Improved estimates of abortion rates in tsetse (<i>Glossina</i>spp)

Hargrove, John W.

doi:10.1101/2022.09.15.508167

2022

DOI: 10.1101/2022.09.15.508167

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Improved estimates of abortion rates in tsetse (Glossinaspp)

John W. Hargrove

Abstract: Abortion rates were assessed for 170, 846 tsetse (154,228 G. pallidipes and 19,618 G. m. morsitans) sampled in Zimbabwe in 1988-1999. Abortions were diagnosed for flies where the uterus was empty and the largest oocyte awaiting ovulation was <82% of the expected mature length. Of tsetse caught in odour-baited traps, 0.64% (95% ci: 0.59-0.69) of G. pallidipes and 1.00% (0.76-1.29) of G. m. morsitans were diagnosed as having suffered a recent abortion. For flies from artificial refuges, abortion rates were hi… Show more

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Cited by 2 publications

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References 36 publications

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“…Estimated abortion rates in field populations are low, however, and do not increase with age (Hargrove, 1999). On the contrary, if empty uteri can be taken as evidence for a recent abortion, it seems that in the field young females have relatively high abortion rates (Hargrove, 1999), and that the rates are not high in older flies (Hargrove, 2023). Physiological and environmental stress are stronger predictors of overall abortion than age in tsetse, in both field and laboratory studies.…”

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“…Abortion rates are elevated for females in low nutritional condition (Mellanby, 1937;Saunders, 1972;Lord et al, 2021) and for tsetse exposed to insecticides (Riordan, 1986). While abortions are infrequent in wild tsetse, they do increase during hotter months of the year (Hargrove, 1999(Hargrove, , 2023, when flies exhibit increased mortality levels (Hargrove, 2001) and are likely experiencing more physiological stress, due for example to host scarcity or thermal costs of mobility.…”

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Investigating the unaccounted ones: insights on age-dependent reproductive loss in a viviparous fly

et al. 2023

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Most empirical and theoretical studies on reproductive senescence focus on observable attributes of offspring produced, such as size or postnatal survival. While harder to study, an important outcome of reproduction for a breeding individual is whether a viable offspring is produced at all. While prenatal mortality can sometimes be directly observed, this can also be indicated through an increase in the interval between offspring production. Both direct reproductive loss and presumed losses have been found to increase in older females across several species. Here, we study such reproductive loss (or “abortion”) in tsetse, a viviparous and relatively long-lived fly with high maternal allocation. We consider how age-dependent patterns of abortion depend on the developmental stage of offspring and find that, as per previous laboratory studies, older females have higher rates of abortion at the late-larval stage, while egg-stage abortions are high both for very young and older females. We track the reproductive output of individual females and find little evidence that experiencing an abortion is an adaptive strategy to improve future reproductive outcomes. After an abortion, females do not generally take less time to produce their next offspring, these offspring are not larger, and there is no sex bias towards females, the sex with presumed higher fitness returns (being slightly larger and longer-lived than males, and with high insemination rates). Abortion rates are higher for breeding females experiencing stress, measured as nutritional deprivation, which echoes previous work in tsetse and other viviparous species, i.e., humans and baboons. We discuss our results in the context of studies on reproductive loss across taxa and argue that this is an important yet often overlooked reproductive trait which can vary with maternal age and can also depend on environmental stressors.

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Investigating the unaccounted ones: insights on age-dependent reproductive loss in a viviparous fly

et al. 2023

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Deep learning approaches to landmark detection in tsetse wing images

Geldenhuys,

Josias,

Brink

et al. 2023

PLoS Comput Biol

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Morphometric analysis of wings has been suggested for identifying and controlling isolated populations of tsetse (Glossina spp), vectors of human and animal trypanosomiasis in Africa. Single-wing images were captured from an extensive data set of field-collected tsetse wings of species Glossina pallidipes and G. m. morsitans. Morphometric analysis required locating 11 anatomical landmarks on each wing. The manual location of landmarks is time-consuming, prone to error, and infeasible for large data sets. We developed a two-tier method using deep learning architectures to classify images and make accurate landmark predictions. The first tier used a classification convolutional neural network to remove most wings that were missing landmarks. The second tier provided landmark coordinates for the remaining wings. We compared direct coordinate regression using a convolutional neural network and segmentation using a fully convolutional network for the second tier. For the resulting landmark predictions, we evaluate shape bias using Procrustes analysis. We pay particular attention to consistent labelling to improve model performance. For an image size of 1024 × 1280, data augmentation reduced the mean pixel distance error from 8.3 (95% confidence interval [4.4,10.3]) to 5.34 (95% confidence interval [3.0,7.0]) for the regression model. For the segmentation model, data augmentation did not alter the mean pixel distance error of 3.43 (95% confidence interval [1.9,4.4]). Segmentation had a higher computational complexity and some large outliers. Both models showed minimal shape bias. We deployed the regression model on the complete unannotated data consisting of 14,354 pairs of wing images since this model had a lower computational cost and more stable predictions than the segmentation model. The resulting landmark data set was provided for future morphometric analysis. The methods we have developed could provide a starting point to studying the wings of other insect species. All the code used in this study has been written in Python and open sourced.

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Improved estimates of abortion rates in tsetse (Glossinaspp)

Cited by 2 publications

References 36 publications

Investigating the unaccounted ones: insights on age-dependent reproductive loss in a viviparous fly

Investigating the unaccounted ones: insights on age-dependent reproductive loss in a viviparous fly

Deep learning approaches to landmark detection in tsetse wing images

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