During pandemics like COVID-19, both the quality and quantity of services offered by businesses and organizations have been severely impacted. They often have applied a hybrid home office setup to ...overcome this problem, although in some situations, working from home lowers employee productivity. So, increasing the rate of presence in the office is frequently desired from the manager's standpoint. On the other hand, as the virus spreads through interpersonal contact, the risk of infection increases when workplace occupancy rises. Motivated by this trade-off, in this paper, we model this problem as a bi-objective optimization problem and propose a practical approach to find the trade-off solutions. We present a new probabilistic framework to compute the expected number of infected employees for a setting of the influential parameters, such as the incidence level in the neighborhood of the company, transmission rate of the virus, number of employees, rate of vaccination, testing frequency, and rate of contacts among the employees. The results show a wide range of trade-offs between the expected number of infections and productivity, for example, from 1 to 6 weekly infections in 100 employees and a productivity level of 65% to 85%. This depends on the configuration of influential parameters and the occupancy level. We implement the model and the algorithm and perform several experiments with different settings of the parameters. Moreover, we developed an online application based on the result in this paper which can be used as a recommender for the optimal rate of occupancy in companies/workplaces.
Quantifying animals' home ranges is a key problem in ecology and has important conservation and wildlife management applications. Kernel density estimation (KDE) is a workhorse technique for range ...delineation problems that is both statistically efficient and nonparametric. KDE assumes that the data are independent and identically distributed (IID). However, animal tracking data, which are routinely used as inputs to KDEs, are inherently autocorrelated and violate this key assumption. As we demonstrate, using realistically autocorrelated data in conventional KDEs results in grossly underestimated home ranges. We further show that the performance of conventional KDEs actually degrades as data quality improves, because autocorrelation strength increases as movement paths become more finely resolved. To remedy these flaws with the traditional KDE method, we derive an autocorrelated KDE (AKDE) from first principles to use autocorrelated data, making it perfectly suited for movement data sets. We illustrate the vastly improved performance of AKDE using analytical arguments, relocation data from Mongolian gazelles, and simulations based upon the gazelle's observed movement process. By yielding better minimum area estimates for threatened wildlife populations, we believe that future widespread use of AKDE will have significant impact on ecology and conservation biology.
Summary
Kernel density estimators are widely applied to area‐related problems in ecology, from estimating the home range of an individual to estimating the geographic range of a species. Currently, ...area estimates are obtained indirectly, by first estimating the location distribution from tracking (home range) or survey (geographic range) data and then estimating areas from that distribution. This indirect approach leads to biased area estimates and difficulty in deriving reasonable confidence intervals.
We introduce a new kernel density estimator (and associated confidence intervals) focused specifically on area estimation that applies to both independently sampled survey data and autocorrelated tracking data. We test our methods against simulated movement data and demonstrate its use with African buffalo data.
The area‐corrected kernel density estimator produces much more accurate area estimates, particularly at small sample sizes, and the newly derived confidence intervals are more reliable than existing alternatives.
This new method is the most efficient nonparametric home‐range estimator for animal tracking data and should also be considered when calculating nonparametric range estimates from survey data. This estimator is now the default method in the ctmm r package.
Summary
Movement ecology has developed rapidly over the past decade, driven by advances in tracking technology that have largely removed data limitations. Development of rigorous analytical tools has ...lagged behind empirical progress, and as a result, relocation data sets have been underutilized.
Discrete‐time correlated random walk models (CRW) have long served as the foundation for analyzing relocation data. Unfortunately, CRWs confound the sampling and movement processes. CRW parameter estimates thus depend sensitively on the sampling schedule, which makes it difficult to draw sampling‐independent inferences about the underlying movement process. Furthermore, CRWs cannot accommodate the multiscale autocorrelations that typify modern, finely sampled relocation data sets.
Recent developments in modelling movement as a continuous‐time stochastic process (CTSP) solve these problems, but the mathematical difficulty of using CTSPs has limited their adoption in ecology. To remove this roadblock, we introduce the ctmm package for the R statistical computing environment. ctmm implements all of the CTSPs currently in use in the ecological literature and couples them with powerful statistical methods for autocorrelated data adapted from geostatistics and signal processing, including variograms, periodograms and non‐Markovian maximum likelihood estimation.
ctmm is built around a standard workflow that begins with visual diagnostics, proceeds to candidate model identification, and then to maximum likelihood fitting and AIC‐based model selection. Once an accurate CTSP for the data has been fitted and selected, analyses that require such a model, such as quantifying home range areas via autocorrelated kernel density estimation or estimating occurrence distributions via time‐series Kriging, can then be performed.
We use a case study with African buffalo to demonstrate the capabilities of ctmm and highlight the steps of a typical CTSP movement analysis workflow.
AIM: Species distribution models (SDMs) are common tools in biogeography and conservation ecology. It has been repeatedly claimed that aggregated (stacked) SDMs (S‐SDMs) will overestimate species ...richness. One recently suggested solution to this problem is to use macroecological models of species richness to constrain S‐SDMs. Here, we examine current practice in the development of S‐SDMs to identify methodological problems, provide tools to overcome these issues, and quantify the performance of correctly stacked S‐SDMs alongside macroecological models. LOCATIONS: Barents Sea, Europe and Dutch Wadden Sea. METHODS: We present formal mathematical arguments demonstrating how S‐SDMs should and should not be stacked. We then compare the performance of macroecological models and correctly stacked S‐SDMs on the same data to determine if the former can be used to constrain the latter. Next, we develop a maximum‐likelihood approach to adjusting S‐SDMs and discuss how it could potentially be used in combination with macroecological models. Finally, we use this tool to quantify how S‐SDMs deviate from observed richness in four very different case studies. RESULTS: We demonstrate that stacking methods based on thresholding site‐level occurrence probabilities will almost always be biased, and that these biases will tend toward systematic overprediction of richness. Next, we show that correctly stacked S‐SDMs perform very similarly to macroecological models in that they both have a tendency to overpredict richness in species‐poor sites and underpredict it in species‐rich sites. MAIN CONCLUSIONS: Our results suggest that the perception that S‐SDMs consistently overpredict richness is driven largely by incorrect stacking methods. With these biases removed, S‐SDMs perform similarly to macroecological models, suggesting that combining the two model classes will not offer much improvement. However, if situations where coupling S‐SDMs and macroecological models would be beneficial are subsequently identified, the tools we develop would facilitate such a synthesis.
An animal's trajectory is a fundamental object of interest in movement ecology, as it directly informs a range of topics from resource selection to energy expenditure and behavioral states. Optimally ...inferring the mostly unobserved movement path and its dynamics from a limited sample of telemetry observations is a key unsolved problem, however. The field of geostatistics has focused significant attention on a mathematically analogous problem that has a statistically optimal solution coined after its inventor, Krige. Kriging revolutionized geostatistics and is now the gold standard for interpolating between a limited number of autocorrelated spatial point observations. Here we translate Kriging for use with animal movement data. Our Kriging formalism encompasses previous methods to estimate animal's trajectories—the Brownian bridge and continuous‐time correlated random walk library—as special cases, informs users as to when these previous methods are appropriate, and provides a more general method when they are not. We demonstrate the capabilities of Kriging on a case study with Mongolian gazelles where, compared to the Brownian bridge, Kriging with a more optimal model was 10% more precise in interpolating locations and 500% more precise in estimating occurrence areas.
The SARS-CoV-2 virus has spread around the world with over 100 million infections to date, and currently many countries are fighting the second wave of infections. With neither sufficient vaccination ...capacity nor effective medication, non-pharmaceutical interventions (NPIs) remain the measure of choice. However, NPIs place a great burden on society, the mental health of individuals, and economics. Therefore the cost/benefit ratio must be carefully balanced and a target-oriented small-scale implementation of these NPIs could help achieve this balance. To this end, we introduce a modified SEIRD-class compartment model and parametrize it locally for all 412 districts of Germany. The NPIs are modeled at district level by time varying contact rates. This high spatial resolution makes it possible to apply geostatistical methods to analyse the spatial patterns of the pandemic in Germany and to compare the results of different spatial resolutions. We find that the modified SEIRD model can successfully be fitted to the COVID-19 cases in German districts, states, and also nationwide. We propose the correlation length as a further measure, besides the weekly incidence rates, to describe the current situation of the epidemic.
The complex network framework has been successfully used to model interactions between entities in Complex Systems in the Biological Sciences such as Proteomics, Genomics, Neuroscience, and Ecology. ...Networks of organisms at different spatial scales and in different ecosystems have provided insights into community assembly patterns and emergent properties of ecological systems. In the present work, we investigate two questions pertaining to fish species assembly rules in US river basins, a) if morphologically similar fish species also tend to be phylogenetically closer, and b) to what extent are co-occurring species that are phylogenetically close also morphologically similar? For the first question, we construct a network of Hydrologic Unit Code 8 (HUC8) regions as nodes with interaction strengths (edges) governed by the number of common species. For each of the modules of this network, which are found to be geographically separated, there is differential yet significant evidence that phylogenetic distance predicts morphological distance. For the second question, we construct and analyze nearest neighbor directed networks of species based on their morphological distances and phylogenetic distances. Through module detection on these networks and comparing the module-level mean phylogenetic distance and mean morphological distance with the number of basins of common occurrence of species in modules, we find that both phylogeny and morphology of species have significant roles in governing species co-occurrence, i.e. phylogenetically and morphologically distant species tend to co-exist more. In addition, between the two quantities (morphological distance and phylogentic distance), we find that morphological distance is a stronger determinant of species co-occurrences.
Bagnoli Coroglio is an urban district of the City of Naples (South Italy), which fronts the Tyrrhenian Sea for nearly 3 km. It is part of the Campi Flegrei caldera, one of the most explosive volcanic ...areas in Europe. The need for redeveloping the site after the intense industrial activities of the twentieth century has prompted a remarkable research effort to investigate the pollution's degree, nature, and extent at both the land and seafloor.
This article focuses on releasing thermal waters from a natural channel as a source of arsenic contamination in the Bagnoli marine sediments; the thermal waters originate from the nearby Agnano hot-springs and have been conveying artificially to the track since the mid-XIX century. As a first part of the outcomes, the work describes the flow regime that characterizes the marine area.
The analysis has been conducted via numerical simulations carried out with the software package Delft3D, developed by Deltares, which employs dynamically interfacing modules to account for wave propagation, generation of currents, and presence of coastal structures. Climatic inputs to the software (waves, winds, and tide) have specifically been gathered and analyzed within this research.
The numerical study has permitted to furnish, for the first time, a clear and systematic view of the hydrodynamic forcings that characterize the area under investigation. In particular, a leading role in the transport of pollutants could be played by rip current systems, whose characteristics vary with climate intensity (waves and wind) and coastal structures characteristics.
Due to its inherently dynamic nature, the proposed approach seems especially desirable in situations where different contamination sources are compared. As such, it could be successfully applied to other sites also.
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•Bagnoli (South Italy) is part of one of the most explosive volcanic areas in Europe.•Bagnoli seabed suffers from strong contamination by Arsenic.•A natural channel (Agnnano Effluent) conveys thermal waters to the sea.•The article investigates the role of the Agnano Effluent on the seabed contamination.•The article studies in detail the marine circulation induced by tide, wind and waves.
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