The rock partridge (Alectoris graeca saxatilis) is an alpine Galliform with high conservation value. Several factors, including parasitic helminths, play a role in population dynamics, and consequently in the conservation management of wild Galliformes. The aim of this study was to assess the epidemiological characteristics of Cheilospirura hamulosa (Nematoda, Acuarioidea) in the Rock partridge population in France. Machine learning modeling algorithms were applied to identify the environmental variables influencing parasite occurrence, and to map parasite presence probability. The present work is based on a long-term sampling (1987-2019) conducted in the French Alps. C. hamulosa was found with a prevalence (P) of 39% (Confidence Interval-CI 95%: 34-43), and mean intensity of 7.7 (7.8 sd). The highest prevalence (P: 67%, CI 95%: 54-80) was detected in the period 2005-2009. Latitude was the most important variable shaping the parasite distribution, followed by altitude, annual mean temperature, temperature seasonality, and the amount of precipitation of the coldest quarter. The area suitable for parasite presence included 73% of the French Alps. This work represents the first epidemiological surveillance on C. hamulosa infection in the rock partridge. It provides evidence of a high level of infection and identifies priority areas at higher infection risk, where a close monitoring of the rock partridge populations should be carried out.
Cheilospirura hamulosa in the rock partridge (Aleactoris graeca saxatilis): epidemiological patterns and prediction of parasite distribution in France
Angela Fanelli;Paolo Tizzani;Ezio Ferroglio;
2020-01-01
Abstract
The rock partridge (Alectoris graeca saxatilis) is an alpine Galliform with high conservation value. Several factors, including parasitic helminths, play a role in population dynamics, and consequently in the conservation management of wild Galliformes. The aim of this study was to assess the epidemiological characteristics of Cheilospirura hamulosa (Nematoda, Acuarioidea) in the Rock partridge population in France. Machine learning modeling algorithms were applied to identify the environmental variables influencing parasite occurrence, and to map parasite presence probability. The present work is based on a long-term sampling (1987-2019) conducted in the French Alps. C. hamulosa was found with a prevalence (P) of 39% (Confidence Interval-CI 95%: 34-43), and mean intensity of 7.7 (7.8 sd). The highest prevalence (P: 67%, CI 95%: 54-80) was detected in the period 2005-2009. Latitude was the most important variable shaping the parasite distribution, followed by altitude, annual mean temperature, temperature seasonality, and the amount of precipitation of the coldest quarter. The area suitable for parasite presence included 73% of the French Alps. This work represents the first epidemiological surveillance on C. hamulosa infection in the rock partridge. It provides evidence of a high level of infection and identifies priority areas at higher infection risk, where a close monitoring of the rock partridge populations should be carried out.File | Dimensione | Formato | |
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