indices show a strong spatial dependency, opposed to the residuals of the models, indicating that the environmental variables explain the spatial variability of the diversity indices adequately. The most important environmental variables structuring evenness are clay and sand fraction, and the minimum annual total suspended matter. Species richness is also affected by the intensity of sand extraction and the amount of gravel of the sea bed

Using formal concept analysis to detect and monitor organised crime

This paper describes some possible uses of Formal Concept Analysis in the detection and monitoring of Organised Crime. After describing FCA and its mathematical basis, the paper suggests, with some simple examples, ways in which FCA and some of its related disciplines can be applied to this problem domain. In particular, the paper proposes FCA-based approaches for finding multiple instances of an activity associated with Organised Crime, finding dependencies between Organised Crime attributes, and finding new indicators of Organised Crime from the analysis of existing data. The paper concludes by suggesting that these approaches will culminate in the creation and implementation of an Organised Crime ‘threat score card’, as part of an overall environmental scanning system that is being developed by the new European ePOOLICE projec