Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-28T10:39:02.903Z Has data issue: false hasContentIssue false

Biogeographical region and host trophic level determine carnivore endoparasite richness in the Iberian Peninsula

Published online by Cambridge University Press:  28 April 2011

L. M. ROSALINO*
Affiliation:
Universidade de Lisboa, Centro de Biologia Ambiental, Faculdade de Ciências de Lisboa, Departamento de Biologia Animal, Ed. C2, 1749-016 Lisboa, Portugal
M. J. SANTOS
Affiliation:
University of California Davis, Center for Spatial Technologies and Remote Sensing, Department of Land, Air and Water Resources, One Shields Avenue, Davis, CA 95616 USA
C. FERNANDES
Affiliation:
Universidade de Lisboa, Centro de Biologia Ambiental, Faculdade de Ciências de Lisboa, Departamento de Biologia Animal, Ed. C2, 1749-016 Lisboa, Portugal
M. SANTOS-REIS
Affiliation:
Universidade de Lisboa, Centro de Biologia Ambiental, Faculdade de Ciências de Lisboa, Departamento de Biologia Animal, Ed. C2, 1749-016 Lisboa, Portugal
*
*Corresponding author: Universidade de Lisboa, Centro de Biologia Ambiental, Faculdade de Ciências de Lisboa, Departamento de Biologia Animal, Ed. C2, 1749-016 Lisboa, Portugal. Tel: +351 217500000 ext. 22541. Fax: +351 217500028. E-mail: [email protected]

Summary

We address the question of whether host and/or environmental factors might affect endoparasite richness and distribution, using carnivores as a model. We reviewed studies published in international peer-reviewed journals (34 areas in the Iberian Peninsula), describing parasite prevalence and richness in carnivores, and collected information on site location, host bio-ecology, climate and detected taxa (Helminths, Protozoa and Mycobacterium spp.). Three hypotheses were tested (i) host based, (ii) environmentally based, and (iii) hybrid (combination of environmental and host). Multicollinearity reduced candidate variable number for modelling to 5: host weight, phylogenetic independent contrasts (host weight), mean annual temperature, host trophic level and biogeographical region. General Linear Mixed Modelling was used and the best model was a hybrid model that included biogeographical region and host trophic level. Results revealed that endoparasite richness is higher in Mediterranean areas, especially for the top predators. We suggest that the detected parasites may benefit from mild environmental conditions that occur in southern regions. Top predators have larger home ranges and are likely to be subjected to cascading effects throughout the food web, resulting in more infestation opportunities and potentially higher endoparasite richness. This study suggests that richness may be more affected by historical and regional processes (including climate) than by host ecological processes.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bates, D. and Maechler, M. (2010). lme4: Linear mixed-effects models using S4 classes. R package version 0.999375-33. (http://www.r-project.orgv – Accessed at 29-07-2010).Google Scholar
Baker, J. R. (1969). Parasitic Protozoa. Hutchinson University Library, London, UK.Google Scholar
Barbosa, A. M., Segovia, J. M., Vargas, J. M., Torres, J., Real, R. and Miquel, J. (2005). Predictors of red fox (Vulpes vulpes) helminth parasite diversity in the provinces of Spain. Wildlife Biology in Practice 1, 314.CrossRefGoogle Scholar
Blondel, J. and Aronson, J. (1999). Biology and Wildlife of the Mediterranean Region. Oxford University Press, Oxford, UK.Google Scholar
Brooker, S. (2007). Spatial epidemiology of human schistosomiasis in Africa: risk models, transmission dynamics and control. Transactions of the Royal Society of Tropical Medicine and Hygiene 101, 18.CrossRefGoogle ScholarPubMed
Burnham, K. P. and Anderson, D. R. (2002). Model Selection and Multimodel Inference: a Practical Information-Theoretic Approach. 2nd Edn. Springer-Verlag, New York, USA.Google Scholar
Burnham, K. P. and Anderson, D. R. (2004). Multimodel Inference: understanding AIC and BIC in model selection. Sociobiological Methods & Research 33, 261304.CrossRefGoogle Scholar
Bush, A. O., Aho, J. M. and Kennedy, C. R. (1990). Ecological versus phylogenetic determinants of helminth parasite community richness. Evolutionary Ecology 4, 120.CrossRefGoogle Scholar
Campillo, M. C., Ordóñez, L. C. and Feo, A. R. (1994). Índice-Catálogo de zooparásitos ibéricos. Universidad de Léon, León, Spain.Google Scholar
Carl, G. and Kühn, I. (2007). Analyzing spatial autocorrelation in species distributions using Gaussian and logit models. Ecological Modelling 207, 159170.CrossRefGoogle Scholar
Chen, H.-S., Liu, W.-C., Davis, A. J., Jordán, F., Hwang, M.-J. and Shao, K.-T. (2008). Network position of hosts in food webs and their parasite diversity. Oikos 117, 18471855.CrossRefGoogle Scholar
Condé, S. and Richard, D. (2002). Europe's Biodiversity – Biogeographical Regions and Seas. EEA Report No 1/2002. European Environment Agency, Copenhagen, Denmark.Google Scholar
Christensen, N. Ø., Frandsen, F. and Roushdy, M. Z. (1980). The effect of some environmental conditions and final-host- and parasite-related factors on the penetration of Schistosoma mansoni cercariae into mice. Parasitology Research 59, 267275.Google Scholar
Dobson, A., Lafferty, K. and Kuris, A. (2006). Parasites and food webs. In Ecological Networks: Linking Structure to Dynamics in Food Webs (ed. Pascual, M. and Dunne, J. A.), pp. 119135. Oxford University Press, New York, USA.Google Scholar
Doby, J. M., Betremieux, C., Barrat, J. and Rolland, C. (1991). Tick spirochetosis by Borrelia burgdorferi in wild carnivores in France. Results of serologic tests in 372 foxes. Bulletin de la Société de Pathologie Exotique et de ses Filiales 84, 4653.Google ScholarPubMed
EEA (2008). Biogeographical Regions in Europe. European Environment Agency, Copenhagen, Denmark.Google Scholar
Fernandes, C. A., Ginja, C., Pereira, I., Tenreiro, R., Bruford, M. W. and Santos-Reis, M. (2008). Species-specific mitochondrial DNA markers for identification of non-invasive samples from sympatric carnivores in the Iberian Peninsula. Conservation Genetics 9, 681690.CrossRefGoogle Scholar
Ferrer, M. and Negro, J. J. (2004). The near extinction of two large European predators: super specialist pay a price. Conservation Biology 18, 344349.CrossRefGoogle Scholar
Flynn, J. J., Finarelli, J. A., Zehr, S., Hsu, J. and Nedbal, M A. (2005). Molecular phylogeny of the Carnivora (Mammalia): assessing the impact of increased sampling on resolving enigmatic relationships. Systematic Biology 54, 317337.CrossRefGoogle ScholarPubMed
Graczyk, T. K., Knight, R. and Tamang, L. (2005). Mechanical transmission of Human protozoan parasites by insects. Clinical Microbiology Reviews 18, 128132.CrossRefGoogle ScholarPubMed
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. and Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25, 19651978.CrossRefGoogle Scholar
Hotez, P. J., Brindley, P. J., Bethony, J. M., King, C. H., Pearce, E. J. and Jacobson, J. (2008). Helminth infections: the great neglected tropical diseases. The Journal of Clinical Investigation 118, 13111321.CrossRefGoogle ScholarPubMed
Hudson, P. J., Dobson, A. P. and Newborn, D. (1998). Prevention of population cycles by parasite removal. Science 282, 22562258.CrossRefGoogle ScholarPubMed
Jobb, G., von Haeseler, A. and Strimmer, K. (2004). TREEFINDER: a powerful graphical analysis environment for molecular phylogenetics. BMC Evolutionary Biology 4, 18.CrossRefGoogle ScholarPubMed
Jones, K. E., Patel, N. G., Storeygard, A., Balk, D., Gittleman, J. L. and Daszak, P. (2008). Global trends in emerging infectious diseases. Nature, London 451, 990994.CrossRefGoogle ScholarPubMed
Kates, K. C. (1965). Ecological aspects of helminth transmission in domestic animals. American Zoologist 5, 95130.CrossRefGoogle Scholar
Legendre, P. (1993). Spatial autocorrelation: trouble or new paradigm? Ecology 74, 16591673.CrossRefGoogle Scholar
Lindenfors, P., Nunn, C. L., Jones, K. E., Cunningham, A., Sechrest, W. and Gittleman, G. J. (2007). Parasite species richness in carnivores: effects of host body mass, latitude, geographical range and population density. Global Ecology and Biogeography 16, 496509.CrossRefGoogle Scholar
Macdonald, D. W. and Kays, R. W. (2005). Carnivores of the world: an introduction. In Walker's Carnivores of the World (ed. Nowak, R. M.), pp. 167. The Johns Hopkins University Press, Baltimore, MD, USA.Google Scholar
Morand, S. and Harvey, P. H. (2000). Mammalian metabolism, longevity and parasite species richness. Proceedings of the Royal Society of London, B 267, 19992003.CrossRefGoogle ScholarPubMed
Murray, D. L., Kapke, C. A., Evermann, J. F. and Fuller, T. K. (1999). Infectious disease and the conservation of free-ranging large carnivores. Animal Conservation 2, 241254.Google ScholarPubMed
Nowak, R. M. (2005). Walker's Carnivores of the World. The Johns Hopkins University Press, Baltimore, MD, USA.Google Scholar
Nunn, C. L., Altizer, S., Jones, K. E. and Sechrest, W. (2003). Comparative tests of parasite species richness in primates. The American Naturalist 162, 597614.CrossRefGoogle ScholarPubMed
Nunn, C. L., Altizer, S. M., Sechrest, W. and Cunningham, A. (2005). Latitudinal gradients of parasite species richness in primates. Diversity and Distributions 11, 249256.CrossRefGoogle Scholar
Oliveira, R., Castro, D., Godinho, R., Luikart, G. and Alves, P. C. (2010). Species identification using a small nuclear gene fragment: application to sympatric wild carnivores from South-western Europe. Conservation Genetics 11, 10231032.CrossRefGoogle Scholar
Palomo, L. J. and Gisbert, J. (2002). Atlas de los mamíferos terrestres de España. Dirección General de Conservación de la Naturaleza-SECEM-SECEMU, Madrid, Spain.Google Scholar
Patz, J. A., Daszak, P., Tabor, G. M., Aguire, A. A., Pearl, M., Epstein, J., Wolfe, N. D., Kirkpatrick, A. M., Foufopoulos, J., Molyneux, D., Bradley, D. J. and Members of the working group on land use change and disease emergence. (2004). Unhealthy landscapes: policy recommendations on land use change and infectious disease emergence. Environmental Health Perspectives 112, 10921098.CrossRefGoogle ScholarPubMed
Paradis, E., Claude, J. and Strimmer, K. (2004). APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289290.CrossRefGoogle ScholarPubMed
Pence, D. B. (1990). Helminth community of mammalian hosts: concepts at the infracommunity, component and compound community levels. In Parasite Communities: Patterns and Processes (ed. Esch, G., Bush, A. and Aho, J.), pp. 233260. Chapman & Hall, London, UK.CrossRefGoogle Scholar
Piekarski, G. (1962). Medical Parasitology in Plates. Farbenfabriken Bayer AG, Leverkusen, Germany.Google Scholar
Poulin, R. (1995). Phylogeny, ecology, and the richness of parasite communities in vertebrates. Ecological Monographs 65, 283302.CrossRefGoogle Scholar
Poulin, R. (1998). Comparison of three estimators of species richness in parasite component communities. The Journal of Parasitology 84, 485490.CrossRefGoogle ScholarPubMed
Poulin, R. (2004). Macroecological patterns of species richness in parasite assemblages. Basic and Applied Ecology 5, 423434.CrossRefGoogle Scholar
R Development Core Team (2008). R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Robertson, L. J., Campbell, A. T. and Smith, H. V. (1992). Survival of Cryptosporidium parvum oocysts under various environmental pressures. Applied and Environmental Microbiology 58, 34943500.CrossRefGoogle ScholarPubMed
Rogers, D. J. and Randolph, S. E. (2003). Studying the global distribution of infectious diseases using GIS and RS. Nature Reviews of Microbiology 1, 231237.CrossRefGoogle ScholarPubMed
Schustera, F. L. and Visvesvara, G. S. (2004). Amebae and ciliated protozoa as causal agents of waterborne zoonotic disease. Veterinary Parasitology 126, 91120.CrossRefGoogle Scholar
Siegle, S. and Castellan, N. J. (1988). Nonparametric Statistics for the Behavioural Sciences. McGraw-Hill, Inc., New York, USA.Google Scholar
Soetaert, K. and Heip, C. (1990). Sample-size dependence of diversity indices and the determination of sufficient sample size in a high-diversity deep-sea environment. Marine Ecology Progress Series 59, 305307.CrossRefGoogle Scholar
Spratt, D. M. (1990). The role of helminths in biological control of mammals. International Journal for Parasitology 20, 543550.CrossRefGoogle ScholarPubMed
Tabachnick, B. G. and Fidell, L. S. (1996). Using Multivariate Statistics. Harper Collins College Publishers, New York, USA.Google Scholar
Torres, J., Miquel, J., Casanova, J.-C., Ribas, A., Feliu, C. and Morand, S. (2006). Endoparasite species richness of Iberian carnivores: influences of host density and range distribution. Biodiversity and Conservation 15, 46194632.CrossRefGoogle Scholar
Torres, J., Miquel, J. and Motjé, M. (2001). Helminth parasites of the Eurasian badger (Meles meles L.) in Spain: a biogeographical approach. Parasitology Research 87, 259263.CrossRefGoogle Scholar
Vitorino, L. R., Margos, G., Feil, E. J., Collares-Pereira, M., Zé-Zé, L. and Kurtenbach, K. (2008). Fine-scale phylogeographic structure of Borrelia lusitaniae revealed by multilocus sequence typing. PLoS ONE 3, e4002.CrossRefGoogle ScholarPubMed
Watve, M. G., Sukumar, R. (1995). Parasite abundance and diversity in mammals: correlates with host ecology. Proceedings of the National Academy of Sciences, USA 92, 89458949.CrossRefGoogle ScholarPubMed
Webb, C. O., Ackerly, D. D. and Kembel, S. W. (2008). Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 20982100.CrossRefGoogle ScholarPubMed
Willig, M. R., Kaufman, D. M. and Stevens, R. D. (2003). Latitudinal gradients of biodiversity: pattern, process, scale, and synthesis. Annual Review of Ecology, Evolution, and Systematics 34, 273309.CrossRefGoogle Scholar
Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. and Smith, G. M. (2009). Mixed Effects Models and Extensions in Ecology. Springer-Verlag, New York, USA.CrossRefGoogle Scholar