Habitat Suitability for Mosquito Larvae and Its Implica;ons for the Hamilton Region Heather Bonn, Rebecca Englert, Kira Moor McMaster University, Advanced Raster GIS
Mosquitoes are prominent disease vectors and are responsible for the transmission of many diseases harming human popula;ons worldwide (Figure 1)1. Of par;cular concern to North America is the West Nile Virus, which ﬁrst emerged in Canada in 2001 and con;nues to appear annually in the summer and fall months2. Speciﬁc condi;ons are required for mosquito Figure 1: A mosquito blood feeding3 breeding and larvae survival making them essen;al to sustaining mosquito popula;ons. Mapping mosquito larvae habitat suitability can be used to determine the vector capacity of an area and the impact this has on nearby human popula;ons.
ESRI ArcMap 10.2 GIS soaware was used to conduct a mul;-‐criteria analysis to generate a habitat suitability model for mosquito larvae. The values of each criterion were standardized using a linear scale transforma;on score range procedure. Each criterion was weighted to reﬂect rela;ve importance by piece-‐wise comparison and combined using a weighted linear combina;on to produce the resul;ng map (Figure 3).
Ra;o es;ma;on and ranked reciprocal methods were used to evaluate the sensi;vity of the model to diﬀerent weigh;ng schemes. Both methods produced a slight decrease in habitat suitability in speciﬁc loca;ons, which was more signiﬁcant in the ra;o es;ma;on method. However, this varia;on between the habitat suitability depicted by each method (Figures 4 & 5) was minimal. This represents the accuracy of the model.
Results & Implica;ons
Study Area .
Figure 2: West Nile Virus incidence in Canada for 2013 as of November7.
Decision Criteria Criterion
Water Body Proximity The closer the beLer Wind Speed
The lower the beLer
The greater the beLer
The ﬂaLer the beLer
The higher the beLer
The lower the beLer
The area under inves;ga;on in this study is the region of Hamilton (Figure 2). This area was selected because of its large popula;on and close proximity to several water bodies with the poten;al to serve as breeding grounds.
Figure 4: Sensi-vity analysis using rank reciprocal. Figure 5: Sensi-vity analysis using ra-o es-ma-on.
Model Improvements Figure 3: Habitat suitability for mosquito larvae in the Hamilton region using pairwise comparison.
Habitat suitability was greatest around Hamilton Harbour (Figure 3). These areas are at the highest risk for infec;on of mosquito transmiLed diseases and have the greatest vector capacity in the Hamilton region. It is recommended that local mosquito control strategies, such as spraying and sampling, should be primarily focused on these areas. This will allow for more eﬀec;ve treatment and more appropriate resource alloca;on in an eﬀort to prevent disease dissemina;on. The ﬁnal model could be used to not only help prevent the spread of West Nile Virus, but to aid in managing future outbreaks of other mosquito transmiLed diseases. As mosquitoes also prey on a host of other animals and birds, preventa;ve measures may also serve to protect na;ve wildlife as well as human health. Ra,onal
Shallow standing water is required for Euclidean breeding4. Distance Interferes with mosquito movement and Spline creates disturbances in water bodies5. Interpola;on Creates damp condi;ons for larvae and excess Spline water forms puddles5. Interpola;on Low slopes are favourable for water reten;on DEM and pooling6. Larvae prefer hot and humid climates5. Spline Interpola;on Greater chance of water pooling6. DEM
Pairwise Comparison Weight
There are several adjustments that could improve the overall accuracy of this model: 1. More factors such as vegeta;on, soil types, preda;on, and rate of water ﬂow could be taken into account to enhance the model’s predic;ve ability. 2. Decision criteria could be speciﬁc to the species of mosquitoes known to carry West Nile Virus instead of general condi;ons for all mosquitoes. 3. Finer resolu;on and more recent data would provide more realis;c results. 4. The study could be expanded to encompass a greater area so comparisons could be made over the province. Addi;onally, this oﬀers the poten;al for correla;ng data with infec;on incidence.
Data Sources & References
Data Sources: • • • • •
DMTI Spa;al Inc. – Digital Eleva;on Model Environment Canada – Canadian Wind Energy Atlas Na;onal Hydro Network – Hamilton’s Hydro Network 2012 Government Canada – Climate Sta;s;cs Canada – Hamilton Region Boundary
1. Fradin, M. S. (1998). Mosquitoes and Mosquito Repellents: A Clinician’s Guide. Annals of Interna-onal Medicine, 128(11), 931 – 940. 2. Canadian Endangered Species Conserva;on Council (CESCC). (2010). Wild Species 2010. Retrieved October 9, 2013, from hLp:// publica;ons.gc.ca/collec;ons/collec;on_2011/ec/CW70-‐7-‐2010-‐eng.pdf. 3. Gathany, J. (2003). Culix Nil, [Photograph]. Retrieved November 22, 2013 from: hLp://commons.wikimedia.org/wiki/File:CulexNil.jpg 4. Vanwambeke et al., (2007). Landscape and land cover factors inﬂuence the presence of Aedes and Anopheles larvae, Journal of Medical Entomology, 44(1), 133-‐144. 5. Dohm, D. J., O’Guinn, M. L., & Turell, M. J. (2002). Eﬀect of Environmental Temperature on the Ability of Culex pipiens (Diptera: Culicidae) to Transmit West Nile Virus. Journal of Medical Entomology, 39(1), 221-‐225. 6. Cooke, W. H., Grala, K., & Wallis, R. C. (2006). Avian GIS models signal human risk for West Nile virus in Mississippi.Interna-onal Journal of Health Geographics, 5(36), na. 7. Public Health Agency of Canada. (2013).West Nile Virus MONITOR Surveillance Maps, [image]. Retrieved November 23, 2013 from: hLp:// www.phac-‐aspc.gc.ca/wnv-‐vwn/map-‐carte/index-‐eng.php