Search

Autonomous vehicles (AV) are expected to play a key role in the future of transportation, introducing a disruptive yet potentially beneficial change for vehicle-wildlife interactions. However, this assumption has not been critically examined. Here, we introduce a new conceptual framework covering the intersection between AV technological innovation and wildlife conservation to reduce wildlife-vehicle collisions. We suggest future research within this framework to focus on developing robust warning systems and animal detection methods for AV systems, and to incorporate wildlife-vehicle interactions into decision-making algorithms. With large-scale deployment a looming reality, it is vital to incorporate conservation and sustainability into the societal, ethical, and legal implications of AV technology. We appeal for further debate and interdisciplinary collaborations between scientists, developers, and policymakers.

The optimal placement of healthcare facilities, including the placement of diagnostic test centers, plays a pivotal role in ensuring efficient and equitable access to healthcare services. However, the emergence of unique complexities in the context of a pandemic, exemplified by the COVID-19 crisis, has necessitated the development of customized solutions. This paper introduces a bi-objective integer linear programming model designed to achieve two key objectives: minimizing average travel time for individuals visiting testing centers and maximizing an equitable workload distribution among testing centers. To address this problem, we propose a customized local search algorithm based on the Voronoi diagram. Additionally, we employ an $\epsilon$-constraint approach, which leverages the Gurobi solver. We rigorously examine the effectiveness of the model and the algorithms through numerical experiments and demonstrate their capability to identify Pareto-optimal solutions. We show that while the Gurobi performs efficiently in small-size instances, our proposed algorithm outperforms it in large-size instances of the problem.

The emergence of a spatially-organized population distribution depends on the dynamics of the population and mediators of interaction (activators and inhibitors). Two broad classes of models have been used to investigate when and how self-organization is triggered, namely, reaction-diffusion and spatially nonlocal models. Nevertheless, these models implicitly assume smooth propagation scenarios, neglecting that individuals many times interact by exchanging short and abrupt pulses of the mediating substance. A recently proposed framework advances in the direction of properly accounting for these short-scale fluctuations by applying a coarse-graining procedure on the pulse dynamics. In this paper, we generalize the coarse-graining procedure and apply the extended formalism to new scenarios in which mediators influence individuals' reproductive success or their motility. We show that, in the slow- and fast-mediator limits, pulsed interactions recover, respectively, the reaction-diffusion and nonlocal models, providing a mechanistic connection between them. Furthermore, at each limit, the spatial stability condition is qualitatively different, leading to a timescale-induced transition where spatial patterns emerge as mediator dynamics becomes sufficiently fast.

Wildlife-vehicle collisions are an ongoing and widespread source of biodiversity loss. Understanding how, when and why these collisions happen is a key challenge in any conservation and management efforts. Data collection with biologgers can reveal information on how animals use their environment, interact with each other, and their adaptive responses to rapid environmental changes and anthropogenic features in the landscape —including their behavioral responses to linear infrastructures. The movement ecology field is rapidly shifting as we open new avenues of research, with increased access to modern tracking technologies, collecting high-volume high-resolution movement datasets for a growing number of animal species worldwide. Using these datasets to reveal road impacts on animal behavior is fundamental, since wildlife-vehicle collisions are the second-largest source of anthropogenic mortality for many vertebrate species. We will explore empirical examples with animal tracking data, as well as information collected through road surveys, and how these can serve as crucial tools to achieve a deeper understanding of animal movement and behavior towards roads and vehicles.

Ecologists have long investigated how the demographic and movement parameters of a population determine its spatial spread and the critical habitat size that can sustain it. Yet, most existing models make oversimplifying assumptions about individual movement behavior, neglecting how landscape heterogeneity influences dispersal. We relax this assumption and introduce a reaction-advection-diffusion model that describes the spatial density distribution of a population with space-dependent movement bias toward preferred regions, including avoidance of degraded habitats. In this scenario, the critical habitat size depends on the spatial location of the habitat edges with respect to the preferred regions and on the intensity of the movement bias components. In particular, we identify parameter regions where the critical habitat size decreases when diffusion increases, a phenomenon known as the “drift paradox”. We also find that biased movement toward low-quality or highly populated regions can reduce the population size, therefore creating ecological traps. Our results emphasize the importance of species-specific movement behavior and habitat selection as drivers of population dynamics in fragmented landscapes and, therefore, the need to account for them in the design of protected areas.

Stochastic simulations of complex systems from domains including physics, biology, ecology or economics often require large system sizes, long time scales, and numerous replications to fully explore model behavior. The simple rules defining many models can lead researchers to prefer familiar but inefficient programming techniques, which severely hinder progress by creating computational bottlenecks. While such studies often benefit from combined domain-specific, statistical, and programming knowledge, few individual researchers span the full range of necessary skills. Here, we present a collaboration on the neutral model of biodiversity in dendritic river networks, where the goal is to analyze biodiversity data across the world’s major river systems. We show how we achieved large performance gains by engaging the problem at its foundations and thereby enabled research at a new scale.

Projects focused on movement behavior and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. The estimation of space-use and movement properties is often necessary to answer basic movement ecology questions, but designing an animal tracking study to produce reliable estimates is often done in an ad hoc manner. We developed 'movedesign', a user-friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed, and distance traveled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design. Leveraging the 'ctmm' R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated Kernel Density Estimators (AKDE) and continuous-time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data. This application addresses key challenges faced by researchers when designing tracking studies, including the trade-off between long battery life and high resolution of GPS devices, which may result in a compromise between reliably estimating home range or speed and distance. 'movedesign' has broad applications for researchers and decision-makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade-off associated with these choices.

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 phylogentically 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

Efficient personnel scheduling plays a significant role in matching workload demand in organizations. However, staff scheduling is sometimes affected by unexpected events, such as the COVID-19 pandemic, that disrupt regular operations. Since infectious diseases like COVID-19 transmit mainly through close contact with individuals, an efficient way to prevent the spread is by limiting the number of on-site employees in the workplace along with regular testing. Thus, determining an optimal scheduling and testing strategy that meets the organization's goals and prevents the spread of the virus is crucial during disease outbreaks. In this paper, we formulate these challenges in the framework of two Mixed Integer Non-linear Programming (MINLP) models. The first model aims to derive optimal staff occupancy and testing strategies to minimize the risk of infection among employees, while the second model aims at only optimal staff occupancy under a random testing strategy. To solve the problems expressed in the models, we propose a canonical genetic algorithm as well as two commercial solvers. Using both real and synthetic contact networks of employees, our results show that following the recommended occupancy and testing strategy reduces the risk of infection 25\%--60\% under different scenarios.

Animals moving through landscapes need to strike a balance between finding sufficient resources to grow and reproduce while minimizing encounters with predators 1,2. Because encounter rates are determined by the average distance over which directed motion persists 1,3–5, this trade-off should be apparent in individuals’ movement. Using GPS data from 1,396 individuals across 62 species of terrestrial mammals, we show how predators maintained directed motion ~7 times longer than for similarly-sized prey, revealing how prey species must trade off search efficiency against predator encounter rates. Individual search strategies were also modulated by resource abundance, with prey species forced to risk higher predator encounter rates when resources were scarce. These findings highlight the interplay between encounter rates and resource availability in shaping broad patterns mammalian movement strategies.

Direct encounters, in which two or more individuals are physically close to one another, are a topic of increasing interest as more and better movement data become available. Recent progress, including the development of statistical tools for estimating robust measures of changes in animals’ space use over time, facilitates opportunities to link direct encounters between individuals with the long-term consequences of those encounters. Working with movement data for coyotes (Canis latrans) and grizzly bears (Ursus arctos horribilis), we investigate whether close intraspecific encounters were associated with spatial shifts in the animals’ range distributions, as might be expected if one or both of the individuals involved in an encounter were seeking to reduce or avoid conflict over space. We analyze the movement data of a pair of coyotes in detail, identifying how a shift in home range location resulting from altered movement behavior was apparently a consequence of a close intraspecific encounter. With grizzly bear 50 movement data, we approach the problem from the perspective of a set of encounter pairs within a population. We find support for the hypotheses that 1) close intraspecific encounters between bears are, on average, associated with subsequent shifts in range distributions and 2) encounters defined at finer spatial scales are followed by greater changes in space use. Our results suggest that animals can undertake long-term, large-scale spatial shifts in response to close intraspecific encounters that have the potential for conflict. These results lend support for existing theory on the evolution of territories and space use (e.g., Maynard-Smith’s bourgeois strategy regarding low-conflict coexistence). Overall, we find that analyses of movement data in a pairwise context can 1) identify distances at which individuals’ proximity to one another may alter behavior and 2) facilitate testing of population-level hypotheses concerning the potential for direct enc

The emergence of a spatially-organized population distribution depends on the dynamics of the population and mediators of interaction (activators and inhibitors). Two broad classes of models have been used to investigate when and how self-organization is triggered, namely, reaction-diffusion and spatially nonlocal models. Nevertheless, these models implicitly assume smooth propagation scenarios, neglecting that individuals many times interact by exchanging short and abrupt pulses of the mediating substance. A recently proposed framework advances in the direction of properly accounting for these short-scale fluctuations by applying a coarse-graining procedure on the pulse dynamics. In this paper, we generalize the coarse-graining procedure and apply the extended formalism to new scenarios in which mediators influence individuals' reproductive success or their motility. We show that, in the slow- and fast-mediator limits, pulsed interactions recover, respectively, the reaction-diffusion and nonlocal models, providing a mechanistic connection between them. Furthermore, at each limit, the spatial stability condition is qualitatively different, leading to a timescale-induced transition where spatial patterns emerge as mediator dynamics becomes sufficiently fast.

The optimal placement of healthcare facilities, including the placement of diagnostic test centers, plays a pivotal role in ensuring efficient and equitable access to healthcare services. However, the emergence of unique complexities in the context of a pandemic, exemplified by the COVID-19 crisis, has necessitated the development of customized solutions. This paper introduces a bi-objective integer linear programming model designed to achieve two key objectives: minimizing average travel time for individuals visiting testing centers and maximizing an equitable workload distribution among testing centers. To address this problem, we propose a customized local search algorithm based on the Voronoi diagram. Additionally, we employ an $\epsilon$-constraint approach, which leverages the Gurobi solver. We rigorously examine the effectiveness of the model and the algorithms through numerical experiments and demonstrate their capability to identify Pareto-optimal solutions. We show that while the Gurobi performs efficiently in small-size instances, our proposed algorithm outperforms it in large-size instances of the problem.

From data we first construct a Langevin equation to describe the movement of the individuals. The ctmm1 package can be used to extract the parameters behind intrinsic dynamics of each individual (potential, diffusion coefficient, etc.). However, individuals’ home-range (i.e. the area they occupy) are also shaped by its interaction with individual nearby. For that we need to resolve the forces felt by individuals during encounters2 and then scale-up their effects. This coarse-graining process generates a PDE for the temporal evolution of the occupancy probability of a given individual. This final equation, extracted from data, provides a data-driven description of the formation and maintenance of home-ranges.

Animal encounters are key components of population dynamics, community dynamics, and evolutionary processes. Consequently, measuring encounter rates (i.e. encounters per time) can be insightful. Encounter rates can be measured from animal tracking data, using metrics that can be split into two groups. The first group consists of trajectory-based metrics, i.e. measures based on serial records of animal locations. This first group includes PROX, the number of observed per number of samples. The second group, in contrast, consists of metrics based on home range overlap, including the Bhattacharyya coefficient (BC). In this study, we argue both types of metrics are limited. Trajectory-based metrics are direct measures of encounter rates but have statistical estimation issues due to their dependency on the frequency of location sampling. Meanwhile, home-rangebased metrics are statistically sound but are not proportional to encounter rates. To overcome both challenges, we proposed a new metric, Relative Encounter Rate (RER). RER increases linearly with the number of encounters and does not depend on the frequency of sampling (i.e. it is scale-invariant). In an individual-based simulation, we measured how RER, BC, and PROX relative error under different sample sizes and sampling frequencies. Further, we compared these metrics in three empirical case studies. We tested Jaguars for polygyny, deforestation effects on tapir connectivity, and an extension of the dearest enemy hypothesis with brown bears. We also compared partner hierarchy according to BC and RER in Jaguar mating clusters. In the simulation study, we found PROX overestimates the encounter rate when data has a low sampling frequency. The simulation also indicates BC overestimated encounters. Furthermore, PROX led to false positives in the Tapir and Bear case studies. In addition, PROX was incapable of detecting many individual relationships in the jaguar polygyny study. RER does not depend on sampling frequency (

The study of movement behavior and home range in animals is fundamental to understanding ecological dynamics, yet designing efficient tracking studies remains a challenge due to the lack of clear guidelines. To address this knowledge gap we created 'movedesign,' a user-friendly application that enables researchers to evaluate the precision of three commonly reported movement and spatial ecology estimates: home range area, speed, and distance traveled. Leveraging the 'ctmm' R package, our application employs autocorrelated kernel density estimators (AKDEs) and continuous-time speed and distance (CTSD) estimators, effectively addressing biases inherent in animal movement datasets. By evaluating the interplay between data resolution and battery life, 'movedesign' guides researchers in making informed decisions to achieve reliable estimates of home range and velocity while considering trade-offs. With broad applications for researchers and decision-makers, this tool empowers efficient deployment strategies, optimizing sampling design for insightful and robust outcomes in movement ecology studies.

Projects focused on movement behavior and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. The estimation of space-use and movement properties is often necessary to answer basic movement ecology questions, but designing an animal tracking study to produce reliable estimates is often done in an ad hoc manner. We developed 'movedesign', a user-friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed, and distance traveled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design. Leveraging the 'ctmm' R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated Kernel Density Estimators (AKDE) and continuous-time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data. This application addresses key challenges faced by researchers when designing tracking studies, including the trade-off between long battery life and high resolution of GPS devices, which may result in a compromise between reliably estimating home range or speed and distance. 'movedesign' has broad applications for researchers and decision-makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade-off associated with these choices.

Efficient personnel scheduling plays a significant role in matching workload demand in organizations. However, staff scheduling is sometimes affected by unexpected events, such as the COVID-19 pandemic, that disrupt regular operations. Since infectious diseases like COVID-19 transmit mainly through close contact with individuals, an efficient way to prevent the spread is by limiting the number of on-site employees in the workplace along with regular testing. Thus, determining an optimal scheduling and testing strategy that meets the organization's goals and prevents the spread of the virus is crucial during disease outbreaks. In this paper, we formulate these challenges in the framework of two Mixed Integer Non-linear Programming (MINLP) models. The first model aims to derive optimal staff occupancy and testing strategies to minimize the risk of infection among employees, while the second model aims at only optimal staff occupancy under a random testing strategy. To solve the problems expressed in the models, we propose a canonical genetic algorithm as well as two commercial solvers. Using both real and synthetic contact networks of employees, our results show that following the recommended occupancy and testing strategy reduces the risk of infection 25\%--60\% under different scenarios.

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 phylogentically 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

Long-term care facilities have been widely affected by the COVID-19 pandemic. Empirical evidence demonstrated that older people are the most impacted and are at higher risk of mortality after being infected. Regularly testing care facility residents is a practical approach to detecting infections proactively. In many cases, the care staff must perform the tests on the residents while also providing essential care, which in turn causes imbalances in their working time. Once an outbreak occurs, suppressing the spread of the virus in retirement homes (RHs) is challenging because the residents are in contact with each other, and isolation measures cannot be widely enforced. Regular testing strategies, on the other hand, have been shown to effectively prevent outbreaks in RHs. However, high-frequency testing may consume substantial staff working time, which results in a trade-off between the time invested in testing and the time spent providing essential care to residents.

Increasing habitat fragmentation and disturbance threaten long-distance movements of ungulates. While the effects of impermeable barriers on ungulate migrations have been well researched, quantitative evidence for gradual, long-term changes of mobility remains rare. We investigated changes in movement behavior of Mongolian gazelle Procapra gutturosa using GPS tracking data collected from 62 gazelle individuals between 2007 and 2021. We quantified 16-day displacement distances as a metric for long-distance movements before using linear mixed models, generalized additive models and quantile regressions to assess how anthropogenic and environmental factors affected gazelle movement behavior. Long-distance 16-day movements decreased by 36 %, from 142 km in 2007 to 92 km in 2021. Changes in mobility were affected by increasing vehicle numbers in Mongolia, but could not be explained by concurrent changes in other environmental factors like temperature, precipitation or vegetation greenness. Gazelle movement decreased close to roads, and gazelles stayed further away from roads during the snow-free season, when traffic likely is most intense. Conserving landscape permeability is essential for maintaining populations of highly mobile species. Our study provides evidence for a gradual decline in gazelle mobility over fifteen years as a response to increasing anthropogenic impact. The transportation infrastructure permeating the Eastern Steppe does not pose physical barriers, yet our findings suggest that increasing traffic volume may create semipermeable barriers to gazelle movement. As human activity is increasing, interactions between ungulates and vehicle traffic need to be closely monitored to identify and mitigate semipermeable barrier effects before landscape permeability is severely altered.

A Shiny R application that aims to assist researchers in designing animal tracking projects related to two main research questions: the estimation of home range and of speed and distance traveled. The application makes use of simulations to assess the degree of estimate precision that may be achieved with a given sampling design; that is, the choices regarding data resolution (sampling interval) and battery life (sampling duration).

The data includes physical contact network of employees.

COVID-19 lockdowns in early 2020 reduced human mobility, providing an opportunity to disentangle its effects on animals from those of landscape modifications. Using GPS data, we compared movements and road avoidance of 2300 terrestrial mammals (43 species) during the lockdowns to the same period in 2019. Individual responses were variable with no change in average movements or road avoidance behavior, likely due to variable lockdown conditions. However, under strict lockdowns 10-day 95th percentile displacements increased by 73%, suggesting increased landscape permeability. Animals’ 1-hour 95th percentile displacements declined by 12% and animals were 36% closer to roads in areas of high human footprint, indicating reduced avoidance during lockdowns. Overall, lockdowns rapidly altered some spatial behaviors, highlighting variable but substantial impacts of human mobility on wildlife worldwide.

All data and codes used for the analyses in the paper "Species invasiveness and community invasibility of US freshwater fish fauna revealed via trait-based analysis

Effective personnel scheduling is crucial for organizations to match workload demands. However, staff scheduling is sometimes affected by unexpected events, such as the COVID-19 pandemic, that disrupt regular operations. Limiting the number of on-site staff in the workplace together with regular testing is an effective strategy to minimize the spread of infectious diseases like COVID-19 because they spread mostly through close contact with people. Therefore, choosing the best scheduling and testing plan that satisfies the goals of the organization and prevents the virus’s spread is essential during disease outbreaks. In this paper, we formulate these challenges in the framework of two Mixed Integer Non-linear Programming (MINLP) models. The first model aims to derive optimal staff occupancy and testing strategies to minimize the risk of infection among employees, while the second is aimed only at optimal staff occupancy under a random testing strategy. To solve the problems expressed in the models, we propose a canonical genetic algorithm as well as two commercial solvers. Using both real and synthetic contact networks of employees, our results show that following the recommended occupancy and testing strategy reduces the risk of infection 25–60% under different scenarios. The minimum risk of infection can be achieved when the employees follow a planned testing strategy. Further, vaccination status and interaction rate of employees are important factors in developing scheduling strategies that minimize the risk of infection.

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.

Resource selection functions are among the most commonly used statistical tools in both basic and applied animal ecology. They are typically parameterized using animal tracking data, and advances in animal tracking technology have led to increasing levels of autocorrelation between locations in such data sets. Because resource selection functions assume that data are independent and identically distributed, such autocorrelation can cause misleadingly narrow confidence intervals and biased parameter estimates. Data thinning, generalized estimating equations, and step selection functions have been suggested as techniques for mitigating the statistical problems posed by autocorrelation, but these approaches have notable limitations that include statistical inefficiency, unclear or arbitrary targets for adequate levels of statistical independence, constraints in input data, and (in the case of step selection functions) scale-dependent inference. To remedy these problems, we introduce a method for likelihood weighting of animal locations to mitigate the negative consequences of autocorrelation on resource selection functions. This method weights each observed location in an animal's movement track according to its level of autocorrelation, expanding confidence intervals to match an objective target (i.e., the effective sample size for Autocorrelated Gaussian Density Estimation) and accounting for bias that can arise when there are gaps in the movement track. In this study, we describe the mathematical principles underlying our method, demonstrate its practical advantages versus conventional approaches using simulations and empirical data on a water mongoose (\textit{Atilax paludinosus}), a caracal (\textit{Caracal caracal}), and a serval (\textit{Leptailurus serval}), and discuss pathways for further development of our method. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using the \texttt{c

The global extent and temporally asynchronous pattern of COVID-19 spread have repeatedly highlighted the role of international borders in the fight against the pandemic. Additionally, the deluge of high resolution, spatially referenced epidemiological data generated by the pandemic provides new opportunities to study disease transmission at heretofore inaccessible scales. Existing studies of cross-border infection fluxes, for both COVID-19 and other diseases, have largely focused on characterizing overall border effects. Here, we couple fine-scale incidence data with localized regression models to quantify spatial variation in the inhibitory effect of an international border. We take as a case study the border region between the German state of Saxony and the neighboring regions in northwestern Czechia, where municipality-level COVID-19 incidence data are available on both sides of the border. Consistent with past studies, we find an overall inhibitory effect of the border, but with a clear asymmetry, where the inhibitory effect is stronger from Saxony to Czechia than vice versa. Furthermore, we identify marked spatial variation along the border in the degree to which disease spread was inhibited. In particular, the area around Löbau in Saxony appears to have been a hotspot for cross-border disease transmission. The ability to identify infection flux hotspots along international borders may help to tailor monitoring programs and response measures to more effectively limit disease spread.

The severe shortfall in testing supplies during the initial phases of the COVID-19 pandemic and ensuing struggle to control disease spread have affirmed the need to plan rigorous optimized supply-constrained resource allocation strategies for the next inevitable novel disease epidemic. To address the challenge of optimizing limited resource usage in the face of complicated disease dynamics, we develop an integro partial differential equation disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals, and we analyze the influence of these ele- ments on controllability and optimal resource allocation between two testing strategies, ‘clinical’ targeting symptomatic individuals and ‘non-clinical’ targeting non-symptomatic individuals, for reducing total infection sizes. We apply our model to not only the original, delta, and omicron COVID-19 variants, but also to generic diseases which have different offsets between latent and incubation period distributions which allow for or prevent varying degrees of presymptomatic transmission or preinfectiousness symptom onset. We find that factors which reduce control- lability generally call for reduced levels of non-clinical testing, while the relationship between symptom onset, controllability, and optimal strategies is complicated. Although greater degrees of presymptomatic transmission reduce disease controllability, they may enhance or reduce the role of non-clinical testing in optimal strategies depending on other disease factors like overall transmissibility and latent period length. Our model allows a spectrum of diseases to be com- pared under the same lens such that the lessons learned from COVID-19 can be adapted to resource constraints in the next emerging epidemic and analyzed for optimal strategies under a consistent mathematical framework.

Self-organized spatial patterns of vegetation are frequent in water-limited regions and have been suggested as important indicators of ecosystem health. However, the mechanisms underlying their emergence remain unclear. Some theories hypothesize that patterns could result from a scale-dependent feedback (SDF), whereby interactions favoring plant growth dominate at short distances and growth-inhibitory interactions dominate in the long range. However, we know little about how net plant-to-plant interactions may change sign with inter-individual distance, and in the absence of strong empirical support, the relevance of this SDF for vegetation pattern formation remains disputed. These theories predict a sequential change in pattern shape from gapped to labyrinthine to spotted spatial patterns as precipitation declines. Nonetheless, alternative theories show that the same sequence of patterns could emerge even if net interactions between plants were always inhibitory (purely competitive feedbacks, PCF). Although these alternative hypotheses lead to visually indistinguishable patterns they predict very different desertification dynamics following the spotted pattern. Moreover, vegetation interaction with other ecosystem components can introduce additional spatio-temporal scales that reshape both the patterns and the desertification dynamics. Therefore, to make reliable ecological predictions for a focal ecosystem, it is crucial that models accurately capture the mechanisms at play in the system of interest. Here, we review existing theories for vegetation self-organization and their conflicting predictions about desertification dynamics. We further discuss possible ways for reconciling these predictions and potential empirical tests via manipulative experiments to improve our understanding of how vegetation self-organizes and better predict the fate of the ecosystems where they form.

While biological invasions are recognized as a major threat to global biodiversity, determining species’ abilities to invade new areas (species invasiveness) and the vulnerability of those areas to invasions (community invasibility) are still poorly understood. Here, we used trait-based analysis to profile invasive species and quantify the community invasibility for >1,800 North American freshwater fish communities. We show that species with higher reproduction rates, longer life spans and larger sizes tend to be more invasive. Community invasibility peaked when the functional distance among native species was high, leaving unoccupied functional space for the establishment of potential invaders. Invasion success is therefore governed by both the functional traits of non-native species determining their invasiveness, and by the functional characteristics of the invaded community determining its invasibility. Considering those two determinants together will allow better predictions of invasions.

Projects focused on movement behavior and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. The estimation of space-use and movement properties is often necessary to answer basic movement ecology questions, but designing an animal tracking study to produce reliable estimates is often done in an ad hoc manner. We developed 'movedesign', a user-friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed, and distance traveled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design. Leveraging the 'ctmm' R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated Kernel Density Estimators (AKDE) and continuous-time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data. This application addresses key challenges faced by researchers when designing tracking studies, including the trade-off between long battery life and high resolution of GPS devices, which may result in a compromise between reliably estimating home range or speed and distance. 'movedesign' has broad applications for researchers and decision-makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade-off associated with these choices.

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 phylogentically 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

From data we first construct a Langevin equation to describe the movement of the individuals. The ctmm1 package can be used to extract the parameters behind intrinsic dynamics of each individual (potential, diffusion coefficient, etc.). However, individuals’ home-range (i.e. the area they occupy) are also shaped by its interaction with individual nearby. For that we need to resolve the forces felt by individuals during encounters2 and then scale-up their effects. This coarse-graining process generates a PDE for the temporal evolution of the occupancy probability of a given individual. This final equation, extracted from data, provides a data-driven description of the formation and maintenance of home-ranges.

Animal encounters are key components of population dynamics, community dynamics, and evolutionary processes. Consequently, measuring encounter rates (i.e. encounters per time) can be insightful. Encounter rates can be measured from animal tracking data, using metrics that can be split into two groups. The first group consists of trajectory-based metrics, i.e. measures based on serial records of animal locations. This first group includes PROX, the number of observed per number of samples. The second group, in contrast, consists of metrics based on home range overlap, including the Bhattacharyya coefficient (BC). In this study, we argue both types of metrics are limited. Trajectory-based metrics are direct measures of encounter rates but have statistical estimation issues due to their dependency on the frequency of location sampling. Meanwhile, home-rangebased metrics are statistically sound but are not proportional to encounter rates. To overcome both challenges, we proposed a new metric, Relative Encounter Rate (RER). RER increases linearly with the number of encounters and does not depend on the frequency of sampling (i.e. it is scale-invariant). In an individual-based simulation, we measured how RER, BC, and PROX relative error under different sample sizes and sampling frequencies. Further, we compared these metrics in three empirical case studies. We tested Jaguars for polygyny, deforestation effects on tapir connectivity, and an extension of the dearest enemy hypothesis with brown bears. We also compared partner hierarchy according to BC and RER in Jaguar mating clusters. In the simulation study, we found PROX overestimates the encounter rate when data has a low sampling frequency. The simulation also indicates BC overestimated encounters. Furthermore, PROX led to false positives in the Tapir and Bear case studies. In addition, PROX was incapable of detecting many individual relationships in the jaguar polygyny study. RER does not depend on sampling frequency (

The study of movement behavior and home range in animals is fundamental to understanding ecological dynamics, yet designing efficient tracking studies remains a challenge due to the lack of clear guidelines. To address this knowledge gap we created 'movedesign,' a user-friendly application that enables researchers to evaluate the precision of three commonly reported movement and spatial ecology estimates: home range area, speed, and distance traveled. Leveraging the 'ctmm' R package, our application employs autocorrelated kernel density estimators (AKDEs) and continuous-time speed and distance (CTSD) estimators, effectively addressing biases inherent in animal movement datasets. By evaluating the interplay between data resolution and battery life, 'movedesign' guides researchers in making informed decisions to achieve reliable estimates of home range and velocity while considering trade-offs. With broad applications for researchers and decision-makers, this tool empowers efficient deployment strategies, optimizing sampling design for insightful and robust outcomes in movement ecology studies.

Stochastic simulations of complex systems from domains including physics, biology, ecology or economics often require large system sizes, long time scales, and numerous replications to fully explore model behavior. The simple rules defining many models can lead researchers to prefer familiar but inefficient programming techniques, which severely hinder progress by creating computational bottlenecks. While such studies often benefit from combined domain-specific, statistical, and programming knowledge, few individual researchers span the full range of necessary skills. Here, we present a collaboration on the neutral model of biodiversity in dendritic river networks, where the goal is to analyze biodiversity data across the world’s major river systems. We show how we achieved large performance gains by engaging the problem at its foundations and thereby enabled research at a new scale.

This dataset includes all data discussed in the publication "A novel, scenario-based approach to comparing non-pharmaceutical intervention strategies across nations".

There are three primary datasets used in this work.These were obtained from Dr. Guohuan Su (co-author on the paper) . 1. Phylogenetic distance data sets between all fish species in the US. 2. Morphological traits dataset that list 10 traits information of fish species. 3. Occurrence dataset that list occurrence information of fish species in HUC8 regions of the US There is one dataset that lists exotic species in the US. The entries for exotic species are removed from about datasets before analysis.

Direct encounters, in which two or more individuals are physically close to one another, are a topic of increasing interest as more and better movement data become available. Recent progress, including the development of statistical tools for estimating robust measures of changes in animals’ space use over time, facilitates opportunities to link direct encounters between individuals with the long-term consequences of those encounters. Working with movement data for coyotes (Canis latrans) and grizzly bears (Ursus arctos horribilis), we investigate whether close intraspecific encounters were associated with spatial shifts in the animals’ range distributions, as might be expected if one or both of the individuals involved in an encounter were seeking to reduce or avoid conflict over space. We analyze the movement data of a pair of coyotes in detail, identifying how a shift in home range location resulting from altered movement behavior was apparently a consequence of a close intraspecific encounter. With grizzly bear 50 movement data, we approach the problem from the perspective of a set of encounter pairs within a population. We find support for the hypotheses that 1) close intraspecific encounters between bears are, on average, associated with subsequent shifts in range distributions and 2) encounters defined at finer spatial scales are followed by greater changes in space use. Our results suggest that animals can undertake long-term, large-scale spatial shifts in response to close intraspecific encounters that have the potential for conflict. These results lend support for existing theory on the evolution of territories and space use (e.g., Maynard-Smith’s bourgeois strategy regarding low-conflict coexistence). Overall, we find that analyses of movement data in a pairwise context can 1) identify distances at which individuals’ proximity to one another may alter behavior and 2) facilitate testing of population-level hypotheses concerning the potential for direct enc

Ecologists have long investigated how the demographic and movement parameters of a population determine its spatial spread and the critical habitat size that can sustain it. Yet, most existing models make oversimplifying assumptions about individual movement behavior, neglecting how landscape heterogeneity influences dispersal. We relax this assumption and introduce a reaction-advection-diffusion model that describes the spatial density distribution of a population with space-dependent movement bias toward preferred regions, including avoidance of degraded habitats. In this scenario, the critical habitat size depends on the spatial location of the habitat edges with respect to the preferred regions and on the intensity of the movement bias components. In particular, we identify parameter regions where the critical habitat size decreases when diffusion increases, a phenomenon known as the “drift paradox”. We also find that biased movement toward low-quality or highly populated regions can reduce the population size, therefore creating ecological traps. Our results emphasize the importance of species-specific movement behavior and habitat selection as drivers of population dynamics in fragmented landscapes and, therefore, the need to account for them in the design of protected areas.

The optimal placement of healthcare facilities, including the placement of diagnostic test centers, plays a pivotal role in ensuring efficient and equitable access to healthcare services. However, the emergence of unique complexities in the context of a pandemic, exemplified by the COVID-19 crisis, has necessitated the development of customized solutions. This paper introduces a bi-objective integer linear programming model designed to achieve two key objectives: minimizing average travel time for individuals visiting testing centers and maximizing an equitable workload distribution among testing centers. To address this problem, we propose a customized local search algorithm based on the Voronoi diagram. Additionally, we employ an $\epsilon$-constraint approach, which leverages the Gurobi solver. We rigorously examine the effectiveness of the model and the algorithms through numerical experiments and demonstrate their capability to identify Pareto-optimal solutions. We show that while the Gurobi performs efficiently in small-size instances, our proposed algorithm outperforms it in large-size instances of the problem.

Projects focused on movement behavior and home range are commonplace, but beyond a focus on choosing appropriate research questions, there are no clear guidelines for such studies. The estimation of space-use and movement properties is often necessary to answer basic movement ecology questions, but designing an animal tracking study to produce reliable estimates is often done in an ad hoc manner. We developed 'movedesign', a user-friendly Shiny application, which can be utilized to investigate the precision of three estimates regularly reported in movement and spatial ecology studies: home range area, speed, and distance traveled. Conceptually similar to statistical power analysis, this application enables users to assess the degree of estimate precision that may be achieved with a given sampling design. Leveraging the 'ctmm' R package, we utilize two methods proven to handle many common biases in animal movement datasets: autocorrelated Kernel Density Estimators (AKDE) and continuous-time speed and distance (CTSD) estimators. Longer sampling durations are required to reliably estimate home range areas via the detection of a sufficient number of home range crossings. In contrast, speed and distance estimation requires a sampling interval short enough to ensure that a statistically significant signature of the animal's velocity remains in the data. This application addresses key challenges faced by researchers when designing tracking studies, including the trade-off between long battery life and high resolution of GPS devices, which may result in a compromise between reliably estimating home range or speed and distance. 'movedesign' has broad applications for researchers and decision-makers, supporting them to focus efforts and resources in achieving the optimal sampling design strategy for their research questions, prioritizing the correct deployment decisions for insightful and reliable outputs, while understanding the trade-off associated with these choices.

Animals moving through landscapes need to strike a balance between finding sufficient resources to grow and reproduce while minimizing encounters with predators 1,2. Because encounter rates are determined by the average distance over which directed motion persists 1,3–5, this trade-off should be apparent in individuals’ movement. Using GPS data from 1,396 individuals across 62 species of terrestrial mammals, we show how predators maintained directed motion ~7 times longer than for similarly-sized prey, revealing how prey species must trade off search efficiency against predator encounter rates. Individual search strategies were also modulated by resource abundance, with prey species forced to risk higher predator encounter rates when resources were scarce. These findings highlight the interplay between encounter rates and resource availability in shaping broad patterns mammalian movement strategies.

The severe shortfall in testing supplies during the initial phases of the COVID-19 pandemic and ensuing struggle to control disease spread have affirmed the need to plan rigorous optimized supply-constrained resource allocation strategies for the next inevitable novel disease epidemic. To address the challenge of optimizing limited resource usage in the face of complicated disease dynamics, we develop an integro partial differential equation disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals, and we analyze the influence of these ele- ments on controllability and optimal resource allocation between two testing strategies, ‘clinical’ targeting symptomatic individuals and ‘non-clinical’ targeting non-symptomatic individuals, for reducing total infection sizes. We apply our model to not only the original, delta, and omicron COVID-19 variants, but also to generic diseases which have different offsets between latent and incubation period distributions which allow for or prevent varying degrees of presymptomatic transmission or preinfectiousness symptom onset. We find that factors which reduce control- lability generally call for reduced levels of non-clinical testing, while the relationship between symptom onset, controllability, and optimal strategies is complicated. Although greater degrees of presymptomatic transmission reduce disease controllability, they may enhance or reduce the role of non-clinical testing in optimal strategies depending on other disease factors like overall transmissibility and latent period length. Our model allows a spectrum of diseases to be com- pared under the same lens such that the lessons learned from COVID-19 can be adapted to resource constraints in the next emerging epidemic and analyzed for optimal strategies under a consistent mathematical framework.

Quantifying animal movements is necessary for answering a wide array of research questions in ecology and conservation biology. Consequently, ecologists have made considerable efforts to identify the best way to estimate an animal’s home range, and many methods of estimating home ranges have arisen over the past half century. Most of these methods fall into two distinct categories of estimators that have only recently been described in statistical detail: those that measure range distributions (methods such as Kernel Density Estimation that quantify the long-run behavior of a movement process that features restricted space use) and those that measure occurrence distributions (methods such as Brownian Bridge Movement Models and the Correlated Random Walk Library that quantify uncertainty in an animal movement path during a specific period of observation). In this paper, we use theory, simulations, and empirical analysis to demonstrate the importance of applying these two classes of space use estimators appropriately and distinctly. Conflating range and occurrence distributions can have serious consequences for ecological inference and conservation practice. For example, in most situations, home-range estimates quantified using occurrence estimators are too small, and this problem is exacerbated by ongoing improvements in tracking technology that enable more frequent and more accurate data on animal movements. We encourage researchers to use range estimators to estimate the area of home ranges and occurrence estimators to answer other questions in movement ecology, such as when and where an animal crosses a linear feature, visits a location of interest, or interacts with other animals.

While biological invasions are recognized as a major threat to global biodiversity, determining species’ abilities to invade new areas (species invasiveness) and the vulnerability of those areas to invasions (community invasibility) are still poorly understood. Here, we used trait-based analysis to profile invasive species and quantify the community invasibility for >1,800 North American freshwater fish communities. We show that species with higher reproduction rates, longer life spans and larger sizes tend to be more invasive. Community invasibility peaked when the functional distance among native species was high, leaving unoccupied functional space for the establishment of potential invaders. Invasion success is therefore governed by both the functional traits of non-native species determining their invasiveness, and by the functional characteristics of the invaded community determining its invasibility. Considering those two determinants together will allow better predictions of invasions.

Personnel scheduling in organizations can be disrupted by unforeseen events that require efficient planning. A recent example is the COVID-19 pandemic that disrupted global operations, compromising people's health and safety. Many organizations were forced to transition to full remote work to prevent the spread of the virus and ensure employee safety. Although working entirely remotely is effective for some organizations, others must balance workplace occupancy and infection risk to keep their operations functioning efficiently despite a global health crisis. We address this issue by developing a days-off scheduling model that captures employees' interactions through the underlying contact network. To solve the problem, we propose a Mixed Integer Linear Programming model considering a Microscopic Markov Chain Approach to determine the probability of infection in a contact network that mimics the employees' interactions. The model determines, during a given planning period, the optimal staffing mix to maximize occupancy while minimizing the risk of infection in the presence of testing protocols. We conduct sensitivity analysis to assess the approach's robustness while considering different contact networks and testing strategies. Through extensive computational analysis, we show that the degree of contact among employees is not the sole factor to consider when defining personnel scheduling policies during disease outbreaks. The decision-maker must balance the employee allocation with tailored testing interventions based on management's priorities to mitigate the effects while ensuring the desired occupancy at a lower risk.

Long-term care facilities have been widely affected by the COVID-19 pandemic. Retirement homes are particularly vulnerable due to the higher mortality risk of infected elderly individuals. Once an outbreak is happening, suppressing the spread of the virus in retirement homes is challenging because the residents are in contact with each other and isolation measures cannot be widely enforced. Regular testing strategies, on the other hand, have been shown to effectively prevent outbreaks in retirement homes. However, high frequency testing may consume substantial staff working time, which results a trade-off between the time invested in testing, and the time spent providing essential care to residents. Thus, developing an optimal testing strategy is crucial to proactively detect infections while guaranteeing efficient use of limited staff time in these facilities. Although numerous efforts have been made to prevent the virus from spreading in long-term care facilities, this is the first study to develop testing strategies based on formal optimization methods. This paper proposes two novel optimization models for testing schedules. The models aim to minimize the risk of infection in retirement homes, considering the trade-off between the probability of infection and staff workload. We employ a probabilistic approach in conjunction with the optimization models, to compute the risk of infection, including contact rates, incidence status, and the probability of infection of the residents. To solve the models, we propose an enhanced local search algorithm by leveraging the \textit{symmetry property} of the optimal solution. We perform several experiments with realistically sized instances and show that the proposed approach can derive optimal testing strategies.