Malaria epidemics in regions with seasonal windows of transmission can vary greatly in size from year to year. A central question has been whether these interannual cycles are driven by climate, are instead generated by the intrinsic dynamics of the disease, or result from the resonance of these two mechanisms. This corresponds to the more general inverse problem of identifying the respective roles of external forcings vs. internal feedbacks from time series for nonlinear and noisy systems. We propose here a quantitative approach to formally compare rival hypotheses on climate vs. disease dynamics, or external forcings vs. internal feedbacks, that combines dynamical models with recently developed, computational inference methods. The interannual patterns of epidemic malaria are investigated here for desert regions of northwest India, with extensive epidemiological records for Plasmodium falciparum malaria for the past two decades. We formulate a dynamical model of malaria transmission that explicitly incorporates rainfall, and we rely on recent advances on parameter estimation for nonlinear and stochastic dynamical systems based on sequential Monte Carlo methods. Results show a significant effect of rainfall in the inter-annual variability of epidemic malaria that involves a threshold in the disease response. The model exhibits high prediction skill for yearly cases in the malaria transmission season following the monsoonal rains. Consideration of a more complex model with clinical immunity demonstrates the robustness of the findings and suggests a role of infected individuals that lack clinical symptoms as a reservoir for transmission. Our results indicate that the nonlinear dynamics of the disease itself play a role at the seasonal, but not the interannual, time scales. They illustrate the feasibility of forecasting malaria epidemics in desert and semi-arid regions of India based on climate variability. This approach should be applicable to malaria in other locations, to other infectious diseases, and to other nonlinear systems under forcing.
Unintended mortality in longlines emerged in the early 1990s as one of the most important threats for pelagic seabirds worldwide. Most of the studies were focused on highly developed industrial fisheries, overlooking bycatch in small-scale artisanal fisheries. However, bycatch in smallscale fisheries might have negative effects similar to those of industrial fisheries when they overlap with hotspot areas of top predators. Moreover, different types of fishing gear coexist in the same oceanographic area, particularly in highly exploited marine ecosystems such as the western Mediterranean. We quantify for the first time the influence of trawling regime on Cory's shearwater Calonectris diomedea bycatch in the western Mediterranean longline artisanal fishery. The availability of trawling discards has substantial influence on the foraging and breeding ecology of many seabirds, and trawling inactivity may drive shearwaters to seek alternative food resources, such as baits used in longline fishing. Based on our previous knowledge of the system, we also tested other variables affecting bycatch over 8 yr (1998 to 2005). Within this 2-fishery framework, we found that trawling regime, longline fishing time and breeding stage were key factors explaining shearwater attendance to longline vessels, but mainly trawling regime and fishing time increased the incidental capture of Cory's shearwaters. More specifically, during the pre-breeding and chick-rearing periods, bycatch dramatically increased during sunrise sets in the absence of trawling activity. Importantly, this study indicates the need for an integrated multi-fisheries management approach for the conservation of seabirds and highlights the necessity of banning longline fishing during periods of trawling inactivity.KEY WORDS: Small-scale fishery · Interactions between fisheries · Multi-fisheries management · Trawling inactivity · Cory's shearwater · Mitigation measures · Western Mediterranean Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 420: [241][242][243][244][245][246][247][248][249][250][251][252] 2010 for an 80% reduction in community biomass, affecting both target and non-target species (Myers & Worm 2003, Lewison et al. 2004b). For instance, many thousands of seabirds (mostly Procellariiformes) are killed annually by longline fisheries, and consequently populations have shown important declines in abundance over the last 3 to 4 decades (Weimerskirch & Jouventin 1987, Gales et al. 1998, Brothers et al. 1999, Nel et al. 2002, Cooper et al. 2003. In fact, one-third of the seabird species accidentally caught are catalogued as globally threatened according to the International Union for Conservation of Nature (IUCN) criteria (Brothers et al. 1999, BirdLife International 2004.Most studies reporting top predator bycatch have been performed on highly developed industrial fisheries, and it has seldom been considered in small-scale artisanal fisheries (but see D'Agrosa et al. 2000, Peckham et al. 2007, Bugoni e...
Many biological systems are appropriately described by partially observed Markov process (POMP) models, also known as state space models. Such models also arise throughout the physical and social sciences, in engineering, and in finance. Statistical challenges arise in carrying out inference on nonlinear, nonstationary, vector-valued POMP models. Methodologies that depend on the Markov process model only through numerical solution of sample paths are said to have the plug-and-play property. This property enables consideration of models for which the evaluation of transition densities is problematic. Our case study employs plug-and-play methodology to investigate malaria transmission in Northwest India. We address the scientific question of the respective roles of environmental factors, immunity, and nonlinear disease transmission dynamics in epidemic malaria. Previous debates on this question have been hindered by the lack of a statistical investigation that gives simultaneous consideration to the roles of human immunity and the fluctations in mosquito abundance associated with environmental or ecological covariates. We present the first time series analysis integrating these various components into a single vector-valued dynamic model. We are led to investigate a POMP involving a system of stochastic differential equations driven by Lévy noise. We find a clear role for rainfall and evidence to support models featuring the possibility of clinical immunity.An online supplement presents details of the methodology implemented and two additional figures.
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