Explore the vast range of titles available.

As the deaths from Covid-19 have mounted, many patients with cancer struggle to process the case fatality rate in the context of all the other statistics their oncologists cite. Chemotherapy, hardly desirable at the best of times, may never have been less appealing.

Spatial patterns of movement are fundamental to the ecology of animal populations, influencing their social organization, mating systems, demography, and the spatial distribution of prey and competitors. However, our ability to understand the causes and consequences of animal home range patterns has been limited by the descriptive nature of the statistical models used to analyze them. InMechanistic Home Range Analysis, Paul Moorcroft and Mark Lewis develop a radically new framework for studying animal home range patterns based on the analysis of correlated random work models for individual movement behavior. They use this framework to develop a series of mechanistic home range models for carnivore populations. The authors' analysis illustrates how, in contrast to traditional statistical home range models that merely describe pattern, mechanistic home range models can be used to discover the underlying ecological determinants of home range patterns observed in populations, make accurate predictions about how spatial distributions of home ranges will change following environmental or demographic disturbance, and analyze the functional significance of the movement strategies of individuals that give rise to observed patterns of space use. By providing researchers and graduate students of ecology and wildlife biology with a more illuminating way to analyze animal movement,Mechanistic Home Range Analysiswill be an indispensable reference for years to come.

Forecasting the occurrence and absence of novel disease outbreaks is essential for disease management, yet existing methods are often context-specific, require a long preparation time, and non-outbreak prediction remains understudied. To address this gap, we propose a novel framework using a feature-based time series classification (TSC) method to forecast outbreaks and non-outbreaks. We tested our methods on synthetic data from a Susceptible–Infected–Recovered (SIR) model for slowly changing, noisy disease dynamics. Outbreak sequences give a transcritical bifurcation within a specified future time window, whereas non-outbreak (null bifurcation) sequences do not. We identified incipient differences, reflected in 22 statistical features and 5 early warning signal indicators, in time series of infectives leading to future outbreaks and non-outbreaks. Classifier performance, given by the area under the receiver-operating curve (AUC), ranged from 0 . 99 for large expanding windows of training data to 0 . 7 for small rolling windows. The framework is further evaluated on four empirical datasets: COVID-19 incidence data from Singapore, 18 other countries, and Edmonton, Canada, as well as SARS data from Hong Kong, with two classifiers exhibiting consistently high accuracy. Our results highlight detectable statistical features distinguishing outbreak and non-outbreak sequences well before potential occurrence, in both synthetic and real-world datasets presented in this study.

State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears ( Ursus maritimus ) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.

Effective management of emerging river‐borne diseases requires early prediction of pathogen spatial distributions. However, data on pathogen locations are notoriously rare in the beginning of disease outbreaks and insufficient to feed existing predictive models. We extended hidden Markov models (HMM) to exploit detailed spatially structured data on environmental covariates that are readily available for many aquatic systems, augmented by arbitrarily sparse spatial data on actual pathogen occurrence, to predict pathogen distribution over large geographical scales. The extended HMM predicted the spread of whirling disease in the Oldman River, Canada, with 0.7 area under the receiver operating characteristic curve (AUC) in the absence of any disease test result, provided that the status of a single pixel can be estimated correctly. The AUC increased with the number of used test results, until it reached 0.9 when 100 test results were used. The model has the potential to provide early warning signals for emerging diseases prior to, or at the early stages of, their emergence in a river.

Planning forest management relies on predicting insect outbreaks such as mountain pine beetle, particularly in the intermediate‐term future, e.g., 5‐year. Machine‐learning algorithms are potential solutions to this challenging problem due to their many successes across a variety of prediction tasks. However, there are many subtle challenges in applying them: identifying the best learning models and the best subset of available covariates (including time lags) and properly evaluating the models to avoid misleading performance‐measures. We systematically address these issues in predicting the chance of a mountain pine beetle outbreak in the Cypress Hills area and seek models with the best performance at predicting future 1‐, 3‐, 5‐ and 7‐year infestations. We train nine machine‐learning models, including two generalized boosted regression trees (GBM) that predict future 1‐ and 3‐year infestations with 92% and 88% AUC, and two novel mixed models that predict future 5‐ and 7‐year infestations with 86% and 84% AUC, respectively. We also consider forming the train and test datasets by splitting the original dataset randomly rather than using the appropriate year‐based approach and show that this may obtain models that score high on the test dataset but low in practice, resulting in inaccurate performance evaluations. For example, a k‐nearest neighbor model with the actual performance of 68% AUC, scores the misleadingly high 78% on a test dataset obtained from a random split, but the more accurate 66% on a year‐based split. We then investigate how the prediction accuracy varies with respect to the provided history length of the covariates and find that neural network and naive Bayes, predict more accurately as history‐length increases, particularly for future 1‐ and 3‐year predictions, and roughly the same holds with GBM. Our approach is applicable to other invasive species. The resulting predictors can be used in planning forest and pest management and planning sampling locations in field studies. There are subtle challenges in applying machine‐learning algorithms to forecasting insect outbreaks in the intermediate‐term future. We address these issues and provide step‐by‐step instructions using the case study of the mountain pine beetle outbreak in the Cypress Hills.

We develop a model of CWD management by nonselective deer harvest, currently the most feasible approach available for managing CWD in wild populations. We use the model to explore the effects of 6 common harvest strategies on disease prevalence and to identify potential optimal harvest policies for reducing disease prevalence without population collapse. The model includes 4 deer categories (juveniles, adult females, younger adult males, older adult males) that may be harvested at different rates, a food-based carrying capacity, which influences juvenile survival but not adult reproduction or survival, and seasonal force of infection terms for each deer category under differing frequency-dependent transmission dynamics resulting from environmental and direct contact mechanisms. Numerical experiments show that the interval of transmission coefficients β where the disease can be controlled is generally narrow and efficiency of a harvest policy to reduce disease prevalence depends crucially on the details of the disease transmission mechanism, in particular on the intensity of disease transmission to juveniles and the potential differences in the behavior of older and younger males that influence contact rates. Optimal harvest policy to minimize disease prevalence for each of the assumed transmission mechanisms is shown to depend on harvest intensity. Across mechanisms, a harvest that focuses on antlered deer, without distinguishing between age classes reduces disease prevalence most consistently, whereas distinguishing between young and older antlered deer produces higher uncertainty in the harvest effects on disease prevalence. Our results show that, despite uncertainties, a modelling approach can determine classes of harvest strategy that are most likely to be effective in combatting CWD.

The founding population in most new species introductions, or at the leading edge of an ongoing invasion, is likely to be small. Severe Allee effects—reductions in individual fitness at low population density—may then result in a failure of the species to colonize, even if the habitat could support a much larger population. Using a simulation model for plant populations that incorporates demography, mating systems, quantitative genetics, and pollinators, we show that Allee effects can potentially be overcome by transient hybridization with a resident species or an earlier colonizer. This mechanism does not require the invocation of adaptive changes usually attributed to invasions following hybridization. We verify our result in a case study of sequential invasions by two plant species where the outcrosser Cakile maritima has replaced an earlier, inbreeding, colonizer Cakile edentula (Brassicaceae). Observed historical rates of replacement are consistent with model predictions from hybrid-alleviated Allee effects in outcrossers, although other causes cannot be ruled out.

Toxic cyanobacterial blooms (CBs) are becoming more frequent globally, posing a threat to freshwater ecosystems. While making long‐range forecasts is overly challenging, predicting imminent CBs is possible from precise monitoring data of the underlying covariates. It is, however, infeasibly costly to conduct precise monitoring on a large scale, leaving most lakes unmonitored or only partially monitored. The challenge is hence to build a predictive model that can use the incomplete, partially‐monitored data to make near‐future CB predictions. By using 30 years of monitoring data for 78 water bodies in Alberta, Canada, combined with data of watershed characteristics (including natural land cover and anthropogenic land use) and meteorological conditions, we train a Bayesian network that predicts future 2‐week CB with an area under the curve (AUC) of 0.83. The only monitoring data that the model needs to reach this level of accuracy are whether the cell count and Secchi depth are low, medium, or high, which can be estimated by advanced high‐resolution imaging technology or trained local citizens. The model is robust against missing values as in the absence of any single covariate, it performs with an AUC of at least 0.78. While taking a major step toward reduced‐cost, less data‐intensive CB forecasting, our results identify those key covariates that are worth the monitoring investment for highly accurate predictions. Key Points Toxic algae blooms pose threats globally, longing for prompt prediction Our work uses less costly, incomplete data sets to make near‐future bloom predictions We predict blooms with area under the curve 0.83 using partial data of regional and local information