Inferring ecological interactions is hard because we often lack suitable parametric representations to portray them. Neural ordinary differential equations (NODEs) provide a way of estimating ...interactions non‐parametrically from time‐series data. NODEs, however, are slow to fit, and inferred interactions usually are not compared with the ground truth.
We provide a fast NODE fitting method, Bayesian neural gradient matching (BNGM), which relies on interpolating time series with neural networks and fitting NODEs to the interpolated dynamics with Bayesian regularisation. We test the accuracy of the approach by inferring ecological interactions in time series generated by an ODE model with known interactions. We compare these results against three existing approaches for estimating ecological interactions, standard NODEs, ODE models and convergent cross‐mapping (CCM). We also infer interactions in experimentally replicated time series of a microcosm featuring an algae, flagellate and rotifer population, in the hare and lynx system, and the Maizuru Bay community featuring 11 species.
Our BNGM approach allows us to reduce the fitting time of NODE systems to only a few seconds and provides accurate estimates of ecological interactions in the artificial system, as true ecological interactions are recovered with minimal error. Our benchmark analysis reveals that our approach is both faster and more accurate than standard NODEs and parametric ODEs, while CCM was found to be faster but less accurate. The analysis of the replicated time series reveals that only the strongest interactions are consistent across replicates, while the analysis of the Maizuru community shows the strong negative impact of the chameleon goby on most species of the community, and a potential indirect negative effect of temperature by favouring goby population growth.
Overall, NODEs alleviate the need for a mechanistic understanding of interactions, and BNGM alleviates the heavy computational cost. This is a crucial step availing quick NODE fitting to larger systems, cross‐validation and uncertainty quantification, as well as more objective estimation of interactions, and complex context dependence, than parametric models.
Most population-level studies of eco-evolutionary dynamics assume that evolutionary change occurs in response to ecological change and vice versa. However, a growing number of papers report ...simultaneous ecological and evolutionary change, suggesting that the eco-evolutionary consequences of environmental change for populations can only be fully understood through the simultaneous analysis of statistics used to describe both ecological and evolutionary dynamics. Here we argue that integral projection models (IPM), and matrix approximations of them, provide a powerful approach to integrate population ecology, life history theory, and evolution. We discuss key questions in population biology that can be examined using these models, the answers to which are essential for a general, population-level understanding of eco-evolutionary change.
Over the past 15 years, the number of papers focused on ‘eco‐evo dynamics’ has increased exponentially (Figure 1). This pattern suggests the rapid growth of a new, integrative discipline. We argue ...this overstates the case. First, the terms ‘eco‐evo dynamics’ and ‘eco‐evo interactions’ are used too imprecisely. As a result, many studies that claim to describe eco‐evo dynamics are actually describing basic ecological or evolutionary processes. Second, these terms are often used as if the study of how ecological and evolutionary processes are intertwined is novel when, in fact, it is not. The result is confusion over what the term ‘eco‐evolution’ and its derivatives describe. We advocate a more precise definition of eco‐evolution that is more useful in efforts to understand and characterise the diversity of ecological and evolutionary processes and that focuses attention on the subset of those processes that occur only when ecological and evolutionary timescales are comparable.
1
Figure
Number of papers returned, by year, by a search in Web of Science with the term ‘eco‐evolutionary dynamics' as accessed on 7 January 2021.
Number of papers returned, by year, by a search in Web of Science with the term ‘eco‐evolutionary dynamics' as accessed on 7 January 2021.
Marked impacts of climate change on biodiversity have frequently been demonstrated, including temperature-related shifts in phenology and life-history traits. One potential major impact of climate ...change is the modification of synchronization between the phenology of different trophic levels. High phenotypic plasticity in laying date has allowed many bird species to track the increasingly early springs resulting from recent environmental change, but although changes in the timing of reproduction have been well studied in birds, these questions have only recently been addressed in mammals. To track peak resource availability, large herbivores like roe deer, with a widespread distribution across Europe, should also modify their life-history schedule in response to changes in vegetation phenology over time. In this study, we analysed the influence of climate change on the timing of roe deer births and the consequences for population demography and individual fitness. Our study provides a rare quantification of the demographic costs associated with the failure of a species to modify its phenology in response to a changing world. Given these fitness costs, the lack of response of roe deer birth dates to match the increasingly earlier onset of spring is in stark contrast with the marked phenotypic responses to climate change reported in many other mammals. We suggest that the lack of phenotypic plasticity in birth timing in roe deer is linked to its inability to track environmental cues of variation in resource availability for the timing of parturition.
The mammalian gut teems with microbes, yet how hosts acquire these symbionts remains poorly understood. Research in primates suggests that microbes can be picked up via social contact, but the role ...of social interactions in non-group-living species remains underexplored. Here, we use a passive tracking system to collect high resolution spatiotemporal activity data from wild mice (Apodemus sylvaticus). Social network analysis revealed social association strength to be the strongest predictor of microbiota similarity among individuals, controlling for factors including spatial proximity and kinship, which had far smaller or nonsignificant effects. This social effect was limited to interactions involving males (male-male and male-female), implicating sex-dependent behaviours as driving processes. Social network position also predicted microbiota richness, with well-connected individuals having the most diverse microbiotas. Overall, these findings suggest social contact provides a key transmission pathway for gut symbionts even in relatively asocial mammals, that strongly shapes the adult gut microbiota. This work underlines the potential for individuals to pick up beneficial symbionts as well as pathogens from social interactions.
Population dynamics result from the interplay of density-independent and density-dependent processes. Understanding this interplay is important, especially for being able to predict near-term ...population trajectories for management. In recent years, the study of model systems-experimental, observational and theoretical-has shed considerable light on the way that the both density-dependent and -independent aspects of the environment affect population dynamics via impacting on the organism's life history and therefore demography. These model-based approaches suggest that (i) individuals in different states differ in their demographic performance, (ii) these differences generate structure that can fluctuate independently of current total population size and so can influence the dynamics in important ways, (iii) individuals are strongly affected by both current and past environments, even when the past environments may be in previous generations and (iv) dynamics are typically complex and transient due to environmental noise perturbing complex population structures. For understanding population dynamics of any given system, we suggest that 'the devil is in the detail'. Experimental dissection of empirical systems is providing important insights into the details of the drivers of demographic responses and therefore dynamics and should also stimulate theory that incorporates relevant biological mechanism.
Environmental change has altered the phenology, morphological traits and population dynamics of many species. However, the links underlying these joint responses remain largely unknown owing to a ...paucity of long-term data and the lack of an appropriate analytical framework. Here we investigate the link between phenotypic and demographic responses to environmental change using a new methodology and a long-term (1976-2008) data set from a hibernating mammal (the yellow-bellied marmot) inhabiting a dynamic subalpine habitat. We demonstrate how earlier emergence from hibernation and earlier weaning of young has led to a longer growing season and larger body masses before hibernation. The resulting shift in both the phenotype and the relationship between phenotype and fitness components led to a decline in adult mortality, which in turn triggered an abrupt increase in population size in recent years. Direct and trait-mediated effects of environmental change made comparable contributions to the observed marked increase in population growth. Our results help explain how a shift in phenology can cause simultaneous phenotypic and demographic changes, and highlight the need for a theory integrating ecological and evolutionary dynamics in stochastic environments.
1. There is a growing number of empirical reports of environmental change simultaneously influencing population dynamics, life history and quantitative characters. We do not have a well-developed ...understanding of links between the dynamics of these quantities. 2. Insight into the joint dynamics of populations, quantitative characters and life history can be gained by deriving a model that allows the calculation of fundamental quantities that underpin population ecology, evolutionary biology and life history. The parameterization and analysis of such a model for a specific system can be used to predict how a population will respond to environmental change. 3. Age-stage-structured models can be constructed from character-demography associations that describe age-specific relationships between the character and: (i) survival; (ii) fertility; (iii) ontogenetic development of the character among survivors; and (iv) the distribution of reproductive allocation. 4. These models can be used to calculate a wide range of useful biological quantities including population growth and structure; terms in the Price equation including selection differentials; estimates of biometric heritabilities; and life history descriptors including generation time. We showcase the method through parameterization of a model using data from a well-studied population of Soay sheep Ovis aries. 5. Perturbation analysis is used to investigate how the quantities listed in summary point 4 change as each parameter in each character-demography function is altered. 6. A wide range of joint dynamics of life history, quantitative characters and population growth can be generated in response to changes in different character-demography associations; we argue this explains the diversity of observations on the consequences of environmental change from studies of free-living populations. 7. The approach we describe has the potential to explain within and between species patterns in quantitative characters, life history and population dynamics.
Introduction:
Postoperative atrial fibrillation (POAF) is common after cardiac surgery and associated with increased hospital length of stay, patient morbidity and mortality. We aimed to identify ...factors associated with POAF and evaluate the accuracy of available POAF prediction models.
Methods:
We screened articles from Ovid MEDLINE® and PubMed Central® (PMC) and included studies that evaluated risk factors associated with POAF or studies that designed or validated POAF prediction models. We only included studies in cardiac surgical patients with sample size n ⩾ 50 and a POAF outcome group ⩾20. We summarised factors that were associated with POAF and assessed prediction model performance by reviewing reported calibration and discriminative ability.
Results:
We reviewed 232 studies. Of these, 142 fulfilled the inclusion criteria. Age was frequently found to be associated with POAF, while most other variables showed contradictory findings, or were assessed in few studies. Overall, 15 studies specifically developed and/or validated 12 prediction models. Of these, all showed poor discrimination or absent calibration in predicting POAF in externally validated cohorts.
Conclusions:
Except for age, reporting of factors associated with POAF is inconsistent and often contradictory. Prediction models have low discrimination, missing calibration statistics, are at risk of bias and show limited clinical applicability. This suggests the need for studies that prospectively collect AF relevant data in large cohorts and then proceed to validate findings in external data sets.
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