The natural history of luck: A synthesis study of structured population models.

Chance pervades life. In turn, life histories are described by probabilities (e.g. survival and breeding) and averages across individuals (e.g. mean growth rate and age at maturity). In this study, we explored patterns of luck in lifetime outcomes by analysing structured population models for a wide array of plant and animal species. We calculated four response variables: variance and skewness in both lifespan and lifetime reproductive output (LRO), and partitioned them into contributions from different forms of luck. We examined relationships among response variables and a variety of life history traits. We found that variance in lifespan and variance in LRO were positively correlated across taxa, but that variance and skewness were negatively correlated for both lifespan and LRO. The most important life history trait was longevity, which shaped variance and skew in LRO through its effects on variance in lifespan. We found that luck in survival, growth, and fecundity all contributed to variance in LRO, but skew in LRO was overwhelmingly due to survival luck. Rapidly growing populations have larger variances in LRO and lifespan than shrinking populations. Our results indicate that luck-induced genetic drift may be most severe in recovering populations of species with long mature lifespan and high iteroparity.

Palmitoylated Proteins in Plasmodium falciparum-Infected Erythrocytes: Investigation with Click Chemistry and Metabolic Labeling.

The examination of the complex cell biology of the human malaria parasite Plasmodium falciparum usually relies on the time-consuming generation of transgenic parasites. Here, metabolic labeling and click chemistry are employed as a fast transfection-independent method for the microscopic examination of protein S-palmitoylation, an important post-translational modification during the asexual intraerythrocytic replication of P. falciparum. Applying various microscopy approaches such as confocal, single-molecule switching, and electron microscopy, differences in the extent of labeling within the different asexual developmental stages of P. falciparum and the host erythrocytes over time are observed.

Time and Chance: Using Age Partitioning to Understand How Luck Drives Variation in Reproductive Success.

AbstractOver the course of individual lifetimes, luck usually explains a large fraction of the between-individual variation in life span or lifetime reproductive output (LRO) within a population, while variation in individual traits or "quality" explains much less. To understand how, where in the life cycle, and through which demographic processes luck trumps trait variation, we show how to partition by age the contributions of luck and trait variation to LRO variance and how to quantify three distinct components of luck. We apply these tools to several empirical case studies. We find that luck swamps effects of trait variation at all ages, primarily because of randomness in individual state dynamics ("state trajectory luck"). Luck early in life is most important. Very early state trajectory luck generally determines whether an individual ever breeds, likely by ensuring that they are not dead or doomed quickly. Less early luck drives variation in success among those breeding at least once. Consequently, the importance of luck often has a sharp peak early in life or it has two peaks. We suggest that ages or stages where the importance of luck peaks are potential targets for interventions to benefit a population of concern, different from those identified by eigenvalue elasticity analysis.

Technical Comment on Pande et al. (2020): Why invasion analysis is important for understanding coexistence.

Pande et al. (2020) point out that persistence time can decrease even as invader growth rates (IGRs) increase, which potentially undermines modern coexistence theory. However, because persistence time increases rapidly with system size only when IGR > 0, to understand how any real community persists, we should first identify the mechanisms producing positive IGR.

An expanded modern coexistence theory for empirical applications.

Understanding long-term coexistence of numerous competing species is a longstanding challenge in ecology. Progress requires determining which processes and species differences are most important for coexistence when multiple processes operate and species differ in many ways. Modern coexistence theory (MCT), formalised by Chesson, holds out the promise of doing that, but empirical applications remain scarce. We argue that MCT's mathematical complexity and subtlety have obscured the simplicity and power of its underlying ideas and hindered applications. We present a general computational approach that extends our previous solution for the storage effect to all of standard MCT's spatial and temporal coexistence mechanisms, and also process-defined mechanisms amenable to direct study such as resource partitioning, indirect competition, and life history trade-offs. The main components are a method to partition population growth rates into contributions from different mechanisms and their interactions, and numerical calculations in which some mechanisms are removed and others retained. We illustrate how our approach handles features that have not been analysed in the standard framework through several case studies: competing diatom species under fluctuating temperature, plant-soil feedbacks in grasslands, facilitation in a beach grass community, and niche differences with independent effects on recruitment, survival and growth in sagebrush steppe.

Size-by-environment interactions: a neglected dimension of species' responses to environmental variation.

In both plant and animal systems, size can determine whether an individual survives and grows under different environmental conditions. However, it is unclear whether and when size-dependent responses to exogenous environmental fluctuations affect population dynamics. Size-by-environment interactions create pathways for environmental fluctuations to influence population dynamics by allowing for negative covariation between sizes within vital rates (e.g. small and large individuals have negatively covarying survival rates) and/or size-dependent variability in a vital rate (e.g. survival of large individuals varies less than small individuals through time). Whether these phenomena affect population dynamics depends on how they are mediated by elasticities (they must affect the sizes and vital rates that matter) and their projected impacts will depend on model functional form (the impact of reduced variance depends on the relationship between the environment and vital rate). We demonstrate these ideas with an analysis of fifteen species from five semiarid plant communities. We find that size-by-environment interactions are common but do not impact long-term population dynamics. Size-by-environment interactions may yet be important for other species. Our approach can be applied to species in other ecosystems to determine if and how size-by-environment interactions allow them to cope with, or exploit, fluctuating environments.

Weak interspecific interactions in a sagebrush steppe? Conflicting evidence from observations and experiments.

Stable coexistence requires intraspecific limitations to be stronger than interspecific limitations. The greater the difference between intra- and interspecific limitations, the more stable the coexistence, and the weaker the competitive release any species should experience following removal of competitors. We conducted a removal experiment to test whether a previously estimated model, showing surprisingly weak interspecific competition for four dominant species in a sagebrush steppe, accurately predicts competitive release. Our treatments were (1) removal of all perennial grasses and (2) removal of the dominant shrub, Artemisia tripartita. We regressed survival, growth, and recruitment on the locations, sizes, and species identities of neighboring plants, along with an indicator variable for removal treatment. If our "baseline" regression model, which accounts for local plant-plant interactions, accurately explains the observed responses to removals, then the removal coefficient should be non-significant. For survival, the removal coefficients were never significantly different from zero, and only A. tripartita showed a (negative) response to removals at the recruitment stage. For growth, the removal treatment effect was significant and positive for two species, Poa secunda and Pseudoroegneria spicata, indicating that the baseline model underestimated interspecific competition. For all three grass species, population models based on the vital rate regressions that included removal effects projected 1.4- to 3-fold increases in equilibrium population size relative to the baseline model (no removal effects). However, we found no evidence of higher response to removal in quadrats with higher pretreatment cover of A. tripartita, or by plants experiencing higher pre-treatment crowding by A. tripartita, raising questions about the mechanisms driving the positive response to removal. While our results show the value of combining observations with a simple removal experiment, more tightly controlled experiments focused on underlying mechanisms may be required to conclusively validate or reject predictions from phenomenological models.

Asymptotic Properties for Methods Combining the Minimum Hellinger Distance Estimate and the Bayesian Nonparametric Density Estimate.

In frequentist inference, minimizing the Hellinger distance between a kernel density estimate and a parametric family produces estimators that are both robust to outliers and statistically efficient when the parametric family contains the data-generating distribution. This paper seeks to extend these results to the use of nonparametric Bayesian density estimators within disparity methods. We propose two estimators: one replaces the kernel density estimator with the expected posterior density using a random histogram prior; the other transforms the posterior over densities into a posterior over parameters through minimizing the Hellinger distance for each density. We show that it is possible to adapt the mathematical machinery of efficient influence functions from semiparametric models to demonstrate that both our estimators are efficient in the sense of achieving the Cramér-Rao lower bound. We further demonstrate a Bernstein-von-Mises result for our second estimator, indicating that its posterior is asymptotically Gaussian. In addition, the robustness properties of classical minimum Hellinger distance estimators continue to hold.

A newly discovered role of evolution in previously published consumer-resource dynamics.

Consumer-resource interactions are fundamental components of ecological communities. Classic features of consumer-resource models are that temporal dynamics are often cyclic, with a »-period lag between resource and consumer population peaks. However, there are few published empirical examples of this pattern. Here, we show that many published examples of consumer-resource cycling show instead patterns indicating eco-evolutionary dynamics. When prey evolve along a trade-off between defence and competitive ability, two-species consumer-resource cycles become longer and antiphase (half-period lag, so consumer maxima coincide with minima of the resource species). Using stringent criteria, we identified 21 two-species consumer-resource time series, published between 1934 and 1997, suitable to investigate for eco-evolutionary dynamics. We developed a statistical method to probe for a transition from classic to eco-evolutionary cycles, and find evidence for eco-evolutionary type cycles in about half of the studies. We show that rapid prey evolution is the most likely explanation for the observed patterns.

An expectation maximization algorithm for high-dimensional model selection for the Ising model with misclassified states.

We propose the misclassified Ising Model: a framework for analyzing dependent binary data where the binary state is susceptible to error. We extend previous theoretical results of a model selection method based on applying the LASSO to logistic regression at each node and show that the method will still correctly identify edges in the underlying graphical model under suitable misclassification settings. With knowledge of the misclassification process, an expectation maximization algorithm is developed that accounts for misclassification during model selection. We illustrate the increase of performance of the proposed expectation maximization algorithm with simulated data, and using data from a functional magnetic resonance imaging analysis.

Induction of CXCR3- and CCR5-associated chemokines during acute hepatitis C virus infection.

BACKGROUND & AIMS: Characterization of inflammatory mediators, such as chemokines, during acute hepatitis C virus (HCV) infection might shed some light on viral clearance mechanisms. METHODS: Plasma levels of CXCR3 (CXCL9-11)- and CCR5 (CCL3-4)-associated chemokines, ALT, and HCV RNA were measured in nine injection drug users (median 26 samples/patient) before and during 10 acute (eight primary and two secondary) HCV infections. Using functional data analysis, we estimated smooth long-term trends in chemokine expression levels to obtain the magnitude and timing of overall changes. Residuals were analyzed to characterize short-term fluctuations. RESULTS: CXCL9-11 induction began 38-53days and peaked 72-83days after virus acquisition. Increases in ALT levels followed a similar pattern. Substantial negative auto-correlations of chemokine levels at 1 week lags suggested substantial week-to-week oscillations. Significant correlations were observed between CXCL10 and HCV RNA as well as ALT and CXCR3-associated chemokines measured in the preceding week, CCL3-4 expression levels did not change appreciably during acute HCV infection. CONCLUSIONS: Elevation of CXCR3-associated chemokines late during acute HCV infection suggests a role for cellular immune responses in chemokine induction. Week-to-week oscillations of HCV RNA, chemokines, and ALT suggest frequent, repeated cycles of gain and loss of immune control during acute hepatitis C.

A practical guide to selecting models for exploration, inference, and prediction in ecology.

Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that much confusion surrounding statistical model selection results from failing to first clearly specify the purpose of the analysis. We argue that there are three distinct goals for statistical modeling in ecology: data exploration, inference, and prediction. Once the modeling goal is clearly articulated, an appropriate model selection procedure is easier to identify. We review model selection approaches and highlight their strengths and weaknesses relative to each of the three modeling goals. We then present examples of modeling for exploration, inference, and prediction using a time series of butterfly population counts. These show how a model selection approach flows naturally from the modeling goal, leading to different models selected for different purposes, even with exactly the same data set. This review illustrates best practices for ecologists and should serve as a reminder that statistical recipes cannot substitute for critical thinking or for the use of independent data to test hypotheses and validate predictions.

PCA of waveforms and functional PCA: A primer for biomechanics.

Principal components analysis (PCA) of waveforms and functional PCA (fPCA) are statistical approaches used to explore patterns of variability in biomechanical curve data, with fPCA being an accepted statistical method grounded within the functional data analysis (FDA) statistical framework. This technical note demonstrates that PCA of waveforms is the most rudimentary form of FDA, and consequently can be rationalised within the FDA framework of statistical processes. Mathematical proofing applied demonstrations of both techniques, and an example of when fPCA may be of greater benefit to control over smoothing of functional principal components is provided using an open access motion sickness dataset. Finally, open access software is provided with this paper as means of priming the biomechanics community for using these methods as a part of future functional data explorations.

Spatiotemporal exploratory models for broad-scale survey data.

The distributions of animal populations change and evolve through time. Migratory species exploit different habitats at different times of the year. Biotic and abiotic features that determine where a species lives vary due to natural and anthropogenic factors. This spatiotemporal variation needs to be accounted for in any modeling of species' distributions. In this paper we introduce a semiparametric model that provides a flexible framework for analyzing dynamic patterns of species occurrence and abundance from broad-scale survey data. The spatiotemporal exploratory model (STEM) adds essential spatiotemporal structure to existing techniques for developing species distribution models through a simple parametric structure without requiring a detailed understanding of the underlying dynamic processes. STEMs use a multi-scale strategy to differentiate between local and global-scale spatiotemporal structure. A user-specified species distribution model accounts for spatial and temporal patterning at the local level. These local patterns are then allowed to "scale up" via ensemble averaging to larger scales. This makes STEMs especially well suited for exploring distributional dynamics arising from a variety of processes. Using data from eBird, an online citizen science bird-monitoring project, we demonstrate that monthly changes in distribution of a migratory species, the Tree Swallow (Tachycineta bicolor), can be more accurately described with a STEM than a conventional bagged decision tree model in which spatiotemporal structure has not been imposed. We also demonstrate that there is no loss of model predictive power when a STEM is used to describe a spatiotemporal distribution with very little spatiotemporal variation; the distribution of a nonmigratory species, the Northern Cardinal (Cardinalis cardinalis).

Dynamic mechanism for initiation of ventricular fibrillation in vivo.

BACKGROUND: Dynamically induced heterogeneities of repolarization may lead to wave-front destabilizations and initiation of ventricular fibrillation (VF). In a computer modeling study, we demonstrated that specific sequences of premature stimuli maximized dynamically induced spatial dispersion of refractoriness and predisposed the heart to the development of conduction block. The purpose of this study was to determine whether the computer model results pertained to the initiation of VF in dogs in vivo. METHODS AND RESULTS: Monophasic action potentials were recorded from right and left ventricular endocardium in anesthetized beagle dogs (n=11) in vivo. Restitution of action potential duration and conduction time and the effective refractory period after delivery of the basic stimulus (S(1)) and each of 3 premature stimuli (S(2), S(3), S(4)) were determined at baseline and during verapamil infusion. The effective refractory period data were used to determine the interstimulus intervals for a sequence of 4 premature stimuli (S(2)S(3)S(4)S(5)=CL(VF)) for which the computer model predicted maximal spatial dispersion of refractoriness. Delivery of CL(VF) was associated with discordant action potential duration alternans and induction of VF in all dogs. Verapamil decreased spatial dispersion of refractoriness by reducing action potential duration and conduction time restitution in a dose-dependent fashion, effects that were associated with reduced inducibility of VF with CL(VF). CONCLUSIONS: Maximizing dynamically induced spatial dispersion of repolarization appears to be an effective method for inducing VF. Reducing spatial dispersion of refractoriness by modulating restitution parameters can have an antifibrillatory effect in vivo.

Forcing function diagnostics for nonlinear dynamics.

This article investigates the problem of model diagnostics for systems described by nonlinear ordinary differential equations (ODEs). I propose modeling lack of fit as a time-varying correction to the right-hand side of a proposed differential equation. This correction can be described as being a set of additive forcing functions, estimated from data. Representing lack of fit in this manner allows us to graphically investigate model inadequacies and to suggest model improvements. I derive lack-of-fit tests based on estimated forcing functions. Model building in partially observed systems of ODEs is particularly difficult and I consider the problem of identification of forcing functions in these systems. The methods are illustrated with examples from computational neuroscience.

Formal Hypothesis Tests for Additive Structure in Random Forests.

While statistical learning methods have proved powerful tools for predictive modeling, the black-box nature of the models they produce can severely limit their interpretability and the ability to conduct formal inference. However, the natural structure of ensemble learners like bagged trees and random forests has been shown to admit desirable asymptotic properties when base learners are built with proper subsamples. In this work, we demonstrate that by defining an appropriate grid structure on the covariate space, we may carry out formal hypothesis tests for both variable importance and underlying additive model structure. To our knowledge, these tests represent the first statistical tools for investigating the underlying regression structure in a context such as random forests. We develop notions of total and partial additivity and further demonstrate that testing can be carried out at no additional computational cost by estimating the variance within the process of constructing the ensemble. Furthermore, we propose a novel extension of these testing procedures utilizing random projections in order to allow for computationally efficient testing procedures that retain high power even when the grid size is much larger than that of the training set.

Predicting frequent ED use by people with epilepsy with health information exchange data.

OBJECTIVES: To describe (1) the predictability of frequent emergency department (ED) use (a marker of inadequate disease control and/or poor access to care), and (2) the demographics, comorbidities, and use of health services of frequent ED users, among people with epilepsy. METHODS: We obtained demographics, comorbidities, and 2 years of encounter data for 8,041 people with epilepsy from a health information exchange in New York City. Using a retrospective cohort design, we explored bivariate relationships between baseline characteristics (year 1) and subsequent frequent ED use (year 2). We then built, evaluated, and compared predictive models to identify frequent ED users (≥4 visits year 2), using multiple techniques (logistic regression, lasso, elastic net, CART [classification and regression trees], Random Forests, AdaBoost, support vector machines). We selected a final model based on performance and simplicity. RESULTS: People with epilepsy who, in year 1, were adults (rather than children or seniors), male, Manhattan residents, frequent users of health services, users of multiple health systems, or had medical, neurologic, or psychiatric comorbidities, were more likely to frequently use the ED in year 2. Predictive techniques identified frequent ED visitors with good positive predictive value (approximately 70%) but poor sensitivity (approximately 20%). A simple strategy, selecting individuals with 11+ ED visits in year 1, performed as well as more sophisticated models. CONCLUSIONS: People with epilepsy with 11+ ED visits in a year are at highest risk of continued frequent ED use and may benefit from targeted intervention to avoid preventable ED visits. Future work should focus on improving the sensitivity of predictions.