Abstract
The data presented in this article are related to the research article entitled “Establishment of Aedes aegypti (L.) in mountainous regions in Mexico: Increasing number of population at risk of mosquito-borne disease and future climate conditions” (M. Equihua, S. Ibáñez-Bernal, G. Benítez, I. Estrada-Contreras, C.A. Sandoval-Ruiz, F.S. Mendoza-Palmero, 2016) [1]. This article provides presence records in shapefile format used to generate maps of potential distribution of Aedes aegypti with different climate change scenarios as well as each of the maps obtained in raster format. In addition, tables with values of potential distribution of the vector as well as the average values of probability of presence including data of the mosquito incidence along the altitudinal range.
Keywords: Ecological niche modelling, Vector, MaxLike, Mobility-oriented parity
Specifications Table
| Subject area | Biology and climate change |
| More specific subject area | Ecological niche modelling |
| Type of data | Maps, tables and figures |
| How data was acquired | A dataset sampling for the state of Veracruz: 100 records of Aedes aegypti from previous surveys, 167 also records provided by the Health Authority for Region V, state of Veracruz and seven records from our sampling data. Potential distribution maps of Aedes aegypti were obtained using the packages “maxlike” ver. 0.1–5, “raster” ver. 2.3–12, “rgdal” ver. 0.9–1, “sp” ver. 1.0–16 and “tcltk2” ver. 1.2–10, in the software R ver. 3.1.2. In addition, a geographic information system was used to analyze the maps obtained. |
| Data format | Shapefile (.shp) and Excel (.xlsx) |
| Data source location | Veracruz, Mexico |
| Data accessibility | Data are available in this article |
Value of the data
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Presence records over a gradient including current boundary conditions is interesting to assess current Aedes aegypti distribution expansion.
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Potential distribution mosquito coverage is useful in planning future strategies to face the human risks produced byAedes aegypti expansion.
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The potential distribution ofAedes aegypticould be used to compare the output of other algorithms used in ecological niche modeling.
1. Data
The dataset of this article provides information about occurrence records used to generate the potential distribution maps of Aedes aegypti, we produced a series of maps about this. This maps are presented and discussed in Equihua et al. [1]. The map included (Map 1) is the spatial distribution of the records used to generate Aedes aegypti potential distribution models (shared in shapefile format). The following five maps are the potential distribution obtained under the different scenarios of climate change we explored. Map 2 is the current potential distribution, Map 3 is the RCP 4.5 to 2030 scenario, Map 4 is the RCP 8.5 to 2030 scenario, Map 5 is the RCP 4. 5 to 2080 scenario and Map 6 is the RCP 8.5 to 2080 scenario. They are shared in raster geo-TIF format. Table 1, Table 2, Table 3 show information about the area, probability of occurrence and potential altitudinal presence in different altitudinal ranges where the potential presence of mosquito is projected (they are shared in.xlsx format).
Table 1.
Potential distribution area of Aedes aegypti in central Veracruz, Mexico.
| Altitudinal range (m) |
Area (km2) |
||||
|---|---|---|---|---|---|
| Current | Near future 4.5 | Near future 8.5 | Distant future 4.5 | Distant future 8.5 | |
| 0–100 | 5963.24 | 2816.5 | 2900.78 | 901.28 | NA |
| 101–200 | 2500.02 | 1547.14 | 1598.74 | 389.58 | 5.16 |
| 201–300 | 1406.96 | 1186.8 | 1232.38 | 382.7 | 43 |
| 301–400 | 1079.3 | 1096.5 | 1099.08 | 479.02 | 135.88 |
| 401–500 | 902.14 | 911.6 | 911.6 | 829.04 | 187.48 |
| 501–600 | 748.2 | 749.06 | 749.06 | 745.62 | 180.6 |
| 601–700 | 634.68 | 634.68 | 634.68 | 633.82 | 174.58 |
| 701–800 | 552.12 | 552.12 | 552.12 | 552.12 | 168.56 |
| 801–900 | 578.78 | 578.78 | 578.78 | 578.78 | 393.88 |
| 901–1000 | 521.16 | 521.16 | 521.16 | 521.16 | 449.78 |
| 1001–1100 | 480.74 | 489.34 | 489.34 | 489.34 | 444.62 |
| 1101–1200 | 467.84 | 495.36 | 495.36 | 495.36 | 468.7 |
| 1201–1300 | 418.82 | 454.94 | 455.8 | 455.8 | 430.86 |
| 1301–1400 | 374.96 | 429.14 | 435.16 | 437.74 | 405.92 |
| 1401–1500 | 261.44 | 344.86 | 348.3 | 360.34 | 337.98 |
| 1501–1600 | 116.96 | 257.14 | 264.02 | 302.72 | 281.22 |
| 1601–1700 | 54.18 | 227.9 | 235.64 | 276.06 | 260.58 |
| 1701–1800 | 8.6 | 155.66 | 172.86 | 229.62 | 230.48 |
| 1801–1900 | 0.86 | 84.28 | 102.34 | 210.7 | 245.96 |
| 1901–2000 | NA | 12.9 | 24.94 | 157.38 | 208.98 |
| 2001–2100 | NA | 0.86 | 1.72 | 61.92 | 155.66 |
| 2101–2200 | NA | 0.86 | 1.72 | 18.06 | 112.66 |
| 2201–2300 | NA | NA | NA | 0.86 | 56.76 |
| 2301–2400 | NA | NA | NA | 0.86 | 25.8 |
| 2401–2500 | NA | NA | NA | NA | 5.16 |
| 2501–2600 | NA | NA | NA | NA | 1.72 |
Table 2.
Mean probability of occurrence of Aedes aegypti in central Veracruz, Mexico.
| Altitudinal range (m) |
Mean probability of occurrence |
||||
|---|---|---|---|---|---|
| Current | Near future 4.5 | Near future 8.5 | Distant future 4.5 | Distant future 8.5 | |
| 0–100 | 0.94 | 0.85 | 0.85 | 0.81 | NA |
| 101–200 | 0.94 | 0.79 | 0.80 | 0.79 | 0.29 |
| 201–300 | 0.99 | 0.71 | 0.73 | 0.85 | 0.44 |
| 301–400 | 0.99 | 0.86 | 0.89 | 0.76 | 0.58 |
| 401–500 | 1.00 | 0.97 | 0.98 | 0.73 | 0.69 |
| 501–600 | 0.99 | 1.00 | 1.00 | 0.87 | 0.67 |
| 601–700 | 1.00 | 1.00 | 1.00 | 0.96 | 0.73 |
| 701–800 | 1.00 | 1.00 | 1.00 | 0.96 | 0.82 |
| 801–900 | 0.99 | 1.00 | 1.00 | 0.98 | 0.71 |
| 901–1000 | 0.99 | 1.00 | 1.00 | 0.99 | 0.85 |
| 1001–1100 | 0.98 | 1.00 | 1.00 | 1.00 | 0.87 |
| 1101–1200 | 0.98 | 1.00 | 1.00 | 1.00 | 0.87 |
| 1201–1300 | 0.98 | 0.98 | 0.99 | 0.99 | 0.86 |
| 1301–1400 | 0.95 | 0.94 | 0.95 | 0.96 | 0.90 |
| 1401–1500 | 0.84 | 0.93 | 0.95 | 0.96 | 0.93 |
| 1501–1600 | 0.86 | 0.90 | 0.90 | 0.89 | 0.92 |
| 1601–1700 | 0.73 | 0.87 | 0.89 | 0.88 | 0.94 |
| 1701–1800 | 0.61 | 0.77 | 0.79 | 0.87 | 0.93 |
| 1801–1900 | 0.31 | 0.67 | 0.69 | 0.85 | 0.88 |
| 1901–2000 | NA | 0.45 | 0.46 | 0.76 | 0.89 |
| 2001–2100 | NA | 0.71 | 0.68 | 0.65 | 0.78 |
| 2101–2200 | NA | 0.54 | 0.51 | 0.58 | 0.58 |
| 2201–2300 | NA | NA | NA | 0.50 | 0.45 |
| 2301–2400 | NA | NA | NA | 0.46 | 0.34 |
| 2401–2500 | NA | NA | NA | NA | 0.29 |
| 2501–2600 | NA | NA | NA | NA | 0.28 |
Table 3.
Mean altitude of potential presence of Aedes aegypti in central Veracruz, Mexico.
| Altitudinal range (m) |
Mean altitude of potential presence (m) |
||||
|---|---|---|---|---|---|
| Current | Near future 4.5 | Near future 8.5 | Distant future 4.5 | Distant future 8.5 | |
| 0–100 | 36.94 | 46.00 | 45.34 | 31.47 | NA |
| 101–200 | 143.53 | 141.71 | 142.60 | 150.82 | 161.00 |
| 201–300 | 248.39 | 252.13 | 251.84 | 250.04 | 259.64 |
| 301–400 | 348.76 | 348.83 | 348.77 | 354.33 | 354.31 |
| 401–500 | 449.68 | 449.63 | 449.63 | 451.98 | 451.68 |
| 501–600 | 549.55 | 549.51 | 549.51 | 549.56 | 551.56 |
| 601–700 | 648.64 | 648.64 | 648.64 | 648.69 | 649.42 |
| 701–800 | 749.71 | 749.71 | 749.71 | 749.71 | 752.05 |
| 801–900 | 850.45 | 850.45 | 850.45 | 850.45 | 854.55 |
| 901–1000 | 949.34 | 949.34 | 949.34 | 949.34 | 951.34 |
| 1001–1100 | 1050.74 | 1050.82 | 1050.82 | 1050.82 | 1051.13 |
| 1101–1200 | 1152.24 | 1152.89 | 1152.89 | 1152.89 | 1153.48 |
| 1201–1300 | 1248.68 | 1249.10 | 1249.11 | 1249.11 | 1248.54 |
| 1301–1400 | 1349.18 | 1349.42 | 1349.64 | 1349.77 | 1350.19 |
| 1401–1500 | 1445.48 | 1447.89 | 1447.86 | 1448.25 | 1449.00 |
| 1501–1600 | 1548.25 | 1550.11 | 1550.19 | 1551.14 | 1550.78 |
| 1601–1700 | 1636.68 | 1648.68 | 1648.72 | 1649.72 | 1650.83 |
| 1701–1800 | 1726.40 | 1746.26 | 1747.41 | 1749.27 | 1750.32 |
| 1801–1900 | 1824.00 | 1839.97 | 1841.95 | 1848.89 | 1848.62 |
| 1901–2000 | NA | 1931.73 | 1934.97 | 1947.19 | 1952.33 |
| 2001–2100 | NA | 2001.00 | 2030.00 | 2039.53 | 2051.32 |
| 2101–2200 | NA | 2182.00 | 2146.50 | 2133.19 | 2146.58 |
| 2201–2300 | NA | NA | NA | 2212.00 | 2235.58 |
| 2301–2400 | NA | NA | NA | 2365.00 | 2346.27 |
| 2401–2500 | NA | NA | NA | NA | 2429.17 |
| 2501–2600 | NA | NA | NA | NA | 2515.50 |
2. Experimental design, materials and methods
We developed ecological niche models of Ae. aegypti for the state of Veracruz with a total of 274 verified records. Seven records from our sampling data, 100 records from previous surveys and 167 records provided by the Health Authority for Region V, state of Veracruz. We verified all of them for geographic accuracy with on-screen visual inspection using a Geographic Information System image.
To develop potential distribution models of Ae. aegypti we used bioclimate variables for current conditions [2] and projected to future [3]. The bioclimate variables used were Bio5: maximum temperature of the warmest month, Bio6: minimum temperature of the coldest month, Bio13: precipitation of the wettest month and Bio14: precipitation of the driest month.
The results of correlation analysis for 19 bioclimate variables indicate that the four variables selected highly correlate with 2 principal component that account for almost 92% of the variability in the data. For each projection into future conditions, we used two Representative Concentration Pathways (RCP): RCP 4.5 and RCP 8.5, which refer to the possible range of radiative forcing values in the year 2100 relative to pre-industrial values, expressed in W/m2 [4].
We standardized all bioclimate variables (current and future) with their corresponding current layer, i. e. for the projected value of each variable we subtracted the mean and then divided it by the standard deviation of the current data subset. We used the MaxLike software package [5] to generate potential distribution maps and we used the packages “maxlike” ver. 0.1–5, “raster” ver. 2.3–12, “rgdal” ver. 0.9–1, “sp” ver. 1.0–16 and “tcltk2” ver. 1.2–10, in the software R ver. 3.1.2.
Then, we randomly selected 65% of the records for training and the remaining 35% for cross-validation each of the 1000 times the process was repeated with the current conditions dataset. The resulting models were deemed adequate, according to Estrada-Contreras et al. [6], if they satisfied the following criteria: a) convergence occurred, b) they had no missing data, and c) proportion of errors of omission was less than or equal to 10. The model coefficients were then used to project the species’ future niche. The resulting models were ranked by how well they matched the relative occurrence area (ROA) [7] values. We chose 10 models around the statistical median that had an average probability of presence obtained with validation records closest to 1, since theoretically the average of this value should be 1. Then we produced a consensus map averaging these 10 maps (the same models set for current and future conditions).
The minimum value of probability of presence was considered indicative of the likely presence of Ae. aegypti, and was obtained by extracting values from the potential distribution map to current conditions with the coordinates of all the records used to generate the models (training and validation). To further evaluate the current presence model we used partial ROC [8] by randomly selecting 35% of the records used to generate the models.
Although ecological niche models were generated for surface analysis of the entire state of Veracruz, elevation increase and changes in the probability of occurrence were conducted only in the rectangle that has its diagonal vertices at points 97 °35’55.78’’W and 20 °28’20.67’’N, and 95 °49’31.07’’W and 18 °39’41.6’’N, which covers an area of 28,167.58 km2. To identify whether the analysis area has combinations of environmental variables similar to those of today, the "Mobility-Oriented Parity"(MOP) tool [9] was used.
Conflict of Interest
There is no conflict of interest.
Map. 1.
Occurrence records of Aedes aegypti.
Acknowledgements
We thank José Luis Antiga Tinoco (Secretario de Salud de Veracruz 2010), Pablo Anaya Rivera (Secretario de Salud de Veracruz 2011–12), Juan Antonio Nemi Dib (Secretario de Salud de Veracruz 2013–14), Irasema Guerrero Lagunes (Directora de los Servicios de Salud de Veracruz-SESVER), Alejandro Escobar Mesa (Director de Enfermedades Transmisibles de la Secretaría de Salud de Veracruz- SESVER), Ruth A. Hernández Xoliot (Jefe de Entomología, del Departamento de Vectores de SESVER), Armando Bustos (Jefe de Departamento de Vectores de SESVER, 2010), Raymundo Hernández (encargado del Departamento de Vectores de SESVER, 2015), Cuauhtémoc Limón, (encargado del programa de Vectores, Jurisdicción Sanitaria V, SESVER, 2010), Enrique Alducin (encargado del programa de Vectores, Jurisdicción Sanitaria V, SESVER, 2011), Israel Villa (encargado del programa de Vectores, Jurisdicción Sanitaria V, SESVER, 2012), and Carlos Roberto García. We are also grateful to Jessica Agastein for conducting the sociological surveillance. The authors are indebted to INE-SEMARNAT, and are particularly grateful for the strong interest in this study, support, and help of Adrián Fernández. We also thank Julia Martínez and Uriel Bando of the Coordinación del Programa de Cambio Climático at the INE. We thank Bianca Delfosse for her invaluable revision of the English version of the manuscript.
Footnotes
Supplementary data associated with this article can be found in the online version at 10.1016/j.dib.2016.12.014.
Appendix A. Supplementary material
Supplementary material
.
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Supplementary Materials
Supplementary material