A random walk model that accounts for space occupation and movements of a large herbivore

Animal movement has been identified as a key feature in understanding animal behavior, distribution and habitat use and foraging strategies among others. Large datasets of invididual locations often remain unused or used only in part due to the lack of practical models that can directly infer the desired features from raw GPS locations and the complexity of existing approaches. Some of them being disputed for their lack of biological justifications in their design. We propose a simple model of individual movement with explicit parameters, based on a two-dimensional biased and correlated random walk with three forces related to advection (correlation), attraction (bias) and immobility of the animal. These forces can be directly estimated using individual data. We demonstrate the approach by using GPS data of 5 red deer with a high frequency sampling. The results show that a simple random walk template can account for the spatial complexity of wild animals. The practical design of the model is also verified for detecting spatial feature abnormalities and for providing estimates of density and abundance of wild animals. Integrating even more additional features of animal movement, such as individuals’ interactions or environmental repellents, could help to better understand the spatial behavior of wild animals.

Importance of light status on phytoplankton biomass in the turbid Bisan Strait, Japan: results from a high-frequency sampling study

The dynamics of phytoplankton biomass in the vertically mixed south-eastern part of the Bisan Strait, in the Seto Inland Sea of Japan, may be considered to be strongly dependent not only on nutrients but also on light status. This was investigated by examining variations in chlorophyll-a (Chl-a), nutrients, and Secchi-disc depth through high-frequency sampling (a mean of once every 1.7 days) at the same station from April to October 2019. Precipitation during the Japanese rainy season (East Asian monsoon rains) was associated with a decrease in salinity from 32 to 31 in late July. The highest concentration of dissolved inorganic nitrogen (DIN), the most deficient nutrient, also was recorded in late July in association with seasonal precipitation. However, the measured Chl-a peak (max. 4 μg l−1) in early August was not as high as expected, possibly due to low water clarity. A relatively small but substantial peak of DIN (max. 4 μM) was recorded in mid-August, which coincided with the passing of a typhoon. The small peak of DIN coupled with higher water clarity thereafter was followed by a phytoplankton bloom from mid-August to early September, at which the highest Chl-a (7 μg l−1) occurred. It is suggested that increased light penetration enhanced the efficiency of nutrient assimilation and thereby triggered the late-summer phytoplankton bloom. In contrast to the adjacent stratified areas, light rather than nutrient status appears to be the key determinant for the onset of phytoplankton blooms in the Bisan Strait.

Responses of abundant and rare bacterioplankton to temporal change in a subtropical urban reservoir

ABSTRACT Investigation of bacterial community dynamics across different time scales is important for understanding how environmental conditions drive community change over time. Bacterioplankton from the surface waters of a subtropical urban reservoir in southeast China were analyzed through high-frequency sampling over 13 months to compare patterns and ecological processes between short (0‒8 weeks), medium (9‒24 weeks) and long (25‒53 weeks) time intervals. We classified the bacterial community into different subcommunities: abundant taxa (AT); conditionally rare taxa (CRT); rare taxa (RT). CRT contributed > 65% of the alpha-diversity, and temporal change of beta-diversities was more pronounced for AT and CRT than RT. The bacterial community exhibited a directional change in the short- and medium-time intervals and a convergent dynamic during the long-time interval due to a seasonal cycle. Cyanobacteria exhibited a strong succession pattern than other phyla. CRT accounted for > 76% of the network nodes in three stations. The bacteria–environment relationship and deterministic processes were stronger for large sample size at station G (n = 116) than small sample size at stations C (n = 12) and L (n = 22). These findings suggest that a high-frequency sampling approach can provide a better understanding on the time scales at which bacterioplankton can change fast between being abundant or rare, thus providing the facts about environmental factors driving microbial community dynamics. Patterns and processes in alpha- and beta-diversities and community assembly of bacterioplankton differ among different time intervals (short-, medium- and long-time intervals) and different subcommunities (abundant, conditionally rare and rare taxa) in a subtropical urban reservoir, demonstrating the importance of temporal scale and high-frequency sampling in microbial community ecology.

Lead-lag statistical analysis of simultaneous high frequency oceanographic parameters in moored coastal systems

<div>High-frequency sampling at fixed positions in oceanography are installed all over the World. These provide time series of different oceanographic parameters over large range of scales and can help obtain informations on the complex coupling existing between physical, biogeochemical and biological parameters.</div><div>Here we explore the lead-lag information existing between two quantities: this is done by extracting the dissymmetry in the cross-correlation, corresponding to the statistical lead or lag of one series with respect to the other one (it is not necessarily a causality information). This analysis is done for all available parameters, two by two, giving way to generate a network of lead-lag influences.</div><div>As example this new approach is applied to the MAREL buoy system installed in Boulogne-sur-mer (France) operated by Ifremer (https://www.seanoe.org/data/00286/39754/). It is a moored buoy equipped with physico-chemical and biological measuring devices working in continuous and autonomous conditions with measurement every 20 minutes. We consider here the measurements at high frequency of air temperature, sea temperature, salinity, dissolved oxygen, fluorescence and turbidity for all year from 2005 to 2013. The new method is applied to the whole data set and also to data every year, in order to see a time evolution of the lead-lag network of relations between all studies parameters.</div><div> </div><div> <div> <div> <div> </div> </div> </div> </div>

Characterization of Marine-Surface-Dissolved Organic Matter via Amino Acid Enantiomers and Its Implications Based on Diel and Seasonal Observations

Due to the essential roles of dissolved organic matter (DOM) in both microbiol food loop and marine carbon cycling, changes in marine DOM composition have an important impact on the marine ecosystem and carbon cycling. In October 2014 and June 2015, two field investigations for the DOM in the upper 200 m were conducted in the slope region of the northern South China Sea to characterize the DOM composition via amino acid enantiomers. In June, our sampling locations were under upwelling impact induced by an eddy-pair event, whereas in October there were no eddies. High-frequency sampling (a few hours interval) over 24 h reveals that the variability of the amino acid carbon yield (min. 0.2%) and the D/L alanine ratio (min. 0.02) is larger than its corresponding analytical and propagated errors, suggesting solid short-term changes for these two molecular-based indicators. Section samples from June showed a lower D/L alanine ratio (0.43 vs. 0.53) and a GABA mol% (1.0% vs. 1.6%) relative to the section samples from October, suggesting that DOM in June is more fresh (less degraded) compared to that in October. A higher serine mol% (19.5% vs. 13.2%) and lower D/L serine ratio (0.06 vs. 0.24) from the diel observation in June relative to October further indicates that phytoplankton, rather than bacteria, plays an more important role in DOM composition alternation. This is consistent with the higher phytoplankton biomass found in June, promoted by the eddy-pair.

Evaluation of sampling frequency and normalization of SARS-CoV-2 wastewater concentrations for capturing COVID-19 burdens in the community

Wastewater surveillance for SARS-CoV-2 provides an approach for assessing the infection burden across a city. For these data to be useful for public health, measurement variability and the relationship to case data need to be established. We measured SARS-CoV-2 RNA concentrations in the influent of twelve wastewater treatment plants from August 2020 to January 2021. Replicate samples demonstrated that N1 gene target concentrations varied by ±21% between technical replicate filters and by ±14% between duplicate assays. COVID-19 cases were correlated significantly (rho≥0.70) to wastewater SARS-CoV-2 RNA concentrations for seven plants, including large and small cities. SARS-CoV-2 data normalized to flow improved correlations to reported COVID-19 cases for some plants, but normalizing to a spiked recovery control (BCoV) or a fecal marker (PMMoV or HF183) generally reduced correlations. High frequency sampling demonstrated that a minimum of two samples collected per week was needed to maintain accuracy in trend analysis. We found a significantly different ratio of COVID-19 cases to SARS-CoV-2 loads in one of three large communities, suggesting a higher rate of undiagnosed cases. These data demonstrate that SARS-CoV-2 wastewater surveillance can provide a useful community-wide metric to assess the course of the COVID-19 pandemic.

A non-intrusive load identification algorithm based on deep learning and a compound feature

Aiming at the limitations of using a single feature for load identification, a non-intrusive load identification algorithm based on deep learning and compound features is proposed. The pixelated V-I trajectory characteristics and current harmonic characteristics are extracted by analyzing the load data under high-frequency sampling. Using the feature extraction capabilities of neural networks, the combination of pixelated V-I trajectory features and current harmonic features is realized. Finally, the composite feature is used as the new load feature to train the neural network for non-invasive load identification. The experimental results show that the two-layer neural network constructed by the algorithm can take advantage of the complementarity between the two features, thereby improving the load identification ability.

Urea Inputs Drive Picoplankton Blooms in Sarasota Bay, Florida, U.S.A.

Recent increases in global urea usage, including its incorporation in slow-release fertilizers commonly used in lawn care in Florida, have the potential to alter the form and amount of nitrogen inputs to coastal waters. This shift may, in turn, impact phytoplankton community diversity and nutrient cycling processes. An autonomous water quality monitoring and sampling platform containing meteorological and water quality instrumentation, including urea and phycocyanin sensors, was deployed between June and November of 2009 in Sarasota Bay, Florida. This shallow, lagoonal bay is characterized by extensive and growing urban and suburban development and limited tidal exchange and freshwater inputs. During the monitoring period, three high-biomass (up to 40 µg chlorophyll-a·L−1) phytoplankton blooms dominated by picocyanobacteria or picoeukaryotes were observed. Each bloom was preceded by elevated (up to 20 μM) urea concentrations. The geolocation of these three parameters suggests that “finger canals” lining the shore of Sarasota Bay were the source of urea pulses and there is a direct link between localized urea inputs and downstream picoplankton blooms. Furthermore, high frequency sampling is required to detect the response of plankton communities to pulsed events.