\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{CJKutf8} 
\usepackage[utf8]{inputenc}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,greek,english]{babel}



\usepackage{float}








\begin{document}
\begin{CJK}{UTF8}{gbsn}

\title{Effects of maternal age and stress on offspring quality in a viviparous
fly}



\author[1]{Jennifer Lord}%
\author[1]{Robert Leyland}%
\author[1]{Lee Haines}%
\author[2]{Antoine Barreaux}%
\author[3]{Michael Bonsall}%
\author[1]{Stephen Torr}%
\author[2]{Sinead English}%
\affil[1]{Liverpool School of Tropical Medicine}%
\affil[2]{University of Bristol}%
\affil[3]{University of Oxford}%


\vspace{-1em}



  
  \date{April 25, 2026}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Many organisms show signs of deterioration with age, both in terms of
survival and reproduction. Theory suggests that variation in such
senescence patterns can be driven by resource availability or
reproductive history. Here, we test this theory by manipulating
nutritional stress and age at first reproduction and measuring
age-dependent reproductive output in tsetse (Glossina morsitans
morsitans), a viviparous fly with high maternal allocation. Across all
treatments, offspring wet weight followed a bell-shaped curve with
maternal age. Nutritionally stressed females had higher probability of
abortion, produced smaller offspring with lower starvation tolerance.
Despite this, there was no strong evidence of differences between
treatments in the pattern of abortion probability, offspring wet weight
or offspring starvation tolerance with age. Therefore, although we found
strong evidence of general reproductive senescence in tsetse, variation
in the onset and rate of senescence was not explained by resource
allocation trade-offs or the costs of reproduction.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Across the natural world, it is commonly observed that as individuals
get older, they are more likely to die and have lower reproductive
output (Nussey \emph{et al.} 2013; Hoekstra \emph{et al.} 2019;
Zajitschek \emph{et al.} 2019). Broad patterns of actuarial and
reproductive senescence have been well described for a wide range of
taxa (Nussey \emph{et al.} 2013; Hoekstra \emph{et al.} 2019;
Zajitschek\emph{et al.} 2019), particularly birds and mammals (Gaillard
\& Lema\selectlanguage{ngerman}ître 2020). There is also extensive variation in the onset and
rate of ageing, both within and across populations (Holand \emph{et
al.}2016; Rodríguez-Muñoz \emph{et al.} 2019; Cayuela \emph{et al.}
2020). The role of individual heterogeneity, and subsequent variation in
the onset and rate of ageing among individuals, however, has been
neglected (Gaillard \& Lemaître 2020).

A central tenet of the evolution of senescence is that the strength of
selection declines with age (Hamilton 1966), with consequences in terms
of antagonistic pleiotropy (Williams 1957) or the accumulation of
somatic damage (Kirkwood 1977). Life-history theory suggests both
antagonistic pleiotropy and the accumulation of somatic damage can be
explained by trade-offs between reproduction and other physiological
processes (Kirkwood 1977; Boggs 2009; Baudisch \& Vaupel 2012;
Davison\emph{et al.} 2014). A key assumption underlying this theory is
that resources are limited and must be allocated either to reproduction
or somatic maintenance (Partridge 1987; Boggs 2009). Reproductive
senescence could also occur through physiological damage incurred
directly from reproduction, or can be an adaptive strategy to prolong
survival (McNamara \emph{et al.} 2009).

Experimental manipulation of access to mates or food can yield insights
on the factors driving variation in senescence, including costs of
reproduction and allocation of resources. For such experiments, insects
are useful organisms as they have relatively short generation times, can
be reared in large numbers, and access to mates and resources can be
easily manipulated. Experiments with Lepidoptera, for example, have
shown that delayed mating reduces fecundity but extends longevity
(Unnithan \& Paye 1991; Jiménez-Pérez \& Wang 2009). A large body of
studies have also shown that females with access to fewer resources have
lower overall reproductive output but longer lifespan (De Sousza Santos
\& Begon 1987; Ernsting \& Isaaks 1991; Kaitala 1991;
Chippindale\emph{et al.} 1993; Tatar \& Carey 1995; Curtis Creighton
\emph{et al.}2009). These studies support the theory that senescence is,
at least in part, caused by direct costs of reproduction through
physiological damage, or arises as an indirect consequence of resource
availability. However, experimental manipulation of both age at
reproduction and nutrition is required to tease apart the contributions
of reproductive history and resource availability to senescence.

Here, we present such an experimental study, focussing on reproductive
senescence in tsetse (\emph{Glossina} \emph{morsitans morsitans} ).
Tsetse are vectors of human and animal trypanosomiasis in Africa. They
give birth to a single live larva weighing the same as the mother
(Hargrove \& Muzari 2015; Haines \emph{et al.} 2020), approximately
every nine days. Immature stages receive energy and nutrients from the
mother only. Adults do not increase in size after emergence and have a
relatively long lifespan for their small size, living for weeks rather
than days (Hargrove \emph{et al.} 2011).

Evidence for age-related changes in reproductive output in field and
laboratory tsetse is mixed (Jordan \emph{et al.} 1969; Langley \&
Clutton-Brock 1998; McIntyre \& Gooding 1998). Key limitations to these
studies are that flies were kept only under optimal laboratory
conditions, not tracked individually and frequently grouped across ages.
Preliminary data from the tsetse colony at the Liverpool School of
Tropical Medicine (LSTM) provided evidence of reproductive senescence
(Supporting Information, S1 File), but these data did not allow for
teasing apart of within-individual from among-individual patterns
(Monaghan \emph{et al.} 2020). In this study, we therefore used a novel
method of housing tsetse females to track individual mothers and their
offspring from this colony.

Our first objective was to confirm reproductive senescence in tsetse, as
shown by a decline in the number and quality of offspring with maternal
age. Our second objective was to determine the effect of maternal
nutrition and the physiological costs of reproduction on age-dependent
patterns of maternal allocation. To achieve this aim, we manipulated
nutritional stress by feeding adult females on a high- or low-quality
diet and we varied potential costs of reproduction by delaying the age
at which females were mated. Our third objective was to assess variation
in reproductive senescence patterns among individuals, due to unmeasured
heterogeneity in factors including size, condition and mate quality.

We hypothesised that: i) females mated later would experience a delayed
and/or slower increase in the probability of abortion and decline in
offspring quality, due to physiological costs of reproduction, resource
allocation trade-offs, or both; and either: ii) nutritionally stressed
mothers would have an earlier, and potentially steeper increase in
probability of abortion and decline in offspring quality, due to
resource allocation-trade-offs; or iii) nutritionally stressed mothers
may senescence more slowly, due to lower physiological costs of
reproduction, if stressed females have lower reproductive output.

\textbf{Methods}

Tsetse from a colony of \emph{G. m. morsitans} maintained at LSTM were
used for the experiments. The colony was originally established using
pupae from Zimbabwe and has been maintained at LSTM since 2004. The
colony was kept at 26°C (± 2 °C), 68--76\% relative humidity and a 12 h
photoperiod. Flies were fed on a Monday, Wednesday and Friday on
defibrinated horse blood (TSC Bioscience) which comprises 40--50\% red
blood cells.

\textbf{Measures of offspring quality}

In addition to spontaneous abortions, we recorded the wet weight of
viable pupae and, for a subset, we measured either fat, or whether they
emerged and their starvation tolerance upon emergence (Fig. 1). Wet
weight of pupae correlates with pupal volume in field flies and does not
require destructive sampling (Hargrove 1999). We recorded emergence as
we hypothesised that offspring with more reserves would be more likely
to emerge. We measured starvation tolerance because, in the wild, newly
emerged tsetse have a high probability of mortality (Hargrove \emph{et
al.} 2011), likely due to failure to find a blood meal before running
out of energy reserves.

From preliminary data on the tsetse colony at LSTM (S1 File), we
hypothesised that there would be an increase in the probability of
abortion with age. If resource-allocation trade-offs exist, we would
also expect that the increase would be steeper for the nutritionally
stressed treatment and potentially shallower for the mating delay
treatment, relative to the control. However, for offspring wet weight
and starvation tolerance we predicted that there would be an initial
increase, reflecting the requirement for newly emerged flies to also
divert energy and nutrients to the development of thoracic muscles
(Anderson \& Finlayson 1973), followed by a decline due to senescence.
Lastly, we hypothesised that the effect of maternal age on starvation
tolerance would be determined largely by energy reserves, as indirectly
measured by offspring wet weight. Heavier offspring would have more
reserves and therefore would be more likely to emerge and have higher
starvation tolerance. Maternal age could also influence offspring
starvation tolerance, independent of effects on wet weight, through the
quality of the resources provided.

\textbf{Selection and mating of female tsetse for the experiment}

The \emph{G. m. morsitans} colony at LSTM is maintained in 12 trays
corresponding to each week of life. Females in the colony older than
this are killed due to a decline in reproductive output. To select
females for the experiment, we collected equal numbers of pupae from
each tray over a one-week period for emergence (S2 File). At the time of
mating (upon emergence or after a delay), females were mated at a ratio
of 2:1 (female:male) with one-week old males for 48 hours. Males were
then removed, and females were selected for the experimental treatments.

\textbf{Treatments}

We set up control, nutritional stress and mating delay treatments, each
consisting of 96 adult female \emph{G. m. morsitans} (Fig. 1, S2 File).
Because reducing the amount of haemoglobin in a bloodmeal results in
lower pupal wet weights (Kabayo \& Langley 1985), we chose to dilute red
blood cells with serum to produce a low-quality diet for the nutritional
stress group. Trials testing different ratios of red blood cells to
serum showed that flies fed on c. 10\% red blood cells produced lighter
pupae but had similar survival over a 50-day period, compared to flies
fed on c. 45\% red blood cells (S3 File). We thus ascribed the 10\% red
blood cell treatment as the `nutritional stress' treatment and 45\% red
blood cells as `control'. For the mating delay treatment, virgin females
were kept in communal cages for three weeks post-emergence. Virgin
females continue to ovulate, but mature eggs eventually disintegrate
(Ejezie \& Davey 1977). Once mated, they were separated into individual
cages. The experiment was then run until mothers were 100 days old. The
probability of survival for females up to 100 days is estimated, from
mark-recapture studies, to be c. 10\% in the wild (Hargrove \emph{et
al.} 2011). In addition, unpublished data from the LSTM colony indicated
that by 100 days the probability of abortion had increased from c. 0 to
0.25 and the size of pupae produced had declined to similar sizes seen
from first-time mothers (S1 File).

\textbf{Housing of individual adult females and feeding regime}

Flies were housed in individual cages, which were placed on pupal
collection trays, made from acrylic extrude (described in S2 File), to
allow individual tracking of female reproductive output and survival.
Cameras were fixed below the cages to record weekend activity and to
identify exact larviposition dates. Females were monitored daily between
Monday and Friday and if death occurred, we recorded the date of death.
To determine the exact date of death for females found dead on a Monday
morning, we reviewed video recordings from Friday evening to Monday
morning (S2 File).

Experimental flies were fed following the colony schedule (see above).
To feed individual females, we placed cages on a shallow grid (S2 File).
The grid ensured each female was provided with the same amount of blood
and had equal opportunity for feeding, with c. 100 \selectlanguage{greek}μ\selectlanguage{english}l of blood provided
per female. We heated the tray to 37\selectlanguage{ngerman}°C and covered the blood with a
silicon membrane, then left flies to feed for 45 minutes, which was
sufficient time for most flies to feed.

\textbf{Offspring measurements}

Time of larviposition was recorded, including whether or not larvae
produced were viable (Baldry \emph{et al.} 1992). Production of
non-viable offspring was considered a spontaneous abortion (`abortion'
hereafter). It was not possible to record egg abortions as they were not
visible due to their small size. For pupae collected on a Monday
morning, we consulted the video recordings to determine the exact day of
larviposition (S2 File). For aborted larvae, however, we could not
determine the date for those aborted over the weekend, because early
larval stages were also not visible on the camera and were therefore
recorded as occurring on the Friday. For viable pupae, we measured the
wet weight, to 0.1 mg. Of the pupae produced, we randomly selected 70\%
of samples to track emergence. The remaining 30\% were destructively
sampled for fat analysis (S2 File) in order to quantify how wet weight
correlates with fat reserves.

Pupae assigned for emergence studies were placed singly into 50 ml
Falcon tubes with a 3 mm hole drilled in the centre of the screw cap to
allow air flow. Pupae were observed daily for date of emergence on
working days and again video recordings were used to determine the date
of emergence on weekends. Pupae were observed for a maximum of 50 days.
Any pupae that had not emerged by 50 days, were recorded as a failed
emergence (Hargrove 2004). Each emerging fly remained in the tube until
it died of starvation. Sex of emerging flies and date of death were
recorded.

\textbf{Statistical analyses}

For each analysis described below, models were compared using Akaike's
information criterion (AIC). If the ratio of the sample size to the
number of model parameters was \textless{}40, we used AIC corrected for
small sample size (AICc) (Burnham \& Anderson 2002). Figures were
produced using the model with the lowest AIC. For model comparison, the
difference in AIC between each model and the lowest AIC model
(\selectlanguage{greek}\emph{Δ\textsubscript{i}}\selectlanguage{english} ) and Akaike weights
(\selectlanguage{greek}\emph{ω\textsubscript{i}}\selectlanguage{english} ) were calculated. Akaike weights sum to one
and provide a relative indication for the weight of evidence for any one
model as the best approximating model (Burnham \& Anderson 2002). We
analysed the data using R version 3.6 (R Core Team 2014). The data and R
scripts to produce the results can be accessed at
(https://github.com/jenniesuz/tsetse\_senescence.git).

\par\null

For the probability of abortion, offspring wet weight, and starvation
tolerance, we carried out statistical analyses for each treatment
separately using linear and generalised linear mixed effects models
implemented with the `lme4' and `nlme' R packages (Bates \emph{et
al.}2015; Pinheiro \emph{et al.} 2018). For each analysis described
below, maternal age in days was incorporated as a continuous variable.
All models with and without random effects were fitted and
simultaneously compared using maximum likelihood estimation, assuming
that the bias in the variance components would be relatively small
(\emph{n} /(\emph{n} -\emph{p} ): 1.009 for the probability of abortion;
1.049 for offspring wet weight; and 1.095 for offspring survival,
largest values across treatments reported). Models with and without a
random intercept were compared to assess evidence for variation among
individual mothers in the probability of abortion, offspring wet weight
and offspring starvation tolerance. We also compared models with and
without a random slope for maternal age, to assess evidence for
variation in senescence patterns among individual mothers.

The effect of maternal age on probability of abortion, offspring wet
weight and offspring starvation tolerance were compared between
treatments using fitted coefficients and 95\% confidence intervals from
the model with the lowest AIC. We took this approach to analysis rather
than including treatment as a factor in models as it is simpler and does
not require the assumption of equal variance between treatments.

Logistic regression was used to quantify the effect of maternal age on
the probability of abortion for each treatment, assuming a linear
relationship between maternal age and the log odds of abortion. For the
effect of maternal age on offspring wet weight and starvation tolerance,
models including maternal age as a cubic, quadratic, logistic or linear
effect were compared. For wet weight, we also compared the most
parsimonious model with a model fit using generalised additive modelling
(GAM). Cubic regression splines were fitted for each treatment, with
maternal age as the explanatory variable and accounting for multiple
offspring from individual mothers, using the mgcv R package (Wood 2017).
Generalised additive model fits with knots -- locations where the slope
changes -- ranging from 3 to 10 were compared using AICc. The
correlation between offspring wet weight and fat was summarised using
Pearson's correlation coefficient.

For offspring starvation tolerance, there was no censoring and the data
were approximately normally distributed (S4 Fig.). The relationship
between offspring wet weight, sex, maternal age and number of days to
starvation was therefore modelled using linear mixed effects models for
each treatment. Maternal age was included in models as described for
offspring wet weight. In addition to maternal age and wet weight, we
also included offspring sex as females are larger than males on
emergence (Hargrove \emph{et al.} 2019).

We repeated the above analyses for the nutritional stress treatment,
excluding females that had died, to ensure our results were not affected
by the possibility that females who died allocated more to their
offspring. For the other two treatments this was not done as
\textgreater{}90\% of mothers were still alive by the end of the
experiment. Lastly, an analysis of maternal survival is provided in S5
File.

\textbf{Results}

\textbf{Abortions}

The probability of abortion increased with age across all treatments
(models without age: control \selectlanguage{greek}\emph{ω =}\selectlanguage{english} 0.000; mating delay \selectlanguage{greek}\emph{ω
=}\selectlanguage{english}0.001; and nutritional stress \selectlanguage{greek}\emph{ω =}\selectlanguage{english} 0.00) (Tables S6 1-3). The
increase in the odds of abortion with age were similar across treatments
(from the model with lowest AIC: control 1.072, C.I. 1.054 -- 1.094;
mating delay 1.053, CI 1.025 -- 1.086; and nutritional stress 1.047, CI
1.034 -- 1.061) (Fig. 2, S6 4-6). Females in the nutritional stress
treatment had higher probability of abortion at any age compared with
the other two treatments (Fig. 2).

For all treatments, including a random intercept and slope in models
resulted in a singular fit indicating that models were overfitted. We
did not therefore assess evidence for individual variation in the effect
of maternal age on probability of abortion. There was, however, strong
evidence for variation among individual mothers in the probability of
abortion for the nutritional stress treatment (model with random
intercept \selectlanguage{greek}\emph{ω} \selectlanguage{english}= 0.985; model without \selectlanguage{greek}\emph{ω} \selectlanguage{english}= 0.015). This was
not the case for the control (model with random intercept \selectlanguage{greek}\emph{ω
=}\selectlanguage{english}0.289; model without \selectlanguage{greek}\emph{ω =}\selectlanguage{english} 0.711) or mating delay treatments
(model with random intercept \selectlanguage{greek}\emph{ω =}\selectlanguage{english} 0.368; model without \selectlanguage{greek}\emph{ω =}\selectlanguage{english}
0.631) (Tables S6 1--3). Plots of the raw data for this analysis, and
analyses described below, can be viewed in S7 File.

\textbf{Offspring wet weight and fat}

Across all treatments, wet weight of pupae increased and then declined
with maternal age until the end of the experiment (Fig. 3). Model
selection results provided strong evidence for a quadratic effect of
maternal age on offspring wet weight (control -- quadratic effect
including random intercept and slope \selectlanguage{greek}\emph{ω} \selectlanguage{english}= 0.719; mating delay --
quadratic effect with random intercept and slope \selectlanguage{greek}\emph{ω} \selectlanguage{english}= 0.562,
without random slope \selectlanguage{greek}\emph{ω} \selectlanguage{english}= 0.272; and nutritional stress --
quadratic effect with random intercept and slope \selectlanguage{greek}\emph{ω} \selectlanguage{english}= 0.866)
(Tables S6 7-9). There was no support for a log, or a linear effect of
maternal age for any of the treatments (\selectlanguage{greek}\emph{ω} \selectlanguage{english}= 0.000). In addition,
across treatments, the predicted effect of mother age at the population
level, assuming a quadratic fit, was within the standard errors of
predictions from GAM fits to the data (S8 File).

The fitted quadratic curves and coefficients were similar between
treatments (from the model with lowest AIC, which included a random
intercept and slope, coefficient for maternal age: control 0.520 (0.466
-- 0.574); mating delay 0.564 (0.406 -- 0.723); and nutritional stress
0.502 (0.420 -- 0.584), coefficient for maternal age squared: control
-0.0040 (-0.0045 -- -0.0035); mating delay -0.0042 (-0.0054 -- -0.0030);
and nutritional stress -0.0040 (-0.0048 -- -0.0032)) (Tables S6 10 -
12).

For all three treatments, there was strong evidence for individual
variation among mothers in the wet weight of their pupae (for all
treatments, all models without random effects \selectlanguage{greek}\emph{ω =}\selectlanguage{english} 0.000). This
variation was highest in the mating delay treatment with a random
intercept variance from the most parsimonious model of 11.820, compared
with 7.307 for the control and 2.379 for the nutritional stress
treatment (Tables S6 13 - 15).

There was evidence for variation among mothers in the effect of maternal
age on wet weight for the control and nutritional stress treatments
(quadratic fit with random intercept and slope: control \selectlanguage{greek}\emph{ω} \selectlanguage{english}=
0.719; and nutritional stress \selectlanguage{greek}\emph{ω =}\selectlanguage{english} 0.866). There was less support
for this in the mating delay treatment (quadratic fit with random
intercept and slope \selectlanguage{greek}\emph{ω =}\selectlanguage{english} 0.562, next lowest AIC model - quadratic
fit with random intercept only \selectlanguage{greek}\emph{ω =}\selectlanguage{english} 0.272).

Offspring fat increased linearly with offspring wet weight (Pearson's
correlation coefficient 0.554) (File S9), thus for brevity we focus on
wet weight as our trait of interest, but the patterns are qualitatively
similar if offspring fat is considered instead.

\textbf{Offspring emergence and starvation tolerance}

During the 50-day pupal observation period, 96\% of 355 pupae from the
control, 93\% of 187 pupae from the mating delay, and 91\% of 232 pupae
from the nutritional stress group emerged successfully. All emerged
offspring subjected to starvation were dead by 15 days post emergence.
Although the wet weight of pupae was similar between females and males
(Fig. 4a), across all three treatments there was strong evidence that
female offspring survived on average longer than males, but not by more
than a day (Tables S6 19 -- 21), with \selectlanguage{greek}\emph{ω [?]}\selectlanguage{english} \selectlanguage{english}0.01 for models not
including sex (Tables S6 16 - 18, Fig. 4b).

There was strong evidence for an additional effect of maternal age on
offspring starvation tolerance, in addition to offspring wet weight, for
the nutritional stress treatment (models without age \selectlanguage{greek}\emph{ω} \selectlanguage{english}= 0.000).
Offspring from young mothers in the nutritional stress treatment were
particularly vulnerable to starvation (Fig. 4b), with evidence for a
cubic effect of maternal age in this treatment (cubic effect including
random intercept \selectlanguage{greek}\emph{ω} \selectlanguage{english}= 0.705). While there was also evidence for
maternal age effects on offspring starvation tolerance in the other two
treatments, there was not sufficient information in the data to quantify
the relationship between maternal age and offspring starvation tolerance
for the control or mating delay treatments (lowest AIC model: control --
quadratic effect and no random effects \selectlanguage{greek}\emph{ω} \selectlanguage{english}= 0.298; mating delay --
quadratic effect with no random effects \selectlanguage{greek}\emph{ω} \selectlanguage{english}= 0.212) (Tables S6
16-17).

For the mating delay treatment, singular fits were obtained for models
assuming a quadratic or cubic effect of mother age and including a
random intercept and slope, therefore there was not sufficient data to
assess for this treatment whether differences among individual mothers
carried through to emerged offspring starvation tolerance. There was
also insufficient information in the data to provide evidence for
among-mother variation in the control treatment, with the difference in
AIC weights between models with and without random effects
\textless{}0.15 (Tables S6 16 - 18). However, there was evidence for an
effect of individual mother on starvation tolerance in the nutritional
stress treatment (all models without random effects \selectlanguage{greek}\emph{ω [?]}\selectlanguage{english} \selectlanguage{english}0.04)
(Tables S6 16 - 18), but not for variation among individual mothers in
the effect of maternal age on offspring starvation tolerance (\selectlanguage{greek}\emph{ω
[?]}\selectlanguage{english}\selectlanguage{english}0.005).

The results of the above analyses for the probability of abortion,
offspring wet weight and offspring starvation tolerance were unaffected
by removing from the analysis mothers in the nutritional stress
treatment that had died before the end of the experiment. The analyses
can be found in S10 File and compared with results in S6 File.

\textbf{Discussion}

While there is substantial evidence of actuarial senescence in insects
and some evidence for reproductive senescence (Nussey \emph{et al.}2013;
Zajitschek \emph{et al.} 2019), the drivers of variation in senescence
patterns are not fully understood. Here we manipulated both age at
mating and nutrition to quantify reproductive senescence in tsetse, a
viviparous fly with high maternal allocation and iteroparous
reproduction. Both offspring weight and starvation tolerance declined
with maternal age, after a peak, yet these patterns of senescence were
similar across treatments. Contrary to predictions from life history
theory, therefore, neither changes in maternal allocation nor resources
affected the timing and rate of reproductive senescence, in terms of
offspring quality.

We did observe a steeper increase in the hazard of mortality with age
for nutritionally stressed mothers (S5 File). This suggests that females
do not have a survival benefit from reduced reproductive effort, as
nutritionally stressed mothers had higher probability of abortion and
produced smaller offspring at any age compared with mothers in the
control and mating delay groups. This contrasts with findings from
studies of other insects where nutritional stress both reduced
reproductive output and either maintained or even extended lifespan
relative to a control group (De Sousza Santos \& Begon 1987; Ernsting \&
Isaaks 1991; Kaitala 1991). Taken together, it may be that factors other
than the direct costs of reproduction through physiological damage, or
indirectly through resource allocation trade-offs, impose higher
mortality rates. Alternatively it may be that, given the extreme
maternal allocation in tsetse, even though females produce relatively
smaller offspring they still pay a high cost of reproduction in terms of
physiological damage; and females on a poor quality diet experience this
cost to a greater extent in terms of impact on mortality. This is
supported by data from field-caught tsetse, where smaller females invest
relatively more of their fat in their offspring, even though their
offspring were smaller (Hargrove \emph{et al.} 2018).

We find that females experiencing nutritional stress have a relatively
higher rate of abortion. Hargrove and Muzari (Hargrove \& Muzari 2015),
using field collected \emph{G. pallidipes} , showed that transfer of the
majority of fat to the larva occurs only after c. 80\% pregnancy has
been completed. Therefore, a female could potentially abort a larva if
there are not enough fat reserves for a full-term pregnancy.
Evolutionary models tailored to tsetse life-history, with high
investment in single offspring across multiple reproductive bouts, could
yield insights into whether such spontaneous abortion is an adaptive
strategy to retain reserves for future reproduction, or a result of
physiological constraints that limit the reserves available
(McNamara\emph{et al.} 2009).

Our study highlights the benefits gained from individual-level data to
understand senescence. The bell-shaped relationship of offspring quality
with age may have contributed to the relatively small effects of age
evident in previous studies where grouped ages and mean values were
used, rather than tracking reproductive output from individual females
(Langley \& Clutton-Brock 1998; McIntyre \& Gooding 1998). The
bell-shaped pattern observed here is strikingly similar in shape across
treatments and reflects the general bell-shaped pattern of reproductive
senescence observed across diverse taxonomic groups e.g. (Velando et al.
2006; Sharp \& Clutton-Brock 2010). As summarised by (Monaghan\emph{et
al.} 2020), a bell-shaped relationship between maternal age and
reproductive output, or offspring quality, can arise from
population-level effects, due to selective disappearance, but also from
individual-level effects. By tracking individual mothers, here we
provide evidence that, for tsetse, the bell-shaped relationship between
offspring wet weight and maternal age is a consequence of individual
effects. The initial increase in offspring wet weight could be a
consequence of mothers accruing reserves each time she takes a
bloodmeal, so that at each sequential reproductive event she has more
energy reserves that can be provided to the offspring. The subsequent
decline could then be explained by senescence.

\par\null

Tracking individual mothers provided insights into individual variation
in maternal allocation. There was marked variation in offspring wet
weight among mothers, particularly for the mating delay treatment and
variation in senescence patterns for wet weight, particularly for
nutritionally stressed mothers. Some females in the mating delay
treatment consistently produced smaller than average offspring, across
all ages, and these females contributed more to the individual
heterogeneity than those producing consistently heavier offspring. For
nutritionally stressed mothers, variation between individuals in
offspring wet weight increased as mothers aged. These observations
suggest that variation in offspring quality is affected not only by
mother size but also unmeasured aspects of her condition. The large
amount of variation between individuals, in offspring weight and changes
in the extent of variation with maternal age in this study, was
unexpected, suggesting that future studies quantifying the relative
roles of mother size and physiological condition on offspring size
across different ages would be valuable.

Offspring from young mothers that were nutritionally stressed had the
lowest starvation tolerance. We also found that maternal age affected
offspring starvation tolerance independently of age-effects mediated
through wet weight, suggesting that there may be other factors
associated with maternal age that influence the quality of offspring.
More subtle effects of maternal age on the quality of resources
transferred to offspring warrant further investigation in tsetse and
other species. For tsetse, during late stages of pregnancy females not
only transfer fat but also amino acids. It may be that young
nutritionally-stressed females are limited in these amino acids. High
amounts of tyrosine in the gut contents of third instar larvae, which is
involved in tanning of larval and adult cuticles have been observed
(Cmelik \emph{et al.} 1969). The authors reasoned that the tyrosine and
phenylalanine obtained from a single bloodmeal is unlikely to be
sufficient to meet the amount required by offspring and that a surplus
stored from previous bloodmeals may be required. This suggests that size
may be limited when nutrients are limited, irrespective of the amount of
energy available. It also demonstrates that resource allocation
processes are likely more complex, and more nuanced studies on the
effects of the quality of resources as well as quantity may be required
to understand the ageing process.

We focused on reproductive senescence in this study. One limitation is
that we did not continue the experiment beyond 100 days to quantify more
fully mother survival for all three treatments. Our evidence of
actuarial senescence in the nutritional stress treatment supports
analysis of mark-recapture studies of \emph{G. m. morsitans} in the
field (Hargrove \emph{et al.} 2011) which also showed an increase in
mortality as a function of age. Considering this, an additional
experiment to test whether females mated later experience a delayed
onset in actuarial senescence would be informative, given the similar
rates of reproductive senescence observed across treatments.

We also note that our conclusions with respect to reproductive
senescence are constrained to this 100-day period. While, in principle,
it would be optimal to run the experiment until all females had died,
the \textgreater{}1/4 of offspring being aborted and fewer females
surviving would result in relatively small sample sizes beyond this time
point. However, we are confident that our study timeframe captures an
ecologically relevant period to study age-dependent allocation in
tsetse, given that field studies show that 90\% of females have died
before 100 days (Hargrove \emph{et al.} 2011). Lastly, we acknowledge
that our conclusions may not necessarily extend to wild flies, and
further study of tsetse in the field would need to be carried out to
confirm this.

To conclude, our results provide evidence of a bell-shaped relationship
between maternal age and offspring quality at the individual-level for
the iteroparous tsetse fly. This complements the existing body of work
from other species, that has predominantly shown a similar curve for the
number of offspring produced as mothers age (Monaghan \emph{et
al.}2020). Contrary to predictions from life history theory, however,
our study did not find evidence of a decline in offspring quality as a
function of direct costs of reproduction or resource allocation
trade-offs.

\textbf{Acknowledgements}

We would like to thank Jessica Lingley for assistance in the analysis of
the fat content of pupae and Glyn Vale for review of a final draft of
the manuscript, and John Hargrove, Matt Keeling and Kat Rock for general
discussion. We thank Daniel May for digitising the LSTM tsetse colony
lab books which provided important pilot data for our experiments. The
research was funded by the Biotechnology and Biological Sciences
Research Council, grant numbers BB/P005888/1 and BB/P006159/1. SE was
supported by a Royal Society Dorothy Hodgkin Fellowship (DH140236).

\textbf{References}

Anderson, M. \& Finlayson, L.H. (1973). Ultrastructural changes during
growth of the flight muscles in the adult tsetse fly, Glossina
austeni.\emph{J. Insect Physiol.} , 19, 1989--1997.

Baldry, D., Boreham, P., Challier, A., Van Etten, J., Everts, J.,
Gravel, J., \emph{et al.} (1992). \emph{Training manual for tsetse
control personnel Volume 1} . Food and Agriculture Organization of the
United Nations.

Bates, D., Maechler, M., Bolker, B. \& Walker, S. (2015). Fitting linear
mixed-effects models using lme4. \emph{J. Stat. Softw.} , 67, 1--48.

Baudisch, A. \& Vaupel, J.W. (2012). Getting to the root of
aging.\emph{Science (80-. ).} , 338, 618--619.

Boggs, C.L. (2009). Understanding insect life histories and senescence
thorugh a resource allocation lens. \emph{Funct. Ecol.} , 23, 27--37.

Burnham, K. \& Anderson, D. (2002). \emph{Model selection and multimodel
inference: a practical information-theoretic approach} . 2nd Editio.
Springer-Verlag New York, Inc.

Cayuela, H., Lema\selectlanguage{ngerman}ître, J.F., Bonnaire, E., Pichenot, J. \& Schmidt, B.R.
(2020). Population position along the fast--slow life-history continuum
predicts intraspecific variation in actuarial senescence. \emph{J. Anim.
Ecol.} , 0--2.

Chippindale, A.K., Leroi, A.M., Kim, S.B. \& Rose, M.R. (1993).
Phenotypic plasticity and selection in Drosophila life\selectlanguage{english}-history
evolution. I. Nutrition and the cost of reproduction. \emph{J. Evol.
Biol.} , 6, 171--193.

Cmelik, S.H.W., Bursell, E. \& Slack, E. (1969). Composition of the gut
contents of third-instar tsetse larvae (Glossina morsitans
westwood).\emph{Comp. Biochem. Physiol.} , 29, 447--453.

Curtis Creighton, J., Heflin, N.D. \& Belk, M.C. (2009). Cost of
reproduction, resource quality, and terminal investment in a burying
beetle. \emph{Am. Nat.} , 174, 673--684.

Davison, R., Boggs, C.L. \& Baudisch, A. (2014). Resource allocation as
a driver of senescence: Life history tradeoffs produce age patterns of
mortality. \emph{J. Theor. Biol.} , 360, 251--262.

Ejezie, G.C. \& Davey, K.G. (1977). Some effects of mating in female
tsetse, Glossina austeni newst. \emph{J. Exp. Zool.} , 200, 303--310.

Ernsting, G. \& Isaaks, J.A. (1991). Accelerated Ageing: A Cost of
Reproduction in the Carabid Beetle Notiophilus biguttatus F.\emph{Funct.
Ecol.} , 5, 299.

Gaillard, J.M. \& Lemaitre, J.F. (2020). An integrative view of
senescence in nature. \emph{Funct. Ecol.} , 34, 4--16.

Haines, L.R., Vale, G.A., Barreaux, A.M.G., Ellstrand, N.C., Hargrove,
J.W. \& English, S. (2020). Big Baby, Little Mother: Tsetse Flies Are
Exceptions to the Juvenile Small Size Principle. \emph{BioEssays} ,
2000049, 2000049.

Hamilton, W. (1966). The moulding of senescence by natural
selection.\emph{J. Theor. Biol.} , 12, 12--45.

Hargrove, J. (1999). Nutritional levels of female tsetse Glossina
pallidipes from artificial refuges. \emph{Med. Vet. Entomol.} , 13,
150--164.

Hargrove, J. (2004). Tsetse population dynamics. In: \emph{The
Trypanosomiases} (eds. Maudlin, I., Holmes, P. \& Miles, M.). CABI
Publishing, pp. 113--135.

Hargrove, J., English, S., Torr, S.J., Lord, J., Haines, L.R., Van
Schalkwyk, C., \emph{et al.} (2019). Wing length and host location in
tsetse (Glossina spp.): Implications for control using stationary
baits.\emph{Parasites and Vectors} , 12, 1--13.

Hargrove, J. \& Muzari, M. (2015). Nutritional levels of pregnant and
postpartum tsetse Glossina pallidipes Austen captured in artificial
warthog burrows in the Zambezi Valley of Zimbabwe. \emph{Physiol.
Entomol.} , 40, 138--148.

Hargrove, J., Muzari, M. \& English, S. (2018). How maternal investment
varies with environmental factors and the age and physiological state of
wild tsetse Glossina pallidipes and Glossina morsitans
morsitans.\emph{R. Soc. Open Sci.} , 5.

Hargrove, J., Ouifki, R. \& Ameh, J. (2011). A general model for
mortality in adult tsetse (Glossina spp.). \emph{Med. Vet. Entomol.} ,
25, 385--94.

Hoekstra, L.A., Schwartz, T.S., Sparkman, A.M., Miller, D.A.W. \&
Bronikowski, A.M. (2019). The untapped potential of reptile biodiversity
for understanding how and why animals age. \emph{Funct. Ecol.} , 38--54.

Holand, H., Kvalnes, T., Gamelon, M., Tufto, J., Jensen, H., Parn,
H.,\emph{et al.} (2016). Spatial variation in senescence rates in a bird
metapopulation. \emph{Oecologia} , 181, 865--871.

Jimenez-Perez, A. \& Wang, Q. (2009). Effect of Mating Delay on the
Reproductive Performance of Cnephasia jactatana (Lepidoptera:
Tortricidae)\_. \emph{J. Econ. Entomol.} , 96, 592--598.

Jordan, A.M., Nash, T.A.M. \& Boyle, J.A. (1969). Pupal weight in
relation to female age in Glossina austeni Newst. \emph{Bull. Entomol.
Res.} , 58, 549--552.

Kabayo, J.P. \& Langley, P.A. (1985). The nutritional importance of
dietary blood components for reproduction in the tsetse fly, Glossina
morsitans. \emph{J. Insect Physiol.} , 31, 619--624.

Kaitala, A. (1991). Phenotypic Plasticity in Reproductive Behaviour of
Waterstriders: Trade-Offs Between Reproduction and Longevity During Food
Stress. \emph{Funct. Ecol.} , 5, 12.

Kirkwood, T. (1977). Evolution of ageing. \emph{Nature} , 270, 301--303.

Langley, P. \& Clutton-Brock, T. (1998). Does reproductive investment
change with age in tsetse flies, Glossina morsitans morsitans (Diptera:
Glossinidae)? \emph{Funct. Ecol.} , 12, 866--870.

McIntyre, G.S. \& Gooding, R.H. (1998). Effect of Maternal Age on
Offspring Quality in Tsetse (Diptera: Glossinidae). \emph{J. Med.
Entomol.} , 35, 210--215.

McNamara, J.M., Houston, A.I., Barta, Z., Scheuerlein, A. \& Fromhage,
L. (2009). Deterioration, death and the evolution of reproductive
restraint in late life. \emph{Proc. R. Soc. B Biol. Sci.} , 276,
4061--4066.

Monaghan, P., Maklakov, A.A. \& Metcalfe, N.B. (2020). Intergenerational
Transfer of Ageing: Parental Age and Offspring Lifespan. \emph{Trends
Ecol. Evol.} , 35, 927--937.

Nussey, D., Froy, H., Lemaitre, J., Gaillard, J. \& Austad, S. (2013).
Senescence in natural populations of animals: widespread evidence and
its implications for bio-gerontology. \emph{Ageing Res. Rev.} , 23,
1--7.

Partridge, L. (1987). Is Accelerated Senescence a Cost of
Reproduction?\emph{Funct. Ecol.} , 1, 317.

Pinheiro, J., Bates, D., DebRoy, S. \& D, S. (2018). nlme: linear and
nonlinear mixed effects models.

R Core Team. (2014). R: A language and environment for statistical
computing.

Rodriguez-Munoz, R., Boonekamp, J.J., Fisher, D., Hopwood, P. \&
Tregenza, T. (2019). Slower senescence in a wild insect population in
years with a more female-biased sex ratio. \emph{Proc. R. Soc. B Biol.
Sci.} , 286.

Sharp, S.P. \& Clutton-Brock, T.H. (2010). Reproductive senescence in a
cooperatively breeding mammal. \emph{J. Anim. Ecol.} , 79, 176--183.

De Sousza Santos, P. \& Begon, M. (1987). Survival costs of reproduction
in grasshoppers. \emph{Funct. Ecol.} , 1, 215--221.

Tatar, M. \& Carey, J.R. (1995). Nutrition mediates reproductive
trade-offs with age-specific mortality in the beetle Callosobruchus
maculatus. \emph{Ecology} , 76, 2066--2073.

Unnithan, G.C. \& Paye, S.O. (1991). Mating, longevity, fecundity, and
egg fertility of Chilo partellus (Lepidoptera: Pyralidae): Effects of
delayed or successive matings and their relevance to pheromonal control
methods. \emph{Environ. Entomol.} , 20, 150--155.

Velando, A., Drummond, H. \& Torres, R. (2006). Senescent birds redouble
reproductive effort when ill: Confirmation of the terminal investment
hypothesis. \emph{Proc. R. Soc. B Biol. Sci.} , 273, 1443--1448.

Williams, G. (1957). Pleiotropy, natural selection and the evolution of
senescence. \emph{Evolution (N. Y).} , 11, 398--411.

Wood, S. (2017). \emph{Generalized additive models: an introduction with
R, 2 edition.} Chapman and Hall/ CRC.

Zajitschek, F., Zajitschek, S. \& Bonduriansky, R. (2019). Senescence in
wild insects: Key questions and challenges. \emph{Funct. Ecol.} ,
26--37.

\textbf{Figure 1 Overview of experiments.} M -- mating. Colour-coded
silhouette of pregnant tsetse with larva, timeline shows a silhouette of
a pupa and tubes containing blood, the dark red indicates red blood cell
and beige indicates serum.

\textbf{Figure 2 Predicted probability of larval abortion as a function
of maternal age, by treatment.} Predicted probabilities from generalised
linear mixed effects model fits to the data and 95\% prediction
intervals. Plots of raw data are provided in S7 File.

\textbf{Figure 3 Offspring wet weight as a function of maternal age and
treatment.} Showing model fits to the data: thick line -- population
level, thinner lines -- individual level. Points -- average wet weights
for 10-day intervals and 95\% confidence intervals. Plots of raw data
are provided in S7 File.

\textbf{Figure 4 Effect of sex, wet weight and maternal age on
starvation tolerance (the number of days a newly emerged fly can survive
starvation).} a) Wet weight as a function of offspring sex by treatment;
b) Predicted survival time based on linear mixed effects model. Days
adults survived starvation is plotted against maternal age. Prediction
for each wet weight quartile shown. Plots of raw data are provided in S7
File.

\textbf{Supplementary Files}

\textbf{S1 File. Pilot data from the tsetse colony at LSTM.}

\textbf{S2 File. Additional methods.}

\textbf{S3 File. Nutritional stress pilot.}

\textbf{S4 Figure. Histogram of offspring survival time by treatment.}

\textbf{S5 File. Analysis of mother survival.}

\textbf{S6 File. Statistics.}

\textbf{S7 File. Raw data plots.}

\textbf{S8 File. Generalised additive models.}

\textbf{S9 File. Offspring fat as a function of wet weight.}

\textbf{S10 File. Statistics excluding mothers in the nutritional stress
treatment that died before the end of the experiment.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig1-experiment-overview/Fig1-experiment-overview}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig2-abortions/Fig2-abortions}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig3-wet-weight/Fig3-wet-weight}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig4-offspringSurv/Fig4-offspringSurv}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{CJK}\end{document}

