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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00005516naa a2200541 4500
001oai:DiVA.org:uu-454523
003SwePub
008210929s2021 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4545232 URI
0247 a https://doi.org/10.1111/eva.132372 DOI
040 a (SwePub)uu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Bishop, Anusha P.u Yale Univ, Dept Ecol & Evolutionary Biol, New Haven, CT USA.;Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA.4 aut
2451 0a A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning
264 c 2021-05-05
264 1b John Wiley & Sons,c 2021
338 a electronic2 rdacarrier
520 a Vector control is an effective strategy for reducing vector-borne disease transmission, but requires knowledge of vector habitat use and dispersal patterns. Our goal was to improve this knowledge for the tsetse species Glossina pallidipes, a vector of human and animal African trypanosomiasis, which are diseases that pose serious health and socioeconomic burdens across sub-Saharan Africa. We used random forest regression to (i) build and integrate models of G. pallidipes habitat suitability and genetic connectivity across Kenya and northern Tanzania and (ii) provide novel vector control recommendations. Inputs for the models included field survey records from 349 trap locations, genetic data from 11 microsatellite loci from 659 flies and 29 sampling sites, and remotely sensed environmental data. The suitability and connectivity models explained approximately 80% and 67% of the variance in the occurrence and genetic data and exhibited high accuracy based on cross-validation. The bivariate map showed that suitability and connectivity vary independently across the landscape and was used to inform our vector control recommendations. Post hoc analyses show spatial variation in the correlations between the most important environmental predictors from our models and each response variable (e.g., suitability and connectivity) as well as heterogeneity in expected future climatic change of these predictors. The bivariate map suggests that vector control is most likely to be successful in the Lake Victoria Basin and supports the previous recommendation that G. pallidipes from most of eastern Kenya should be managed as a single unit. We further recommend that future monitoring efforts should focus on tracking potential changes in vector presence and dispersal around the Serengeti and the Lake Victoria Basin based on projected local climatic shifts. The strong performance of the spatial models suggests potential for our integrative methodology to be used to understand future impacts of climate change in this and other vector systems.
650 7a NATURVETENSKAPx Biologix Evolutionsbiologi0 (SwePub)106152 hsv//swe
650 7a NATURAL SCIENCESx Biological Sciencesx Evolutionary Biology0 (SwePub)106152 hsv//eng
653 a disease vector
653 a gene flow
653 a habitat suitability
653 a landscape genetics
653 a random forest
653 a spatial modeling
7001 a Amatulli, Giuseppeu Yale Univ, Sch Environm, New Haven, CT USA.4 aut
7001 a Hyseni, Chazu Uppsala universitet,Zooekologi4 aut0 (Swepub:uu)chahy946
7001 a Pless, Evlynu Yale Univ, Dept Ecol & Evolutionary Biol, New Haven, CT USA.;Univ Calif Davis, Dept Anthropol, Davis, CA 95616 USA.4 aut
7001 a Bateta, Rosemaryu Kenya Agr & Livestock Res Org, Biotechnol Res Inst, Nairobi, Kenya.4 aut
7001 a Okeyo, Winnie A.u Kenya Agr & Livestock Res Org, Biotechnol Res Inst, Nairobi, Kenya.;Maseno Univ, Dept Biomed Sci & Technol, Sch Publ Hlth & Community Dev, Maseno, Kisumu, Kenya.4 aut
7001 a Mireji, Paul O.u Kenya Agr & Livestock Res Org, Biotechnol Res Inst, Nairobi, Kenya.;Kenya Govt Med Res Ctr, Ctr Geog Med Res Coast, Kilifi, Kenya.4 aut
7001 a Okoth, Sylvanceu Kenya Agr & Livestock Res Org, Biotechnol Res Inst, Nairobi, Kenya.4 aut
7001 a Malele, Imnau Tanzania Vet Lab Agcy, Vector & Vector Borne Dis Res Inst, Tanga, Tanzania.4 aut
7001 a Murilla, Graceu Kenya Agr & Livestock Res Org, Biotechnol Res Inst, Nairobi, Kenya.4 aut
7001 a Aksoy, Serapu Yale Sch Publ Hlth, Dept Epidemiol Microbial Dis, New Haven, CT USA.4 aut
7001 a Caccone, Adalgisau Yale Univ, Dept Ecol & Evolutionary Biol, New Haven, CT USA.4 aut
7001 a Saarman, Norah P.u Yale Univ, Dept Ecol & Evolutionary Biol, New Haven, CT USA.;Utah State Univ, Dept Biol, Logan, UT 84322 USA.4 aut
7102 a Yale Univ, Dept Ecol & Evolutionary Biol, New Haven, CT USA.;Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA.b Yale Univ, Sch Environm, New Haven, CT USA.4 org
7730 t Evolutionary Applicationsd : John Wiley & Sonsg 14:7, s. 1762-1777q 14:7<1762-1777x 1752-4571
8564 u https://doi.org/10.1111/eva.13237y Fulltext
8564 u https://uu.diva-portal.org/smash/get/diva2:1598520/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 u https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/eva.13237
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-454523
8564 8u https://doi.org/10.1111/eva.13237

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