Biomedical subjects

J W Hargrove

Publications and source records attributed to J W Hargrove.

9 recordsLinked to original sources

PubMed1995

A cost-benefit analysis of feeding in female tsetse.

Three models for feeding in female tsetse are considered. Model I: there is a prolonged non-feeding phase after each meal followed by feeding at a constant rate, with a constant probability of dying as a consequence of feeding. Model II: the feeding rate increases linearly after each meal. Model III: the feeding rate increases exponentially after each meal. In Models II and III the feeding hazard is a linear function of the probability of feeding. Production of viable female offspring is estimated under each model, making allowance for losses of adults due to starvation and to background and feeding mortality, losses of pupae due to predation and parasitization, and losses of young flies if their mothers take insufficient blood during pregnancy. Under Model I, if females require three meals to produce viable pupae in 9 days, then for a non-decreasing population with a background mortality of 1%/day, and 25% pupal losses due to predation and parasitism, the feeding risk must be < or = 5%/feed. At this maximum level the non-feeding phase should be 2-2.5 days for optimal productivity, with a mean feeding interval of 60-72 h. If the background mortality is 2%/day, feeding losses cannot exceed 1%/feed for a non-decreasing population. If four or five meals are required for the production of fully viable pupae, the optimal values of the non-feeding phase and mean feeding interval tend towards 1 and 2 days respectively. Under Models II and III the mean feeding interval is 50-60 h for optimal productivity (with variances 3 times as large as for Model I), in good agreement with estimates from recent models for feeding and digestion. Field evidence suggests that feeding tsetse take greater risks as their fat levels dwindle. This should result in feeding (and feeding mortality) rates which increase during the feeding phase--as assumed in Models II and III but not in Model I. These models allow greater flexibility than Model I, because flies can feed early in the hunger cycle, at low probability, as long as the feeding risk is also low.

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PubMed1994

Pooled population parameter from mark-recapture data.

The reduced capture history (RCH), compiled from complete capture histories of uniquely marked animals, for a given pooling interval contains the same information as would be obtained from experiments where (i) a single sample lasts the duration of the pooling interval; (ii) an identical batch mark is applied to animals captured in a series of samples carried out during the pooling interval. For stationary populations, biases are calculated for the RCH estimates for all parameters in the Jolly-Seber (J-S) model. The results are verified using simulation. The biases are functions of the survival and capture probabilities and the degree of pooling; they are less than 5% for the total population, birth and survival rates, and probability of capture during the pooling interval if the mortality and capture probabilities do not exceed about 50% per pooling interval. The marked population, marked fraction, and probability of recapture cannot estimated directly by the RCH method but can be obtained iteratively from the bias formulae. The biases in other parameters can be reduced by the same procedure. Alternative estimates are derived that are not detectably biased, for any estimate, for mortality and capture probability up to about 60% per pooling period. The new estimates have higher sample variances than the RCH estimates, but for large populations with high mortalities and capture probabilities the difference is small.

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PubMed1993

Epidemiology of trypanosome infections of the tsetse fly Glossina pallidipes in the Zambezi Valley.

The epidemiology of trypanosome infections of Glossina pallidipes was studied at a riverine site in the Zambezi Valley, Zimbabwe for a period of 13 months. Over 9000 flies were captured using a single trap. These flies were dissected, screened for trypanosome infection, sexed, and aged using both wing fray and (for females) ovarian category indices. Midgut infections were identified to species using recently developed DNA probes. The overall prevalence of mature infections was 5.5%, comprising 3.1% Trypanosoma vivax-type and 2.4% T. congolense-type (which included very low prevalences of T. brucei, T. simiae and another Nannomonas species). The prevalence of infection increased with age. For T. vivax-type infections in flies aged by ovarian category this relationship could be described by a simple 'catalytic' model assuming a constant per capita rate of infection. For T. congolense-type infections this model tended to over-estimate prevalence in older age classes, implying that the rate of infection decreases with age, and/or that infected flies have higher mortality rates, and/or that a significant fraction of the population is resistant to infection. Prevalences of infection also varied between months. This variation was more marked for T. vivax-type infections and was negatively correlated with both temperature and rainfall. The shape of the age-prevalence relationship, however, did not vary significantly between months. These observations are not fully explained by variation in the age-structure of the tsetse population and are consistent with temporal variation in the rate of infection (rather than in the trypanosome developmental period or in effects of infection on fly mortality). Possible causes of this variation are discussed.

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PubMed1988

Tsetse: the limits to population growth.

Growth rates of tsetse populations were estimated by calculating the dominant eigenvalues of appropriate Leslie matrices. The individual effects of four variables (pre-adult and adult survival probability, interlarval period and pupal duration), have been investigated by varying each one over a wide range of values, while the other three are held constant. R, the log of the growth rate, was found to vary approximately linearly with adult and pre-adult death rate; a 1% change in the adult death rate causes approximately a 10-fold change in R. R varies linearly with the log of fecundity and of the pupal duration. An increase in the pupal duration results in a decrease in the growth rate for populations which have a positive growth rate, but an increase for populations which have a negative growth rate. For a population at equilibrium, a change in the pupal duration has no effect. Small changes in fecundity have less effect on the growth rate than small changes in the death rate; this fact is advanced as an important contributor to the generally very cautious nature of female tsetse, and their aversion to man, particularly as a potential host. A simple linear model is described which relates R to all four variables and their first order interactions. The model is used to produce a set of graphs which encapsulate the relationship between the growth rate and the vital parameters over a wide range of values. It is also used to draw the loci on one side of which tsetse populations grow, and on the other of which they decline. Population resilience is discussed in relation to the problem of tsetse eradication; it is concluded that if one can impose and sustain an added mortality of 4% per day on any female tsetse population then it must go extinct, regardless of the strength of the density dependent processes; and it seems likely that in most field conditions only an added 2-3% is required. It is pointed out that ground and aerial spraying techniques produce much higher daily mortalities than this, but they may often not be sustained for sufficiently long to achieve eradication. When odour-baited targets are used the increased death rate is much smaller, but it can be sustained as required; recent work in Zimbabwe shows that there is a good correspondence between the calculated imposed death rate and the observed rate of decline of tsetse populations.

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PubMed1976