Spatial distribution on pasture of infective larvae of the gastro-intestinal nematode parasites of sheep
Scottish Crop Research Institute, Invergowrie, Dundee DD2 5DA, Scotland, U.K.International Journal for Parasitology (Impact Factor: 3.87). 10/1989; 19(6):681-5. DOI: 10.1016/0020-7519(89)90047-7
The horizontal distributions of infective larvae on pasture grazed by sheep have been investigated. Using Taylor's Power Law it was found that larvae had a more aggregated distribution in September than August, the Law index of aggregation being 1.97 and 1.89 for the 2 months, respectively. However, at each time the degree of aggregation remained fairly constant for a range of spacings between points from 5 to 30 m. These results suggest that Taylor's Power Law could be used as a basis for devising an efficient pasture sampling strategy. More data are required, however, to determine the extent to which aggregation of the larvae varies with time of the year.
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- "The use of PLC in validating nematode vaccination strategies and targeted control programmes and as input for nematode model predictions , however, requires good estimates for both low and high degrees of pasture contamination. Unfortunately, only limited data is available on the spatial distribution of trichostrongyle larvae on cattle pastures (Gruner and Sauve, 1982; Flota-Bã nuelos et al., 2013), as most studies consider data collected on sheep pastures (Crofton, 1954; Tallis and Donald, 1964; Donald, 1967; Boag et al., 1989). Extrapolation of results between host species is objectionable because differences in faecal morphology (i.e. "
ABSTRACT: Assessing levels of pasture larval contamination is frequently used to study the population dynamics of the free-living stages of parasitic nematodes of livestock. Direct quantification of infective larvae (L3) on herbage is the most applied method to measure pasture larval contamination. However, herbage collection remains labour intensive and there is a lack of studies addressing the variation induced by the sampling method and the required sample size. The aim of this study was (1) to compare two different sampling methods in terms of pasture larval count results and time required to sample, (2) to assess the amount of variation in larval counts at the level of sample plot, pasture and season, respectively and (3) to calculate the required sample size to assess pasture larval contamination with a predefined precision using random plots across pasture. Eight young stock pastures of different commercial dairy herds were sampled in three consecutive seasons during the grazing season (spring, summer and autumn). On each pasture, herbage samples were collected through both a double-crossed W-transect with samples taken every 10 steps (method 1) and four random located plots of 0.16m(2) with collection of all herbage within the plot (method 2). The average (±standard deviation (SD)) pasture larval contamination using sampling methods 1 and 2 was 325 (±479) and 305 (±444)L3/kg dry herbage (DH), respectively. Large discrepancies in pasture larval counts of the same pasture and season were often seen between methods, but no significant difference (P=0.38) in larval counts between methods was found. Less time was required to collect samples with method 2. This difference in collection time between methods was most pronounced for pastures with a surface area larger than 1ha. The variation in pasture larval counts from samples generated by random plot sampling was mainly due to the repeated measurements on the same pasture in the same season (residual variance component=6.2), rather than due to pasture (variance component=0.55) or season (variance component=0.15). Using the observed distribution of L3, the required sample size (i.e. number of plots per pasture) for sampling a pasture through random plots with a particular precision was simulated. A higher relative precision was acquired when estimating PLC on pastures with a high larval contamination and a low level of aggregation compared to pastures with a low larval contamination when the same sample size was applied. In the future, herbage sampling through random plots across pasture (method 2) seems a promising method to develop further as no significant difference in counts between the methods was found and this method was less time consuming. Copyright © 2015 Elsevier B.V. All rights reserved.Veterinary Parasitology 04/2015; 210(3-4). DOI:10.1016/j.vetpar.2015.03.031 · 2.46 Impact Factor
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- "To analyze such aggregated parasite data, the fitting of the negative binomial distribution is a common method, as in  to model the abundance of the fluke Diplostomum spathaceum in fish, in  for European red mite on apple leaves, in  for the tapeworms Echinococcus granulosus and multilocularis in dogs, in  for the nematode Trichinella spiralis in rabbits and in  for the larval stage of the mites Allothrombium pulvinum Ewing in lice. However, these models do not take into account the age of the hosts, which is known to influence the parasite pattern   . To incorporate age, negative binomial regression can be used, as in modeling the age-dependent frequency of the nematode Wuchereria bancrofti in humans , or of the nematodes Ostertagia gruehneri and Marshallagia marshalli in reindeer . "
ABSTRACT: Compound processes are proposed as models for the acquisition of hydatid cysts in sheep, caused by the parasite Echinococcus granulosus. The hypothesis of a clumped infection process against single ingestions is tested and it is shown that the clump-based approach provides a more accurate description of the two data sets investigated. Models with simple and mixed Poisson incidence processes and different clump size distributions are compared. A mixed Poisson incidence process with a zero-truncated negative binomial distribution for the clump sizes is shown to give an adequate description, suggesting that the acquisition of hydatid cysts in the sheep population is heterogeneous, and that the clump sizes are aggregated. The estimates of the parameters derived from the data take plausible values. The average infection rate and the clump size distribution are comparable in both data sets. Goodness-of-fit measures indicate that the model fits the data reasonably well.Mathematical biosciences 09/2009; 222(1):27-35. DOI:10.1016/j.mbs.2009.08.007 · 1.30 Impact Factor
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- "Within a host population, helminths tend to be overdispersed ; that is, there are usually a large number of hosts with light or no infection, and a few host individuals that harbour the majority of the worms (Anderson and Gordon, 1982; Grenfell and Dobson, 1995; Grenfell et al., 1995; Stear et al., 1995; Paterson et al., 2000; Paterson and Viney, 2000, 2003). Trichostronglyoid infective larvae are clustered on pasture (Boag et al., 1989). The implication of this overdispersion of adult parasites and larvae for parasite population genetics is that in most (lightly infected) hosts, the eggs released onto the pasture are likely to form aggregations of related individuals. "
ABSTRACT: We have used a mitochondrial marker to explore the population genetics of an economically important parasite of sheep, Teladorsagia. We examined diversity within and between parasites from three very different host populations, as well as within and between individual hosts. One of our study populations, the Soay sheep on Hirta, St Kilda, is unusually isolated with no sheep having been introduced to the island since 1932. Worm haplotypes from Hirta were compared with those from two other host populations. Remarkably, despite its historical isolation the Hirta population shows similar levels of within-population diversity to the other study populations. No divergence between the three Teladorsagia populations was found, consistent with gene flow between the populations. The high diversity within Teladorsagia populations provides compelling evidence that this variability is a general feature of parasitic nematode populations. Such diversity may be caused by high effective population size, coupled with an increased mutation rate for mtDNA, which has important implications for the spread of anthelmintic resistance in nematode populations.International Journal for Parasitology 10/2004; 34(10):1197-204. DOI:10.1016/j.ijpara.2004.06.005 · 3.87 Impact Factor
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