The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

A unified approach to linking experimental, statistical and computational analysis of spike train data

A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach – linking statistical, computational, and experimental neuroscience – provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.Published versio

Urban fire dynamics and its association with urban growth: evidence from Nanjing

Many Chinese cities currently are facing increased urban fire risks particularly at places such as urban villages, high-rise buildings and large warehouses. Using a unique historical fire incident dataset (2002–2013), this paper is intended to explore the urban fire dynamics and its association with urban growth in Nanjing, China, with a geographical information system (GIS)-based spatial analytics and remote sensing (RS) techniques. A new method is proposed to define a range of fire hot spots characterizing different phases of fire incident evolution, which are compared with the urban growth in the same periods. The results suggest that the fire events have been largely concentrated in the city proper and meanwhile expanding towards the suburbs, which has a similar temporal trend to the growth of population and urban land at the city level particularly since 2008. Most intensifying and persistent fire hot spots are found in the central districts, which have limited urban expansion but high population densities. Most new hot spots are located in the suburban districts, which have seen both rapid population growth and urban expansion in recent years. However, the analysis at a finer spatial scale (500 m × 500 m) shows no evidences of an explicit connection between the locations of new fire hot spots and recently developed urban land. The findings can inform future urban and emergency planning with respect to the deployment of fire and rescue resources, ultimately improving urban fire safety

Multi-scale topography assessment for site-specific drought management in Sweden

Globally, as well as in Sweden, the occurrence of droughts is expected to increase due to global warming. The drought summer of 2018 revealed the vulnerability of Sweden’s agriculture – with cereal yield losses of up to 50 %. Subsequently, the development of more resilience crop production systems to mitigate future droughts is required. Precision agriculture practices (PAP), widely applied in Sweden, are promising to base such developments upon. Hence, the aim of this study was to investigate the potential usage of topography for site-specific decision support, to extend PAP for advanced drought management in Sweden. Therefore, the drought effect along the study period (between crop development stages DC31-DC75) on crop growth development and related to field topography was assessed in a dry year (2018) and compared to a non-dry year (2019). Two common cereals i.e., Winter wheat and spring barely were selected to conduct this study. The study area was in the south-eastern region of Skåne in Sweden. The scale varied from the whole study area to within the field. Crop growth development was monitored using different vegetation and drought indices i.e., normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), normalized difference water index (NDWI) and the normalized difference drought index (NDDI). Topography was analysed at and within the field using different topographic indices i.e., slope, relative height (RE) and the topographic wetness index (TWI). The data required to conduct this study was publicly available and consisted of a highresolution digital elevation model, Sentinel-2 remote sensing data, weather data, field polygon as well as soil texture data. Overall, the results clearly showed an average NDVI, NDRE and NDWI reduction over the study period in 2018 compared to 2019 for both cereals; this reduction was about 25 %, 32 % and 58 % for winter wheat and about 36 %, 43 % and 69 % for spring barley. Topographic related within-field crop growth variations were prominent under dry conditions in 2018 and not present under non-dry conditions in 2019. Within-field crop growth variation increased with an increase in average field slope under dry conditions. The TWI was the most promising index explaining within-field crop growth development. Further studies should include other sitespecific field characteristics besides topography to better delineate within-field drought management zones for PAP