Habitat suitability of Anopheles vector species and association with human malaria in the Atlantic Forest in south-eastern Brazil

Laporta, Gabriel Zorello; Ramos, Daniel; Ribeiro, Milton Cézar; Sallum, Maria Anice Mureb

doi:10.1590/s0074-02762011000900029

Cited by 37 publications

(31 citation statements)

References 26 publications

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“…(2015) and others (Biek & Real, 2010; Bouyer & Lancelot, 2017; Dicko et al., 2014; Kulkarni, Desrochers, & Kerr, 2010; Laporta, Ramos, Ribeiro, & Sallum, 2011). However, it differs from previous methods in several ways that we suggest improve our predictive power to describe tsetse movement over both ecological and evolutionary time scales.…”

Section: Discussionmentioning

confidence: 99%

“…We think this approach increases the practical utility of the isolated habitat patches that we identified (Figure 2: O 6 ) over what could be found using a landscape friction model using GPS coordinates from a limited population genetics sample (Bouyer et al., 2015; Dicko et al., 2014; Kulkarni et al., 2010; Laporta et al., 2011), or from field‐survey data alone as would be possible from results provided by Wint and Rogers (2000). The fact that this pipeline (1) identified a habitat patch the indeed includes tsetse flies and that (2) the fly population in this patch is significantly more genetically differentiated than others located in connected habitat and separated by similar geographic distances lends support to the ability of the method to produce accurate models of past and current movement of the target species across the landscape.…”

Section: Discussionmentioning

confidence: 99%

“…(2015) and others (Dicko et al., 2014; Kulkarni et al., 2010; Laporta et al., 2011) to develop an approach that produce a connectivity surface to identify isolated habitat patches that reflect both genetic connectivity and ecological connectivity at a spatial scale of interest. Our goals were threefold: to identify isolated habitat patches as candidates for local eradication, to improve the G. f. fuscipes habitat suitability model for a particularly high disease risk region of Uganda, and to produce a pipeline that integrates historical and current species movement patterns.…”

Section: Discussionmentioning

confidence: 99%

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A spatial genetics approach to inform vector control of tsetse flies (Glossina fuscipes fuscipes) in Northern Uganda

Saarman

Burak

Opiro

et al. 2018

Ecology and Evolution

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Tsetse flies (genus Glossina) are the only vector for the parasitic trypanosomes responsible for sleeping sickness and nagana across sub‐Saharan Africa. In Uganda, the tsetse fly Glossina fuscipes fuscipes is responsible for transmission of the parasite in 90% of sleeping sickness cases, and co‐occurrence of both forms of human‐infective trypanosomes makes vector control a priority. We use population genetic data from 38 samples from northern Uganda in a novel methodological pipeline that integrates genetic data, remotely sensed environmental data, and hundreds of field‐survey observations. This methodological pipeline identifies isolated habitat by first identifying environmental parameters correlated with genetic differentiation, second, predicting spatial connectivity using field‐survey observations and the most predictive environmental parameter(s), and third, overlaying the connectivity surface onto a habitat suitability map. Results from this pipeline indicated that net photosynthesis was the strongest predictor of genetic differentiation in G. f. fuscipes in northern Uganda. The resulting connectivity surface identified a large area of well‐connected habitat in northwestern Uganda, and twenty‐four isolated patches on the northeastern margin of the G. f. fuscipes distribution. We tested this novel methodological pipeline by completing an ad hoc sample and genetic screen of G. f. fuscipes samples from a model‐predicted isolated patch, and evaluated whether the ad hoc sample was in fact as genetically isolated as predicted. Results indicated that genetic isolation of the ad hoc sample was as genetically isolated as predicted, with differentiation well above estimates made in samples from within well‐connected habitat separated by similar geographic distances. This work has important practical implications for the control of tsetse and other disease vectors, because it provides a way to identify isolated populations where it will be safer and easier to implement vector control and that should be prioritized as study sites during the development and improvement of vector control methods.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A spatial genetics approach to inform vector control of tsetse flies (Glossina fuscipes fuscipes) in Northern Uganda

Saarman

Burak

Opiro

et al. 2018

Ecology and Evolution

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“…This metric was selected because it has been indicated as one of the main landscape properties influencing species distribution at varying scales for a number of groups, e.g., mammals (Pardini et al, 2010, Jorge et al, 2013, and mosquitoes (Laporta et al, 2011). A 200 m radius was used to delimit the virtual landscape around the sampling points.…”

Section: Environmental Predictorsmentioning

confidence: 99%

“…Bioclimatic variables have been intensively used in studies using species distribution models (Hijmans et al, 2005;Loiselle et al, 2010;Laporta et al, 2011). Such models search for biogeographic patterns associated with the occurrence of species, which can be based on presence/ absence records, and through ecological niches concepts (Elith and Graham, 2009;Loiselle et al, 2010).…”

Section: Environmental Predictorsmentioning

confidence: 99%

The taxonomic distinctness of macroinvertebrate communities of Atlantic Forest streams cannot be predicted by landscape and climate variables, but traditional biodiversity indices can

et al. 2014

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Predicting how anthropogenic activities may influence the various components of biodiversity is essential for finding ways to reduce diversity loss. This challenge involves: a) understanding how environmental factors influence diversity across different spatial scales, and b) developing ways to measure these relationships in a way that is fast, economical, and easy to communicate. In this study, we investigate whether landscape and bioclimatic variables could explain variation in biodiversity indices in macroinvertebrate communities from 39 Atlantic Forest streams. In addition to traditional diversity measures, i.e., species richness, abundance and Shannon index, we used a taxonomic distinctness index that measures the degree of phylogenetic relationship among taxa. The amount of variation in the diversity measures that was explained by environmental and spatial variables was estimated using variation partitioning based on multiple regression. Our study demonstrates that taxonomic distinctness does not respond in the same way as the traditional used in biodiversity studies. We found no evidence that taxonomic distinctness responds predictably to variation in landscape metrics, indicating the need for the incorporation of predictors at multiple scales in this type of study. The lack of congruence between taxonomic distinctness and other indices and its low predictability may be related to the fact that this measure expresses long-term evolutionary adaptation to ecosystem conditions, while the other traditional biodiversity metrics respond to short-term environmental changes.Keywords: biomonitoring, diversity indices, spatial structure, taxonomic diversity. A distinção taxonômica de comunidades de macroinvertebrados de córregos da Mata Atlântica não pode ser previsto pela paisagem e por variáveis climáticas, mas os índices de biodiversidade tradicional podem ResumoPrever como as atividades antrópicas podem influenciar os vários componentes da biodiversidade é essencial para encontrar maneiras de reduzir a perda de diversidade. Este desafio envolve: a) a compreensão de como os fatores ambientais influenciam a diversidade em diferentes escalas espaciais e, b) desenvolver formas de medir essas relações de uma maneira rápida, econômica e de fácil comunicação. Neste estudo, nós investigamos se a paisagem e as variáveis bioclimáticas podem explicar a variação nos índices de biodiversidade em comunidades de macroinvertebrados de 39 riachos de Mata Atlântica. Adicionalmente às medidas tradicionais de diversidade, por ex.: riqueza de espécies, abundância e índice de Shannon, nos utilizamos índice de distinção taxonômica que mede o grau de relação filogenética dentre os taxa. A quantidade de variação nas medidas de diversidade que foi explicado por variáveis ambientais e espaciais foi estimada utilizando a variação particionada baseada em regressão múltipla. O presente estudo demonstraBraz.

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Ecological Niche Modeling and Other Tools for the Study of Avian Malaria Distribution in the Neotropics: A Short Literature Review

Prieto‐Torres

Rojas‐Soto

Lira‐Noriega

2020

Avian Malaria and Related Parasites in the Tropics

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Habitat suitability of Anopheles vector species and association with human malaria in the Atlantic Forest in south-eastern Brazil

Cited by 37 publications

References 26 publications

A spatial genetics approach to inform vector control of tsetse flies (Glossina fuscipes fuscipes) in Northern Uganda

A spatial genetics approach to inform vector control of tsetse flies (Glossina fuscipes fuscipes) in Northern Uganda

The taxonomic distinctness of macroinvertebrate communities of Atlantic Forest streams cannot be predicted by landscape and climate variables, but traditional biodiversity indices can

Ecological Niche Modeling and Other Tools for the Study of Avian Malaria Distribution in the Neotropics: A Short Literature Review

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