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While biological invasions are recognized as a major threat to global biodiversity, determining non-native species’ abilities to establish in new areas (species invasiveness) and the vulnerability of those areas to invasions (community invasibility) is challenging. Here, we use trait-based analysis to profile invasive species and quantify the community invasibility for >1,800 North American freshwater fish communities. We show that, in addition to effects attributed to propagule pressure caused by human intervention, species with higher fecundity, longer lifespan and larger size tend to be more invasive. Community invasibility peaks when the functional distance among native species was high, leaving unoccupied functional space for the establishment of potential invaders. Our findings illustrate how the functional traits of non-native species determining their invasiveness, and the functional characteristics of the invaded community determining its invasibility, may be identified. Considering those two determinants together will enable better predictions of invasions.

Home-range estimates are a common product of animal tracking data, as each range represents the area needed by a given individual. Population-level inference of home-range areas—where multiple individual home ranges are considered to be sampled from a population—is also important to evaluate changes over time, space or covariates such as habitat quality or fragmentation, and for comparative analyses of species averages. Population-level home-range parameters have traditionally been estimated by first assuming that the input tracking data were sampled independently when calculating home ranges via conventional kernel density estimation (KDE) or minimal convex polygon (MCP) methods, and then assuming that those individual home ranges were measured exactly when calculating the population-level estimates. This conventional approach does not account for the temporal autocorrelation that is inherent in modern tracking data, nor for the uncertainties of each individual home-range estimate, which are often large and heterogeneous. Here, we introduce a statistically and computationally efficient framework for the population-level analysis of home-range areas, based on autocorrelated kernel density estimation (AKDE), that can account for variable temporal autocorrelation and estimation uncertainty. We apply our method to empirical examples on lowland tapir Tapirus terrestris, kinkajou Potos flavus, white-nosed coati Nasua narica, white-faced capuchin monkey Cebus capucinus and spider monkey Ateles geoffroyi, and quantify differences between species, environments and sexes. Our approach allows researchers to more accurately compare different populations with different movement behaviours or sampling schedules while retaining statistical precision and power when individual home-range uncertainties vary. Finally, we emphasize the estimation of effect sizes when comparing populations, rather than mere significance tests. published

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 and long-term changes of mobility in response to anthropogenic disturbance remains relatively rare.We investigated changes in movement behavior of Mongolian gazelleProcapra gutturosa, a nomadic ungulate species native to the Mongolian steppe. Using GPS tracking data collected from 62 gazelle individuals between 2007 and 2021, we quantified 16-day displacement distances for each individual as a metric for long-distance movements. We used generalized linear mixed models, generalized additive models and additive quantile mixed models to assess how anthropogenic and environmental factors affected gazelle movement behavior.Long distance 16-day movements decreased significantly by up to 36 %, from 142 km in 2007 to 92 km in 2021. Changes in gazelle mobility were affected by the increasing number of vehicles in Mongolia, but could not be explained by concurrent changes in other environmental factors like temperature, precipitation or vegetation greenness that often drive ungulate migration behavior. Moreover, we found that gazelle movement decreased close to roads, and that gazelles stayed further away from roads during the snow-free season, when vehicular 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. To date, 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 throughout the Eastern Steppe, interactions between ungulates and vehicle traffic need to be closely monitored in order to identify, localize, and mitigate semipermeable barrier effects before landscape permeability is severely altered.

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. Without these guidelines, designing an animal tracking study to produce reliable estimates of space-use and movement properties (necessary to answer basic movement ecology questions), is often done in anad hocmanner.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;i.e., the choices regarding data resolution (sampling interval) and battery life (sampling duration).Leveraging the ‘ctmm’Rpackage, 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 locations collected by the 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.

SummaryAnimals moving through landscapes need to strike a balance between finding sufficient resources to grow and reproduce while minimizing encounters with predators1,2. Because encounter rates are determined by the average distance over which directed motion persists1,3–5, 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.

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.

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{ctmm R} package.

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.Open Research StatementTracking data on Aepyceros melampus, Beatragus hunteri, Bycanistes bucinator, Cerdocyon thous, Eulemur rufifrons, Glyptemys insculpta, Gyps coprotheres, Madoqua guentheri, Ovis canadensis, Propithecus verreauxi, Sus scrofa, and Ursus arctos are publicly archived in the Dryad repository (Noonan et al. 2018; https://doi.org/10.5061/dryad.v5051j2), as are data from Procapra gutturosa (Fleming et al. 2014a; https://doi.org/10.5061/dryad.45157). Data on Panthera onca were taken from (Morato et al. 2018). Additional data are publicly archived in the Movebank repository under the following identifiers: Canis latrans, 8159699; Canis lupus, 8159399; Chrysocyon brachyurus, 18156143; Felis silvestris, 40386102; Gyps africanus, 2919708; Lepus europaeus, 25727477; Martes pennanti, 2964494; Panthera leo, 220229; Papio cynocephalus, 222027; Syncerus caffer, 1764627; Tapirus terrestris, 443607536; Torgos tracheliotus, 2919708; and Ursus americanus, 8170674.

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.

Estimating animal home ranges is a primary purpose of collecting tracking data. All conventional home range estimators in widespread usage, including minimum convex polygons and kernel density estimators, assume independently sampled data. In stark contrast, modern GPS animal tracking datasets are almost always strongly autocorrelated. This incongruence between estimator assumptions and empirical reality leads to systematically underestimated home ranges. Autocorrelated kernel density estimation (AKDE) resolves this conflict by modeling the observed autocorrelation structure of tracking data during home range estimation, and has been shown to perform accurately across a broad range of tracking datasets. However, compared to conventional estimators, AKDE requires additional modeling steps and has heretofore only been accessible via the command-line ctmm R package. Here, we introduce ctmmweb, which provides a point-and-click graphical interface to ctmm, and streamlines AKDE, its prerequisite autocorrelation modeling steps, and a number of additional movement analyses. We demonstrate ctmmweb’s capabilities, including AKDE home range estimation and subsequent home range overlap analysis, on a dataset of four jaguars from the Brazilian Pantanal. We intend ctmmweb to open AKDE and related autocorrelation-explicit analyses to a wider audience of wildlife and conservation professionals.

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.In this study, we demonstrate that this method weights each observed location in an animal’s movement track according to its level of non-independence, expanding confidence intervals and reducing bias that can arise when there are missing data in the movement track.Ecologists and conservation biologists can use this method to improve the quality of inferences derived from resource selection functions. We also provide a complete, annotated analytical workflow to help new users apply our method to their own animal tracking data using the ctmm R package.

The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supply-constrained resource allocation strategies for controlling novel disease epidemics. To address the challenge of constrained resource optimization for managing diseases with complications like pre- and asymptomatic transmission, we develop an integro partial differential equation compartmental disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals. Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission. To analyze the influence of these realistic features on disease controllability, we find optimal strategies for reducing total infection sizes that allocate limited testing resources between ‘clinical’ testing, which targets symptomatic individuals, and ‘non-clinical’ testing, which targets non-symptomatic individuals. We apply our model not only to the original, delta, and omicron COVID-19 variants, but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions, which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness. We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies, while the relationship between incubation-latent mismatch, controllability, and optimal strategies is complicated. In particular, though greater degrees of presymptomatic transmission reduce disease controllability, they may increase or decrease the role of non-clinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length. Importantly, our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.

Home-range estimates are a common product of animal tracking data, as each range informs on the area needed by a given individual. Population-level inference on home-range areas—where multiple individual home-ranges are considered to be sampled from a population—is also important to evaluate changes over time, space, or covariates, such as habitat quality or fragmentation, and for comparative analyses of species averages. Population-level home-range parameters have traditionally been estimated by first assuming that the input tracking data were sampled independently when calculating home ranges via conventional kernel density estimation (KDE) or minimal convex polygon (MCP) methods, and then assuming that those individual home ranges were measured exactly when calculating the population-level estimates. This conventional approach does not account for the temporal autocorrelation that is inherent in modern tracking data, nor for the uncertainties of each individual home-range estimate, which are often large and heterogeneous.Here, we introduce a statistically and computationally efficient framework for the population-level analysis of home-range areas, based on autocorrelated kernel density estimation (AKDE), that can account for variable temporal autocorrelation and estimation uncertainty.We apply our method to empirical examples on lowland tapir (Tapirus terrestris), kinkajou (Potos flavus), white-nosed coati (Nasua narica), white-faced capuchin monkey (Cebus capucinus), and spider monkey (Ateles geoffroyi), and quantify differences between species, environments, and sexes.Our approach allows researchers to more accurately compare different populations with different movement behaviors or sampling schedules, while retaining statistical precision and power when individual home-range uncertainties vary. Finally, we emphasize the estimation of effect sizes when comparing populations, rather than mere significance tests.

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 COVID-19 pandemic has had unprecedented health and economic impacts worldwide. Various fields of science approached this topic to better understand the pandemic’s spread characteristics and provide a factual base for nonpharmaceutical interventions. One of the interesting non-invasive methodologies, wastewater epidemiology, looks for the correlation between the outcomes of the wastewater-based PCR analysis and the epidemiological situation in their catchment (e.g., Bogler et al., 2020; Farkas et al., 2020). This approach may efficiently predict the outbreak in the endemic phase when the conventional testing is usually underrated. Therefore a visual analytical tool may be beneficial for observing the spatiotemporal correlations of the wastewater indicators and the focal incidence levels. However, based on the analysis of existing tools (Naughton et al., 2021), most dashboards integrating wastewater indicators do not use information about geographical space at all, or the functionality of the map component is limited to the identification of the particular wastewater plant. In the where2test project (www.where2test.de), we established a scalable operational data store (ODS) to automatically collect, store and integrate heterogeneous epidemiology-related data for various administrative levels of several European countries, focusing primarily on the region of Saxony. The project includes various activities, including spatiotemporal analyses (Mertel et al., 2022), epidemiological models and applications (Davoodi et al., 2022), and forecasts. In this paper, we present a prototype of an interactive dashboard to explore the correlation between the local incidence as estimated by conventional testing and the wastewater-based PCR indicators (cT values) in Saxony. Another goal of this application is to focus on the geospatial aspect of these data. The top part of the dashboard features an interactive timeline linked to a multidimensional heatmap. Here, the user can navigate in time and explore the correlation between the epidemiological situation of the neighborhood of the wastewater plants (defined by the temporal distance) and the cT indicators in time. For the week selected in the timeline, the user can observe the spatial context within the Saxonian municipalities on the map below. The map allows interaction with the single municipalities and all the wastewater plants integrated with our ODS to access detailed information and additional charts. In the future, we plan to integrate more metrics and parameters that may be beneficial for exploring spatiotemporal inference. Our scalable infrastructure also easily allows the integration of more wastewater plants. The application is available on www.where2test.de/wastewater from August 2022.

Accurately quantifying species’ area requirements is a prerequisite for effective area‐based conservation. This typically involves collecting tracking data on species of interest and then conducting home‐range analyses. Problematically, autocorrelation in tracking data can result in space needs being severely underestimated. Based on the previous work, we hypothesized the magnitude of underestimation varies with body mass, a relationship that could have serious conservation implications. To evaluate this hypothesis for terrestrial mammals, we estimated home‐range areas with global positioning system (GPS) locations from 757 individuals across 61 globally distributed mammalian species with body masses ranging from 0.4 to 4000 kg. We then applied block cross‐validation to quantify bias in empirical home‐range estimates. Area requirements of mammals 1, meaning the scaling of the relationship changed substantially at the upper end of the mass spectrum., Article impact statement: Due to autocorrelation‐induced bias, conventional methods severely underestimate the area requirements of GPS‐tracked large mammals.

Virginia has seen dramatic increases in reported cases of Lyme disease and Rocky Mountain spotted fever, but basic knowledge on the community ecology of these tick-borne diseases is poor. We examined the tick burdens of 5 small-mammal species in northwest Virginia from October 2011 to December 2012. We live-trapped individuals, quantified the tick burdens, assessed the burden structure, and tested a subset of the ticks for tick-borne pathogens. We found the tick burdens to be composed predominantly of Ixodes scapularis (Black-Legged Tick), and Ixodes sp. ticks, with Amblyomma americanum (Lone Star Tick) and Dermacentor variabilis (American Dog Tick) also present at lower densities. We detected Borrelia burgdorferi (prevalence 15%), Rickettsia spp. (4%), Anaplasma phagocytophilum (4%), and Hepatozoon spp. (1%). Black-Legged Ticks, a species which has shown range expansion in recent decades, tested positive for B. burgdorferi (17%) and for multiple pathogens in individual ticks. For better predictions of tick-borne disease risk across the Mid-Atlantic region, we recommend tracking changes in tick communities by continuous monitoring of tick burdens, densities of questing ticks, and prevalence of tick-borne pathogens.

While reports of mass mortality events (MMEs) are increasing in the literature, comparing the incidence of MMEs through time, among locations, or taxa is problematic without accounting for detection probabilities. MMEs involving small, cryptic species can be difficult to detect even during the event, and degradation and scavenging of carcasses can make the window for detection very short. As such, the number or occurrence rate of MMEs may often be severely underestimated, especially with infrequent observations. We develop a simple modeling framework to quantify the probability of detecting an MME as a function of the observation frequency relative to the rate at which MMEs become undetectable. This framework facilitates the design of surveillance programs and may be extended to correct estimates of the incidence of MMEs from actual surveillance data for more appropriate analyses of trends through time and among taxa.

Nomadic movements are often a consequence of unpredictable resource dynamics. However, how nomadic ungulates select dynamic resources is still understudied. Here we examined resource selection of nomadic Mongolian gazelles (Procapra gutturosa) in the Eastern Steppe of Mongolia. We used daily GPS locations of 33 gazelles tracked up to 3.5 years. We examined selection for forage during the growing season using the Normalized Difference Vegetation Index (NDVI). In winter we examined selection for snow cover which mediates access to forage and drinking water. We studied selection at the population level using resource selection functions (RSFs) as well as on the individual level using step-selection functions (SSFs) at varying spatio-temporal scales from 1 to 10 days. Results from the population and the individual level analyses differed. At the population level we found selection for higher than average NDVI during the growing season. This may indicate selection for areas with more forage cover within the arid steppe landscape. In winter, gazelles selected for intermediate snow cover, which may indicate preference for areas which offer some snow for hydration but not so much as to hinder movement. At the individual level, in both seasons and across scales, we were not able to detect selection in the majority of individuals, but selection was similar to that seen in the RSFs for those individuals showing selection. Difficulty in finding selection with SSFs may indicate that Mongolian gazelles are using a random search strategy to find forage in a landscape with large, homogeneous areas of vegetation. The combination of random searches and landscape characteristics could therefore obscure results at the fine scale of SSFs. The significant results on the broader scale used for the population level RSF highlight that, although individuals show uncoordinated movement trajectories, they ultimately select for similar vegetation and snow cover.

Animal movement is a key ecological process that is tightly coupled to local environmental conditions. While agriculture, urbanisation, and transportation infrastructure are critical to human socio-economic improvement, these have spurred substantial changes in animal movement across the globe with potential impacts on fitness and survival. Notably, however, human disturbance can have differential effects across species, and responses to human activities are thus largely taxa and context specific. As human disturbance is only expected to worsen over the next decade it is critical to better understand how species respond to human disturbance in order to develop effective, case-specific conservation strategies. Here, we use an extensive telemetry dataset collected over 22 years to fill a critical knowledge gap in the movement ecology of lowland tapirs (Tapirus terrestris) across a gradient of human disturbance within three biomes in southern Brazil: the Pantanal, Cerrado, and Atlantic Forest.From these data we found that the mean home range size across all monitored tapirs was 8.31 km2 (95% CI: 6.53 - 10.42), with no evidence that home range sizes differed between sexes nor age groups. Interestingly, although the Atlantic Forest, Cerrado, and Pantanal vary substantially in habitat composition, levels of human disturbance, and tapir population densities, we found that lowland tapir movement behaviour and space use were consistent across all three biomes. Human disturbance also had no detectable effect on lowland tapir movement. Lowland tapirs living in the most altered habitats we monitored exhibited movement behaviour that was comparable to that of tapirs living in a near pristine environment.Contrary to our expectations, we observed very little individual variability in lowland tapir space use and movement, and human impacts on the landscape also had no measurable effect on their movement. Lowland tapir movement behaviour thus appears to exhibit very little phenotypic plasticity. Crucially, the lack of any detectable response to anthropogenic disturbance suggests that human modified habitats risk being ecological traps for tapirs and this information should be factored into conservation actions and species management aimed towards protecting lowland tapir populations.

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., This work is supported by FAPESP through grants ICTP-SAIFR 2016/01343-7, and Programa Jovens Pesquisadores em Centros Emergentes 2019/24433-0 and 2019/05523-8, Instituto Serrapilheira through grant Serra-1911-31200, and the Simons Foundation (RMG); the Princeton University May Fellowship in the department of Ecology and Evolutionary Biology (CC); MINECO/AEI/FEDER through the María de Maeztu Program for Units of Excellence in R&D (MDM-2017-0711, Spain) (EHG & CL); The Gordon and Betty Moore Foundation, grant #7800 (CET & JAB); and the NSF grant DMS-2052616 (JAB). This work was partially funded by the Center of Advanced Systems Understanding (CASUS) which is financed by Germany’s Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament (JMC).

Made available in DSpace on 2021-06-25T10:58:24Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Ecologists have long been interested in linking individual behaviour with higher level processes. For motile species, this ‘upscaling’ is governed by how well any given movement strategy maximizes encounters with positive factors and minimizes encounters with negative factors. Despite the importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with developments in animal tracking or movement modelling. Furthermore, existing work has focused primarily on the relationship between animal movement and encounter rates while the relationship between individual movement and the spatial locations of encounter events in the environment has remained conspicuously understudied. Here, we bridge this gap by introducing a method for describing the long-term encounter location probabilities for movement within home ranges, termed the conditional distribution of encounters (CDE). We then derive this distribution, as well as confidence intervals, implement its statistical estimator into open-source software and demonstrate the broad ecological relevance of this distribution. We first use simulated data to show how our estimator provides asymptotically consistent estimates. We then demonstrate the general utility of this method for three simulation-based scenarios that occur routinely in biological systems: (a) a population of individuals with home ranges that overlap with neighbours; (b) a pair of individuals with a hard territorial border between their home ranges; and (c) a predator with a large home range that encompassed the home ranges of multiple prey individuals. Using GPS data from white-faced capuchins Cebus capucinus, tracked on Barro Colorado Island, Panama, and sleepy lizards Tiliqua rugosa, tracked in Bundey, South Australia, we then show how the CDE can be used to estimate the locations of territorial borders, identify key resources, quantify the potential for competitive or predatory interactions and/or identify any changes in behaviour that directly result from location-specific encounter probability. The CDE enables researchers to better understand the dynamics of populations of interacting individuals. Notably, the general estimation framework developed in this work builds straightforwardly off of home range estimation and requires no specialized data collection protocols. This method is now openly available via the ctmm R package. Department of Biology The Irving K. Barber Faculty of Science The University of British Columbia Smithsonian Conservation Biology Institute National Zoological Park ICTP South American Institute for Fundamental Research & Instituto de Fisica Teorica – UNESP Department of Anthropology University of California Smithsonian Tropical Research Institute Department for the Ecology of Animal Societies Max Planck Institute of Animal Behavior Department of Biology University of Konstanz Centre for the Advanced Study of Collective Behaviour University of Konstanz North Carolina Museum of Natural Sciences and North Carolina State University College of Science and Engineering James Cook University Department of Environmental Science and Policy University of California Davis School of Zoology Faculty of Life Sciences Tel Aviv University Department of Biology University of Maryland Center for Advanced Systems Understanding (CASUS) Helmholtz-Zentrum Dresden Rossendorf (HZDR) Department of Ecological Modelling Helmholtz Centre for Environmental Research (UFZ) ICTP South American Institute for Fundamental Research & Instituto de Fisica Teorica – UNESP

Insufficient testing capacity continues to be a critical bottleneck in the worldwide fight against COVID-19. Optimizing the deployment of limited testing resources has therefore emerged as a keystone problem in pandemic response planning. Here, we use a modified SEIR model to optimize testing strategies under a constraint of limited testing capacity. We define pre-symptomatic, asymptomatic, and symptomatic infected classes, and assume that positively tested individuals are immediately moved into quarantine. We further define two types of testing. Clinical testing focuses only on the symptomatic class. Non-clinical testing detects pre- and asymptomatic individuals from the general population, and an “information” parameter governs the degree to which such testing can be focused on high infection risk individuals. We then solve for the optimal mix of clinical and non-clinical testing as a function of both testing capacity and the information parameter. We find that purely clinical testing is optimal at very low testing capacities, supporting early guidance to ration tests for the sickest patients. Additionally, we find that a mix of clinical and non-clinical testing becomes optimal as testing capacity increases. At high but empirically observed testing capacities, a mix of clinical testing and unfocused (information=0) non-clinical testing becomes optimal. We further highlight the advantages of early implementation of testing programs, and of combining optimized testing with contact reduction interventions such as lockdowns, social distancing, and masking.

Nomadic movements are often a consequence of unpredictable resource dynamics. However, how nomadic ungulates select dynamic resources is still understudied. Here we examined resource selection of nomadic Mongolian gazelles (Procapra gutturosa) in the Eastern Steppe of Mongolia. We used daily GPS locations of 33 gazelles tracked up to 3.5 years. We examined selection for forage during the growing season using the Normalized Difference Vegetation Index (NDVI). In winter we examined selection for snow cover which mediates access to forage and drinking water. We studied selection at the population level using resource selection functions (RSFs) as well as on the individual level using step-selection functions (SSFs) at varying spatio-temporal scales from 1 to 10 days. Results from the population and the individual level analyses differed. At the population level we found selection for higher than average NDVI during the growing season. This may indicate selection for areas with more forage cover within the arid steppe landscape. In winter, gazelles selected for intermediate snow cover, which may indicate preference for areas which offer some snow for hydration but not so much as to hinder movement. At the individual level, in both seasons and across scales, we were not able to detect selection in the majority of individuals, but selection was similar to that seen in the RSFs for those individuals showing selection. Difficulty in finding selection with SSFs may indicate that Mongolian gazelles are using a random search strategy to find forage in a landscape with large, homogeneous areas of vegetation. The combination of random searches and landscape characteristics could therefore obscure results at the fine scale of SSFs. The significant results on the broader scale used for the population level RSF highlight that, although individuals show uncoordinated movement trajectories, they ultimately select for similar vegetation and snow cover.

Ecologists have long been interested in linking individual behavior with higher-level processes. For motile species, this ‘upscaling’ is governed by how well any given movement strategy maximizes encounters with positive factors, and minimizes encounters with negative factors. Despite the importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with developments in animal tracking or movement modeling. Furthermore, existing work has focused primarily on the relationship between animal movement and encounterrateswhile no theoretical framework exists for directly relating individual movement with the spatiallocationsof encounter events in the environment.Here, we bridge this gap by introducing a new theoretical concept describing the long-term encounter location probabilities for movement within home ranges, termed the conditional distribution of encounters (CDE). We then derive this distribution, as well as confidence intervals, implement its statistical estimator into open source software, and demonstrate the broad ecological relevance of this novel concept.We first use simulated data to show how our estimator provides asymptotically consistent estimates. We then demonstrate the general utility of this method for three simulation-based scenarios that occur routinely in biological systems: i) a population of individuals with home ranges that overlap with neighbors; ii) a pair of individuals with a hard territorial border between their home ranges; and iii) a predator with a large home range that encompassed the home ranges of multiple prey individuals. Using GPS data from white-faced capuchins (Cebus capucinus) tracked on Barro Colorado Island, Panama, and sleepy lizards (Tiliqua rugosa) tracked in Bundey, South Australia, we then show how the CDE can be used to estimate the locations of territorial borders, identify key resources, quantify the location-specific potential for competition, and/or identify any changes in behaviour that directly result from location-specific encounter probability.This novel target distribution enables researchers to better understand the dynamics of populations of interacting individuals. Notably, the general estimation framework developed in this work builds straightforwardly off of home range estimation and requires no specialised data collection protocols. This method is now openly available via the ctmm R package.

Animal tracking data are being collected more frequently, in greater detail, and on smaller taxa than ever before. These data hold the promise to increase the relevance of animal movement for understanding ecological processes, but this potential will only be fully realized if their accompanying location error is properly addressed. Historically, coarsely-sampled movement data have proved invaluable for understanding large scale processes (e.g., home range, habitat selection, etc.), but modern fine-scale data promise to unlock far more ecological information. While GPS location error can often be ignored in coarsely sampled data, fine-scale data require more care, and tools to do this have not kept pace. Current approaches to dealing with location error largely fall into two categories—either discarding the least accurate location estimates prior to analysis or simultaneously fitting movement and error parameters in a hidden-state model. In some cases these approaches can provide a level of correction, but they have known limitations, and in some cases they can be worse than doing nothing. Here, we provide a general framework to account for location error in the analysis of triangulated and trilatcralizcd animal tracking data, which includes GPS, Argos Doppler-shift, triangulated VHF, trilatcralized acoustic and cellular location data. We apply our error-modelselection framework to 190 GPS, cellular, and acoustic devices representing 27 models from 14 manufacturers. Collectively, these devices were used to track a wide range of taxa comprising birds, fish, reptiles, and mammals of different sizes and with different behaviors, in urban, suburban, and wild settings. In almost half of the tested device models, error-model selection was necessary to obtain the best performing error model, and in almost a quarter of tested device models, the reported DOP values were actually misinformative. Then, using empirical tracking data from multiple species, we provide an overview of modern, error-informed movement analyses, including continuous-time path reconstruction, home-range distribution, home-range overlap, speed, and distance estimation. Adding to these techniques, we introduce new error-informed estimators for outlier detection and autocorrelation visualization. Because error-induced biases depend on many factors—sampling schedule, movement characteristics, tracking device, habitat, etc.—differential bias can easily confound biological inference and lead researchers to draw false conclusions. We demonstrate how error-informed analyses on calibrated tracking data can provide more accurate estimates are that are insensitive to location error, and allow researchers to use all of their data.

Estimating animal home ranges is a primary purpose of collecting tracking data. All conventional home range estimators in widespread usage, including minimum convex polygons and kernel density estimators, assume independently sampled data. In stark contrast, modern GPS animal tracking datasets are almost always strongly autocorrelated. This incongruence between estimator assumptions and empirical reality leads to systematically underestimated home ranges. Autocorrelated kernel density estimation (AKDE) resolves this conflict by modeling the observed autocorrelation structure of tracking data during home range estimation, and has been shown to perform accurately across a broad range of tracking datasets. However, compared to conventional estimators, AKDE requires additional modeling steps and has heretofore only been accessible via the command-linectmmR package. Here, we introducectmmweb, which provides a point-and-click graphical interface toctmm, and streamlines AKDE, its prerequisite autocorrelation modeling steps, and a number of additional movement analyses. We demonstratectmmweb’scapabilities, including AKDE home range estimation and subsequent home range overlap analysis, on a dataset of four jaguars from the Brazilian Pantanal. We intendctmmwebto open AKDE and related autocorrelation-explicit analyses to a wider audience of wildlife and conservation professionals.

BackgroundGlobal positioning systems (GPS) and altimeters are increasingly used to monitor vertical space use by aerial species, a key aspect of their ecological niche, that we need to know to manage our own use of the airspace, and to protect those species. However, there are various sources of error in flight height data (“height” above ground, as opposed to “altitude” above a reference like the sea level). First the altitude is measured with a vertical error from the devices themselves. Then there is error in the ground elevation below the tracked animals, which translates into error in flight height computed as the difference between altitude and ground elevation. Finally, there is error in the horizontal position of the animals, which translates into error in the predicted ground elevation below the animals. We used controlled field trials, simulations, and the reanalysis of raptor case studies with state-space models to illustrate the effect of improper error management.ResultsErrors of a magnitude of 20 m appear in benign conditions for barometric altimeters and GPS vertical positioning (expected to be larger in more challenging context). These errors distort the shape of the distribution of flight heights, inflate the variance in flight height, bias behavioural state assignments, correlations with environmental covariates, and airspace management recommendations. Improper data filters such as removing all negative flight height records introduce several biases in the remaining dataset, and preclude the opportunity to leverage unambiguous errors to help with model fitting. Analyses that ignore the variance around the mean flight height, e.g., those based on linear models of flight height, and those that ignore the variance inflation caused by telemetry errors, lead to incorrect inferences.ConclusionThe state-space modelling framework, now in widespread use by ecologists and increasingly often automatically implemented within on-board GPS data processing algorithms, makes it possible to fit flight models directly to the output of GPS devices, with minimal data pre-selection, and to analyse the full distribution of flight heights, not just the mean. In addition to basic research about aerial niches, behaviour quantification, and environmental interactions, we highlight the applied relevance of our recommendations for airspace management and the conservation of aerial wildlife.

The SARS-CoV-2 virus has spread around the world with over 90 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 SEIR-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 SEIR 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.

We introduce fast implementations for the likelihood functions, telemetry error filters, probabilistic trajectory and velocity reconstructions, and movement-path simulations for a large class of continuous-time movement models. This class of models includes all of the basic continuous-time models that have been applied to animal movement. A diverse array of movement behaviors can be modeled from within this framework, including range residence, persistence of motion, migration, range shifting, and territorial patrol. The fast algorithms presented here, based upon the Kalman filter, are critical for applying movement analyses to the evergrowing number of modern datasets that feature thousands or more observed animal locations, and they are key to the continuous-time movement modeling ( ctmm ) R package.

Summary Many animals undertake movements that are longer scaled and more directed than their typical home ranging behaviour. These movements include seasonal migrations (e.g. between breeding and feeding grounds), natal dispersal, nomadic range shifts and responses to local environmental disruptions. While various heuristic tools exist for identifying range shifts and migrations, none explicitly model the movement of the animals within a statistical framework that facilitates quantitative comparisons. We present the mechanistic range shift analysis (MRSA), a method to estimate a suite of range shift parameters: times of initiation, duration of transitions, centroids and areas of respective ranges. The method can take the autocorrelation and irregular sampling that is characteristic of much movement data into account. The mechanistic parameters suggest an intuitive measure, the range shift index, for the extent of a range shift. The likelihood based estimation further allows for statistical tests of several relevant hypotheses, including a range shift test, a stopover test and a site fidelity test. The analysis tools are provided in an R package (marcher). We applied the MRSA to a population of GPS tracked roe deer (Capreolus capreolus) in the Italian Alps between 2005 and 2008. With respect to seasonal migration, this population is extremely variable and difficult to classify. Using the MRSA, we were able to quantify the behaviours across the population and among individuals across years, identifying extents, durations and locations of seasonal range shifts, including cases that would have been ambiguous to detect using existing tools. The strongest patterns were differences across years: many animals simply did not perform a seasonal migration to wintering grounds during the mild winter of 2006–2007, even though some of these same animals did move extensively in other, harsher winters. For seasonal migrants, however, site fidelity across years was extremely high, even after skipping an entire seasonal migration. These results suggest that for roe deer behavioural plasticity and tactical responses to immediate environmental cues are reflected in the decision of whether rather than where to migrate. The MRSA also revealed a trade-off between the probability of migrating and the size of a home range.

Most species exhibit periodic behaviors in response to cycles in resources and risks in the environment (circadian, lunar, seasonal, and so on). The ability to respond to anthropogenic perturbations by modifying periodic behaviors remains little studied, as does the question of whether and how periodic behaviors translate into periodic patterns in animal space use, on which we focus. Extending existing continuous-time stochastic movement models, we propose two new parametric approaches to detect and quantify periodic patterns of space use in animal tracking data, via periodicity in the expected position or circulation in the stochastic component of the path. We use them to study the movements of maned wolves (Chrysocyon brachyurus) and coyotes (Canis latrans) along anthropization gradients. These case studies illustrate how periodic patterns can be of natural origin (cycles in the environment) or anthropogenic origin (periodicity in human activity or restrictions on available habitat), suggesting a role for periodic patterns of space use in species persistence in anthropized areas. The method builds upon and extends existing functionalities in the R-package ctmm, in which the necessary tools are made available.

BackgroundSpeed and distance traveled provide quantifiable links between behavior and energetics, and are among the metrics most routinely estimated from animal tracking data. Researchers typically sum over the straight-line displacements (SLDs) between sampled locations to quantify distance traveled, while speed is estimated by dividing these displacements by time. Problematically, this approach is highly sensitive to the measurement scale, with biases subject to the sampling frequency, the tortuosity of the animal’s movement, and the amount of measurement error. Compounding the issue of scale-sensitivity, SLD estimates do not come equipped with confidence intervals to quantify their uncertainty.MethodsTo overcome the limitations of SLD estimation, we outline a continuous-time speed and distance (CTSD) estimation method. An inherent property of working in continuous-time is the ability to separate the underlying continuous-time movement process from the discrete-time sampling process, making these models less sensitive to the sampling schedule when estimating parameters. The first step of CTSD is to estimate the device’s error parameters to calibrate the measurement error. Once the errors have been calibrated, model selection techniques are employed to identify the best fit continuous-time movement model for the data. A simulation-based approach is then employed to sample from the distribution of trajectories conditional on the data, from which the mean speed estimate and its confidence intervals can be extracted.ResultsUsing simulated data, we demonstrate how CTSD provides accurate, scale-insensitive estimates with reliable confidence intervals. When applied to empirical GPS data, we found that SLD estimates varied substantially with sampling frequency, whereas CTSD provided relatively consistent estimates, with often dramatic improvements over SLD.ConclusionsThe methods described in this study allow for the computationally efficient, scale-insensitive estimation of speed and distance traveled, without biases due to the sampling frequency, the tortuosity of the animal’s movement, or the amount of measurement error. In addition to being robust to the sampling schedule, the point estimates come equipped with confidence intervals, permitting formal statistical inference. All the methods developed in this study are now freely available in the package or the point-and-click web based graphical user interface.

Encounter rates link movement strategies to intra- and inter-specific interactions, and therefore translate individual movement behavior into higher-level ecological processes. Indeed, a large body of interacting population theory rests on the law of mass action, which can be derived from assumptions of Brownian motion in an enclosed container with exclusively local perception. These assumptions imply completely uniform space use, individual home ranges equivalent to the population range, and encounter dependent on movement paths actually crossing. Mounting empirical evidence, however, suggests that animals use space non-uniformly, occupy home ranges substantially smaller than the population range, and are often capable of nonlocal perception. Here, we explore how these empirically supported behaviors change pairwise encounter rates. Specifically, we derive novel analytical expressions for encounter rates under Ornstein-Uhlenbeck motion, which features non-uniform space use and allows individual home ranges to differ from the population range. We compare OU-based encounter predictions to those of Reflected Brownian Motion, from which the law of mass action can be derived. For both models, we further explore how the interplay between the scale of perception and home range size affects encounter rates. We find that neglecting realistic movement and perceptual behaviors can systematically bias encounter rate predictions., 21 pages, 7 figures, 10 appendices

Conservation of nomadic species presents significant conservation challenges because of unpredictability in their movements and space use. Long‐term studies on nomadic species offering insights into the variability in space use within and between years are largely missing but are necessary to develop effective conservation strategies. We examined the temporal variability in space‐use of Mongolian gazelle, a nomadic species. We tracked 22 individuals for 1–3 years with GPS and used the resulting movement patterns to evaluate conservation strategies associated with their nomadic movements in the intact open plain grasslands of Mongolia. Individuals exhibited a high degree of variability in space use within and between years, often using different wintering areas in different years. The individual range size varied as much as threefold between years, with an estimated average annual individual range size of ~19,000 km² and a lifetime range of ~100,000 km². Comparing simulated and empirical GPS trajectories for the Mongolian gazelle showed that they avoided disturbed areas (e.g. oil fields) and did not prefer protected areas. Importantly, no single protected area in the region was large enough to cover the annual range of any of the tracked gazelle. Because of their wide‐ranging movements, the presence of linear infrastructure and the resulting barrier effects are a particular concern. We found that fences along the national border were absolute barriers affecting movements of about 80% of all tracked individuals. When gazelle encounter the border fence, they moved a median distance of 11 km along fences, suggesting frequent crossing options are needed to make barriers permeable. Synthesis and applications. We show that for nomadic species whose space use varies greatly across years, multiyear movement data are essential for sound conservation planning. We emphasize that place‐based approaches alone are insufficient to conserve wide‐ranging nomadic species and that linear infrastructure, including fences, highways and railroads, is of particular concern. Because nomadic species lack defined movement corridors, we advocate integrated land use planning that prioritizes permeability across the entire landscape and facilitates long‐distance movements. We suggest that conservation strategies for nomadic species in arid and semi‐arid regions be reconsidered based on multiyear connectivity assessments at the landscape scale.

Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function [AKDE], Silverman´s rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ((Formula presented.)) to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the hold-out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing (Formula presented.). To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal´s movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small (Formula presented.). While 72% of the 369 empirical data sets had >1,000 total observations, only 4% had an (Formula presented.) >1,000, where 30% had an (Formula presented.)

Aim: Animal movement is an important determinant of individual survival, population dynamics and ecosystem structure and function. Nonetheless, it is still unclear how local movements are related to resource availability and the spatial arrangement of resources. Using resident bird species and migratory bird species outside the migratory period, we examined how the distribution of resources affects the movement patterns of both large terrestrial birds (e.g., raptors, bustards and hornbills) and waterbirds (e.g., cranes, storks, ducks, geese and flamingos). Location: Global. Time period: 2003–2015. Major taxa studied: Birds. Methods: We compiled GPS tracking data for 386 individuals across 36 bird species. We calculated the straight‐line distance between GPS locations of each individual at the 1‐hr and 10‐day time‐scales. For each individual and time‐scale, we calculated the median and 0.95 quantile of displacement. We used linear mixed‐effects models to examine the effect of the spatial arrangement of resources, measured as enhanced vegetation index homogeneity, on avian movements, while accounting for mean resource availability, body mass, diet, flight type, migratory status and taxonomy and spatial autocorrelation. Results: We found a significant effect of resource spatial arrangement at the 1‐hr and 10‐day time‐scales. On average, individual movements were seven times longer in environments with homogeneously distributed resources compared with areas of low resource homogeneity. Contrary to previous work, we found no significant effect of resource availability, diet, flight type, migratory status or body mass on the non‐migratory movements of birds. Main conclusions: We suggest that longer movements in homogeneous environments might reflect the need for different habitat types associated with foraging and reproduction. This highlights the importance of landscape complementarity, where habitat patches within a landscape include a range of different, yet complementary resources. As habitat homogenization increases, it might force birds to travel increasingly longer distances to meet their diverse needs. National Trust for Scotland; Penguin Foundation; The U.S. Department of Energy, Grant/Award Number: DE-EE0005362; Australian Research Council; NASA's Arctic Boreal Vulnerability Experiment (ABoVE), Grant/Award Number: NNX15AV92A; Netherlands Organization for Scientific Research, Grant/Award Number: VIDI 864.10.006; BCC; NSF Award, Grant/Award Number: ABI-1458748; U.K. Department for Energy and Climate Change; ‘Juan de la Cierva ‐ Incorporación’ postdoctoral grant; Irish Research Council, Grant/Award Number: GOIPD/2015/81 ; DECC; Goethe International Postdoctoral Programme, People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007‐2013/ under REA grant agreement no [291776]; German Aerospace Center Award, Grant/Award Number: 50JR1601; Scottish Natural Heritage; Solway Coast AONB Sustainable Development Fund; COWRIE Ltd.; Heritage Lottery Fund; Robert Bosch Stiftung; NSF Division of Biological Infrastructure Award, Grant/Award Number: 1564380; Spanish Ministry of Economy and Competitiveness, Grant/Award Number: IJCI-2014-19190; Energinet.dk; NASA Award, Grant/Award Number: NNX15AV92A; MAVA Foundation; Fundação para a Ciência e Tecnologia, Grant/Award Number: SFRH/BPD/118635/2016; National Key R&D Program of China, Grant/Award Number: 2016YFC0500406; Green Fund of the Greek Ministry of Environment

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 apply 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 non-parametric home-range estimator for animal tracking data, and should also be considered when calculating non-parametric range estimates from survey data. This estimator is now the default method in the ctmm R package. This article is protected by copyright. All rights reserved.

Emergence may involve invasive mosquitoes other than Asian tiger mosquitoes, climate change, and changes in wildlife densities., La Crosse encephalitis is a viral disease that has emerged in new locations across the Appalachian region of the United States. Conventional wisdom suggests that ongoing emergence of La Crosse virus (LACV) could stem from the invasive Asian tiger (Aedes albopictus) mosquito. Efforts to prove this, however, are complicated by the numerous transmission routes and species interactions involved in LACV dynamics. To analyze LACV transmission by Asian tiger mosquitoes, we constructed epidemiologic models. These models accurately predict empirical infection rates. They do not, however, support the hypothesis that Asian tiger mosquitoes are responsible for the recent emergence of LACV at new foci. Consequently, we conclude that other factors, including different invasive mosquitoes, changes in climate variables, or changes in wildlife densities, should be considered as alternative explanations for recent increases in La Crosse encephalitis.

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.

International audience; In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ in their behaviour and performance. Here, we review the mathematical foundations of model averaging along with the diversity of approaches available. We explain that the error in model‐averaged predictions depends on each model's predictive bias and variance, as well as the covariance in predictions between models, and uncertainty about model weights. We show that model averaging is particularly useful if the predictive error of contributing model predictions is dominated by variance, and if the covariance between models is low. For noisy data, which predominate in ecology, these conditions will often be met. Many different methods to derive averaging weights exist, from Bayesian over information‐theoretical to cross‐validation optimized and resampling approaches. A general recommendation is difficult, because the performance of methods is often context dependent. Importantly, estimating weights creates some additional uncertainty. As a result, estimated model weights may not always outperform arbitrary fixed weights, such as equal weights for all models. When averaging a set of models with many inadequate models, however, estimating model weights will typically be superior to equal weights. We also investigate the quality of the confidence intervals calculated for model‐averaged predictions, showing that they differ greatly in behaviour and seldom manage to achieve nominal coverage. Our overall recommendations stress the importance of non‐parametric methods such as cross‐validation for a reliable uncertainty quantification of model‐averaged predictions.

Tightly synchronized reproduction in vast wildebeest herds underpins the keystone role this iconic species plays in the Serengeti. However, despite decades of study, the proximate synchronizing mechanism remains unknown. Combining a season-long field experiment with simple stochastic process models, we show that females exposed to playback of male rutting vocalizations are over three times more synchronous in their expected time to mating than a control group isolated from all male stimuli. Additionally, predictions of both mating and calving synchrony based on the playback group were highly consistent with independent data on wildebeest mating and calving synchrony, while control-based predictions were inconsistent with the data. Taken together, our results provide the first experimental evidence that male rutting vocalizations alone could account for the highly synchronized reproduction observed in Serengeti wildebeest. Given anthropogenically driven losses in many areas, a mechanistic understanding of synchrony can highlight additional risks declining wildebeest populations may face.