Environmental forcing and density-dependent controls of Culex pipiens abundance in a temperate climate (Northeastern Italy)
Introduction
Emerging and re-emerging mosquito-borne diseases in temperate regions have become progressively more common in recent years as ever increasing human and vector mobility creates conditions which are favorable for their diffusion (Enserink, 2008, Randolph and Rogers, 2010). This is a source of concern, particularly in areas with high human population density characterized by low immunity and uncertain vector-host relationships (Hayes et al., 2005, Enserink, 2008, Lafferty, 2009, Randolph and Rogers, 2010). Well-known and relevant examples include the West Nile Virus (WNV) diffusion in the United States and Europe, mostly associated with the Culex genus (Hayes et al., 2005), and the increasingly frequent and numerous Chikungunya and Dengue fever cases in Europe, spread by Aedes albopictus (Enserink, 2008).
A fundamental step toward understanding and predicting mosquito-borne disease transmission in temperate climates requires the development of a mechanistic modeling framework of vector population dynamics (Stenseth et al., 2002, Jones et al., 2008). However, work on modeling mosquito population dynamics in temperate climates remains relatively limited. It is often based on empirical regressions of mosquito population on environmental drivers, and usually assumes a fast response of population abundance to meteorological forcings. This assumption is likely to hold in environments where there is a clear-cut, on/off, water control on the mosquito life cycle, such as in areas where rainfall is strongly seasonal. On the contrary, temperate areas are characterized by sometimes subtle links between mosquito population dynamics and highly heterogeneous environmental drivers (e.g. due to a lack of sharp transitions between dry/wet rainfall regimes). Temperate regions also usually exhibit a high human density and degree of anthropogenic control on water availability (e.g. irrigation, diffuse water impoundments, etc.). As a result, reliable models of mosquito abundance remain elusive in temperate areas (Shaman et al., 2006, Linard et al., 2009). In this context, it is important to provide a realistic representation of water dynamics in the soil and at its surface, jointly controlled by several hydrologic processes, such as infiltration, evapotranspiration and runoff generation. However, most approaches to modeling water controls on mosquito population dynamics simply use rainfall as a direct driver (e.g. Yang et al., 2008b, Chaves et al., 2012) and relatively few contributions use explicit hydrological models (Shaman et al., 2006, Bomblies et al., 2008, Day and Shaman, 2008, Montosi et al., 2012) and/or satellite remote sensing (Chuang et al., 2012, Midekisa et al., 2012) to drive mosquito population models.
The importance of endogenous controls, such as density dependence and delayed population responses (Fowler, 1981, Turchin, 1990, Sibly et al., 2005) should also not be underestimated. Only recently they have been recognized as important regulatory mechanisms of mosquito population and they have been rarely incorporated in modeling formulations (Yang et al., 2008a, Yang et al., 2008b, Chaves et al., 2012). We hypothesize that endogenous controls may be particularly important in humid temperate climates, where water is less often limiting and the interaction of multiple environmental controls may result in a wider distribution of adult emergence times with respect to those occurring under more homogeneous environmental conditions (e.g. in tropical climates).
From a population dynamical perspective, previous works on mosquito modeling typically made use of either detailed entomological approaches or of empirical approaches (LaDeau et al., 2011). The first approach uses detailed descriptions of the mosquito life cycle and consequently involves a relatively large number of parameters (Bomblies et al., 2008, Linard et al., 2009, Cailly et al., 2012). These approaches are effective in the presence of a large amount of data to constrain model parameters and when controlling factors are known in detail. But very often this is not the case, because of sampling limitations (of both mosquito abundance and atmospheric forcings) and of the difficulty of transferring to field conditions parameter values obtained from lab experiments. Furthermore, noise is always present both in measurements and in population-dynamic processes, but detailed formulations of mosquito life cycles do not typically allow for stochastic components, thus presenting a quite fundamental mismatch between model assumptions and actual population behavior.
Empirical models, at the other end of the spectrum, may be able to explain a large proportion of the observed variability by simply using meteorological and habitat information. However, forecasting skills are typically poor beyond the conditions under which the model is calibrated, because of the intrinsic lack of a foundation in the governing physical mechanisms (LaDeau et al., 2011). Furthermore, empirical models do not allow insights into the relative importance of controlling mechanisms because such mechanisms are not explicitly resolved in the model structure. A balance is thus desirable between a very detailed model, posing fundamental problems of parameter identification, and completely empirical models, lacking a robust process basis.
Based on the above considerations, the overall goals of the present work are: (i) explore the relative importance of endogenous (e.g. density dependence) vs. exogenous (meteorological and hydrological forcing) factors. We explore in particular the relationship between per capita growth rate and population abundance, the magnitude of the delays with which population responses become manifested, and how they are influenced by environmental factors; (ii) evaluate if and how the use of a full-fledged hydrological model of infiltration, percolation, and runoff to estimate soil moisture affects the model ability to explain the variability in mosquito population abundance in a temperate climate; (iii) develop a sound hierarchical state-space modeling approach (Clark, 2007, Gelman and Hill, 2007) to optimally exploit spatially-distributed observations and minimize the impact of mosquito population sampling schemes which do not fully represent all scales of spatial and temporal heterogeneity.
Section snippets
Study area and data collection
Western Europe in general is identified as a hotspot for the emergence and re-emergence of vector-borne diseases (Jones et al., 2008), spread by 96 endemic mosquito species. Of these species, 62 were found in Italy in 1999 (Snow and Ramsdale, 1999), and this number has probably increased significantly since then. The Po river delta in Northeastern Italy, where our study site is located (Fig. 1), is in particular characterized by a high mosquito abundance, due to favorable climatic and
Results
In general, mosquito abundance in both years started from about zero in May, reached a peak in late June-early July and then gradually decreased in August and September (Fig. 3). The variation among sites was very significant. The number of captured Cx. pipiens in a given night ranged between 0 and 4000. However, consistence was observed across the two years: sites displaying high abundance maintained this characteristic in both years.
The model performance was first evaluated in terms of
Discussion and conclusions
Our analysis of the data through a new model of mosquito population dynamics shows that endogenous and exogenous factors are both important regulators of mosquito abundance and that they interact in a complex way. Density dependence was observed under laboratory conditions for Cx. pipiens larvae: higher larvae density led to larger mortality of the immature stages and longer development times (Vinogradova, 2000). However, the relevance of density dependence mechanisms in the field had not been
Acknowledgements
We acknowledge support by the Nicholas School of Environment (graduate support to YJ, and funding to MM and SS.) and by the “Progetto Pilota di Lotta alle Zanzare nella provincia di Rovigo” funded by Regione Veneto and by Parco Delta del Po. Valuable comments were provided by Dr. William Pan, Dr. Song Qian and Dr. Massimiliano Ignaccolo. We thank Dr. Mario Putti, for making available the Cathy model code. We thank Consorzio di Bonifica del Delta del Po, and in particular Dott. Matteo Bozzolan,
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2020, Environmental ResearchCitation Excerpt :Nine studies used the normalized difference vegetation index (NDVI), a variable related to vegetation presence and photosynthetic activity (Bisanzio et al., 2011; Calzolari et al., 2015; Conte et al., 2015b; Jian et al., 2014; Marcantonio et al., 2015; Rosa et al., 2014; Semenza et al., 2016; Tran et al., 2014; Valiakos et al., 2014). Three studies found a positive relationship between NDVI and vector density (Bisanzio et al., 2011; Calzolari et al., 2015; Jian et al., 2014). Three of the 7 studies that made predictions used it as a key predictor (Bisanzio et al., 2011; Calzolari et al., 2015; Conte et al., 2015b).