Sex-specific effects of fisheries and climate on the demography of sexually dimorphic seabirds.

Many animal taxa exhibit sex-specific variation in ecological traits, such as foraging and distribution. These differences could result in sex-specific responses to change, but such demographic effects are poorly understood. Here, we test for sex-specific differences in the demography of northern (NGP, Macronectes halli) and southern (SGP, M. giganteus) giant petrels - strongly sexually size-dimorphic birds that breed sympatrically at South Georgia, South Atlantic Ocean. Both species feed at sea or on carrion on land, but larger males (30% heavier) are more reliant on terrestrial foraging than the more pelagic females. Using multi-event mark-recapture models, we examine the impacts of long-term changes in environmental conditions and commercial fishing on annual adult survival and use two-sex matrix population models to forecast future trends. As expected, survival of male NGP was positively affected by carrion availability, but negatively affected by zonal winds. Female survival was positively affected by meridional winds and El Niño-Southern Oscillation (ENSO), and negatively affected by sea ice concentration and pelagic longline effort. Survival of SGPs did not differ between sexes; however, survival of males only was positively correlated with the Southern Annular Mode (SAM). Two-sex population projections indicate that future environmental conditions are likely to benefit giant petrels. However, any potential increase in pelagic longline fisheries could reduce female survival and population growth. Our study reveals that sex-specific ecological differences can lead to divergent responses to environmental drivers (i.e. climate and fisheries). Moreover, because such effects may not be apparent when all individuals are considered together, ignoring sex differences could underestimate the relative influence of a changing environment on demography.

Identifying on admission patients likely to develop acute kidney injury in hospital.

BACKGROUND: The incidence of Acute Kidney Injury (AKI) continues to increase in the UK, with associated mortality rates remaining significant. Approximately one fifth of hospital admissions are associated with AKI and approximately a third of patients with AKI in hospital develop AKI during their time in hospital. A fifth of these cases are considered avoidable. Early risk detection remains key to decreasing AKI in hospitals, where sub-optimal care was noted for half of patients who developed AKI. METHODS: Electronic anonymised data for adults admitted into the Royal Cornwall Hospitals Trust (RCHT) between 18th March and 31st December 2015 was trimmed to that collected within the first 24 h of hospitalisation. These datasets were split according to three separate time periods: data used for training the Takagi-Sugeno Fuzzy Logic Systems (FLS) and the multivariable logistic regression (MLR) models; data used for testing; and data from a later patient spell used for validation. Three fuzzy logic models and three MLR models were developed to link characteristics of patients diagnosed with a maximum stage AKI within 7 days of admission: the first models to identify any AKI Stage (FLS I, MLR I), the second for patterns of AKI Stage 2 or 3 (FLS II, MLR II), and the third to identify AKI Stage 3 (FLS III, MLR III). Model accuracy is expressed by area under the curve (AUC). RESULTS: Accuracy for each model during internal validation was: FLS I and MLR I (AUC 0.70, 95% CI: 0.64-0.77); FLS II (AUC 0.77, 95% CI: 0.69-0.85) and MLR II (AUC 0.74, 95% CI: 0.65-0.83); FLS III and MLR III (AUC 0.95, 95% CI: 0.92-0.98). CONCLUSIONS: FLS II and FLS III (and the respective MLR models) can identify with a high level of accuracy patients at high risk of developing AKI in hospital. These two models cannot be properly assessed against prior studies as this is the first attempt at quantifying the risk of developing specific Stages of AKI for a broad cohort of both medical and surgical inpatients. FLS I and MLR I performance is comparable to other existing models.

A necessary condition for dispersal driven growth of populations with discrete patch dynamics.

We revisit the question of when can dispersal-induced coupling between discrete sink populations cause overall population growth? Such a phenomenon is called dispersal driven growth and provides a simple explanation of how dispersal can allow populations to persist across discrete, spatially heterogeneous, environments even when individual patches are adverse or unfavourable. For two classes of mathematical models, one linear and one non-linear, we provide necessary conditions for dispersal driven growth in terms of the non-existence of a common linear Lyapunov function, which we describe. Our approach draws heavily upon the underlying positive dynamical systems structure. Our results apply to both discrete- and continuous-time models. The theory is illustrated with examples and both biological and mathematical conclusions are drawn.

Robust set-point regulation for ecological models with multiple management goals.

Population managers will often have to deal with problems of meeting multiple goals, for example, keeping at specific levels both the total population and population abundances in given stage-classes of a stratified population. In control engineering, such set-point regulation problems are commonly tackled using multi-input, multi-output proportional and integral (PI) feedback controllers. Building on our recent results for population management with single goals, we develop a PI control approach in a context of multi-objective population management. We show that robust set-point regulation is achieved by using a modified PI controller with saturation and anti-windup elements, both described in the paper, and illustrate the theory with examples. Our results apply more generally to linear control systems with positive state variables, including a class of infinite-dimensional systems, and thus have broader appeal.

The evolution of multivariate maternal effects.

There is a growing interest in predicting the social and ecological contexts that favor the evolution of maternal effects. Most predictions focus, however, on maternal effects that affect only a single character, whereas the evolution of maternal effects is poorly understood in the presence of suites of interacting traits. To overcome this, we simulate the evolution of multivariate maternal effects (captured by the matrix M) in a fluctuating environment. We find that the rate of environmental fluctuations has a substantial effect on the properties of M: in slowly changing environments, offspring are selected to have a multivariate phenotype roughly similar to the maternal phenotype, so that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phenotype which substantially differs from the maternal phenotype. Moreover, when fluctuating selection on one maternal character is temporally delayed relative to selection on other traits, we find a striking pattern of cross-trait maternal effects in which maternal characters influence not only the same character in offspring, but also other offspring characters. Additionally, when selection on one character contains more stochastic noise relative to selection on other traits, large cross-trait maternal effects evolve from those maternal traits that experience the smallest amounts of noise. The presence of these cross-trait maternal effects shows that individual maternal effects cannot be studied in isolation, and that their study in a multivariate context may provide important insights about the nature of past selection. Our results call for more studies that measure multivariate maternal effects in wild populations.

Integral control for population management.

We present a novel management methodology for restocking a declining population. The strategy uses integral control, a concept ubiquitous in control theory which has not been applied to population dynamics. Integral control is based on dynamic feedback-using measurements of the population to inform management strategies and is robust to model uncertainty, an important consideration for ecological models. We demonstrate from first principles why such an approach to population management is suitable via theory and examples.

Bounds on the dynamics of sink populations with noisy immigration.

Sink populations are doomed to decline to extinction in the absence of immigration. The dynamics of sink populations are not easily modelled using the standard framework of per capita rates of immigration, because numbers of immigrants are determined by extrinsic sources (for example, source populations, or population managers). Here we appeal to a systems and control framework to place upper and lower bounds on both the transient and future dynamics of sink populations that are subject to noisy immigration. Immigration has a number of interpretations and can fit a wide variety of models found in the literature. We apply the results to case studies derived from published models for Chinook salmon (Oncorhynchus tshawytscha) and blowout penstemon (Penstemon haydenii).

Global asymptotic stability of density dependent integral population projection models.

Many stage-structured density dependent populations with a continuum of stages can be naturally modeled using nonlinear integral projection models. In this paper, we study a trichotomy of global stability result for a class of density dependent systems which include a Platte thistle model. Specifically, we identify those systems parameters for which zero is globally asymptotically stable, parameters for which there is a positive asymptotically stable equilibrium, and parameters for which there is no asymptotically stable equilibrium.

A framework for studying transient dynamics of population projection matrix models.

Empirical models are central to effective conservation and population management, and should be predictive of real-world dynamics. Available modelling methods are diverse, but analysis usually focuses on long-term dynamics that are unable to describe the complicated short-term time series that can arise even from simple models following ecological disturbances or perturbations. Recent interest in such transient dynamics has led to diverse methodologies for their quantification in density-independent, time-invariant population projection matrix (PPM) models, but the fragmented nature of this literature has stifled the widespread analysis of transients. We review the literature on transient analyses of linear PPM models and synthesise a coherent framework. We promote the use of standardised indices, and categorise indices according to their focus on either convergence times or transient population density, and on either transient bounds or case-specific transient dynamics. We use a large database of empirical PPM models to explore relationships between indices of transient dynamics. This analysis promotes the use of population inertia as a simple, versatile and informative predictor of transient population density, but criticises the utility of established indices of convergence times. Our findings should guide further development of analyses of transient population dynamics using PPMs or other empirical modelling techniques.

AI-based healthcare: a new dawn or apartheid revisited?

The Bubonic Plague outbreak that wormed its way through San Francisco's Chinatown in 1900 tells a story of prejudice guiding health policy, resulting in enormous suffering for much of its Chinese population. This article seeks to discuss the potential for hidden "prejudice" should Artificial Intelligence (AI) gain a dominant foothold in healthcare systems. Using a toy model, this piece explores potential future outcomes, should AI continue to develop without bound. Where potential dangers may lurk will be discussed, so that the full benefits AI has to offer can be reaped whilst avoiding the pitfalls. The model is produced using the computer programming language MATLAB and offers visual representations of potential outcomes. Interwoven with these potential outcomes are numerous historical models for problems caused by prejudice and recent issues in AI systems, from police prediction and facial recognition software to recruitment tools. Therefore, this research's novel angle, of using historical precedents to model and discuss potential futures, offers a unique contribution. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00146-020-01120-w.

Modeling and analysis of COVID-19 epidemics with treatment in fractional derivatives using real data from Pakistan.

Coronaviruses are a large family of viruses that cause different symptoms, from mild cold to severe respiratory distress, and they can be seen in different types of animals such as camels, cattle, cats and bats. Novel coronavirus called COVID-19 is a newly emerged virus that appeared in many countries of the world, but the actual source of the virus is not yet known. The outbreak has caused pandemic with 26,622,706 confirmed infections and 874,708 reported deaths worldwide till August 31, 2020, with 17,717,911 recovered cases. Currently, there exist no vaccines officially approved for the prevention or management of the disease, but alternative drugs meant for HIV, HBV, malaria and some other flus are used to treat this virus. In the present paper, a fractional-order epidemic model with two different operators called the classical Caputo operator and the Atangana-Baleanu-Caputo operator for the transmission of COVID-19 epidemic is proposed and analyzed. The reproduction number R 0 is obtained for the prediction and persistence of the disease. The dynamic behavior of the equilibria is studied by using fractional Routh-Hurwitz stability criterion and fractional La Salle invariant principle. Special attention is given to the global dynamics of the equilibria. Moreover, the fitting of parameters through least squares curve fitting technique is performed, and the average absolute relative error between COVID-19 actual cases and the model's solution for the infectious class is tried to be reduced and the best fitted values of the relevant parameters are achieved. The numerical solution of the proposed COVID-19 fractional-order model under the Caputo operator is obtained by using generalized Adams-Bashforth-Moulton method, whereas for the Atangana-Baleanu-Caputo operator, we have used a new numerical scheme. Also, the treatment compartment is included in the population which determines the impact of alternative drugs applied for treating the infected individuals. Furthermore, numerical simulations of the model and their graphical presentations are performed to visualize the effectiveness of our theoretical results and to monitor the effect of arbitrary-order derivative.

Patterns and rules for sensitivity and elasticity in population projection matrices.

Sensitivity and elasticity analysis of population projection matrices (PPMs) are established tools in the analysis of structured populations, allowing comparison of the contributions made by different demographic rates to population growth. In some commonly used structures of PPM, however, there are mathematically inevitable patterns in the relative sensitivity and elasticity of certain demographic rates. We take a simulation approach to investigate these mathematical constraints for a range of PPM models. Our results challenge some previously proposed constraints on sensitivity and elasticity. We also identify constraints beyond those that have already been proven mathematically and promote them as candidates for future mathematical proof. A general theme among these rules is that changes to the demographic rates of older or larger individuals have less impact on population growth than do equivalent changes among younger or smaller individuals. However, the validity of these rules in each case depends on the choice between sensitivity and elasticity, the growth rate of the population, and the PPM structure used. If the structured population conforms perfectly to the assumptions of the PPM used to model it, the rules we describe represent biological reality, allowing us to prioritize management strategies in the absence of detailed demographic data. Conversely, if the model is a poor fit to the population (specifically, if demographic rates within stages are heterogeneous), such analyses could lead to inappropriate management prescriptions. Our results emphasize the importance of choosing a structured population model that fits the demographics of the population.

Parameterizing the growth-decline boundary for uncertain population projection models.

We consider discrete time linear population models of the form n(t+1)=An(t) where A is a population projection matrix or integral projection operator, and n(t) represents a structured population at time t. It is well known that the asymptotic growth or decay rate of n(t) is determined by the leading eigenvalue of A. In practice, population models have substantial parameter uncertainty, and it might be difficult to quantify the effect of this uncertainty on the leading eigenvalue. For a large class of matrices and integral operators A, we give sufficient conditions for an eigenvalue to be the leading eigenvalue. By preselecting the leading eigenvalue to be equal to 1, this allows us to easily identify, which combination of parameters, within the confines of their uncertainty, lead to asymptotic growth, and which lead to asymptotic decay. We then apply these results to the analysis of uncertainty in both a matrix model and an integral model for a population of thistles. We show these results can be generalized to any preselected leading eigenvalue.

Predicting the impact of stage-specific harvesting on population dynamics.

1. Perturbation analyses of population projection matrices predict the response of a population's growth rate to changes in lifestage-specific vital rates. Such predictions have been widely used in population management but their reliability remains hotly debated. 2. We grew replicate populations of the water flea Daphnia magna in controlled, density-independent conditions and subjected treatment populations to harvesting of the largest lifestage. We predicted the growth rate of treatment populations using sensitivity analysis (a linear approximation), and transfer function analysis (TFA; which captures nonlinear responses) applied to projection matrix models parameterized from the control populations. 3. When perturbation analyses considered only the direct effect of harvesting on adult survival, the growth rate of harvested populations (averaging 0.051) was significantly overestimated (average of 0.112) by TFA and non-significantly underestimated (average of 0.012) by sensitivity. 4. When the indirect effects of harvesting on other vital rates were accounted for in a structured perturbation, TFA gave accurate predictions (average growth rate of 0.068), while sensitivity gave significant underestimates (average of -0.043). 5. Our results demonstrate two crucial sources of error that may influence predictions of the impacts of demographic perturbations on population dynamics. First, impacts of stage-specific harvesting are inherently nonlinear, hence predictions based on sensitivity must be treated with caution. Second, stage-specific perturbations can change non-target demographic rates, even in the absence of adaptation. 6. Population managers should consider both nonlinear and indirect effects of perturbations when designing management interventions. We encourage the development of methods to assess the robustness of predictions to unforeseen perturbation structures and indirect harvesting impacts.

Demographic amplification is a predictor of invasiveness among plants.

Invasive plant species threaten native biodiversity, ecosystems, agriculture, industry and human health worldwide, lending urgency to the search for predictors of plant invasiveness outside native ranges. There is much conflicting evidence about which plant characteristics best predict invasiveness. Here we use a global demographic survey for over 500 plant species to show that populations of invasive plants have better potential to recover from disturbance than non-invasives, even when measured in the native range. Invasives have high stable population growth rates in their invaded ranges, but this metric cannot be predicted based on measurements in the native ranges. Recovery from demographic disturbance is a measure of transient population amplification, linked to high levels of reproduction, and shows phylogenetic signal. Our results demonstrate that transient population dynamics and reproductive capacity can help to predict invasiveness across the plant kingdom, and should guide international policy on trade and movement of plants.

On second order sensitivity for stage-based population projection matrix models.

In this paper we present a simple method for identifying life-history perturbations in population projection matrices that yield an accelerating population growth rate. Accelerating growth means that the dependence of the growth rate on the perturbation is convex. Convexity, when the second sensitivity of the growth rate is positive, is calculated using a new formula derived from the transfer function of the perturbed system. This formula is used to explore the relationship between stasis and growth probabilities from stage-structured population projection matrices.

Nonlinearity in eigenvalue-perturbation curves of simulated population projection matrices.

Sensitivity and elasticity analyses of population projection matrices (PPMs) are established tools in the analysis of structured populations, but they make a linear approximation of the usually nonlinear relationship between population growth and matrix elements. The evaluation of alternative population management interventions may be particularly vulnerable to error if the degree of nonlinearity depends on the element of the PPM that is targeted. The second self-derivative and the self-sensitivity of elasticity can be used to estimate the degree and sign of nonlinearity in sensitivity and elasticity analyses, respectively. Using simulated matrices, we demonstrate that the magnitude and sign of the second self-derivative and the self-sensitivity of elasticity vary systematically, according to the element of the PPM subject to perturbation. Population management prescriptions based on sensitivity and elasticity analysis should always be backed up by nonlinear analyses.

Inferring transient dynamics of human populations from matrix non-normality.

In our increasingly unstable and unpredictable world, population dynamics rarely settle uniformly to long-term behaviour. However, projecting period-by-period through the preceding fluctuations is more data-intensive and analytically involved than evaluating at equilibrium. To efficiently model populations and best inform policy, we require pragmatic suggestions as to when it is necessary to incorporate short-term transient dynamics and their effect on eventual projected population size. To estimate this need for matrix population modelling, we adopt a linear algebraic quantity known as non-normality. Matrix non-normality is distinct from normality in the Gaussian sense, and indicates the amplificatory potential of the population projection matrix given a particular population vector. In this paper, we compare and contrast three well-regarded metrics of non-normality, which were calculated for over 1000 age-structured human population projection matrices from 42 European countries in the period 1960 to 2014. Non-normality increased over time, mirroring the indices of transient dynamics that peaked around the millennium. By standardising the matrices to focus on transient dynamics and not changes in the asymptotic growth rate, we show that the damping ratio is an uninformative predictor of whether a population is prone to transient booms or busts in its size. These analyses suggest that population ecology approaches to inferring transient dynamics have too often relied on suboptimal analytical tools focussed on an initial population vector rather than the capacity of the life cycle to amplify or dampen transient fluctuations. Finally, we introduce the engineering technique of pseudospectra analysis to population ecology, which, like matrix non-normality, provides a more complete description of the transient fluctuations than the damping ratio. Pseudospectra analysis could further support non-normality assessment to enable a greater understanding of when we might expect transient phases to impact eventual population dynamics.

Divergent demographic strategies of plants in variable environments.

One of the best-supported patterns in life history evolution is that organisms cope with environmental fluctuations by buffering their most important vital rates against them. This demographic buffering hypothesis is evidenced by a tendency for temporal variation in rates of survival and reproduction to correlate negatively with their contribution to fitness. Here, we show that widespread evidence for demographic buffering can be artefactual, resulting from natural relationships between the mean and variance of vital rates. Following statistical scaling, we find no significant tendency for plant life histories to be buffered demographically. Instead, some species are buffered, whereas others have labile life histories with higher temporal variation in their more important vital rates. We find phylogenetic signal in the strength and direction of variance-importance correlations, suggesting that clades of plants are prone to being either buffered or labile. Species with simple life histories are more likely to be demographically labile. Our results suggest important evolutionary nuances in how species deal with environmental fluctuations.

Transients drive the demographic dynamics of plant populations in variable environments.

The dynamics of structured plant populations in variable environments can be decomposed into the 'asymptotic' growth contributed by vital rates, and 'transient' growth caused by deviation from stable stage structure.We apply this framework to a large, global data base of longitudinal studies of projection matrix models for plant populations. We ask, what is the relative contribution of transient boom and bust to the dynamic trajectories of plant populations in stochastic environments? Is this contribution patterned by phylogeny, growth form or the number of life stages per population and per species?We show that transients contribute nearly 50% or more to the resulting trajectories, depending on whether transient and stable contributions are partitioned according to their absolute or net contribution to population dynamics.Both transient contributions and asymptotic contributions are influenced heavily by the number of life stages modelled. We discuss whether the drivers of transients should be considered real ecological phenomena, or artefacts of study design and modelling strategy. We find no evidence for phylogenetic signal in the contribution of transients to stochastic growth, nor clear patterns related to growth form. We find a surprising tendency for plant populations to boom rather than bust in response to temporal changes in vital rates and that stochastic growth rates increase with increasing tendency to boom. Synthesis. Transient dynamics contribute significantly to stochastic population dynamics but are often overlooked in ecological and evolutionary studies that employ stochastic analyses. Better understanding of transient responses to fluctuating population structure will yield better management strategies for plant populations, and better grasp of evolutionary dynamics in the real world.