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Mercedes Pascual

Publications and source records attributed to Mercedes Pascual.

At least 19 recordsLinked to original sources
PubMed2025

A paradoxical population structure of var DBLα types in Africa.

The var multigene family encodes Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), central to host-parasite interactions. Genome structure studies have identified three major groups of var genes by specific upstream sequences (upsA, B, or C). Var with these ups groups have different chromosomal locations, transcriptional directions, and associations with disease severity. Here we explore temporal and spatial diversity of a region of var genes encoding the DBLα domain of PfEMP1 in Africa. By applying a novel ups classification algorithm (cUps) to publicly-available DBLα sequence datasets, we categorised DBLα according to association with the three ups groups, thereby avoiding the need to sequence complete genes. Data from deep sequencing of DBLα types in a local population in northern Ghana surveyed seven times from 2012 to 2017 found variants with rare-to-moderate-to-extreme frequencies, and the common variants were temporally stable in this local endemic area. Furthermore, we observed that every isolate repertoire, whether mono- or multiclonal, comprised DBLα types occurring with these frequency ranges implying a common genome structure. When comparing African countries of Ghana, Gabon, Malawi, and Uganda, we report that some DBLα types were consistently found at high frequencies in multiple African countries while others were common only at the country level. The implication of these local and pan-Africa population patterns is discussed in terms of advantage to the parasite with regards to within-host adaptation and resilience to malaria control.

Plasmodium falciparum↗
PubMed2006

Epochal evolution shapes the phylodynamics of interpandemic influenza A (H3N2) in humans.

Human influenza A (subtype H3N2) is characterized genetically by the limited standing diversity of its hemagglutinin and antigenically by clusters that emerge and replace each other within 2 to 8 years. By introducing an epidemiological model that allows for differences between the genetic and antigenic properties of the virus's hemagglutinin, we show that these patterns can arise from cluster-specific immunity alone. Central to the formulation is a genotype-to-phenotype mapping, based on neutral networks, with antigenic phenotypes, not genotypes, determining the degree of strain cross-immunity. The model parsimoniously explains well-known, as well as previously unremarked, features of interpandemic influenza dynamics and evolution. It captures the observed boom-and-bust pattern of viral evolution, with periods of antigenic stasis during which genetic diversity grows, and with episodic contraction of this diversity during cluster transitions.

Amino Acid Substitution↗
PubMed2006

Serotype cycles in cholera dynamics.

Interest in understanding strain diversity and its impact on disease dynamics has grown over the past decade. Theoretical disease models of several co-circulating strains indicate that incomplete cross-immunity generates conditions for strain-cycling behaviour at the population level. However, there have been no quantitative analyses of disease time-series that are clear examples of theoretically expected strain cycling. Here, we analyse a 40-year (1966-2005) cholera time-series from Bangladesh to determine whether patterns evident in these data are compatible with serotype-cycling behaviour. A mathematical two-serotype model is capable of explaining the oscillations in case patterns when cross-immunity between the two serotypes, Inaba and Ogawa, is high. Further support that cholera's serotype-cycling arises from population-level immunity patterns is provided by calculations of time-varying effective reproductive rates. These results shed light on historically observed serotype dominance shifts and have important implications for cholera early warning systems.

Bangladesh↗
PubMed2006

Comment on "A keystone mutualism drives pattern in a power function".

Vandermeer and Perfecto (Reports, 17 February 2006, p. 1000) reported a general power law pattern in the distribution of a common agricultural pest. However, there is an exact analytical solution for the expected cluster distribution under the proposed null model of density-independent growth in a patchy landscape. Reanalysis of the data shows that the system is not in a critical state but confirms the importance of a mutualism.

Animals↗
PubMed2006

Seasonality and the dynamics of infectious diseases.

Seasonal variations in temperature, rainfall and resource availability are ubiquitous and can exert strong pressures on population dynamics. Infectious diseases provide some of the best-studied examples of the role of seasonality in shaping population fluctuations. In this paper, we review examples from human and wildlife disease systems to illustrate the challenges inherent in understanding the mechanisms and impacts of seasonal environmental drivers. Empirical evidence points to several biologically distinct mechanisms by which seasonality can impact host-pathogen interactions, including seasonal changes in host social behaviour and contact rates, variation in encounters with infective stages in the environment, annual pulses of host births and deaths and changes in host immune defences. Mathematical models and field observations show that the strength and mechanisms of seasonality can alter the spread and persistence of infectious diseases, and that population-level responses can range from simple annual cycles to more complex multiyear fluctuations. From an applied perspective, understanding the timing and causes of seasonality offers important insights into how parasite-host systems operate, how and when parasite control measures should be applied, and how disease risks will respond to anthropogenic climate change and altered patterns of seasonality. Finally, by focusing on well-studied examples of infectious diseases, we hope to highlight general insights that are relevant to other ecological interactions.

Animal Diseases↗
PubMed2006

Phenotypic plasticity opposes species invasions by altering fitness surface.

Understanding species invasion is a central problem in ecology because invasions of exotic species severely impact ecosystems, and because invasions underlie fundamental ecological processes. However, the influence on invasions of phenotypic plasticity, a key component of many species interactions, is unknown. We present a model in which phenotypic plasticity of a resident species increases its ability to oppose invaders, and plasticity of an invader increases its ability to displace residents. Whereas these effects are expected due to increased fitness associated with phenotypic plasticity, the model additionally reveals a new and unforeseen mechanism by which plasticity affects invasions: phenotypic plasticity increases the steepness of the fitness surface, thereby making invasion more difficult, even by phenotypically plastic invaders. Our results should apply to phenotypically plastic responses to any fluctuating environmental factors including predation risk, and to other factors that affect the fitness surface such as the generalism of predators. We extend the results to competition, and argue that phenotypic plasticity's effect on the fitness surface will destabilize coexistence at local scales, but stabilize coexistence at regional scales. Our study emphasizes the need to incorporate variable interaction strengths due to phenotypic plasticity into invasion biology and ecological theory on competition and coexistence in fragmented landscapes.

Adaptation, Physiological↗
PubMed2006

Climate cycles and forecasts of cutaneous leishmaniasis, a nonstationary vector-borne disease.

BACKGROUND: Cutaneous leishmaniasis (CL) is one of the main emergent diseases in the Americas. As in other vector-transmitted diseases, its transmission is sensitive to the physical environment, but no study has addressed the nonstationary nature of such relationships or the interannual patterns of cycling of the disease. METHODS AND FINDINGS: We studied monthly data, spanning from 1991 to 2001, of CL incidence in Costa Rica using several approaches for nonstationary time series analysis in order to ensure robustness in the description of CL's cycles. Interannual cycles of the disease and the association of these cycles to climate variables were described using frequency and time-frequency techniques for time series analysis. We fitted linear models to the data using climatic predictors, and tested forecasting accuracy for several intervals of time. Forecasts were evaluated using "out of fit" data (i.e., data not used to fit the models). We showed that CL has cycles of approximately 3 y that are coherent with those of temperature and El Niño Southern Oscillation indices (Sea Surface Temperature 4 and Multivariate ENSO Index). CONCLUSIONS: Linear models using temperature and MEI can predict satisfactorily CL incidence dynamics up to 12 mo ahead, with an accuracy that varies from 72% to 77% depending on prediction time. They clearly outperform simpler models with no climate predictors, a finding that further supports a dynamical link between the disease and climate.

Animals↗
PubMed2005

Refractory periods and climate forcing in cholera dynamics.

Outbreaks of many infectious diseases, including cholera, malaria and dengue, vary over characteristic periods longer than 1 year. Evidence that climate variability drives these interannual cycles has been highly controversial, chiefly because it is difficult to isolate the contribution of environmental forcing while taking into account nonlinear epidemiological dynamics generated by mechanisms such as host immunity. Here we show that a critical interplay of environmental forcing, specifically climate variability, and temporary immunity explains the interannual disease cycles present in a four-decade cholera time series from Matlab, Bangladesh. We reconstruct the transmission rate, the key epidemiological parameter affected by extrinsic forcing, over time for the predominant strain (El Tor) with a nonlinear population model that permits a contributing effect of intrinsic immunity. Transmission shows clear interannual variability with a strong correspondence to climate patterns at long periods (over 7 years, for monsoon rains and Brahmaputra river discharge) and at shorter periods (under 7 years, for flood extent in Bangladesh, sea surface temperatures in the Bay of Bengal and the El Niño-Southern Oscillation). The importance of the interplay between extrinsic and intrinsic factors in determining disease dynamics is illustrated during refractory periods, when population susceptibility levels are low as the result of immunity and the size of cholera outbreaks only weakly reflects climate forcing.

Bangladesh↗
PubMed2005

Pathogen adaptation to seasonal forcing and climate change.

Many diverse infectious diseases exhibit seasonal dynamics. Seasonality in disease incidence has been attributed to seasonal changes in pathogen transmission rates, resulting from fluctuations in extrinsic climate factors. Multi-strain infectious diseases with strain-specific seasonal signatures, such as cholera, indicate that a range of seasonal patterns in transmission rates is possible in identical environments. We therefore consider pathogens capable of evolving their 'seasonal phenotype', a trait that determines the sensitivity of their transmission rates to environmental variability. We introduce a theoretical framework, based on adaptive dynamics, for predicting the evolution of disease dynamics in seasonal environments. Changes in the seasonality of environmental factors are one important avenue for the effects of climate change on disease. This model also provides a framework for examining these effects on pathogen evolution and associated disease dynamics. An application of this approach gives an explanation for the recent cholera strain replacement in Bangladesh, based on changes in monsoon rainfall patterns.

Adaptation, Physiological↗
PubMed2005

Dynamics of the 'echo' effect in a phytoplankton system with nitrogen fixation.

Consideration of nitrogen fixation adds a positive nonlinear feedback to plankton ecosystem models. We investigate the consequences of this feedback for secondary phytoplankton blooms and the response of phytoplankton dynamics to physical forcing. The dynamics of phytoplankton, Trichodesmium (the nitrogen fixer), and nutrients is modeled with a system of three differential equations. The model includes two types of nonlinear interactions: the competition of phytoplankton and Trichodesmium for light, and the positive feedback resulting from Trichodesmium recycling. A typical simulation of the model in time, with forcing by a varying mixed-layer depth, reveals a clear successional sequence including a secondary or 'echo' bloom of the phytoplankton. We explain this sequence of events through the stability analysis of three different steady states of the model. Our analysis shows the existence of a critical biological parameter, the ratio of normalized growth rates, determining the occurrence of 'echo' blooms and the specific sequence of events following a physical perturbation. The interplay of positive and negative feedbacks appears essential to the timing and the type of events following such a perturbation.

Algorithms↗
PubMed2004

Criticality and disturbance in spatial ecological systems.

Classical criticality describes sudden changes in the state of a system when underlying processes change slightly. At this transition, patchiness develops which lacks a characteristic or dominant spatial scale. Thus, criticality lies at the interface of two important subjects in ecology, threshold behavior and patchiness. Most ecological examples of criticality involve processes of disturbance and recovery; the spatial and temporal scales of these processes enable three different types of critical system to be distinguished: classical phase transitions, self organized criticality (SOC) and 'robust' criticality. Here, we review the properties defining these three types and their implications for threshold behavior and large intermittent temporal fluctuations, with examples taken from spatial stochastic models for predator-prey, infected-susceptible, and disturbance-recovery interactions. In critical systems, spatial properties of patchiness alone are insufficient indicators of impending sudden changes, unless complemented by the spatial and temporal scales of disturbance and recovery themselves.

Journal Article↗
PubMed2004

Disentangling extrinsic from intrinsic factors in disease dynamics: a nonlinear time series approach with an application to cholera.

Alternative explanations for disease and other population cycles typically include extrinsic environmental drivers, such as climate variability, and intrinsic nonlinear dynamics resulting from feedbacks within the system, such as species interactions and density dependence. Because these different factors can interact in nonlinear systems and can give rise to oscillations whose frequencies differ from those of extrinsic drivers, it is difficult to identify their respective contributions from temporal population patterns. In the case of disease, immunity is an important intrinsic factor. However, for many diseases, such as cholera, for which immunity is temporary, the duration and decay pattern of immunity is not well known. We present a nonlinear time series model with two related objectives: the reconstruction of immunity patterns from data on cases and population sizes and the identification of the respective roles of extrinsic and intrinsic factors in the dynamics. Extrinsic factors here include both seasonality and long-term changes or interannual variability in forcing. Results with simulated data show that this semiparametric method successfully recovers the decay of immunity and identifies the origin of interannual variability. An application to historical cholera data indicates that temporary immunity can be long-lasting and decays in approximately 9 yr. Extrinsic forcing of transmissibility is identified to have a strong seasonal component along with a long-term decrease. Furthermore, noise appears to sustain the multiple frequencies in the long-term dynamics. Similar semiparametric models should apply to population data other than for disease.

Cholera↗
PubMed2004

Competitive coexistence in a dynamic landscape.

This paper investigates the effect of a dynamic landscape on the persistence of many interacting species. We develop a multi-species community model with an evolving landscape in which the creation and destruction of habitat are dynamic and local in space. Species interactions are also local involving hierarchical competitive trade-offs. We show that dynamic landscapes can reverse the trend of increasing species richness with higher fragmentation observed in static landscapes. The increase in the species-area exponent from a homogeneous to a fragmented landscape does not occur when dynamics are turned on. Thus, temporal aspects of the processes that generate and destroy habitat appear dominant relative to spatial characteristics. We also demonstrate, however, that temporal and spatial aspects interact to influence the persistence time of individual species, and therefore, rank-abundance curves. Specifically, persistence in the model increases in habitats with faster local turnover because of the presence of dynamic corridors.

Animals↗
PubMed2004

Skeletons, noise and population growth: the end of an old debate?

Population dynamics models remain largely deterministic, although the presence of random fluctuations in nature is well recognized. This deterministic approach is based on the implicit assumption that systems can be separated into a deterministic part that captures the essential features of the system and a random part that can be neglected. But is it possible, in general, to understand population dynamics without the explicit consideration of random fluctuations? Here, we suggest perhaps not, and argue that the dynamics of many systems are a result of interactions between the deterministic nonlinear skeleton and noise.

Journal Article↗