Ecological theory predicts that disease incidence increases with increasing density of host networks, yet evolutionary theory suggests that host resistance increases accordingly. To test the combined ...effects of ecological and evolutionary forces on host-pathogen systems, we analyzed the spatiotemporal dynamics of a plant (Plantago Ianceolata)–fungal pathogen (Podosphaera plantaginis) relationship for 12 years in over 4000 host populations. Disease prevalence at the metapopulation level was low, with high annual pathogen extinction rates balanced by frequent (re-)colonizations. Highly connected host populations experienced less pathogen colonization and higher pathogen extinction rates than expected; a laboratory assay confirmed that this phenomenon was caused by higher levels of disease resistance in highly connected host populations.
Increasing body of experimental evidence suggests that anticancer and antimicrobial therapies may themselves promote the acquisition of drug resistance by increasing mutability. The successful ...control of evolving populations requires that such biological costs of control are identified, quantified and included to the evolutionarily informed treatment protocol. Here we identify, characterise and exploit a trade-off between decreasing the target population size and generating a surplus of treatment-induced rescue mutations. We show that the probability of cure is maximized at an intermediate dosage, below the drug concentration yielding maximal population decay, suggesting that treatment outcomes may in some cases be substantially improved by less aggressive treatment strategies. We also provide a general analytical relationship that implicitly links growth rate, pharmacodynamics and dose-dependent mutation rate to an optimal control law. Our results highlight the important, but often neglected, role of fundamental eco-evolutionary costs of control. These costs can often lead to situations, where decreasing the cumulative drug dosage may be preferable even when the objective of the treatment is elimination, and not containment. Taken together, our results thus add to the ongoing criticism of the standard practice of administering aggressive, high-dose therapies and motivate further experimental and clinical investigation of the mutagenicity and other hidden collateral costs of therapies.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The epidemiological dynamics of potentially free-living pathogens are often studied with respect to a specific pathogen species (e.g., cholera) and most studies concentrate only on host-pathogen ...interactions. Here we show that metacommunity-level interactions can alter conventional spatial disease dynamics. We introduce a pathogen eating consumer species and investigate a deterministic epidemiological model of two habitat patches, where both patches can be occupied by hosts, pathogens, and consumers of free-living pathogens. An isolated habitat patch shows periodic disease outbreaks in the host population, arising from cyclic consumer-pathogen dynamics. On the other hand, consumer dispersal between the patches generate asymmetric disease prevalence, such that the host population in one patch stays disease-free, while disease outbreaks occur in the other patch. Such asymmetry can also arise with host dispersal, where infected hosts carry pathogens to the other patch. This indirect movement of pathogens causes also a counter-intuitive effect: decreasing morbidity in a focal patch under increasing pathogen immigration. Our results underline that community-level interactions influence disease dynamics and consistent spatial asymmetry can arise also in spatially homogeneous systems.
We construct a model that combines extinction‐colonization dynamics with the dynamics of local adaptation in a network of habitat patches of dissimilar qualities. We derive a deterministic ...approximation for the stochastic model that allows the calculation of patch‐specific incidences of occupancy and levels of adaptation at steady state. Depending on (i) the strength of local selection, (ii) the amount of genetic variance, (iii) the demographic cost of maladaptation, (iv) the spatial scale of gene flow, and (v) the amount of habitat heterogeneity, the model predicts adaptation at different spatial scales. Local adaptation is predicted when there is much genetic variance and strong selection, while network‐level adaptation occurs when the demographic cost of maladaptation is low. For little genetic variance and high cost of maladaptation, the model predicts network‐level habitat specialization in species with long‐range migration but an intermediate scale of adaptation (mosaic specialization) in species with short‐range migration. In fragmented landscapes, the evolutionary dynamics of adaptation may both decrease and enhance metapopulation viability in comparison with no evolution. The model can be applied to real patch networks with given sizes, qualities, and spatial positions of habitat patches.
Evolutionary change in individual species has been hypothesized to have far-reaching consequences for entire ecological communities 1–3, and such coupling of ecological and evolutionary dynamics ...(“eco-evolutionary dynamics”) has been demonstrated for a variety systems 4–7. However, the general importance of evolutionary dynamics for ecological dynamics remains unclear. Here, we investigate how spatial patterns of local adaptation in the stick insect Timema cristinae, driven by the interaction between multiple evolutionary processes, structure metapopulations, communities, and multitrophic interactions.
Observations of a wild T. cristinae metapopulation show that locally imperfect camouflage reduces population size and that the effect of such maladaptation is comparable to the effects of more traditional ecological factors, including habitat patch size and host-plant species identity. Field manipulations of local adaptation and bird predation support the hypothesis that maladaptation reduces population size through an increase in bird predation. Furthermore, these field experiments show that maladaptation in T. cristinae and consequent increase in bird predation reduce the pooled abundance and species richness of the co-occurring arthropod community, and ultimately cascade to decrease herbivory on host plants. An eco-evolutionary model of the observational data demonstrates that the demographic cost of maladaptation decreases habitat patch occupancy by T. cristinae but enhances metapopulation-level adaptation.
The results demonstrate a pervasive effect of ongoing evolution in a spatial context on population and community dynamics. The eco-evolutionary model makes testable predictions about the influence of the spatial configuration of the patch network on metapopulation size and the spatial scale of adaptation.
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•Local maladaptation reduces population size in nature by attracting bird predators•Maladaptation increases the spatial scale of adaptation•Maladaptation reduces density and diversity of arthropods, and decreases herbivory
Different neuroimaging methods can yield different views of task-dependent neural engagement. Studies examining the relationship between electromagnetic and hemodynamic measures have revealed ...correlated patterns across brain regions but the role of the applied stimulation or experimental tasks in these correlation patterns is still poorly understood. Here, we evaluated the across-tasks variability of MEG-fMRI relationship using data recorded during three distinct naming tasks (naming objects and actions from action images, and objects from object images), from the same set of participants. Our results demonstrate that the MEG-fMRI correlation pattern varies according to the performed task, and that this variability shows distinct spectral profiles across brain regions. Notably, analysis of the MEG data alone did not reveal modulations across the examined tasks in the time-frequency windows emerging from the MEG-fMRI correlation analysis. Our results suggest that the electromagnetic-hemodynamic correlation could serve as a more sensitive proxy for task-dependent neural engagement in cognitive tasks than isolated within-modality measures.
A tumour grows when the total division (birth) rate of its cells exceeds their total mortality (death) rate. The capability for uncontrolled growth within the host tissue is acquired via the ...accumulation of driver mutations which enable the tumour to progress through various hallmarks of cancer. We present a mathematical model of the penultimate stage in such a progression. We assume the tumour has reached the limit of its present growth potential due to cell competition that either results in total birth rate reduction or death rate increase. The tumour can then progress to the final stage by either seeding a metastasis or acquiring a driver mutation. We influence the ensuing evolutionary dynamics by cytotoxic (increasing death rate) or cytostatic (decreasing birth rate) therapy while keeping the effect of the therapy on net growth reduction constant. Comparing the treatments head to head we derive conditions for choosing optimal therapy. We quantify how the choice and the related gain of optimal therapy depends on driver mutation, metastasis, intrinsic cell birth and death rates, and the details of cell competition. We show that detailed understanding of the cell population dynamics could be exploited in choosing the right mode of treatment with substantial therapy gains.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The impact of fitness landscape features on evolutionary outcomes has attracted considerable interest in recent decades. However, evolution often occurs under time‐dependent selection in so‐called ...fitness seascapes where the landscape is under flux. Fitness seascapes are an inherent feature of natural environments, where the landscape changes owing both to the intrinsic fitness consequences of previous adaptations and extrinsic changes in selected traits caused by new environments. The complexity of such seascapes may curb the predictability of evolution. However, empirical efforts to test this question using a comprehensive set of regimes are lacking. Here, we employed an in vitro microbial model system to investigate differences in evolutionary outcomes between time‐invariant and time‐dependent environments, including all possible temporal permutations, with three subinhibitory antimicrobials and a viral parasite (phage) as selective agents. Expectedly, time‐invariant environments caused stronger directional selection for resistances compared to time‐dependent environments. Intriguingly, however, multidrug resistance outcomes in both cases were largely driven by two strong selective agents (rifampicin and phage) out of four agents in total. These agents either caused cross‐resistance or obscured the phenotypic effect of other resistance mutations, modulating the evolutionary outcome overall in time‐invariant environments and as a function of exposure epoch in time‐dependent environments. This suggests that identifying strong selective agents and their pleiotropic effects is critical for predicting evolution in fitness seascapes, with ramifications for evolutionarily informed strategies to mitigate drug resistance evolution.
Ecology Letters (2011) 14: 1025–1034
Evolutionary changes in natural populations are often so fast that the evolutionary dynamics may influence ecological population dynamics and vice versa. Here we ...construct an eco‐evolutionary model for dispersal by combining a stochastic patch occupancy metapopulation model with a model for changes in the frequency of fast‐dispersing individuals in local populations. We test the model using data on allelic variation in the gene phosphoglucose isomerase (Pgi), which is strongly associated with dispersal rate in the Glanville fritillary butterfly. Population‐specific measures of immigration and extinction rates and the frequency of fast‐dispersing individuals among the immigrants explained 40% of spatial variation in Pgi allele frequency among 97 local populations. The model clarifies the roles of founder events and gene flow in dispersal evolution and resolves a controversy in the literature about the consequences of habitat loss and fragmentation on the evolution of dispersal.
In Bayesian statistics, the marginal likelihood (evidence) is one of the key factors that can be used as a measure of model goodness. However, for many practical model families it cannot be computed ...analytically. An alternative solution is to use some approximation method or time-consuming sampling method. The widely applicable Bayesian information criterion (WBIC) was developed recently to have a marginal likelihood approximation that works also with singular models. The central idea of the approximation is to select a single thermodynamic integration term (power posterior) with the (approximated) optimal temperature
β
∗
=
1
/
log
(
n
)
, where
n
is the data size. We apply this new approximation to the analytically solvable Gaussian process regression case to show that the optimal temperature may depend also on data itself or other variables, such as the noise level. Moreover, we show that the steepness of a thermodynamic curve at the optimal temperature indicates the magnitude of the error that WBIC makes.
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