Identificación de áreas vulnerables a la presencia actual y futura de la Tórtola (zenaida auriculata) en la sierra ecuatoriana, en el período 2015
The research identified the potential vulnerable areas to the presence of the turtledove (Zenaida auriculata) in 11 provinces of the Ecuadorian highlands, it was used records presence of the species, environmental variables collected in digital maps and software Maxent was modeled the current and fu...
Αποθηκεύτηκε σε:
| Κύριος συγγραφέας: | |
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| Άλλοι συγγραφείς: | |
| Μορφή: | bachelorThesis |
| Γλώσσα: | spa |
| Έκδοση: |
2016
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| Θέματα: | |
| Διαθέσιμο Online: | http://repositorio.utc.edu.ec/handle/27000/7761 |
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Προσθήκη ετικέτας | Περίληψη: | The research identified the potential vulnerable areas to the presence of the turtledove (Zenaida auriculata) in 11 provinces of the Ecuadorian highlands, it was used records presence of the species, environmental variables collected in digital maps and software Maxent was modeled the current and future potential distribution. The aim of this study was to determine the possible vulnerable areas to the presence of Zenaida auriculata in relation with current and future distribution models. The resulting surface of current distribution map, with a high probability, obtained for the species was 15734.14 km2 equivalent to 24.47% of the total research area. The map of crops that helps to the presence of the turtledove was done based with their diet, which was used the identification of significant vulnerable areas, getting as result the cities: Latacunga, with an area of 369.207 km2, Quito, with area of 353.806 km2 and Riobamba, with an area of 254.23 km2. Likewise, was compared the current potential distribution with 8 maps of the future distribution in two global circulation models (CCCMA-CGCM31 and CSIRO_MK30) with two scenarios (A2 and B1) and along two time horizons (2050 and 2080). It was determined that Sierra region presents potential areas for the establishment of the species at the present as in future being climate and anthropogenic factors that could strongly influence the distribution. This model will help to generate preventive and measure controls for the species in the region, avoiding that the community take bad decisions that includes handling of chemicals that trigger ecosystems lost and environmental pollution. |
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