Modeling of the ecological niches of the anopheles spp in Ecuador by the use of geo-informatic tools

Padilla, Oswaldo; Rosas, Pablo; Moreno, Wilson Delgado; Toulkeridis, Theofilos

doi:10.1016/j.sste.2016.12.001

Cited by 15 publications

(8 citation statements)

References 40 publications

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“…MaxEnt, Boosted Regression Tree (BRT), Random Forest are some of the widely used machine learning SDM algorithms. Among others, MaxEnt, which requires presence-only data (instead of presence/absence) is widely used among the mapping methods due to robust predictive performance compared to other contemporary methods [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Present and Future of Dengue Fever in Nepal: Mapping Climatic Suitability by Ecological Niche Model

Acharya

Cao

et al. 2018

IJERPH

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Both the number of cases of dengue fever and the areas of outbreaks within Nepal have increased significantly in recent years. Further expansion and range shift is expected in the future due to global climate change and other associated factors. However, due to limited spatially-explicit research in Nepal, there is poor understanding about the present spatial distribution patterns of dengue risk areas and the potential range shift due to future climate change. In this context, it is crucial to assess and map dengue fever risk areas in Nepal. Here, we used reported dengue cases and a set of bioclimatic variables on the MaxEnt ecological niche modeling approach to model the climatic niche and map present and future (2050s and 2070s) climatically suitable areas under different representative concentration pathways (RCP2.6, RCP6.0 and RCP8.5). Simulation-based estimates suggest that climatically suitable areas for dengue fever are presently distributed throughout the lowland Tarai from east to west and in river valleys at lower elevations. Under the different climate change scenarios, these areas will be slightly shifted towards higher elevation with varied magnitude and spatial patterns. Population exposed to climatically suitable areas of dengue fever in Nepal is anticipated to further increase in both 2050s and 2070s on all the assumed emission scenarios. These findings could be instrumental to plan and execute the strategic interventions for controlling dengue fever in Nepal.

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Section: Introductionmentioning

confidence: 99%

Present and Future of Dengue Fever in Nepal: Mapping Climatic Suitability by Ecological Niche Model

Acharya

Cao

et al. 2018

IJERPH

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“…We applied a correlation analysis and selected variables based on the variance inflation factor (VIF < 10) to avoid an over-adjustment in the models ( 17 ). We made a current species distribution model using the MaxEnt algorithm (Maxent v.3.4.1) ( 20 ) with 50 replicates ( 26 ) and 3,600 pseudoabsences ( 27 ).…”

Section: Methodsmentioning

confidence: 99%

“…(Ano.) pseudopunctipennis, as demonstrated for other anopheline species (16)(17)(18). Important knowledge gaps remain regarding the potential effects of climate and climate change on the emergence of several vector-borne diseases in the world.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Potential Distribution of the Malaria Vector Anopheles (Ano.) pseudopunctipennis Theobald (Diptera: Culicidae) in Arid Regions of Northern Chile

Valderrama

Ayala

Reyes

et al. 2021

Front. Public Health

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The extreme north of Chile presents a subtropical climate permissive of the establishment of potential disease vectors. Anopheles (Ano.) pseudopunctipennis is distributed from the south of the United States to the north of Argentina and Chile, and is one of the main vectors of malaria in Latin America. Malaria was eradicated from Chile in 1945. Nevertheless, the vector persists in river ravines of the Arica and Tarapacá regions. The principal effect of climate change in the north of Chile is temperature increase. Precipitation prediction is not accurate for this region because records were erratic during the last century. The objective of this study was to estimate the current and the projected distribution pattern of this species in Chile, given the potential impact due to climate change. We compiled distributional data for An. (Ano.) pseudopunctipennis and constructed species distribution models to predict the spatial distribution of this species using the MaxEnt algorithm with current and RCP 4.5 and 8.5 scenarios, using environmental and topographic layers. Our models estimated that the current expected range of An. (Ano.) pseudopunctipennis extends continuously from Arica to the north of Antofagasta region. Furthermore, the RCP 4.5 and 8.5 projected scenarios suggested that the range of distribution of An. (Ano.) pseudopunctipennis may increase in longitude, latitude, and altitude limits, enhancing the local extension area by 38 and 101%, respectively, and local presence probability (>0.7), from the northern limit in Arica y Parinacota region (18°S) to the northern Antofagasta region (23°S). This study contributes to geographic and ecologic knowledge about this species in Chile, as it represents the first local study of An. (Ano.) pseudopunctipennis. The information generated in this study can be used to inform decision making regarding vector control and surveillance programs of Latin America. These kinds of studies are very relevant to generate human, animal, and environmental health knowledge contributing to the “One Health” concept.

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“…where Dt is the percentage of test data and p the number of predictor variables (Padilla et al, 2017). The percentage for Maxent training of data consists of 75% presence points, whereas the remaining 25% of points are used for validation (Khatchikian et al, 2011).…”

Section: Species Distribution Modellingmentioning

confidence: 99%

Development of the first georeferenced map of Rhipicephalus (Boophilus) spp. in Mexico from 1970 to date and prediction of its spatial distribution

Alcalá-Canto¹,

Figueroa-Castillo²,

Ibarra-Velarde³

et al. 2018

Geospat Health

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The tick genus Ripicephalus (Boophilus), particularly R. microplus, is one of the most important ectoparasites that affects livestock health and considered an epidemiological risk because it causes significant economic losses due, mainly, to restrictions in the export of infested animals to several countries. Its spatial distribution has been tied to environmental factors, mainly warm temperatures and high relative humidity. In this work, we integrated a dataset consisting of 5843 records of Rhipicephalus spp., in Mexico covering close to 50 years to know which environmental variables mostly influence this ticks' distribution. Occurrences were georeferenced using the software DIVA-GIS and the potential current distribution was modelled using the maximum entropy method (Maxent). The algorithm generated a map of high predictive capability (Area under the curve = 0.942), providing the various contribution and permutation importance of the tested variables. Precipitation seasonality, particularly in March, and isothermality were found to be the most significant climate variables in determining the probability of spatial distribution of Rhipicephalus spp. in Mexico (15.7%, 36.0% and 11.1%, respectively). Our findings demonstrate that Rhipicephalus has colonized Mexico widely, including areas characterized by different types of climate. We conclude that the Maxent distribution model using Rhipicephalus records and a set of environmental variables can predict the extent of the tick range in this country, information that should support the development of integrated control strategies.

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Modeling of the ecological niches of the anopheles spp in Ecuador by the use of geo-informatic tools

Cited by 15 publications

References 40 publications

Present and Future of Dengue Fever in Nepal: Mapping Climatic Suitability by Ecological Niche Model

Present and Future of Dengue Fever in Nepal: Mapping Climatic Suitability by Ecological Niche Model

Modeling the Potential Distribution of the Malaria Vector Anopheles (Ano.) pseudopunctipennis Theobald (Diptera: Culicidae) in Arid Regions of Northern Chile

Development of the first georeferenced map of Rhipicephalus (Boophilus) spp. in Mexico from 1970 to date and prediction of its spatial distribution

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