Jeffrey Shaman, Marc Stieglitz, Colin Stark, Sylvie Le Blancq, and Mark Cane. 2002. Using a dynamic hydrology model to predict mosquito abundances in flood and swamp water. Emerging Infectious Diseases 8: 1-13.
Blog Author: Altangerel Tsogtsaikhan
JEFFREY SHAMAN: Associate Professor
Environmental Health Sciences (in the International Research Institute for Climate and Society/Earth Institute) also he is a director, Climate and Health Program.
EDUCATION
Ph.D., 2003 Columbia University
MA, 2000, Columbia University
BA, 1990, University of Pennsylvania
BIOGRAPHY: Jeffrey Shaman, Ph.D., focuses on climate, atmospheric science and hydrology, as well as biology, and studies the environmental determinants of infectious disease transmission and infectious disease forecast. For the former, Dr. Shaman investigates how hydrologic variability affects mosquito ecology and mosquito-borne disease transmission, how atmospheric conditions impact the survival, transmission, and seasonality of pathogens, and, how meteorology affects human health, in general. For the latter, he is engaged in developing mathematical and statistical systems for generating forecasts of infectious disease outbreaks at a range of timescales. In addition, Dr. Shaman is studying a number of climate phenomena, including Rossby wave dynamics, atmospheric jet waveguides, the coupled South Asian monsoon-ENSO system, extratropical precipitation, and tropical cyclogenesis.
Background:It is important to determine mosquito abundance to monitor mosquito fluctuations in mosquito populations to prevent or predict the impact of mosquito-borne diseases. They are the vectors for many diseases, unfortunately, many of the health organizations do not have resources to sample and monitor of the spatial and temporal distributions of mosquito populations.
Goals:The overall goal of this research is to demonstrate the possible application of using dynamic hydrology model to detect mosquito abundance in flood and swamp water habitats.
Methods:The current study uses the dynamic hydrology model to hindcast the surface wetness including puddles, bogs, and ponds which would potentially hold floodwater and swamp water mosquito larvae. Also, the spatial-temporal variability model to predict the surface wetness which is the main association with the spatial-temporal variability of floodwater and swamp water mosquito abundance.
- A logistic regression model fit analysis to determine the local surface wetness to subsequent mosquito species abundance considering meteorological variables such as evaporation, soil properties, and antecedent conditions. Also, the model tracks the expansion and contraction of lowland saturated zones in time.
- An adaptation of TOPMODEL was used to supplement the rainfall-runoff modeling.
- For the statistical analysis, the mean depth of water table and topography within a watershed were used to calculate the saturated areas of the watershed and groundwater flow.
-A mosaic of cells was created for each local model surface wetness which depicts the spatial variability of conditions of land surface using topography, vegetation, and soil type.
-The current weather conditions were used to depicts real-time surface wetness.
Application and Validation of the Hydrology model
-A multidirectional flow routing algorithm was used to determine the probability distribution of the soil moisture for topography statistical analysis.
_Hydrology model uses hourly records of air, temperature, precipitation, relative humidity, surface pressure, wind speed, incident long-wave radiation, and solar radiation.
-Hourly and daily hydrologic variables including mean water table depth (WTD).
Linking the Model to Mosquitos
The impact of mosquito development on saturated surface expansion and contraction provided by the model using the mean catchment WTD in different time series. The local WTD was used to statistical estimation of potential surface saturation.
Data collecting analysis
-Adult mosquito was collected from New Jersey were used
1. 13-year time series of daily mosquito counts from Request River
2. 15-year time series of daily mosquito counts from a single site in the Great Swamp National Wildlife Refuge.
Light traps were used to collect mosquitos.
-National Climate Data Center (NCDC) provided the hourly meteorological data.
-Northeastern Regional Climate Center (NRCC) used to analyze solar energy data for the model.
-Indices of local wetness (ILWs) was generated from WTD.
-Surface wetness and the ILW and mosquito prevalence were estimated by statistical analysis.
-The tendency and strength of the association between mosquito abundance and the collected ILW determined by time series regression analysis.
Results/Conclusion:
-1987 is the only year the association is positive and statistically significant (P<0.01)
-Full 15-year record, the ILW was positively correlated with a 10-day lag (P<0.0001) to Ae. vexansand An. walkeri.
-The ILW was negatively correlated with a 10-day lag (p<0.0001)
-Unfortunately, ILW fails to explain more than 12% of the variance of the mosquito species.
-Year to year variability at Great Swamp site, ILW. Aed. vexansis significantly correlated with the ILW only 9 of 15 individual years.
- The years 1986 and 1994, the association was negative. We have to look at the graphs.
The surface wetness was correlated with the subsequent abundance of three New Jersey mosquito species including Ae. vexans, An. wakeri,and Cx. pipiens. The association of modeled surface wetness and species abundance seems to be the function of mosquito biology. Finally, these correlations can be used to conduct probabilistic forecast of mass emergence of mosquito outbreaks through land surface hydrology modeling. The mosquito abundance and hydrology model can be used for monitoring larger regions.
General thoughts
The reason I choose this paper is to see a potential application of predictor models using species abundance data with the abiotic and biotic variables. These kind approaches are important for not only for predicting zoonotic diseases for public health but also for the anticipation of what is coming from driven climate change impacts. Species abundance data plays the key role in this process. Zika virus, west nile virus, chikungunya virus, dengue, and malaria are some of the associated examples of deadly mosquito-borne diseases. Document, assess, monitor and act.
This is a topic growing in importance as mosquitoes are carrying more disease in the US and spreading the diseases farther. The best treatment is prevention and if we can target these areas and treat them ahead of time we prevent the spread to other animals and humans. By studying the actual aquatic environments and hydrology of the area, they can target specific species of mosquitoes.
ReplyDeleteI understand the importance of developing predictive models for mosquito abundances and the impact accurate models would have on disease restriction and research. However, I did not understand much of the methods and results section, basically what they were doing and the raw data produced. This paper seems to be written for people within that modeling field and those that are used to the jargon.
ReplyDeleteThere was a lot of lingo thrown around in this paper, so I didn't really understand how the model operates. However, I think the implications are important regarding mosquito movement and disease protection.
ReplyDeleteI like the approach the authors show with this model. I think it is a very interesting and useful way to take preventive actions regarding mosquitoes. I wonder if this model has been applied and tested in other regions, specially in the tropics where the weather predictions are not as robust but the mosquitoes are so abundant and carry so many diseases. Also, it caught my attention their definition of mass emergence (>128,>32 mosquitoes), I thought that for an animal that reproduces in terms of hundreds of eggs, those thresholds seemed too low to define a mass emergence.
ReplyDeleteI was impressed by their dataset: "a 15-year time series of daily mosquito counts (June through September)"? That's insane. I feel bad for the field tech who had to count 600 mosquitos caught on a day in June 1989 (see Figure 3).
ReplyDeleteLaura, I agree that 128 mosquitos doesn't sound like a "mass" emergence. However, based on Figure 3, it looks like trapping more than 100 mosquitos in a day (with the light trap) was a rare occurrence. I'm guessing they would have had much higher numbers if they had put a human in the trap as bait - I've definitely done fieldwork where I was swarmed by more than 100 mosquitos.
A lot of the terminologies in this paper are not familiar to me so it was a little hard for me to understand the paper fully. Also, Maria, I would love to hear about your experience as human bait although that sounds so terrifying to me. It would also be great if the light trap method is explained more in class.
ReplyDeleteAlthough I could probably not replicate this study if I attempted, their modelling process was really neat! It kinda grossed me out reading about all the mosquitos, but the idea of applying the model was really interesting. I liked that they “learned” they didn’t take the most efficient statistical approach and recommend doing it differently.
ReplyDeleteI like the idea of using an almost real time model of surface water availability to predict mosquito abundances based on the knowledge of the species biology. Nonetheless, I don´t know how useful it could be for implementing control actions as only a portion of the variance was explained by this variable.
ReplyDeleteOn another subject, it appears that these data contradicts Browns paper as there is no peak in abundance on the soil wetness gradient. Maybe not the full range is encompassed in the study area/time? or for some variables (niche dimensions), abundance correlates in a linear way?
Water table depths really consider quite a bit of variant elevation changes that seem quite insignificant (tracks, prints etc.) and I enjoy how they clarified these details being taken into consideration. It’s an interesting idea to experiment on predicting mosquito abundances out of all the organisms in and around swamp waters that can cause a number of health issues as well. Their finding on how swamp waters can also negatively affect a species’ abundance was useful as well. It seemed quite futile at first, but getting to the conclusion and truly understanding that all this information is in some way still useful to the cause made it an understandable point of research.
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