Dogs’ health and demographics in wildlife-populated and tsetse-infested villages of Mambwe district, eastern Zambia.

Good dog-keeping practices and access to veterinary care are essential for the well-being of dogs. As the main causes of morbidity and mortality in the rural canine population in Zambia are poorly understood, we followed a cohort of 162 indigenous dogs for six months in wildlife-populated and tsetse-infested villages of Mambwe district, eastern Zambia to gain deeper insights. Dogs lacked basic home and veterinary care, they were often starved and burdened with ticks, and some passed live adult worms in their stool. The frequent exposure of dogs to tsetse bites and consumption of fresh raw game meat and bones puts them at greater risk of acquiring African trypanosomiasis. Nearly 20% of dogs were lost to follow-up, with the main causes being poor health (58.1%), predation by wild carnivores (29%), and owner culling or euthanasia (12.9%). We observed that indigenous dogs' general well-being and survival were largely influenced by their environment, infectious diseases, injuries sustained during interaction with conspecifics and wildlife, and community attitudes and practices associated with dog ownership

Impact of habitat fragmentation on tsetse populations and trypanosomosis risk in Eastern Zambia

[b]Background[/b]: Fragmentation of tsetse habitat in eastern Zambia is largely due to encroachments by subsistence farmers into new areas in search of new agricultural land. The impact of habitat fragmentation on tsetse populations is not clearly understood. This study was aimed at establishing the impact of habitat fragmentation on physiological and demographic parameters of tsetse flies in order to enhance the understanding of the relationship between fragmentation and African animal trypanosomosis (AAT) risk. [b]Methods[/b]: A longitudinal study was conducted to establish the age structure, abundance, proportion of females and trypanosome infection rate of Glossina morsitans morsitans Westwood (Diptera: Glossinidae) in areas of varying degrees of habitat fragmentation in Eastern Zambia. Black screen fly rounds were used to sample tsetse populations monthly for 1 year. Logistic regression was used to analyse age, proportion of females and infection rate data. [br/][b]Results[/b]: Flies got significantly older as fragmentation increased (p < 0.004). The proportion of old flies, i.e. above ovarian category four, increased significantly (P < 0.001) from 25.9 % (CI 21.4-31.1) at the least fragmented site (Lusandwa) to 74.2 % (CI 56.8-86.3) at the highly fragmented site (Chisulo). In the most fragmented area (Kasamanda), tsetse flies had almost disappeared. In the highly fragmented area a significantly higher trypanosome infection rate in tsetse (P < 0.001) than in areas with lower fragmentation was observed. Consequently a comparatively high trypanosomosis incidence rate in livestock was observed there despite lower tsetse density (p < 0.001). The overall proportion of captured female flies increased significantly (P < 0.005) as fragmentation reduced. The proportion increased from 0.135 (CI 0.10-0.18) to 0.285 (CI 0.26-0.31) at the highly and least fragmented sites, respectively. [br/][b]Conclusions[/b]: Habitat fragmentation creates conditions to which tsetse populations respond physiologically and demographically thereby affecting tsetse-trypanosome interactions and hence influencing trypanosomosis risk. Temperature rise due to fragmentation coupled with dominance of old flies in populations increases infection rate in tsetse and hence creates high risk of trypanosomosis in fragmented areas. Possibilities of how correlations between biological characteristics of populations and the degree of fragmentation can be used to structure populations based on their well-being, using integrated GIS and remote sensing techniques are discussed

Wing length of tsetse caught by stationary and mobile sampling methods

INTRODUCTION : A variety of techniques have been used to control tsetse with varying degrees of success. In a study on the population structure of Glossina fuscipes fuscipes that recovered after a previous vector control trial on two Kenyan islands, it was reported that the average fly size on the intervention islands was significantly smaller than on the none intervention islands and also compared to the size before the intervention. The conclusion was that vector control using tiny targets exerted size selection pressure on the population. The study recommended for further studies and suggested that this phenomenon could be among the reasons why targets used as a sole control method have rare reports of successful elimination of tsetse populations. Therefore, in this paper we report on a study of body size of tsetse flies caught in epsilon traps (as a stationary device) and black screen fly rounds (as a mobile trapping device). MATERIALS AND METHODS : The study was carried out in eastern Zambia to test the hypothesis that the body size (measured as wing length) of G. m. morsitans males or females, captured by epsilon traps and fly rounds is the same. RESULTS : A total of 1442 (489 females and 953 males) wing length measurements of G. m. morsitans were used in the analysis. It was established that tsetse flies caught by epsilon traps are on average larger than those caught by fly rounds. The likelihood of a large female or male fly being caught by traps, relative to a small one, significantly increased by 5.088 times (95% CI: 3.138–8.429) and by 2.563 times (95% CI: 1.584–4.148), respectively, p < 0.0001, compared with being caught by fly rounds. The hypothesis was rejected. CONCLUSION : This study showed that epsilon traps capture significantly larger G. m. morsitans than fly rounds do. Therefore, further research is recommended to verify i) whether the predilection of traps to capture larger flies has an effect on the process of tsetse elimination when targets are used e.g. targets may take longer to reach elimination than if the predilection was not there, ii) whether different results can be obtained on ecogeographic distribution of different sizes of the species if fly rounds are used for sampling instead of epsilon traps. The results from such studies could influence the strategies used in future control operations.Data for: Wing length of tsetse caught by stationary and mobile sampling methods The disease of interest is trypanosomiasis that is transmitted by tsetse flies (Glossina sp). The disease affects both human and livestock. (https://data.mendeley.com/datasets/jpx5brm3bp/1)The Wellcome Trusthttp://www.elsevier.com/locate/actatropica2021-04-01hj2020Veterinary Tropical Disease

Effect of wing length on the prevalence of trypanosomes in Glossina morsitans morsitans in eastern Zambia

BACKGROUND: Tsetse fies (Diptera: Glossinidae) transmit trypanosomiasis (sleeping sickness in humans and nagana in livestock). Several studies have indicated that age, sex, site of capture, starvation and microbiome symbionts, among others, are important factors that infuence trypanosome infection in tsetse fies. However, reasons for a higher infection rate in females than in males still largely remain unknown. Considering that tsetse species and sexes of larger body size are the most mobile and the most available to stationary baits, it was hypothesized in this study that the higher trypanosome prevalence in female than in male tsetse fies was a consequence of females being larger than males. METHODS: Black screen fy rounds and Epsilon traps were used to collect tsetse fies in eastern Zambia. Measurement of wing vein length and examination for presence of trypanosomes in the fies were carried out by microscopy. Principal component method was carried out to assess the potential of wing vein length as a predictor variable. The multilevel binary logistic regression method was applied on whole data, one-method data and one-sex data sets to evaluate the hypothesis. RESULTS: Data derived from a total of 2195 Glossina morsitans morsitans were evaluated (1491 males and 704 females). The wing length variable contributed the highest variance percentage (39.2%) to the frst principal component. The variable showed signifcant infuence on prevalence of trypanosomes when the analysis was applied on the whole data set, with the log odds for the prevalence of trypanosomes signifcantly increasing by 0.1 (P = 0.032), per unit increase in wing length. Females had higher trypanosome prevalence rates than males, though not always signifcant. Furthermore, moving from females to males, wing length signifcantly reduced by 0.2 (P < 0.0001). CONCLUSIONS: We conclude that wing length is an important predictor variable for trypanosome prevalence in Glossina morsitans morsitans and could partially explain the higher prevalence of trypanosomes in females than in males. However, reasonably representative population data are required for analysis—a serious challenge with the current tsetse sampling methods. Thus, analysis combining data from mobile and stationary methods that include both sexes’ data could be useful to verify this hypothesis.Wellcome Trust, Londonhttps://parasitesandvectors.biomedcentral.compm2022Veterinary Tropical Disease

Tsetse population net change in the prediction zone for each season and summed for the entire period.

<p>The latter is stable or growing if the net change is equal to or larger than 0; otherwise it is declining. The upper-right corner map shows the position of the predicted area (red square) in Zambia.</p

Mean values of the net change during the three seasons in the four sites in Zambia.

<p>Rainy (January to April), cold-dry (May to August) and hot-dry (September to December) seasons; and the mean values throughout the year.</p

Microsatellite Genotype

All microsatellite markers were originally from other publications cited in the publication. Available data IDs are included in the table

Details of the sample sites and data collection periods (from – to).

<p>‘Stops’ are the number of fly catching points on each fly-round.</p

Estimates of the different parameters in the model.

<p><i>b</i>, fertility rate; <i>a</i>, population size at which the density dependent mortality effect starts; <i>α</i>, angle of the density dependent effect; <i>β₁</i>, intercept of the linear regression for the density independent mortality effect; <i>β₂</i> regression coefficients; <i>φ</i>, spatial range; <i>ρ</i>, temporal range; <i>δ</i>, interactive term for the spatio-temporal effect; , spatial variance; , error variance.</p