Inter-annual variability of the effects of intrinsic and extrinsic drivers affecting West Nile virus vector Culex pipiens population dynamics in Northeastern Italy
Background: Vector-borne infectious diseases (VBDs) represent a major public health concern worldwide. Among VBDs, West Nile Virus (WNV) showed an increasingly wider spread in temperate regions of Europe, including Italy. During the last decade, WNV outbreaks have been recurrently reported in mosquitoes, horses, wild birds, and humans, showing great variability in the temporal and spatial distribution pattern. Due to the complexity of the environment-host-vector-pathogen interaction and the incomplete understanding of the epidemiological pattern of the disease, WNV occurrences can be hardly predictable. The analyses of ecological drivers responsible for the earlier WNV reactivation and transmission are pivotal; in particular, variations in the vector population dynamics may represent a key point of the recent success of WNV and, more in general, of the VBDs.
Methods: We investigated the variations of Culex pipiens population abundance using environmental, climatic and trapping data obtained over nine years (2010 to 2018) through the WNV entomological surveillance program implemented in northeastern Italy. An Information Theoretic approach (IT-AICc) and model-averaging algorithms were implemented to examine the relationship between the seasonal mosquito population growth rates and both intrinsic (e.g. intraspecific competition) and extrinsic (e.g. environmental and climatic variables) predictors, to identify the most significant combinations of variables outlining the Cx. pipiens population dynamics.
Results: Population abundance (proxy for intraspecific competition) and length of daylight were the predominant factors regulating the mosquito population dynamics; however, also other drivers encompassing environmental and climatic variables had a significant impact, although sometimes counterintuitive and not univocal. The analyses of the single-year datasets, and the comparison with the results obtained from the overall model (all data available from 2010 to 2018), highlighted remarkable differences in coefficients magnitude, sign, and significance. These outcomes indicate that different combinations of factors might have distinctive, and sometimes divergent, effects on mosquito population dynamics.
Conclusions: A more realistic acquaintance of the intrinsic and extrinsic mechanism of mosquito population fluctuations in relation to continuous changes in environmental and climatic conditions is paramount to properly reinforce VBDs risk-based surveillance activities, to plan targeted density control measures and to implement effective early detection programs.
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
Posted 15 Apr, 2020
On 09 Apr, 2020
On 08 Apr, 2020
On 08 Apr, 2020
On 25 Feb, 2020
Received 23 Feb, 2020
On 02 Feb, 2020
Received 02 Feb, 2020
On 30 Jan, 2020
Invitations sent on 29 Jan, 2020
On 02 Jan, 2020
On 02 Jan, 2020
On 02 Jan, 2020
On 31 Dec, 2019
Inter-annual variability of the effects of intrinsic and extrinsic drivers affecting West Nile virus vector Culex pipiens population dynamics in Northeastern Italy
Posted 15 Apr, 2020
On 09 Apr, 2020
On 08 Apr, 2020
On 08 Apr, 2020
On 25 Feb, 2020
Received 23 Feb, 2020
On 02 Feb, 2020
Received 02 Feb, 2020
On 30 Jan, 2020
Invitations sent on 29 Jan, 2020
On 02 Jan, 2020
On 02 Jan, 2020
On 02 Jan, 2020
On 31 Dec, 2019
Background: Vector-borne infectious diseases (VBDs) represent a major public health concern worldwide. Among VBDs, West Nile Virus (WNV) showed an increasingly wider spread in temperate regions of Europe, including Italy. During the last decade, WNV outbreaks have been recurrently reported in mosquitoes, horses, wild birds, and humans, showing great variability in the temporal and spatial distribution pattern. Due to the complexity of the environment-host-vector-pathogen interaction and the incomplete understanding of the epidemiological pattern of the disease, WNV occurrences can be hardly predictable. The analyses of ecological drivers responsible for the earlier WNV reactivation and transmission are pivotal; in particular, variations in the vector population dynamics may represent a key point of the recent success of WNV and, more in general, of the VBDs.
Methods: We investigated the variations of Culex pipiens population abundance using environmental, climatic and trapping data obtained over nine years (2010 to 2018) through the WNV entomological surveillance program implemented in northeastern Italy. An Information Theoretic approach (IT-AICc) and model-averaging algorithms were implemented to examine the relationship between the seasonal mosquito population growth rates and both intrinsic (e.g. intraspecific competition) and extrinsic (e.g. environmental and climatic variables) predictors, to identify the most significant combinations of variables outlining the Cx. pipiens population dynamics.
Results: Population abundance (proxy for intraspecific competition) and length of daylight were the predominant factors regulating the mosquito population dynamics; however, also other drivers encompassing environmental and climatic variables had a significant impact, although sometimes counterintuitive and not univocal. The analyses of the single-year datasets, and the comparison with the results obtained from the overall model (all data available from 2010 to 2018), highlighted remarkable differences in coefficients magnitude, sign, and significance. These outcomes indicate that different combinations of factors might have distinctive, and sometimes divergent, effects on mosquito population dynamics.
Conclusions: A more realistic acquaintance of the intrinsic and extrinsic mechanism of mosquito population fluctuations in relation to continuous changes in environmental and climatic conditions is paramount to properly reinforce VBDs risk-based surveillance activities, to plan targeted density control measures and to implement effective early detection programs.
Figure 1
Figure 2