Volume 28, 2021
Special Issue – Combatting Anthelmintic resistance in ruminants. Invited Editors: Johannes Charlier, Hervé Hoste, and Smaragda Sotiraki
Article Number 46
Number of page(s) 16
Published online 27 May 2021
  1. Albergel C, Dutra E, Munier S, Calvet JC, Munoz-Sabater J, De Rosnay P, Balsamo G. 2018. ERA-5 and ERA-Interim driven ISBA land surface model simulations: Which one performs better? Hydrology and Earth System Sciences, 22(6), 3515–3532. [Google Scholar]
  2. Arbabi M, Nezami E, Hooshyar H, Delavari M. 2018. Epidemiology and economic loss of fasciolosis and dicrocoeliosis in Arak, Iran. Veterinary World, 11(12), 1648. [Google Scholar]
  3. Beck J, Böller M, Erhardt A, Schwanghart W. 2014. Spatial bias in the GBIF database and its effect on modeling species’ geographic distributions. Ecological Informatics, 19, 10–15. [Google Scholar]
  4. Bennema S, Vercruysse J, Claerebout E, Schnieder T, Strube C, Ducheyne E, Hendrickx G, Charlier J. 2009. The use of bulk-tank milk ELISAs to assess the spatial distribution of Fasciola hepatica, Ostertagia ostertagi and Dictyocaulus viviparus in dairy cattle in Flanders (Belgium). Veterinary Parasitology, 165(1–2), 51–57. [Google Scholar]
  5. Bennema SC, Ducheyne E, Vercruysse J, Claerebout E, Hendrickx G, Charlier J. 2011. Relative importance of management, meteorological and environmental factors in the spatial distribution of Fasciola hepatica in dairy cattle in a temperate climate zone. International Journal for Parasitology, 41(2), 225–233. [Google Scholar]
  6. Bosco A, Rinaldi L, Musella V, Pintus D, Santaniello M, Morgoglione M, Zacometti G, Cringoli G. 2013. Helminths in Sheep on Farms of the Basilicata Region of Southern Italy, in Trends in Veterinary Sciences, Boiti C, Ferlazzo A, Gaiti A, Pugliese A, Editors. Springer: Berlin, Heidelberg. [Google Scholar]
  7. Bosco A, Rinaldi L, Musella V, Amadesi A, Cringoli G. 2015. Outbreak of acute fascioliosis in sheep farms in a Mediterranean area arising as a possible consequence of climate change. Geospatial Health, 9(2), 319–324. [Google Scholar]
  8. Colwell DD, Goater CP. 2010. Dicrocoelium dendriticum in cattle from Cypress Hills, Canada: Humoral response and preliminary evaluation of an ELISA. Veterinary Parasitology, 174(1–2), 162–165. [Google Scholar]
  9. Cringoli G, Rinaldi L, Veneziano V, Capelli G, Scala A. 2004. The influence of flotation solution, sample dilution and the choice of McMaster slide area (volume) on the reliability of the McMaster technique in estimating the faecal egg counts of gastrointestinal strongyles and Dicrocoelium dendriticum in sheep. Veterinary Parasitology, 123(1), 121–131. [Google Scholar]
  10. Ducheyne E, Charlier J, Vercruysse J, Rinaldi L, Biggeri A, Demeler J, Brandt C, de Waal T, Selemetas N, Höglund J, Kaba J, Kowalczyk SJ, Hendrickx G. 2015. Modelling the spatial distribution of Fasciola hepatica in dairy cattle in Europe. Geospatial Health, 9(2), 261–270. [Google Scholar]
  11. Ekstam B, Johansson B, Dinnétz P, Ellström P. 2011. Predicting risk habitats for the transmission of the small liver fluke, Dicrocoelium dendriticum to grazing ruminants. Geospatial Health, 6(1), 125–131. [Google Scholar]
  12. Elith J, Leathwick JR. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40(1), 677–697. [Google Scholar]
  13. Estrada-Peña A, Estrada-Sánchez A, de la Fuente J. 2014. A global set of Fourier-transformed remotely sensed covariates for the description of abiotic niche in epidemiological studies of tick vector species. Parasites Vectors, 7, 302. [Google Scholar]
  14. Ezatpour B, Hasanvand A, Azami M, Anbari K, Ahmadpour F. 2015. Prevalence of liver fluke infections in slaughtered animals in Lorestan. Iranian Journal of Parasitic Diseases, 39(4), 725–729. [Google Scholar]
  15. Fairweather I, Brennan GP, Hanna REB, Robinson MW, Skuce PJ. 2020. Drug resistance in liver flukes. International Journal for Parasitology: Drugs and Drug Resistance, 12, 39–59. [Google Scholar]
  16. Farber O, Kadmon R. 2003. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecological Modelling, 160(1–2), 115–130. [Google Scholar]
  17. Fick SE, Hijmans RJ. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. [Google Scholar]
  18. González-Warleta M, Lladosa S, Castro-Hermida JA, Martínez-Ibeas AM, Conesa D, Muñoz F, López-Quílez A, Manga-González Y, Mezo M. 2013. Bovine paramphistomosis in Galicia (Spain): prevalence, intensity, aetiology and geospatial distribution of the infection. Veterinary Parasitology, 191(3–4), 252–263. [Google Scholar]
  19. Gordon DK, Zadoks RN, Stevenson H, Sargison ND, Skuce PJ. 2012. On farm evaluation of the coproantigen ELISA and coproantigen reduction test in Scottish sheep naturally infected with Fasciola hepatica. Veterinary Parasitology, 187(3–4), 436–444. [Google Scholar]
  20. Hendrickx G. 1999. Georeferenced decision support methodology towards trypanosomosis management in West Africa. Ghent, Belgium: Universiteit Gent. [Google Scholar]
  21. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965–1978. [Google Scholar]
  22. Jeandron A, Rinaldi L, Abdyldaieva G, Usubalieva J, Steinmann P, Cringoli G, Utzinger J. 2011. Human infections with Dicrocoelium dendriticum in Kyrgyzstan: the tip of the iceberg? Journal of Parasitology, 97(6), 1170–1172. [Google Scholar]
  23. Jithendran KP, Bhat TK. 1996. Prevalence of dicrocoeliosis in sheep and goats in Himachal Pradesh, India. Veterinary Parasitology, 61(3–4), 265–271. [Google Scholar]
  24. Liaw A, Wiener M. 2002. Classification and regression by randomForest. R news, 2(3), 18–22. [Google Scholar]
  25. Manga-González MY, Ferreras MC. 2019. Dicrocoeliidae family: Major species causing veterinary diseases. Adv Exp Med Biol, 1154, 279–319. PMID: 31297766. [Google Scholar]
  26. Mateo RG, Felicísimo ÁM, Muñoz J. 2010. Effects of the number of presences on reliability and stability of MARS species distribution models: the importance of regional niche variation and ecological heterogeneity. Journal of Vegetation Science, 21(5), 908–922. [Google Scholar]
  27. Meshgi B, Majidi-Rad M, Hanafi-Bojd AA, Kazemzadeh A. 2019. Predicting environmental suitability and geographical distribution of Dicrocoelium dendriticum at littoral of Caspian Sea: an ecological niche-based modeling. Preventive Veterinary Medicine, 170, 104736. [Google Scholar]
  28. Morgan E, Charlier J, Hendrickx G, Biggeri A, Catalan D, von Samson-Himmelstjerna G, Demeler J, Müller E, van Dijk J, Kenyon F, Skuce P, Höglund J, O’Kiely P, van Ranst B, de Waal T, Rinaldi L, Cringoli G, Hertzberg H, Torgerson P, Wolstenholme A, Vercruysse J. 2013. Global change and helminth infections in grazing ruminants in Europe: impacts, trends and sustainable solutions. Agriculture, 3(3), 484–502. [Google Scholar]
  29. Musella V, Catelan D, Rinaldi L, Lagazio C, Cringoli G, Biggeri A. 2011. Covariate selection in multivariate spatial analysis of ovine parasitic infection. Preventive Veterinary Medicine, 99(2–4), 69–77. [Google Scholar]
  30. O’Donnell MS, Ignizio DA. 2012. Bioclimatic predictors for supporting ecological applications in the conterminous United States. U.S. Geological Survey Data Series, 691, 10. [Google Scholar]
  31. Otranto D, Traversa D. 2003. Dicrocoeliosis of ruminants: a little known fluke disease. Trends in Parasitology, 19(1), 12–15. [Google Scholar]
  32. Phelan P, Morgan ER, Rose H, Grant J, O’Kiely P. 2016. Predictions of future grazing season length for European dairy, beef and sheep farms based on regression with bioclimatic variables. Journal of Agricultural Science, 154(5), 765. [Google Scholar]
  33. Robinson TP, William Wint GR, Conchedda G, Van Boeckel TP, Ercoli V, Palamara E, Cinardi G, D’Aietti L, Hay SI, Gilbert M. 2014. Mapping the global distribution of livestock. PLoS One, 9(5), e96084. [Google Scholar]
  34. Rojo-Vázquez FA, Meana A, Valcárcel F, Martínez-Valladares M. 2012. Update on trematode infections in sheep. Veterinary Parasitology, 189(1), 15–38. [Google Scholar]
  35. Scala A, Tamponi C, Dessì G, Sedda G, Sanna G, Carta S, Corda A, Jacquiet P, Varcasia A, Ligios C. 2019. Dicrocoeliosis in extensive sheep farms: a survey. Parasites & Vectors, 12(1), 1–7. [Google Scholar]
  36. Shinggu PA, Olufemi OT, Nwuku JA, Baba-Onoja EBT, Iyawa PD. 2019. Liver flukes egg infection and associated risk factors in white Fulani cattle slaughtered in Wukari, southern Taraba State, Nigeria. Advances in Preventive Medicine, 2019, 5, Article ID 2671620. [Google Scholar]
  37. Stockwell DRB, Peterson AT. 2002. Effects of sample size on accuracy of species distribution models. Ecological Modelling, 148, 1–13. [Google Scholar]
  38. Syfert MM, Smith MJ, Coomes DA. 2013. The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PLoS One, 8(2), e55158. [Google Scholar]
  39. Team R. 2019. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. [Google Scholar]
  40. Walker PA, Cocks KD. 2009. HABITAT: a procedure for modelling a disjoint environmental envelope for a plant or animal species. Global Ecology and Biogeography, 1(4), 108–118. [Google Scholar]
  41. Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A, Group NPSDW. 2008. Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14(5), 763–773. [Google Scholar]
  42. Zurell D, Franklin J, König C, Bouchet PJ, Dormann CF, Elith J, Fandos G, Feng X, Guillera-Arroita G, Guisan A. 2020. A standard protocol for reporting species distribution models. Ecography, 43(9), 1261–1277. [Google Scholar]
  43. R Core Team. 2017. R: A Language and Environment for Statistical Computing. [Google Scholar]

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