Research Article Ecological Niche Modeling of Seventeen … · 2019. 7. 31. · Research Article...

Research ArticleEcological Niche Modeling of Seventeen Sandflies Species(Diptera Psychodidae Phlebotominae) from Venezuela

Iomar Sanchez1 Jonathan Liria12 and Mariacutea Dora Feliciangeli3

1Departamento de Biologıa Facultad Experimental de Ciencias y Tecnologıa Universidad de Carabobo Valencia 2005Carabobo Venezuela2Laboratorio Museo de Zoologıa Universidad de Carabobo Valencia 2005 Carabobo Venezuela3Centro Nacional de Referencia de Flebotomos y otros Vectores (CNRFV) BIOMED Facultad de Ciencias de la SaludUniversidad de Carabobo Maracay 2101 Aragua Venezuela

Correspondence should be addressed to Jonathan Liria jonathanliriagmailcom

Received 13 August 2014 Accepted 21 January 2015

Academic Editor Ajai K Srivastav

Copyright copy 2015 Iomar Sanchez et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The purpose of this study is to create distribution models of seventeen Lutzomyia species in Venezuela Presence records wereobtained from field collections over 30 years by several research teamsWe usedmaximum entropymethod for model constructionbased on 30 arc-second resolution environmental layers 19 bioclimatic variables elevation and land cover Three species weredistributed throughout north-central Venezuelan two restricted to northern Venezuelan coast and three throughout the west fivewere restricted mainly to the Andean and finally two species within sparse pattern The most important variables that contributedwere related to precipitationThe environmental niche model of sandflies might be a useful tool to contribute to the understandingof the ecoepidemiological complexity of the transmission dynamics of the leishmaniases

1 Introduction

The phlebotomine sandflies are important vectors of bar-tonellosis [1] flaviviruses orbiviruses phleboviruses andvesiculoviruses [2 3] as well as leishmaniases [4]

Cutaneous leishmaniasis in Venezuela is endemic andfocal but is distributed almost all over the country wherethe population is about 25000000 The number of casesreported by the Ministry of Health during the period 1988ndash2007 was 52402 with an average incidence of 1023 in 100000inhabitants 9867 were diagnosed as localized cutaneousleishmaniasis 11 as mucocutaneous leishmaniasis and023 as diffuse leishmaniasis [5 6] In relation to visceralleishmaniasis (VL) for the period 1990ndash2009 the number ofregistered cases was 597 and the average incidence was 012per 100000 inhabitants (unpublished files of the Departmentof Informatics Instituto de Biomedicina Caracas)

Feliciangeli [7] reported about 100 species of phle-botomine sandflies known in Venezuela 30 of which areanthropophilic and eight are recognized as suspected orproven vectors of the leishmaniases Following Young and

Duncanrsquos classification [8] we list here the proven vectorsof cutaneous leishmaniasis (CL) Lutzomyia ovallesi (Ortız1952) L gomezi (Nitzulescu 1931) L panamensis (Shannon1926) L youngi Feliciangeli amp Murillo 1985 L spinicrassaMorales Osorno-Mesa Osorno and Munoz de Hoyos 1969and L migonei (Franca 1920) and the proven vectors ofvisceral leishmaniasis (VL) L longipalpis sl (Lutz amp Neiva1912) L pseudolongipalpis Arrivillaga amp Feliciangeli 2001and L evansi (Nunez-Tovar 1924)

Earlier studies have demonstrated that historical field andlaboratory data regarding the ecology and distribution ofvector-borne diseases can provide the basis for developmentof spatial-temporal environmental risk models by the use ofstandardized analysis of Geographical Information Systemsremote sensing data from earth-observing satellites andecological niche modeling [9ndash13] Ecological niche modeling(ENM) uses record data in conjunction with environmentaldata to develop models of habitat range for a given organism[14 15] Some authors stated that ENM today plays a centralrole in many areas of ecology conservation and evolutionarybiology both because they can fill gaps in knowledge and

Hindawi Publishing CorporationInternational Journal of ZoologyVolume 2015 Article ID 108306 9 pageshttpdxdoiorg1011552015108306

2 International Journal of Zoology

allow a better estimate of multiple components of speciesdiversity [16ndash18] This technique has been used in modelingdistribution of diseases such as dengue malaria and WestNile and yellow fever vectors such as Aedes Meigen 1818[19] Anopheles Meigen 1818 [20ndash22] Culex Linnaeus 1758[23] and Haemagogus Williston 1896 [24] respectively Inaddition ENM has been used to examine the potentialdistribution of Lutzomyia Franca 1924 in America [25ndash30]and PhlebotomusRondani 1840 inwesternAsia [31] RecentlyFoley et al [32] proposed a new map service that allows freepublic online access to global sandfly collection records andhabitat suitability models

In this study we use ENM to develop distributionmodelsfor seventeen species of phlebotomine sandflies (sixteen fromLutzomyia and one of Brumptomyia Franca amp Parrot 1921)in Venezuela Using these models we attempt to identifyenvironmental factors which influence the distribution ofthese species in order to contribute to leading to a moresophisticated risk analysis for leishmaniasis in Venezuela

2 Material and Methods

21 Species Records As part of a sandfly biogeographicalstudy presence data for the species were taken fromVenezue-lan collections performed by the Centro Nacional de Refer-encia de Flebotomos y otros Vectores (CNRFV) BIOMEDUniversidad de Carabobo Maracay Venezuela during 30years and bibliographic revision in scientific literature datingfrom 1970 through the present compiled by one of the authors(Marıa Dora Feliciangeli) This study was restricted onlyto species with ten or more presence records and includedsix species of medical importance Small sample sizes posechallenges to any statistical analysis and result in decreasedpredictive potential when compared to models developedwithmore occurrences Hernandez et al [33] evaluate severalENM algorithms and found that model accuracy increasedwith larger sample sizes for all modeling methods acrossthe taxa tested Nonetheless useful models were producedwith as few as 5ndash10 positive observations Because of thatwe included the sandflies species within small records sizesin order to evaluate the accuracy of obtained models in suchtaxaWe compiled a country database (from21 administrativestates excluding Monagas Vargas and Delta Amacuro)with 681 specimen records (98 represents CNRFV sitecollections) of 17 species Brumptomyia beaupertuyi (Ortiz1954) (119899 = 18) Lutzomyia antunesi (Coutinho 1939) (24)L atroclavata (Knab 1913) (80) L cayennensis cayennensis(Floch amp Abonnenc 1941) (81) L dubitans (Sherlock 1962)(38) L evansi (43) L gomezi (92) L lichyi (Floch ampAbonnenc 1950) (49) L longipalpis sl (67) L micropyga(Mangabeira 1942) (19) L migonei (23) L nuneztovari(Ortiz 1954) (12) L olmeca bicolor Fairchild amp Theodor1971 (12) L ovallesi (47) L panamensis (33) L pilosa(Damasceno amp Causey 1944) (14) and L punctigeniculata(Floch amp Abonnenc 1944) (29) Lutzomyia pseudolongipalpiswas not included in this study because its known distributionis only restricted to three close localities in Lara state Thedatabase coordinates were converted to the decimal degrees

format using GPS coordinates 1 100000 regional mapsand geographical gazetteers and for database standardizationprotocols we followed [34 35]

22 Environmental Layers We used 19 bioclimatic vari-ables [36] derived from climatic data from the 1950ndash2000period annual mean temperature (BIO1) mean diurnalrange (BIO2) isothermality (BIO3) temperature seasonality(BIO4) maximum temperature of warmest month (BIO5)minimum temperature of coldestmonth (BIO6) temperatureannual range (BIO7) mean temperature of wettest quarter(BIO8) mean temperature of driest quarter (BIO9) meantemperature of warmest quarter (BIO10) mean temperatureof coldest quarter (BIO11) annual precipitation (BIO12)precipitation of wettestmonth (BIO13) precipitation of driestmonth (BIO14) precipitation seasonality (BIO15) precip-itation of wettest quarter (BIO16) precipitation of driestquarter (BIO17) precipitation of warmest quarter (BIO18)and precipitation of coldest quarter (BIO19) and one topo-graphic variable [37] elevation (ELEV) and one ecological[38] fifteen land cover classes (LAND) All variables werereduced to a grid resolution of 30 arc-seconds or 0008333∘(approximately 1 Km2) for the analysis

23 Model Building and Evaluation We modeled the speciesdistribution using maximum entropy method [17 39] withMaxEnt 23 [17] The analysis was performed using thedefault parameters maximum iterations to 500 and usingconvergence threshold in 10119864 minus 5 [40 41] Seventy-fivepercent of the data points for each species were randomlyselected as training points and used in model building Theremaining 25 of the records were test points and used inmodel validation Duplicate presence records were removedby the program prior to model development The modeloutput was set to logistic it is an attempt to get as close as wecan to estimate the relative habitat suitability that the speciesis present given the environment The program returns anestimated presence probability for a given location betweencell values of 0 (no probability of species presence) and 1(species is certain to be present) we based our predictionsmaps on a probability of 070 (values above this thresh-old were considered presence and they represented highersuitability) The model was evaluated using the area underthe curve (AUC) of the receiver operating characteristic(ROC) this technique computes the total area under thecurve created by plotting sensitivity against the fractionalpredicted area for the species Also a one-tailed binomialtest was calculated with the null hypothesis being that themodel does not predict the test points better than random[17 31 39 42]

3 Results

Table 1 represents the accuracy of the niche models in thesandflies species Of these 17 niche models all possessedan AUC greater than 080 with 15 of them being statisti-cally significant (119875 lt 005) as indicated by the binomialtest Niche models were produced for Lutzomyia antunesi

International Journal of Zoology 3

Table 1 Accuracy of the niche models in the sandflies species

Species AUC Omission rate 119875 valueBrumptomyia beaupertuyi 0882 0000 nsLutzomyia antunesi 0825 0000 lt005L atroclavata 0872 0067 lt0001L c cayennensis 0929 0065 lt0001L dubitans 0953 0034 lt0001L evansi 0926 0061 lt0001L gomezi 0805 0087 lt0001L lichyi 0977 0054 lt0001L longipalpis sl 0802 0137 lt0001Lmicropyga 0948 0067 lt001Lmigonei 0888 0000 lt005L nuneztovari 0967 0000 lt0001L olmeca bicolor 0969 0000 lt005L ovallesi 0900 0083 lt0001L panamensis 0889 0040 lt0001L pilosa 0821 0091 nsL punctigeniculata 0829 0045 lt005ns no significance

L atroclavata L c cayennensis L dubitans L evansi Lgomezi L lichyi L longipalpis sl L micropyga L migoneiL nuneztovari L olmeca bicolor L ovallesi L panamensisand L punctigeniculata Figures 1ndash3 present maps of thepredicted geographical distributions of sandflies speciesDarker regions are those of high relative probability (gt070) ofoccurrence while lighter areas are those in which the relativeprobability of occurrence is low (lt070)The species present avariety of distributional patterns acrossVenezuelaLutzomyiaevansi and L panamensiswere distributed throughout north-central Venezuela L cayennensis and L olmeca bicolor wererestricted to northern Venezuelan coast L nuneztovariL longipalpis sl L punctigeniculata and L gomezi weredistributed throughout the west L micropyga L migonei Llichyi and L ovallesi were restricted mainly to the Andeanregion L atroclavata is distributed throughout northernVenezuela finally L dubitans and L antunesi were present ina sparse pattern in the country

Table 2 informs about the environmental variable con-tribution based on the Jackknife test first the AUC of eachniche model was calculated using each of the environmentalvariables individually Those variables that resulted in thehighest AUC were interpreted as those which possessedthe most information regarding the niche of a speciesSecond the AUC of each niche model was calculated afteromitting each of the environmental variables one at atime In our study the variables that increment the AUCvalue when included separately were precipitation of wettestquarter (BIO16) precipitation of wettest month (BIO13)annual precipitation (BIO12) and precipitation of coldestquarter (BIO19) The remaining variables showed smallestimportance in the AUC contribution precipitation of dri-est month (BIO14) elevation (ELEV) temperature annualrange (BIO7) land cover (LAND) and so forth However

some variables decrease the AUC when they are omittedfrom the models isothermality (BIO3) BIO12 mean diurnalrange (BIO2) LAND and ELEV For additional MaxEntoutput see the Supplementary Material available onlineat httpdxdoiorg1011552015108306 within the omissionand predicted area and Jackknife regularized trained gainfigures for each species

4 Discussion

The predicted distributions of Venezuela sandflies produceuseful models within species that differ from 12 records(L olmeca bicolor and L nuneztovari) to 92 records (Lgomezi) however in L pilosa (14 records) and B beaupertuyi(18) the models performance was poor Some studies havedemonstrated deterioration in predictive performance assample sizes are decreased [26 42] In others accuracymodels were produced with few records maybe due toecologically specialized species with smallest geographicextent of occurrence and very low tolerance [33] Ourrecords showed L olmeca bicolor to be restricted to lowlanddeciduous forests (100ndash250masl) L nuneztovari to montaneevergreen forests (500ndash1900masl) L gomezi to various landcover types and wide elevation range L pilosa to montaneevergreen forest agriculture types and wide elevation rangeand finally B beaupertuyi to various land cover types andwide elevation range Our results are similar to ten Lutzomyiaspecies potential distribution maps generated by the globalgeospatially referenced clearinghouse for sandfly disease vec-tor species collection records (httpwwwsandflymaporgaccess 1192015) However the differences may be dueto the environmental variables used to build the modeland the number of records (presences) in each species InVenezuela there are only 40 records for seventeen species inthe SandflyMap database in contrast to the 681 records in ourstudy

In strict epidemiological sense the Venezuelan Andes(Trujillo Merida Tachira states) and the Lara state showedto be the most important area with CL transmission [43ndash46]Feliciangeli et al [47] stated that the distribution ofLutzomyiaevansi (VL vector) and L ovallesi almost overlaps the Andeanlowland forests as well in the Northern Coast Cordillera Lovallesi is restricted to 0ndash800masl and L nuneztovari to areasclose or to above 1000 masl In a review of the subgenusMicropygomyia of Lutzomyia Feliciangeli [48] showed thatthe geographical distribution of L micropyga to the Andeanstates may represent the natural continuity of the Colombiandistribution L c cayennensis occupies wide range of lifezones (dry to moist tropical forests) whereas L atroclavatais normally found in the lowlands (0ndash400masl)

Rotureau [49] stated that Lutzomyia vectors are generallyabundant year round in theAmazonNevertheless the periodencompassing the end of the dry season and the beginningof the rainy season is more propitious for their developmentPeterson and Shaw [25] proposed a niche model for cuta-neous leishmaniasis vectors from southern Brazil they foundthat L migonei had the broadest ecological distribution andLutzomyia whitmani (Antunes amp Coutinho 1939) tended to


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Figure 1 Distribution of sandflies species in Venezuela and gray points denoted occurrences and black areas high probability of relativehabitat suitability Maps are provided for (a) Lutzomyia antunesi (b) L atroclavata (c) L c cayennensis (d) L dubitans and (e) L evansi and(f) L gomezi


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Figure 2 Distribution of sandflies species in Venezuela and gray points denoted occurrences and black areas high probability of relativehabitat suitability Maps are provided for (a) L lichyi (b) L longipalpis sl (c) L micropyga (d) L migonei (e) L nuneztovari and (f) Lolmeca bicolor


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(c)

Figure 3 Distribution of sandflies species in Venezuela and gray points denoted occurrences and black areas high probability of relativehabitat suitability Maps are provided for (a) L ovallesi (b) L panamensis and (c) L punctigeniculata

be the most tropical in distribution (high precipitation hightemperature wet climates and high vapour pressure) Morerecently a niche model for Leishmaniasis in north Americasuggests that the most important environmental variableswere mean temperatures of the wettest and warmest quartersin Lutzomyia anthophora (Addis 1945) and annual meantemperature and the minimum temperature of the coldestmonth inL diabolica (Hall 1936) [27] Colacicco-Mayhugh etal [31] determined the nichemodel in Phlebotomus alexandriSinton 1928 and P papatasi (Scopoli 1786) using land coverclasses derived fromNormalizedDifferenceVegetation Index(NVDI) and 19 bioclimatic variables The variables with highprobabilities presence were urban fieldwoody savanna and

woody savannah coverage while the remaining variableshave very modest training gains They consider that this maybe partly due to sampling bias as collections of phlebotominesand flies tend to be associated with research related tohuman leishmaniasis and anthropogenic biomes Almeidaet al [50] observed in Mato Grosso state (Brazil) similarpredicted areas between niche models for L longipalpisand L cruzi (Mangabeira 1938) when NVDI land coverclasses and climatic variables were included together or in aseparate analysis Finally they reported high abundance ofL longipalpis in the rainy season and in L cruzi followedmoderate to regular precipitation andminimum temperatureelevation Quintana et al [29] showed that precipitation


Table 2 Jackknife AUC contributions of the environmental variables of the niche models

Species Variables that produce the largest AUCwhen included separately

Variables that produce thesmallest AUC when omitted

B beaupertuyi BIO19 (0892) BIO16 (0874) BIO12 (0866) ELEV (0849) BIO2 (0872) BIO11 (0879)L antunesi BIO7 (0915) BIO19 (0886) BIO5 (0845) BIO2 (0812) BIO3 (0819) BIO16 (0821)L atroclavata BIO13 (0856) BIO19 (0855) BIO16 (0848) BIO12 (0839) ELEV (0858) BIO5 (0859)L c cayennensis BIO16 (0908) BIO19 (0906) BIO13 (0901) BIO12 (0898) BIO2 (0907) BIO3 (0909)L dubitans BIO12 (0957) BIO13 (0952) BIO16 (0950) BIO2 (0942) BIO19 (0950) ELEV (0951)L evansi BIO12 (0926) BIO13 (0921) BIO16 (0917) BIO3 (0912) BIO2 (0920) BIO12 (0922)L gomezi BIO16 (0872) BIO12 (0859) BIO13 (0856) BIO11 (0792) BIO4 (0793) LAND (0793)L lichyi BIO6 (0964) BIO11 (0961) BIO9 (0959) ELEV (0965) BIO16 (0973) BIO2 (0975)L longipalpis sl BIO12 (0934) BIO13 (0930) BIO16 (0925) BIO12 (0786) BIO3 (0789) BIO9 (0803)Lmicropyga LAND (0921) BIO13 (0826) BIO16 (0819) BIO3 (0944) BIO12 (0945) BIO13 (0946)Lmigonei BIO17 (0888) BIO7 (0886) BIO14 (0870) LAND (0867) BIO2 (0875) BIO3 (0876)L nuneztovari BIO5 (0989) BIO1 (0987) BIO10 (0986) BIO18 (0957) BIO15 (0962) BIO12 (0967)L olmeca bicolor BIO12 (0953) BIO19 (0940) ELEV (0727) LAND (0961) BIO17 (0972) BIO3 (0973)L ovallesi BIO16 (0906) BIO13 (0902) BIO12 (0884) BIO12 (0876) LAND (0896) BIO6 (0898)L panamensis BIO19 (0935) BIO16 (0913) BIO12 (0912) BIO12 (0879) LAND (0882) BIO3 (0886)L pilosa BIO15 (0868) BIO14 (0849) ELEV (0802) BIO15 (0810) BIO8 (0816) BIO10 (0817)L punctigeniculata BIO14 (0896) BIO4 (0883) BIO17 (0882) BIO13 (0822) BIO17 (0824) BIO7 (0827)

seasonality and precipitation in the warmest quarter were thevariables that most contributed to the ENM of Lutzomyianeivai and Lutzomyia migonei in Argentina Our resultssuggest that precipitation of driest quarter and precipitationof driest month were the most important variables in Lmigonei model prediction In Colombia [30] noted thatdistributions of L longipalpis and L evansi are stronglycorrelated with precipitation their breeding success dependson the availability of rich humid soils and precipitationduring dry months when the river flow can decrease canbecome a limiting factor These findings are similar to oursannual precipitation is the most important variable for thesespecies that contributes to the model construction

The VL vectors L evansi and L longipalpis sl showedto be sympatric in northern Venezuela [49 51] Then Lpanamensis and L evansi were also reported to be focused inCaribbean coast of eastern Venezuela where cutaneous andvisceral leishmaniasis coexist [52] Lutzomyia gomezi and Lovallesi in the north-central Venezuela are the main anthro-pophilic species implicated as vectors of this disease [49 53]Feliciangeli [7] reported the geographic distribution of eightleishmaniasis vectors of these five species showed the widestdistribution within fourteen to twenty administrative statesand the remaining species were restricted to one to fourstates Our results are congruent with this author showingwide predicted distributions for L longipalpis sl L evansiL ovallesi and L gomezi however the potential distributionin L panamensis was limited to fewer states

Recently in Mexico [27] estimate the probable distribu-tion of all sandfly species with potential medical importancefor leishmaniasis and related it to the transmission areas

In conclusion although we are aware about the lim-itations of using secondary data collected with differentmethods across many years our results show that the ENM

might be a useful tool to contribute to the understandingof the ecoepidemiological complexity of the transmissiondynamics of the leishmaniases To reach this goal furtherstudies are needed which must include epidemiological vari-ables such as the distribution of CLVL cases and reservoirs

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

The authors thank the Venezuelan Ministry of Science andTechnology for supporting bymeans of the ProjectsMISIONCIENCIA 2008000911-2 and 2008000911-4

References

[1] R J Birtles ldquoCarrions diseaserdquo in The Encyclopedia ofArthropod-Transmitted Infections M W Service Ed pp 104ndash106 CABI Publishing Wallingford UK 2001

[2] J A Comer and R B Tesh ldquoPhlebotomine sand flies as vectorsof vesiculoviruses a reviewrdquo Parassitologia vol 33 pp 143ndash1501991

[3] R W Ashford ldquoPhlebotomus feversrdquo in The Encyclopedia ofArthropod-Transmitted Infections M W Service Ed pp 397ndash401 CABI Publishing Wallingford UK 2001

[4] R Killick-Kendrick ldquoPhlebotomine vectors of the leishmani-ases a reviewrdquoMedical and Veterinary Entomology vol 4 no 1pp 1ndash24 1990

[5] H de Lima R H Borges J Escobar and J Convit ldquoLeish-maniasis cutanea americana en Venezuela un analisis clınico


epidemiologico a nivel nacional y por entidad federal 1988ndash2007rdquo Boletin de Malariologıa y Salud Ambiental vol 50 pp283ndash299 2010

[6] HDe Lima RH Borges J Escobar and J CGarcıa ldquoAmericancutaneous leishmaniasis in Venezuela biennium 2008-2009rdquoBoletin deMalariologia y Salud Ambiental vol 51 no 2 pp 215ndash224 2011

[7] M D Feliciangeli ldquoSobre los Flebotomos (Diptera Psycho-didae Phlebotominae) con especial referencia a las especiesconocidas enVenezuelardquoActa Biologica Venezuelica vol 26 no2 pp 61ndash80 2006

[8] D Young and M Duncan Guide to the Identification andGeographic Distribution of Lutzomyia Sand Flies in Mexico theWest Indies Central and South America (Diptera Psychodidae)vol 54 of Memoirs of the American Entomological InstituteAssociated Publishers Gainesville Fla USA 1994

[9] J B Malone ldquoBiology-based mapping of vectormdashborne para-sites by Geographic information systems and remote sensingrdquoParassitologia vol 47 no 1 pp 27ndash50 2005

[10] P Nieto J B Malone and M E Bavia ldquoEcological nichemodeling for visceral leishmaniasis in the state of Bahia Brazilusing genetic algorithm for rule-set prediction and growingdegree day-water budget analysisrdquo Geospatial Health vol 1 no1 pp 115ndash126 2006

[11] A T Peterson ldquoBiogeography of diseases a framework foranalysisrdquoNaturwissenschaften vol 95 no 6 pp 483ndash491 2008

[12] G S Bhunia S Kesari A Jeyaram V Kumar and P DasldquoInfluence of topography on the endemicity of Kala-azar astudy based on remote sensing and geographical informationsystemrdquo Geospatial Health vol 4 no 2 pp 155ndash165 2010

[13] G Parra-Henao ldquoSistemas de informacion geografica y sen-sores remotos Aplicaciones en enfermedades transmitidas porvectoresrdquo CES Medicina vol 24 pp 75ndash89 2010

[14] A T Peterson ldquoPredicting speciesrsquo geographic distributionsbased on ecological niche modelingrdquo The Condor vol 103 no3 pp 599ndash605 2001

[15] P Illoldi-Rangel and T Escalante ldquoDe los modelos de nichoecologico a las areas de distribucion geograficardquo Biogeografiavol 3 pp 7ndash12 2008

[16] M B Araujo and A Guisan ldquoFive (or so) challenges for speciesdistribution modellingrdquo Journal of Biogeography vol 33 no 10pp 1677ndash1688 2006

[17] S J Phillips R P Anderson and R E Schapire ldquoMaximumentropy modeling of species geographic distributionsrdquo Ecologi-cal Modelling vol 190 no 3-4 pp 231ndash259 2006

[18] L C Terribile J A F Diniz-Filho and P de Marco Jr ldquoHowmany studies are necessary to compare niche-based models forgeographic distributions Inductive reasoning may fail at theendrdquo Brazilian Journal of Biology vol 70 no 2 pp 263ndash2692010

[19] A T Peterson C Martınez-Campos Y Nakazawa and EMartınez-Meyer ldquoTime-specific ecological nichemodeling pre-dicts spatial dynamics of vector insects and human denguecasesrdquoTransactions of the Royal Society of TropicalMedicine andHygiene vol 99 no 9 pp 647ndash655 2005

[20] R S Levine A T Peterson and M Q Benedict ldquoGeographicand ecological distributions of the Anopheles gambiae complexpredicted using a genetic algorithmrdquo The American Journal ofTropical Medicine and Hygiene vol 70 no 2 pp 105ndash109 2004

[21] A Moffett N Shackelford and S Sarkar ldquoMalaria in Africavector speciesrsquo niche models and relative risk mapsrdquo PLoS ONEvol 2 no 9 article e824 2007

[22] D H Foley L M Rueda A T Peterson and R C WilkersonldquoPotential distribution of two species in the medically impor-tant Anopheles minimus complex (Diptera Culicidae)rdquo Journalof Medical Entomology vol 45 no 5 pp 852ndash860 2008

[23] S R Larson J P Degroote L C Bartholomay and R Sugu-maran ldquoEcological niche modeling of potential west nile virusvector mosquito species in Iowardquo Journal of Insect Science vol10 pp 1ndash17 2010

[24] J Liria and J C Navarro ldquoModelo de nicho ecologico enHaemagogus Williston (Diptera Culicidae) vectores del virusde la Fiebre Amarillardquo Revista Biomedica vol 21 pp 149ndash1612010

[25] A T Peterson and J Shaw ldquoLutzomyia vectors for cutaneousleishmaniasis in Southern Brazil ecological niche modelspredicted geographic distributions and climate change effectsrdquoInternational Journal for Parasitology vol 33 no 9 pp 919ndash9312003

[26] A Townsend Peterson R Scachetti Pereira and V FonsecaDe Camargo Neves ldquoUsing epidemiological survey data toinfer geographic distributions of leishmaniasis vector speciesrdquoRevista da Sociedade Brasileira de Medicina Tropical vol 37 no1 pp 10ndash14 2004

[27] C Gonzalez O Wang S E Strutz C Gonzalez-Salazar VSanchez-Cordero and S Sarkar ldquoClimate change and risk ofleishmaniasis in North America predictions from ecologicalniche models of vector and reservoir speciesrdquo PLoS NeglectedTropical Diseases vol 4 no 1 article e585 2010

[28] P S deAlmeida A Sciamarelli PMira Batista et al ldquoPredictingthe geographic distribution of Lutzomyia longipalpis (DipteraPsychodidae) and visceral leishmaniasis in the state of MatoGrosso do Sul BrazilrdquoMemorias do Instituto Oswaldo Cruz vol108 no 8 pp 992ndash996 2013

[29] M Quintana O Salomon R Guerra M Lizarralde de Grossoand A Fuenzalida ldquoPhlebotominae of epidemiological impor-tance in cutaneous leishmaniasis in northwestern Argentinarisk maps and ecological niche modelsrdquoMedical and VeterinaryEntomology vol 27 no 1 pp 39ndash48 2013

[30] C Gonzalez A Paz and C Ferro ldquoPredicted altitudinal shiftsand reduced spatial distribution of Leishmania infantum vectorspecies under climate change scenarios in Colombiardquo ActaTropica vol 129 no 1 pp 83ndash90 2014

[31] M G Colacicco-Mayhugh P M Masuoka and J P GriecoldquoEcological niche model of Phlebotomus alexandri and P pap-atasi (Diptera Psychodidae) in the Middle eastrdquo InternationalJournal of Health Geographics vol 9 article 2 2010

[32] D H Foley R C Wilkerson L L Dornak et al ldquoSandflyMapleveraging spatial data on sand fly vector distribution for diseaserisk assessmentsrdquo Geospatial Health vol 6 pp S25ndashS30 2012

[33] P A Hernandez C H Graham L L Master and D LAlbert ldquoThe effect of sample size and species characteristicson performance of different species distribution modelingmethodsrdquo Ecography vol 29 no 5 pp 773ndash785 2006

[34] S Sua R D Mateus and J C Vargas Georrefenciacion deRegistros Biologicos y Gacetero Digital de Localidades Institutode Investigacion de Recursos Biologicos Alexander von Hum-boldt Bogota Colombia 2004

[35] G Rodrıguez and T Escalante ldquoManejo e importancia delas bases de datos en colecciones biologicasrdquo in ColeccionesMastozoologicas deMexico C Lorenzo E EspinozaM Brionesand and F Cervantes Eds pp 133ndash147 Instituto de BiologıamdashUNAM Mexico City Mexico 2006


[36] R J Hijmans S Cameron and J Parra WorldClim Ver-sion 13 University of California Berkeley 2005 httpwwwworldclimorg

[37] United States Geological SurveymdashUSGS ldquoHYDRO1k ElevationDerivative Databaserdquo 2001 httpsltacrusgsgovHYDRO1K

[38] H D Eva A S Belward E E de Miranda et al ldquoA land covermap of South Americardquo Global Change Biology vol 10 no 5pp 731ndash744 2004

[39] J Elith S J Phillips T Hastie M Dudık Y E Chee and CJ Yates ldquoA statistical explanation of MaxEnt for ecologistsrdquoDiversity and Distributions vol 17 no 1 pp 43ndash57 2011

[40] S Pawar M S Koo C Kelley M F Ahmed S Chaudhuriand S Sarkar ldquoConservation assessment and prioritization ofareas in Northeast India priorities for amphibians and reptilesrdquoBiological Conservation vol 136 no 3 pp 346ndash361 2007

[41] T Escalant M Linaje P Illoldi-Rangel et al ldquoEcological nichemodels and patterns of richness and endemism of the SouthernAndean genus Eurymetopum (Coleoptera Cleridae)rdquo RevistaBrasileira de Entomologia vol 53 no 3 pp 379ndash385 2009

[42] R G Pearson C J RaxworthyMNakamura andA TownsendPeterson ldquoPredicting species distributions from small numbersof occurrence records a test case using cryptic geckos inMadagascarrdquo Journal of Biogeography vol 34 no 1 pp 102ndash1172007

[43] R Bonfante-Garrido R Urdaneta I Urdaneta and J AlvaradoldquoNatural infection of Lutzomyia ovallesi (Diptera Psychodidae)with Leishmania in Duaca Lara State Venezuelardquo Transactionsof the Royal Society of TropicalMedicine andHygiene vol 85 no1 p 61 1991

[44] R Maingon D Feliciangeli B Guzman et al ldquoCutaneousleishmaniasis in Tachira state Venezuelardquo Annals of TropicalMedicine and Parasitology vol 88 no 1 pp 29ndash36 1994

[45] G Perruolo N N Rodrıguez and M D Feliciangeli ldquoIsolationof Leishmania (Viannia) braziliensis from Lutzomyia spinicrassa(species group Verrucarum) Morales Osorno Mesa Osorno ampHoyos 1969 in the Venezuelan Andean regionrdquo Parasite vol 13no 1 pp 17ndash22 2006

[46] L E Traviezo ldquoFlebotofauna al suroeste del estado LaraVenezuelardquo Biomedica vol 26 pp 73ndash81 2006

[47] M D Feliciangeli C Arredondo and R Ward ldquoPhlebotominesandflies in Venezuela review of the verrucarum species group(in part) of Lutzomyia (Diptera Psychodidae) with descriptionof a new species from Larardquo Journal of Medical Entomology vol29 no 5 pp 729ndash744 1992

[48] M D Feliciangeli ldquoPhlebotomine sandflies in Venezuela IVReview of the Lutzomyia subgenus Micropygomyia (DipteraPsychodidae) with a description of L absonodonta n spand the male of L lewisirdquo Annals of Tropical Medicine andParasitology vol 89 no 5 pp 551ndash567 1995

[49] B Rotureau ldquoEcology of the Leishmania species in the Guiananecoregion complexrdquo American Journal of Tropical Medicine andHygiene vol 74 no 1 pp 81ndash96 2006

[50] P S Almeida E Ramos J Oliveira-Da-Silva M A Teixeiraand A Sciamarelli ldquoModelos de distribuicao de vetores deleishmaniose visceral no Estado de Mato Grosso do Sulcom dados bioclimaticos e espectraisrdquo in Anais 3ordmSimposio deGeotecnologias no Pantanal pp 941ndash950 Embrapa InformaticaAgropecuariaINPE Mato Grosso Brazil Outubro 2010

[51] M D Feliciangeli N Rodriguez Z de Guglielmo and ARodriguez ldquoThe re-emergence of American visceral leishma-niasis in an old focus in Venezuela II Vectors and parasitesrdquoParasite vol 6 no 2 pp 113ndash120 1999

[52] R Gonzalez L De Sousa R Devera A Jorquera and ELedezma ldquoSeasonal and nocturnal domiciliary human land-ingbiting behaviour of Lutzomyia (Lutzomyia) evansi andLutzomyia (Psychodopygus) panamensis (Diptera Psychodidae)in a periurban area of a city on the Caribbean coast of easternVenezuela (Barcelona Anzoategui State)rdquo Transactions of theRoyal Society of Tropical Medicine and Hygiene vol 93 no 4pp 361ndash364 1999

[53] M D Feliciangeli and J Rabinovich ldquoAbundance of Lutzomyiaovallesi but not Lu gomezi (Diptera Psychodidae) corre-lated with cutaneous leishmaniasis incidence in north-centralVenezuelardquo Medical and Veterinary Entomology vol 12 no 2pp 121ndash131 1998

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


allow a better estimate of multiple components of speciesdiversity [16ndash18] This technique has been used in modelingdistribution of diseases such as dengue malaria and WestNile and yellow fever vectors such as Aedes Meigen 1818[19] Anopheles Meigen 1818 [20ndash22] Culex Linnaeus 1758[23] and Haemagogus Williston 1896 [24] respectively Inaddition ENM has been used to examine the potentialdistribution of Lutzomyia Franca 1924 in America [25ndash30]and PhlebotomusRondani 1840 inwesternAsia [31] RecentlyFoley et al [32] proposed a new map service that allows freepublic online access to global sandfly collection records andhabitat suitability models

In this study we use ENM to develop distributionmodelsfor seventeen species of phlebotomine sandflies (sixteen fromLutzomyia and one of Brumptomyia Franca amp Parrot 1921)in Venezuela Using these models we attempt to identifyenvironmental factors which influence the distribution ofthese species in order to contribute to leading to a moresophisticated risk analysis for leishmaniasis in Venezuela

2 Material and Methods

21 Species Records As part of a sandfly biogeographicalstudy presence data for the species were taken fromVenezue-lan collections performed by the Centro Nacional de Refer-encia de Flebotomos y otros Vectores (CNRFV) BIOMEDUniversidad de Carabobo Maracay Venezuela during 30years and bibliographic revision in scientific literature datingfrom 1970 through the present compiled by one of the authors(Marıa Dora Feliciangeli) This study was restricted onlyto species with ten or more presence records and includedsix species of medical importance Small sample sizes posechallenges to any statistical analysis and result in decreasedpredictive potential when compared to models developedwithmore occurrences Hernandez et al [33] evaluate severalENM algorithms and found that model accuracy increasedwith larger sample sizes for all modeling methods acrossthe taxa tested Nonetheless useful models were producedwith as few as 5ndash10 positive observations Because of thatwe included the sandflies species within small records sizesin order to evaluate the accuracy of obtained models in suchtaxaWe compiled a country database (from21 administrativestates excluding Monagas Vargas and Delta Amacuro)with 681 specimen records (98 represents CNRFV sitecollections) of 17 species Brumptomyia beaupertuyi (Ortiz1954) (119899 = 18) Lutzomyia antunesi (Coutinho 1939) (24)L atroclavata (Knab 1913) (80) L cayennensis cayennensis(Floch amp Abonnenc 1941) (81) L dubitans (Sherlock 1962)(38) L evansi (43) L gomezi (92) L lichyi (Floch ampAbonnenc 1950) (49) L longipalpis sl (67) L micropyga(Mangabeira 1942) (19) L migonei (23) L nuneztovari(Ortiz 1954) (12) L olmeca bicolor Fairchild amp Theodor1971 (12) L ovallesi (47) L panamensis (33) L pilosa(Damasceno amp Causey 1944) (14) and L punctigeniculata(Floch amp Abonnenc 1944) (29) Lutzomyia pseudolongipalpiswas not included in this study because its known distributionis only restricted to three close localities in Lara state Thedatabase coordinates were converted to the decimal degrees

format using GPS coordinates 1 100000 regional mapsand geographical gazetteers and for database standardizationprotocols we followed [34 35]

22 Environmental Layers We used 19 bioclimatic vari-ables [36] derived from climatic data from the 1950ndash2000period annual mean temperature (BIO1) mean diurnalrange (BIO2) isothermality (BIO3) temperature seasonality(BIO4) maximum temperature of warmest month (BIO5)minimum temperature of coldestmonth (BIO6) temperatureannual range (BIO7) mean temperature of wettest quarter(BIO8) mean temperature of driest quarter (BIO9) meantemperature of warmest quarter (BIO10) mean temperatureof coldest quarter (BIO11) annual precipitation (BIO12)precipitation of wettestmonth (BIO13) precipitation of driestmonth (BIO14) precipitation seasonality (BIO15) precip-itation of wettest quarter (BIO16) precipitation of driestquarter (BIO17) precipitation of warmest quarter (BIO18)and precipitation of coldest quarter (BIO19) and one topo-graphic variable [37] elevation (ELEV) and one ecological[38] fifteen land cover classes (LAND) All variables werereduced to a grid resolution of 30 arc-seconds or 0008333∘(approximately 1 Km2) for the analysis

23 Model Building and Evaluation We modeled the speciesdistribution using maximum entropy method [17 39] withMaxEnt 23 [17] The analysis was performed using thedefault parameters maximum iterations to 500 and usingconvergence threshold in 10119864 minus 5 [40 41] Seventy-fivepercent of the data points for each species were randomlyselected as training points and used in model building Theremaining 25 of the records were test points and used inmodel validation Duplicate presence records were removedby the program prior to model development The modeloutput was set to logistic it is an attempt to get as close as wecan to estimate the relative habitat suitability that the speciesis present given the environment The program returns anestimated presence probability for a given location betweencell values of 0 (no probability of species presence) and 1(species is certain to be present) we based our predictionsmaps on a probability of 070 (values above this thresh-old were considered presence and they represented highersuitability) The model was evaluated using the area underthe curve (AUC) of the receiver operating characteristic(ROC) this technique computes the total area under thecurve created by plotting sensitivity against the fractionalpredicted area for the species Also a one-tailed binomialtest was calculated with the null hypothesis being that themodel does not predict the test points better than random[17 31 39 42]

3 Results

Table 1 represents the accuracy of the niche models in thesandflies species Of these 17 niche models all possessedan AUC greater than 080 with 15 of them being statisti-cally significant (119875 lt 005) as indicated by the binomialtest Niche models were produced for Lutzomyia antunesi







4 Discussion





10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(a)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(b)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(c)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(d)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(e)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(f)



10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(a)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(b)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(c)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(d)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(e)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(f)



10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

HighLow

(a)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

(b)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

(c)
















Acknowledgment


References
































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology







4 Discussion





10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(a)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

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∘30

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∘20

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∘10

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∘00

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∘50

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71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(b)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

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∘30

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∘20

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∘10

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∘00

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∘50

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71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(c)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

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∘20

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∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(d)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(e)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(f)



10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

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∘20

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∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(a)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(b)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(c)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

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∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(d)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(e)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(f)



10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

HighLow

(a)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

(b)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

(c)
















Acknowledgment


References
































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

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∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(a)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(b)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

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∘30

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∘20

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∘10

99840061

∘00

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∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(c)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

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∘40

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∘30

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∘20

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∘10

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∘00

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∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(d)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(e)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(f)



10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(a)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(b)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(c)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(d)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(e)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(f)



10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

HighLow

(a)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

(b)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

(c)
















Acknowledgment


References
































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(a)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

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99840063

∘10

99840061

∘00

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∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(b)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(c)

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

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∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(d)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(e)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

N

E

S

W

200 0 200

(km)

(f)



10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

HighLow

(a)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

(b)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

(c)
















Acknowledgment


References
































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

HighLow

(a)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

(b)

HighLow

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

10∘40

998400

8∘30

998400

6∘20

998400

4∘10

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

71∘50

99840069

∘40

99840067

∘30

99840065

∘20

99840063

∘10

99840061

∘00

99840058

∘50

998400

NE

S

W

200 0 200

(km)

(c)
















Acknowledgment


References
































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology













Acknowledgment


References
































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology



























































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

Research Article Ecological Niche Modeling of Seventeen … · 2019. 7. 31. · Research Article...

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Transcript of Research Article Ecological Niche Modeling of Seventeen … · 2019. 7. 31. · Research Article...