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Historical Human Fecundity info?

Historical Human Fecundity info?

I'm looking for information about the number of children a woman has over their life time. I get an average to be 5-7 or 10-12 with 1-2 or ~50% dying pre-puberty.

What I'm looking for now is how those children are spaced between 14 and 55 usually, or rather have been across history, rather than modern times because I know its a bit off in modern times.

Is it more likely that a woman would just go and have 7 children in a row between 14 and 20, or space it out at 1 every other year, or 1 every 4 years? I know that in modern times there seems to be a common trend of a child every 2.5 to every 5 years in smaller families, while in bigger they seem to try to get as many kids as possible early… and often couples do kids at the beginning of a marriage. And I know that there are rarities of 10 and 20 year gaps and some women holding off till their forties.

Has this always been the norm or are these trends the norm throughout history?


Such a study would be quite local, requiring a detailed study of church records for baptisms, marriages and deaths.

I can provide some information from 19th century:

1802: Lucinda Pixley, age 19, married at age 19 to David Powers, in Vermont They had two daughters, in 1802 and 1808. The family moved to New York; the husband died in 1813, while serving in the US Army. They may have had some other difficulties as well, but documentation is sparse.

1817: Vinera Powers, age 15, married Samuel Stillman Glover, in upstate New York. They had 11 children, with one set of twins, between 1818 and 1847. Most of the gaps were 2 years or so, with one much longer gap of five years. The family moved to Michigan. The mother and new born twins died in 1847.

1860: Annette Glover, age 16, married Jesse Youngs in Michigan. They were married for 64 years. They had 17 children, of whom 9 lived to adulthood, between 1861 and 1898. There were several long gaps of 5 and 6 years. She was 54 when her final child was born; the final seven all died as infants, and were left out of the family bible record, but have birth and death certificates.

Annette Glover & Jesse Youngs, 50nd wedding anniversary. Image from our family archive. Ella (Youngs) Kelly and her husband, C.J. Kelly, are on the left.

1879: Ella Youngs, age 16, married Christian James Kelly in Michigan. They were married for 38 years, and had 11 children between 1880 and 1896. Eight of the children lived to adulthood, with three dying as young children.

1913: Eloise Kelly, age 17, married Harlan Foster in Michigan. They were married for seven years and had three children, all living to adulthood. Her husband was absent for two years while a member of the AEF serving in Europe, and they divorced shortly after his return.

These are five generations of the same family. The large families all lived on farms, under stable circumstances. The husbands were often much older than the wives. By 1900 the economic situation was changing, and only two of the children of this final generation had large families.

From general research on these and other families the typical spacing of children is every two years, with the first child born within a year of the marriage. Note that a nursing mother will ordinarily not conceive, so unless a wet nurse is being used, spacing of less than two years is uncommon in older times, though goats milk is a workable replacement.


Fecundity

Abstract

Fecundity is the physiological maximum potential reproductive output of an individual (usually female) over its lifetime and represents one of the major cornerstones of theoretical and applied population biology. Fertility, a related concept, is defined as the current (actual) reproductive performance of an individual. Many strategies have evolved to shape patterns in fecundity so that lifetime reproductive success (the genetic contribution to the next generation) is maximized under the evolutionary selective pressures experienced by an organism. Fecundity is an important component of both sexual and asexual reproduction, and it can be viewed as a direct (production of offspring) or an indirect (assisting in the reproduction of related individuals) process. Temporal patterns in fecundity fall under two main categories: (1) semelparity – the production of offspring only once during an organism’s lifetime and (2) iteroparity – the repeated production of offspring. Factors influencing temporal variation in fecundity and fertility include age, body size (allometric) relationships, the effects of population density, mate choice, and environmental variability. A particularly important framework for understanding fecundity patterns examines the energetic tradeoffs that exist between reproduction and survival, that is, opting to reproduce at the expense of surviving or vice versa. From a genetic perspective, reproductive fitness is the combination of fecundity and survival and is used to measure the effects of inbreeding depression on populations.


Why the Concept of Historical Trauma is Important for Human Services Agencies

Human services programs are provided to a wide range of individuals including members of groups who may experience historical trauma. By being mindful of unresolved grief and distrust of majority groups or government programs, human service providers can more readily deliver programs to reduce family stress, child abuse and neglect, substance misuse, mental health challenges, and domestic violence. Human services providers and staff can better understand present day reactions to events in the context of individual trauma narratives. Because trauma-related events have occurred in the context of service provision, it is also important to be mindful of a potential lack of trust in government-funded services, in research, and in health and mental health care. To build trust, providers can be respectful, cognizant of different reactions to traumatic events within communities, and focus on community strengths and resilience. With the understanding that all communities are unique with distinct cultural norms and belief systems, human services personnel are in a good position to support to members of the community with whom they are working.


How Cave Dwellers Work

We know that cavepeople existed -- early humans and other species closely related to humans inhabited caves. The question is, how important were cave dwellings to these primitive peoples? We'll probably never really know, because they left no historical records other than a few cave paintings and scattered artifacts. However, the general consensus among anthropologists and archaeologists is that caves very rarely served as permanent settlements. They may have provided seasonal shelter or been temporary camp sites for nomadic groups that moved from place to place, following the herd animals they hunted for food.

Some of the prehuman or humanlike species that may have lived in caves include Homo antecessor, Homo neanderthalensis (Neanderthals), Homo erectus and Homo heidelbergensis. Early humans, Homo sapiens, also used caves sporadically. Living as hunter-gatherers, these species didn't create permanent settlements. They had several ways of building shelters for themselves, such as stretching animal hides over bone, building rough wooden lean-tos or creating earthen mounds. When they came across a cave suitable for shelter, they used it.

The most common caves in the world are made of limestone, which is eroded by acidic water. Although there are millions of caves, many of them are unsuitable for shelter. The entrances may be located on an inaccessible cliff face, or the entrance itself may be a long vertical shaft. Surrounding terrain often blocks the view of the entrance to casual observers, which is why many previously inhabited caves remained hidden until modern humans rediscovered them. And it's not just the outside that's intimidating -- cave interiors are rarely safe places. They're filled with crevices, unstable gravel slopes, multiple entrances and exits, shafts and potential rockfalls. Once you move more than a few dozen meters from the entrance, they're also utterly dark. And without naturally occurring ventilation shafts, the air could quickly become unbreathable. A cave suitable for living in is actually quite rare.

The Neanderthals are one particular species known to have had a predilection for cave living. They existed throughout a wide swath of Europe during a glacial period. The harsh climate forced Neanderthals to be adaptive, creative survivors. Archaeologists believe they used two main strategies: circulating mobility and radiating mobility. With circulating mobility, each group of Neanderthals had several temporary camps, some of which included caves, spread throughout a region. They moved from place to place in search of the best hunting grounds. With radiating mobility, the group had one central camp. Hunting parties headed out from camp, moving farther and farther afield to find food. In at least several cases, these main camps were caves [source: Tattersall]. The caves suited the Neanderthals' purposes especially well because they lived in very small groups of about a dozen individuals. Few caves could support a larger population. There is evidence that in at least one case, Neanderthals and early humans lived in the same cave at the same time and shared resources [source: Viegas].

In the next section, we'll examine the archaeological evidence of prehistoric cave life -- in particular, cave paintings.

The era that most people think of when they talk about "cavemen" is the Paleolithic Era, sometimes referred to as the Stone Age (it's actually one part of the Stone Age). It extends from more than 2 million years into the past until sometime between 40,000 and 10,000 years ago (depending on who you ask). Ironically, there are probably more humans permanently living in caves today that at any time in human history.


Plasmodium infection decreases fecundity and increases survival of mosquitoes

Long-lived mosquitoes maximize the chances of Plasmodium transmission. Yet, in spite of decades of research, the effect of Plasmodium parasites on mosquito longevity remains highly controversial. On the one hand, many studies report shorter lifespans in infected mosquitoes. On the other hand, parallel (but separate) studies show that Plasmodium reduces fecundity and imply that this is an adaptive strategy of the parasite aimed at redirecting resources towards longevity. No study till date has, however, investigated fecundity and longevity in the same individuals to see whether this prediction holds. In this study, we follow for both fecundity and longevity in Plasmodium-infected and uninfected mosquitoes using a novel, albeit natural, experimental system. We also explore whether the genetic variations that arise through the evolution of insecticide resistance modulate the effect of Plasmodium on these two life-history traits. We show that (i) a reduction in fecundity in Plasmodium-infected mosquitoes is accompanied by an increase in longevity (ii) this increase in longevity arises through a trade-off between reproduction and survival and (iii) in insecticide-resistant mosquitoes, the slope of this trade-off is steeper when the mosquito is infected by Plasmodium (cost of insecticide resistance).

1. Introduction

The pattern and intensity of transmission of malaria parasites within a population depends critically on the fitness of their vectors. For this reason, the effect of Plasmodium on factors such as the fecundity and longevity of mosquitoes has traditionally received a lot of attention [1–3].

Mosquito survival is important because it affects Plasmodium transmission in two ways [4]. First, it allows the parasite to complete its extrinsic incubation period within the mosquito. This period (or sporogonic cycle) lasts 10–14 days, depending on the species and on environmental conditions [5,6]. Second, because it increases the potential for infective bites to hosts. Mosquitoes need to take a minimum of two blood meals in order to transmit the parasite, and blood meals are paced by lengthy gonotrophic cycles (the process of host seeking, blood feeding, egg production and oviposition), which last 2–4 days [6]. One would therefore not expect Plasmodium to decrease the survival of their vectors [1,3,7]. And yet, in a meta-analysis on Plasmodium-infected mosquito survival conducted in 2002, Ferguson & Read [3] showed that although half of the studies reported no effect of Plasmodium on survival, the other half reported shorter lifespans in infected than in uninfected mosquitoes. Although the reasons for these contradictory results are probably multifactorial, the negative effects of Plasmodium on survival were more likely to appear in non-natural mosquito–Plasmodium combinations, which lead Ferguson & Read [3] to conclude that Plasmodium may be harmful only in novel vector species. Unfortunately, there are few experimental (non-human) malaria models that combine mosquito and Plasmodium species with a common evolutionary history, so the question of the effect of Plasmodium on mosquito longevity is still largely unresolved.

As Plasmodium only transmits horizontally, mosquito fecundity is, in contrast, of no direct consequence for the parasite's fitness. Yet, a consistent observation from most mosquito–Plasmodium studies is that the parasite has a strong detrimental effect on mosquito fecundity. Indeed, several species of malaria have been shown to significantly reduce the fecundity (number of eggs) and fertility (number of hatched larvae) of different species of mosquitoes (reviewed in Hurd [1]). Hurd [1] has convincingly argued that this parasite-mediated life-history shift cannot be explained by differences in the quantity or quality of the blood meal taken by infected and uninfected mosquitoes. Rather, the ovaries of Plasmodium-infected females undergo a series of physiological changes (reduction in oocyte vitellogenin content, apoptosis of follicular epithelial cells) that culminate in the resorption of a considerable proportion of the developing oocytes (reviewed in Hurd [1,2]). Although no molecule of Plasmodium origin has been identified that would justify talking about parasite manipulation, it has been widely assumed that reproductive curtailment is an adaptive strategy of the parasite to increase mosquito survival through a trade-off in energy allocation between reproduction and survival [1,7,8]. To our knowledge, however, the existence of a physiological trade-off between fecundity and longevity in mosquitoes has never been formally demonstrated. Indeed, none of the longevity studies reviewed by Ferguson & Read [3] or published since [9–11] has measured the effects of Plasmodium on fecundity and longevity in the same individual.

Here, we revisit the survival and fecundity effects of Plasmodium on mosquitoes, taking advantage of the recent development of the avian malaria system [12], the only currently available non-human experimental model that uses a natural mosquito–Plasmodium combination. This new animal model associates the avian malaria parasite Plasmodium relictum (SGS1 lineage) and its natural vector, the mosquito Culex pipiens [13,14]. We carried out two different experiments. The first of these was performed in much the same way as previous longevity experiments: mosquitoes were given either an infected or an uninfected blood meal, and their mortality was recorded daily in cages until all mosquitoes died. In the second experiment, the protocol was repeated except that this time mosquitoes were followed individually to quantify first their fecundity (defined here as the number of eggs laid during the first gonotrophic cycle) and then their (post-reproductive) survival.

In both of these experiments, we explored whether genetically distinct mosquito lines respond differently to the same Plasmodium infection. There is, indeed, substantial evidence that host genetic factors play a major role in determining the outcome of Plasmodium infections [15–17]. In the field, a key source of mosquito genetic variation is associated with the evolution of insecticide resistance. Following intensive insecticide use, many mosquito populations have evolved several genetically distinct mechanisms of insecticide resistance. These mechanisms can be broadly classified into two types: metabolic resistance (the detoxification of the insecticide through the overproduction of specific enzymes) and target-site resistance (point mutations that insensitize the molecular targets of the insecticide [18]). Culex pipiens has a well-deserved reputation for being one of the mosquito species where the molecular and genetic bases of these two mechanisms of insecticide resistance are best understood [19–21]. In addition, the evolution of these two mechanisms of insecticide resistance in Cx. pipiens has been shown to entail a battery of correlated life-history changes in the insect, which have been widely interpreted as being the result of pleiotropic effects of the insecticide-resistant genes [22]. Yet the role of insecticide resistance in determining the outcome of mosquito-Plasmodium interactions has been largely unexplored ([22], but see [23]).

The specific aims of the present study are thus to determine: (i) whether Plasmodium alters the survival and/or fecundity of mosquitoes using an experimentally novel, albeit natural, mosquito–Plasmodium combination (ii) whether there is a negative correlation (trade-off) between fecundity and survival and (iii) whether the genetic variations that arise through the evolution of insecticide resistance modulate the effect of Plasmodium on these life-history traits.

2. Material and methods

Three isogenic Cx. pipiens mosquito strains were used in the experiments: one insecticide susceptible strain (SLAB), one insecticide-resistant strain through the overproduction of carboxylesterases (SA4B4) and one insecticide-resistant strain through the modification of the acetylcholinesterase (SR). These lines were obtained by repeated backcrossing of field-collected insecticide-resistant strains into a common (SLAB) susceptible background (see Berticat et al. [24] for details). Since their creation, these lines have been kept under identical rearing conditions. To avoid genetic drift, and owing to the occasional contamination of the lines, these lines are regularly backcrossed into the SLAB background. Larvae were reared as previously described [12]. Larval trays (n = 300 larvae per tray, n = 8 trays per strain in each experiment) were placed inside emergence cages (27 × 40 × 35 cm) with an ad libitum source of a 10 per cent sugar solution for the emerged adults.

Plasmodium relictum (lineage SGS1) is the aetiological agent of the most prevalent form of avian malaria in Europe [13]. This generalist Plasmodium parasite lineage was originally isolated from wild sparrows caught in the region of Dijon (France) in 2009 and subsequently passaged to naive canaries (Serinus canaria) by intraperitoneal injection. Bird experimental infections took place by intraperitoneal injection of ca 50–100 µl of blood from our infected bird stock. Mosquito blood feeding took place 10 days after the infection, to coincide with the acute phase of the bird's parasitaemia (J. Vézilier 2007, unpublished results).

(a) Experiment 1: survival

To explore the effect of Plasmodium on mosquito survival, we first adopted the most consensual protocol followed in longevity studies published to date (see electronic supplementary material, table S1): longevity was quantified in the absence of oviposition, and food was provided ad libitum. For this purpose, 70 female mosquitoes from each of the three strains (SLAB, SA4B4, SR) were haphazardly chosen from the different emergence cages and placed inside an experimental cage (n = 10 experimental cages). Half of these cages were then provided overnight with an infected canary and the other half with a non-infected (control) canary (see Vézilier et al. [12] for details). The following day, unfed and dead female mosquitoes were removed from the experimental cages. One of the infected cages had less than 50 per cent of blood fed mosquitoes and was therefore discarded from the study (see electronic supplementary material, table S2).

To obtain an estimate of blood feeding, infection success and mosquito size, on day 1 post blood meal (pbm), 15 mosquitoes were haphazardly sampled from each of the cages and placed individually in 30 ml plastic tubes covered with a mesh. Food was provided in the form of a cotton pad soaked in a 10 per cent glucose solution placed on top of each tube and replaced daily. Four days later (day 5 pbm), the mosquito was taken out of the tube, and the amount of haematin excreted at the bottom of each tube was quantified as an estimate of the blood meal size [12]. One wing was also removed from each female and measured under a binocular microscope along its longest axis as an index of body size [25,26]. In addition, mosquitoes that had been exposed to the infected canaries were dissected, and the number of oocysts in their midgut counted using a binocular microscope [12]. The rest of the mosquitoes (ca 195 mosquitoes per cage) were kept in the cages and provided with ad libitum food in the form of a 10 per cent sugar solution. Survival of these mosquitoes was assessed every ca 12 h by counting dead individuals lying at the bottom of each cage until all females died. Dead mosquitoes were kept at −20°C and subsequently allocated to one of the three insecticide resistance strains using a RFLP analysis as described in [27].

(b) Experiment 2: fecundity and survival

In the second experiment, where we aimed to quantify fecundity and longevity simultaneously, 70 female mosquitoes from each of the three strains (SLAB, SA4B4, SR) were haphazardly chosen from the different emergence cages and placed together to feed overnight inside an experimental cage (n = 5 infected cages, n = 5 control cages). To simplify the identification of the strains, however, 4 days before the blood meal, the mosquitoes were marked using a small amount (1 µg per female) of either pink, blue or yellow fluorescent powder (RadGlo JST) applied as a dust storm [28]. Preliminary trials have shown that at this concentration the dust has no effect on mosquito survival or oocyst count (Vézilier 2010, unpublished data), and is detectable only by using a binocular microscope. The three colours were used in rotation to mark the three strains so that the strain-colour code was switched from cage to cage.

On day 1 pbm, all engorged females were placed individually in numbered plastic tubes (30 ml) covered with a mesh (haematin tubes). Food was provided in the form of a cotton pad soaked in a 10 per cent glucose solution (as in experiment 1). Four days later (day 5 pbm), all mosquitoes were transferred to a new tube containing 4 ml of mineral water to allow the females to lay their eggs (oviposition tube). The oviposition tubes were provided daily with a cotton pad soaked in mineral water placed on top of each tube. In these conditions, 90 per cent of the females lay their eggs in a single day in the form of a single raft (Vézilier 2010, unpublished results). To obtain an estimate of the infection success, on day 7 pbm, 10 females from each of the infected cages were haphazardly sampled, taken out of the oviposition tubes, dissected and the number of oocysts in their midguts counted with the aid of a binocular microscope.

The rest of the oviposition tubes were checked daily for the presence of eggs. Once oviposition took place, the females were transferred to a new tube to measure their survival (longevity tube), and the egg rafts were photographed using a binocular microscope equipped with a numeric camera, after which they were put back in the insectary where they were checked daily until the emergence of the larvae. Eggs in the photographs were counted using the Mesurim Pro freeware (Academie d'Amiens, France). Larvae were killed by adding 5 ml of 100 per cent ethanol to the tube and counted using a binocular microscope. The longevity tubes were provided with a cotton pad soaked in water (as described earlier) and were monitored daily until the death of the female. On the day of death, the females were measured (wing length) and allocated to one of the three insecticide-resistant strains by examining their colour under a binocular microscope.

(c) Statistical analysis

Analyses were carried out using the r statistical package (v. 2.12.0). The different statistical models built to analyse the data are described in the electronic supplementary material, table S4. The general procedure for building the statistical models was as follows. Models were built by including mosquito strain (SLAB, SA4B4 and SR), parasite treatment (exposed to an infected or a control bird) and mosquito wing size (experiment 2 only) as fixed explanatory variables, and experimental cage as a random explanatory variable. Maximal models, including all higher-order interactions, were simplified by sequentially eliminating non-significant terms and interactions to establish a minimal model [29]. The significance of the explanatory variables was established using a likelihood ratio test (LRT), which is approximately distributed as a chi-square distribution [30] and using p = 0.05 as a cut-off p-value. The significant chi-square values given in the text are for the minimal model, whereas non-significant values correspond to those obtained before the deletion of the variable from the model. A posteriori contrasts were carried out by aggregating factor levels together and by testing the fit of the simplified model using an LRT [29].

Survival data were analysed using Cox proportional hazards mixed effect models (coxme, kinship package). Hazard ratios (HRs) were obtained from these models as an estimate of the ratio between the instantaneous risk of dying between two given factor levels. Two additional standard measurements of survival were obtained from Kaplan–Maier estimates of the survival distribution in each cage: the median survival (the time at which 50% of the population is still alive) and the proportion of mosquitoes that survived till day 14 (the average time at which Plasmodium completes its sporogonic cycle and the mosquito becomes infective [5]).

When the response variable was a proportion (e.g. hatching rate), the data were analysed using a linear mixed effect model with a binomial error distribution (lmer, lme4 package), otherwise mixed effect models with a normal error distribution were used (lme, nlme package). The differences in wing length between mosquito strains (experiment 1) were analysed using standard general linear models (glm, with the associated F-statistics addressing a given factor significance).

(d) Ethics statement

Animal experiments were carried out in strict accordance with the ‘National Charter on the Ethics of Animal Experimentation’ of the French Government, and all efforts were made to minimize suffering. Experiments were approved by the Ethical Committee for Animal Experimentation established by the authors’ institution (CNRS) under the auspices of the French Ministry of Education and Research (permit no. CEEA-LR-1051).

3. Results

(a) Experiment 1: survival

Midgut dissections revealed that most of the mosquitoes fed on an infected canary contained at least one oocyst (83% on average, see electronic supplementary material, table S2). This high-infection rate agrees with previous studies carried out in this system [12]. For this reason, and to simplify the reminder of the text, we refer to mosquitoes fed on an infected canary as being infected.

The Cox proportional hazards model revealed that P. relictum had no effect on mosquito survival ( p = 0.103, see electronic supplementary material, table S4 model 1). There was, however, a strong insecticide resistance effect (model 1, p < 0.001, figure 1a–d): the instantaneous risk of death was twice as high for esterase-resistant (SA4B4) than for susceptible (SLAB) mosquitoes (model 1, HR ± s.e. = 2.23 ± 0.07), and somewhat lower for acetylcholinesterase-resistant (SR) mosquitoes (model 1, HR ± s.e.: 0.82 ± 0.07). The analysis of the median survival and of the survival to day 14 gave identical results: a strong strain effect (median survival: model 2, p < 0.001 survival to day 14: model 3, p < 0.001) but no effect of Plasmodium infection (model 2, p = 0.096 and model 3, p = 0.656, respectively). This insecticide resistance effect on mosquito survival does not stem from differences in mosquito size, as the three mosquito stains used in the experiment had similar wing length (model 4, F114,2 = 2.38, p = 0.099).

Figure 1. Mosquito survivorship in experiment 1. Kaplan–Meier survival curve for the insecticide susceptible strain (a) SLAB and two insecticide-resistant strains: (b) SA4B4 and (c) SR after feeding on control uninfected (dashed line) or infected (full line) canaries. (d) Mean ± s.e. of the median survival of mosquitoes (i.e. time at which 50% of the females were still alive) for each strain for each treatment (empty circles: mosquitoes exposed to control uninfected canaries, grey circles: females that fed on infected birds).

(b) Experiment 2: fecundity and survival

On average, 87 per cent of the mosquitoes exposed to a control bird and 61 per cent of mosquitoes exposed to an infected bird took a blood meal (model 5, p = 0.010). Midgut dissections revealed that almost 90 per cent of the mosquitoes fed on an infected canary were infected (see electronic supplementary material, table S2).

(i) Fecundity and hatching success

As expected, haematin, its quadratic term haematin 2 and mosquito size were found to be strong predictors of the amount of eggs laid in both control (model 7, p < 0.001, p < 0.001 and p < 0.001, respectively) and infected mosquitoes (model 8, p < 0.001, and p < 0.001, respectively). The number of eggs laid by females (henceforth fecundity) was strongly dependent on whether the females were infected or not (model 6, p = 0.003 figure 2a). Egg rafts of infected females contained on average 55 ± 4 eggs less than rafts from their uninfected counterparts. Insecticide resistance, however, had no effect on fecundity (model 6, p = 0.261 figure 2a).

Figure 2. Plasmodium infection effect on (a) fecundity and (b) egg hatching rate of Culex pipiens females. (a) Box and whisker plots of the number of eggs laid by the insecticide susceptible strain SLAB and the two insecticide-resistant strains SA4B4 and SR after mosquitoes have fed on control uninfected (empty boxes) or Plasmodium-infected canaries (grey boxes). Bold horizontal black bars show the median number of eggs. Boxes above and below the median show the first and third quartiles respectively. Dashed lines delimit 1.5 times the inter-quartile range on both side of the box, above which individual counts are considered outliers and marked as empty circles. (b) Mean hatching rate (± s.e.) for uninfected (empty circles) or infected mosquitoes (grey circles) of the three strains. Only mosquitoes whose eggs were productive (i.e. from which at least one larva emerged) were included in the analysis.

On average, 92 per cent of the egg rafts laid by Cx. pipiens females produced at least one larva (see electronic supplementary material, table S3). The proportion of larvae hatched in each raft (hatching rate) was dependent on the interaction between the strain and infection status of the female (model 9, p < 0.001). Hatching rate was significantly lower for SA4B4 mosquitoes, but the effect of Plasmodium was apparent only in SR females (figure 2b).

(ii) Survival

Post-egg-laying survival was significantly higher for infected mosquitoes than for control mosquitoes (model 10, p = 0.012). This result was consistent across the three strains (model 10, strain × infection: p = 0.141 figure 3a–d). The Cox proportional hazards model revealed that the instantaneous risk of death of infected mosquitoes was 35 per cent lower than that of control ones (model 10, HR ± s.e.: 1.51 ± 0.14). In addition, infection significantly increased the median survival by 1.3 days (model 11, , p = 0.010). The proportion of mosquitoes that survived till day 14 also increased following Plasmodium infection, although only for SA4B4 mosquitoes (model 12, strain × infection interaction, , p = 0.015). As expected, mosquito wing size was strongly correlated to their survival (model 10, , p = 0.003). Adding the number of eggs laid by female mosquitoes as a covariate into the Cox proportional hazards model improved the model fit (model 13, , p < 0.001) and removed the significance of the main infection effect (model 13, , p = 0.620), which suggests that the effect of Plasmodium on mosquito survival is mediated through a reduction in female fecundity.

Figure 3. Mosquito survivorship in experiment 2. Kaplan–Meier survival curve for the insecticide susceptible strain (a) SLAB and two insecticide-resistant strains (b) SA4B4 and (c) SR after feeding on control uninfected (dashed line) or infected (full line) canaries. Mosquito survival was recorded daily from their entry in the oviposition tube, 5 days after the blood meal. (d) Mean ± s.e. of the median survival of mosquitoes (i.e. time at which 50% of the females were still alive) for each strain for each treatment (empty circles: mosquitoes exposed to control uninfected canaries, grey circles: females that fed on infected birds).

Mosquito strain was also found to have a strong effect on survival (model 10, , p = 0.001): the instantaneous risk of death of metabolic-resistant SA4B4 mosquitoes was found to be 30 per cent higher than for susceptible SLAB and target-site-resistant SR mosquitoes (model 10, HR ± s.e. = 0.77 ± 0.07), while the latter two strains a similar survivorship (model 10, , p = 0.689). Similar results were obtained when analysing the median survival (model 11, , p < 0.001), although in this case SLAB mosquitoes lived significantly shorter than SR mosquitoes (model 11, , p = 0.009 figure 3d).

(iii) Fecundity–survival trade-off

Mosquito survival and fecundity were strongly negatively correlated: the higher the number of eggs laid by the females, the lower their subsequent survival (model 14, , p < 0.001). This result was consistent across the three strains (figure 4a–c). The effect of infection, however, differed between the strains (model 15, strain × infection × fecundity: , p = 0.015). Analysing each mosquito strain separately unravelled that in SA4B4 females and, to a lesser extent, in SR females the slope of the fecundity–survival relationship was significantly steeper for infected females (infection × fecundity: model 17, SA4B4: , p = 0.012, SR: model 18, , p = 0.054 figure 4b,c). No such effect was apparent in SLAB mosquitoes (model 16, , p = 0.444 figure 4a).

Figure 4. Longevity–fecundity physiological trade-off in Culex pipiens mosquitoes. Raw plots of the number of eggs that the insecticide susceptible strain (a) SLAB and the two insecticide-resistant strains (b) SA4B4 and (c) SR laid against their survival after oviposition. Empty circles show uninfected mosquitoes plots, filled circles represent infected females. The linear regression of mosquito survival against the number of eggs each female laid was plotted using a dashed line for uninfected mosquitoes and a full line for infected females.

4. Discussion

(a) Longevity and fecundity

In this study, we revisited the question of the effect of Plasmodium on mosquito survival and fecundity using a natural vector–parasite combination (the mosquito Cx. pipiens and the avian malaria parasite P. relictum). In the first experiment, we compared the survival of infected and uninfected mosquitoes in much the same way as in most longevity studies published to date (see electronic supplementary material, table S1): female mosquitoes were not provided an oviposition substrate, food was provided ad libitum and mosquito longevity was recorded daily within their experimental cages. In these conditions, we found no effect of Plasmodium on mosquito survival. This result was consistent across the three mosquito strains and agrees with the studies reported by Ferguson & Read [3] where none of the natural mosquito–parasite associations showed any significant effect on survival. In the second experiment, however, both oviposition and subsequent (post-reproductive) survival were individually monitored. In these conditions, we found a drastic decrease of fecundity with infection, which was consistent across the three mosquito lines. Infected females laid, on average 30 per cent less eggs than uninfected ones, a reduction equivalent to that previously reported in other mosquito–Plasmodium combinations [8,31–33]. The widespread effect of Plasmodium on mosquito fecundity has been interpreted in terms of energy reallocation between reproduction and survival [1,3,7,8]. The results from our second experiment show, for the first time, that a reduction in fecundity in infected mosquitoes is indeed associated to an increase in survival. Infected mosquitoes had significantly longer lifespans than their uninfected counterparts, an effect that was consistent across the different mosquito lines and lifespan measurements used. Two lines of statistical evidence suggest that there is a causal relationship between the decrease in fecundity and the increase in survival observed. First, the number of eggs laid by each individual female was negatively correlated to their subsequent survival (figure 4a–c). Second, adding the number of eggs laid as a covariate in our Cox proportional hazard model removed the significance of infection on survival, implying that parasite-induced increase in survival was mediated through an alteration of mosquito reproductive output.

Why was this increase in longevity not found in our first experiment or in any of the survival experiments carried to date using natural mosquito–Plasmodium combinations? One possibility is that the discrepancy is due to minor, but potentially important differences in the experimental protocols. In our first experiment, longevity was measured in cages, whereas in the second experiment, mosquitoes were isolated in tubes. In the first experiment, mosquitoes were provided sugar ad libitum for the duration of the experiment, whereas in the second experiment, for practical reasons, sugar was provided only until the fourth day pbm. We can think of no reason why sugar restriction could explain why infected mosquitoes lived longer, unless by restricting sugar we forced the trade-off between fecundity and longevity. Previous studies show that, if anything, sugar restrictions tend to reduce the effect of Plasmodium on longevity [9]. We believe, however, that there is an alternative and potentially more likely explanation. Something that our first experiment and all previous longevity experiments have in common is that mosquitoes were not allowed to oviposit (or were allowed to oviposit for a very short, and possibly insufficient, amount of time, electronic supplementary material, table S1). Oviposition deprivation can have two potential effects on longevity. On the one hand, there is ample evidence that egg resorption kicks in when insects are forced to retain their eggs [34,35], which seems to be an adaptive strategy to redirect resources against other physiological processes, including longevity [36]. It is thus possible that in oviposition-deprived females the surplus nutrients resulting from egg resorption are redirected towards maintenance, thereby obscuring any eventual differences in longevity between infected and uninfected mosquitoes (note that as uninfected females have a higher fecundity, the net gain in surplus nutrients obtained from resorption would be also higher, thereby compensating for their lower longevity). On the other hand, oviposition-deprived females do not incur the costs of egg laying. Oviposition has indeed been reported to have a negative effect on insect fitness [37,38], suggesting that egg laying per se is indeed costly.

We do not know what is the actual mechanism of Plasmodium-associated fecundity reduction in our system. As both infected and uninfected mosquitoes took a blood meal, the reduction in fecundity must be directly associated to the presence of the parasite in the blood of the birds. There are different ways in which this could have happened. Our haematin quantifications suggested that infected females may have taken a smaller blood meal than uninfected ones. However, haematin (a product of the degradation of haemoglobin) does not quantify blood meal volume but the haemoglobin (red blood cells) ingested. As infected birds are strongly anaemic [39], an equal volume of blood automatically renders lower haematin values in infected birds. Although haemoglobin represents approximately 80 per cent of the proteins in the blood, haematin may not provide an accurate estimate of the total contribution of the blood meal to egg production. We cannot, however, totally eliminate the possibility that our mosquitoes took a smaller blood meal when feeding from an infected bird, although to our knowledge this effect has not been reported in studies that quantify mosquito blood meal size using gravimetric methods [40]. In addition, Hurd et al. [1] have convincingly argued that the fecundity reductions they find in their system are not associated to differences in haematin. Differences in blood quantity aside, a second possibility is that P. relictum induces changes in the nutritional value of the bird's blood. Anaemia, for one, inevitably reduces the amount of protein available for egg production. In addition, there is abundant evidence that Plasmodium decreases the nutrient composition of blood [41], either because these nutrients are scavenged by the parasite or as a host's response to the infection. Host blood quality has been found to be crucial for mosquito fecundity, although most of the evidence available comes from studies comparing mosquitoes fed on different host species [42]. The final, and most intriguing, possibility is that fecundity reduction is directly or indirectly associated with the presence of the parasite within the mosquito (most of the mosquitoes fed on an infected bird were infected, and had relatively high parasitaemias). Hurd et al. [1] have shown that the presence of Plasmodium within mosquitoes reduces fecundity through a combination of an impaired intake of yolk protein by the ovaries coupled with an increase in egg resorption mediated by follicular cells apoptosis. The proximate mechanism triggering these changes remains to be established, but both the cost of the immune system activation [43,44] and the consumption of resources by the developing oocysts have been pointed out as likely explanations [45].

Speculations as to whether this decrease in fecundity and correlated increase in longevity is adaptive for the parasite, for the mosquito or whether it is a simple pathological by-product of the infection are beyond the scope of this study. Indeed, the parasite could manipulate the host physiology to its own advantage and it is not difficult to argue why it would be adaptive for a parasite with no vertical transmission to reduce fecundity in order to increase longevity (and thus, as we have argued earlier, transmission). Another possibility that has been invoked [2] is that the fecundity reduction could be an adaptive strategy of sick mosquitoes, aimed at curtailing current reproduction in order to favour the chances of reproducing when conditions get better. We would argue, however, that such a current versus future reproductive trade-off is unlikely to take place in mosquitoes. It is indeed difficult to imagine how, in a mosquito world where every oviposition event needs to be preceded by a highly risky blood feeding event (arguably, the most likely source of mortality of mosquitoes in the wild [7]), it could be advantageous for a mosquito to dispose of ready-to-lay eggs in this way.

For technical reasons (inability to follow mosquitoes individually over several different blood feeding events), our experiment ran through a single gonotrophic cycle (a blood feeding event followed by an oviposition). In Cx. pipiens, the proportion of multiparous females is estimated to be less than 20 per cent [46], but multi-feeding, multiparous females are the most interesting individuals epidemiologically speaking. Extensive work of Hurd and co-workers on this subject in Anopheles mosquitoes seems to suggest that the Plasmodium-induced reproductive curtailment may persist for several gonotrophic cycle [47–49], but the concomitant effects on longevity have, to our knowledge, never been investigated.

(b) Insecticide resistance

In both experiments, esterase-resistant (SA4B4) mosquitoes had significantly shorter lifespans than either susceptible (SLAB) or target-site-resistant (SR) mosquitoes. Lifespan reductions in insecticide-resistant Culex mosquitoes have been reported before, albeit under extreme experimental conditions (non-sugar fed, non-blood fed), where mosquitoes survived a maximum of ca 3 days [25], way below the intrinsic incubation time of malarial parasites. Interestingly, however, our results agree with field estimates of mosquito overwintering survival in the field, where esterase-resistant Cx. pipiens mosquitoes have been shown to fare considerably worse than acetylcholinesterase or susceptible mosquitoes [50]. Our results also show that esterase-overproducing (SA4B4) and, to a lesser extent, acetylcholinesterase (SR) mosquitoes suffered a higher cost of infection than their susceptible (SLAB) counterparts. Indeed, while in the latter strain, the longevity-fecundity trade-off is independent of infection, in SA4B4 and SR strains, each additional egg laid costs more in terms of survival units when the mosquito has a Plasmodium infection. The isogenic strains, we used in this experiment, have a single (SLAB) genetic background. Further work needs to be carried out, ideally using field-collected sympatric insecticide-resistant and susceptible mosquitoes, to establish whether our results are generalizable to other genetic backgrounds.

(c) Conclusion

In conclusion, we provide the first reported account of an increase in longevity associated to a decrease in fecundity in Plasmodium-infected mosquitoes. Whether differences in the experimental protocol can explain the differences obtained between the results of our second experiment and previous results is beyond the scope of this paper and would need further experiments. However, we contend that mosquito longevity and fecundity should, whenever possible, be quantified concomitantly for two reasons: first, because oviposition deprivation is a situation unlikely to be encountered by mosquitoes in the field, whose main reason to blood feed is, after all, to obtain sufficient proteins to mature, and lay, a batch of eggs. Second, because as life-history theory predicts (and our own results show), fecundity and longevity are inextricably linked. Irrespective of what caused the discrepancies between this and previous experiments, the substantial increase in longevity found in Plasmodium-infected mosquitoes deserves further attention for its important implications in disease transmission.


When fecundity does not equal fitness: evidence of an offspring quantity versus quality trade-off in pre-industrial humans

Maternal fitness should be maximized by the optimal division of reproductive investment between offspring number and offspring quality. While evidence for this is abundant in many taxa, there have been fewer tests in mammals, and in particular, humans. We used a dataset of humans spanning three generations from pre-industrial Finland to test how increases in maternal fecundity affect offspring quality and maternal fitness in contrasting socio-economic conditions. For ‘resource-poor’ landless families, but not ‘resource-rich’ landowning families, maternal fitness returns diminished with increased maternal fecundity. This was because the average offspring contribution to maternal fitness declined with increased maternal fecundity for landless but not landowning families. This decline was due to reduced offspring recruitment with increased maternal fecundity. However, in landowning families, recruited offspring fecundity increased with increased maternal fecundity. This suggests that despite decreased offspring recruitment, maternal fitness is not reduced in favourable socio-economic conditions due to an increase in subsequent offspring fecundity. These results provide evidence consistent with an offspring quantity–quality trade-off in the lifetime reproduction of humans from poor socio-economic conditions. The results also highlight the importance of measuring offspring quality across their whole lifespan to estimate reliably the fitness consequences of increased maternal fecundity.

1. Introduction

The life-history trade-off between the number of offspring produced and their quality is a fundamental idea in evolutionary biology (Lack 1947 Smith & Fretwell 1974 Stearns 1992). It assumes that resources are limited and is based on three principles: (i) as investment in offspring number is increased, investment per offspring is decreased, (ii) increased investment in each offspring enhances offspring reproductive success, and (iii) maternal fitness (i.e. a mother's contribution to population growth) is determined by the number of offspring recruited into the breeding population (i.e. those that breed) and their subsequent lifetime reproductive success (LRS Roff 2002). The original formulation of the offspring quantity versus quality hypothesis by Lack (1947) used these principles to suggest that maternal fecundity in natural populations represents an evolved balance between offspring number and quality, which maximizes maternal fitness. This led to the general expectation that maternal genotypes reflect a process of natural selection that has favoured an optimal division of resources between offspring quantity and quality (Falconer 1989 Stearns 1992). However, few studies have investigated the effects of increased maternal fecundity on overall offspring quality (i.e. both offspring recruitment and their subsequent reproductive success) in wild populations.

Some of the best evidence for a trade-off between offspring quantity and quality in the wild has come from experimental studies of bird populations such as collared flycatchers (Ficedula albicollis: Gustafsson & Sutherland 1988) and kestrels (Falco tinnunculus: Dijkstra et al. 1990). These studies showed that offspring from enlarged clutches have a lower recruitment probability and lower clutch sizes in their first year of breeding than offspring from natural clutch sizes. A similar study of the bank vole (Clethrionomys glareolus) showed that offspring from enlarged litters can have a lower weaning mass and recruitment probability, but appear not to have reduced litter sizes in their first year of breeding (Koskela 1998). However, few studies in the wild have been able to follow offspring throughout their lives and to record their LRS, or have been able to link variation in LRS with measures of resource availability to breeding females. The need for such information was highlighted by a laboratory study of the parasitoid wasp, Goniozus nephantidis (Hardy et al. 1992). This showed that enlarged clutches had little effect on offspring survival, but did reduce offspring size and subsequent fecundity due to increased resource competition within the clutch. Thus, without reliable information on the traits influencing offspring LRS, there is a risk of misrepresenting the costs to offspring and the fitness benefits to mothers of increased brood size. That the most common clutch size in wild bird populations is often below the calculated optimum for maximal maternal fitness suggests that this problem could be real (e.g. Dijkstra et al. 1990 but see Godfray et al. (1991) for a discussion of potential deviations from the optimal clutch size).

In humans, a trade-off is expected between maternal lifetime fecundity and offspring quality due to the need to divide limited resources between several simultaneously dependent offspring (Kaplan 1996 Hill & Kaplan 1999 Gurven & Walker 2006). Human studies offer a way around the problem of tracking individuals throughout life because the LRS of all offspring may be extracted from census records (e.g. Lahdenperä et al. 2004). Importantly, there is also evidence suggesting that humans are sensitive to a trade-off between offspring quantity and quality. For example, the production of twins has been shown to increase maternal fitness only where resources are abundant and relatively constant (Lummaa et al. 1998). In addition, intermediate inter-birth intervals and offspring numbers have been shown to correspond with maximal offspring survivorship (Blurton Jones 1997 Strassmann & Gillespie 2002 Mace 2007). Finally, increased birth rates can also increase malnutrition among offspring (Gibson & Mace 2006).

However, some studies of contemporary hunter-gatherer populations have not shown the expected trade-off, but instead observed that offspring quality increases rather than decreases with increasing maternal fecundity or birth rate (Pennington & Harpending 1988 Hill & Hurtado 1996). These observations might be due to the particular measures of offspring used in the studies or social or ecological situations within these communities. For example, other studies have indicated that the maternal fitness derived from different levels of maternal fecundity may be dependent on individual wealth or social status (Borgerhoff Mulder 2000 Hagen et al. 2006). Thus, individuals with easy access to abundant resources might experience a relaxation of the reproductive constraints imposed by nutrition and workload, and this could lead to observed positive correlations between traits that are expected to trade-off (van Noordwijk & de Jong 1986 Mace 1996). These conflicting results have led to unresolved questions regarding the existence of an overall quantity versus quality trade-off in human lifetime reproduction (Hill & Hurtado 1996 Blurton Jones 1997 Hill & Kaplan 1999), and a major reason for this is the difficulty of linking maternal fecundity directly with subsequent offspring fecundity and maternal fitness.

The aim of our study was to investigate the presence of a trade-off between the number of offspring (i.e. maternal fecundity) versus offspring recruitment and subsequent offspring fecundity under contrasting socio-economic conditions. We used a three-generation, individual-based dataset of pre-industrial Finns from the eighteenth and nineteenth centuries. These demographic data have at least three benefits for addressing our aim. First, we have reliable information about the total number of offspring and grand-offspring of each woman in our dataset. This is because it was collated from church registers maintained by local clergymen who were obliged by law to submit to the state accurate records of the survival and reproductive history of all individuals in their parish area (Luther 1993). Migration rates were low and, in most cases, the parish migration registers allow the LRS of dispersers to be determined. Second, the study period coincides with periods of natural fertility and mortality and ends before healthcare and more liberal economics improved standards of living in Finland (Soininen 1974). Third, the data include the socio-economic status of each woman, assigned according to her husband's occupation. These socio-economic data can be taken to represent differences in resource availability in terms of nutrition, wealth and workload between individuals, which have already been shown to influence long-term fitness in our study population (Pettay et al. 2007) as well as other pre-industrial populations (e.g. Voland 1990).

We first investigate how both maternal fecundity and the proportion of offspring recruited (i.e. survived to breed) combine to predict total grand-offspring number in contrasting socio-economic conditions. We use total grand-offspring number as our measure of maternal fitness. This enables us to link offspring number to maternal fitness through the effects of offspring recruitment (survival to breed) and the subsequent fecundity of recruited offspring. Second, for each socio-economic condition, we focus on how maternal fecundity specifically predicts three measures of offspring quality. Our three measures of offspring quality are as follows. (i) The average number of grand-offspring that each birth contributed to maternal fitness, as an overall measure of offspring quality. This combines both offspring recruitment and subsequent offspring fecundity, with higher recruitment and subsequent fecundity indicating higher quality. (ii) The proportion of offspring recruited. (iii) Subsequent offspring fecundity. In keeping with Lack's hypothesis, we predict that in poor socio-economic conditions, maternal fecundity beyond the population mean will lead to diminishing returns in maternal fitness due to reductions in offspring quality. Conversely, in favourable socio-economic conditions, maternal fecundity beyond the population mean should continue to give proportional returns in maternal fitness due to a relaxation of constraints on offspring quality.

2. Material and methods

Our data contain three generations of full reproductive history and survival details collected by genealogists for women sampled from four geographically isolated parishes in Finland: Ikaalinen (61°45′ N, 23° E) Hiittinen (60° N, 22°30′ E) Kustavi (60°30′ N, 21°30′ E) and Rymättyla (60°15′ N, 22° E). From these parish church registers, we obtained a sample of 446 women who gave birth to at least one offspring in their lifetime. Of these, only those whose complete lifetime fecundity could be reliably determined were included in the analysis (resulting sample sizes are shown in table 1). All presented results are from this full dataset of 437 women, who were born between 1709 and 1815. These women had a total of 2888 offspring born from 1741 to 1858, and 6470 grand-offspring born from 1766 to 1901. Our study period thus ended before healthcare and more liberal economics began to improve standards of living in Finland (Soininen 1974). In addition, in most parts of the country during our study period (1709–1901), frosty nights during early summer and rain at harvest time in the autumn often led to unpredictable crop failures and subsequent famines, and even during average harvest years, 5–10% of people consumed emergency foods (Jutikkala et al. 1980). During famines, the most common causes of death were infectious diseases such as tuberculosis, cholera, scarlet fever and smallpox, which readily spread among the malnourished people (Turpeinen 1978). Indeed, infection is likely to have been the primary cause of infant mortality, particularly among poorer families (Moring 1998).

Table 1 Women from landowning families have more offspring, recruited offspring (survived to breed) and grand-offspring compared with women from landless families. (There were 248 landless women and 189 women from landowning families. The sample size (n) of the numbers of offspring, recruited offspring and grand-offspring is shown for each socio-economic category. We applied Box–Cox power transformations to each of the three response variables and investigated the influence of socio-economic status using linear models.)

Socio-economic status was initially assigned with advice from historians, on the basis of occupations that had similar access to wealth and food (see Pettay et al. 2007). As women rarely had a profession, their socio-economic status was the same as their husband's at the time the offspring were born. We simplified the socio-economic status classification into two groups, hereafter referred to as landowners and landless, according to those owning land versus those either renting or having no access to land at all. Although there is likely to be high levels of variation within these groups, this allowed us to observe the major effects of differences in socio-economic status. Importantly, landownership in pre-industrial populations has been shown to have effects on both fecundity (Easterlin 1976) and other life-history traits such as offspring survival and marriage probability (Voland & Dunbar 1995). During the study era, inheritance usually favoured the eldest son. The mating system was monogamous to an unusually high degree, with almost all reproductive individuals married and divorce forbidden (Moring 1993).

We carried out all statistical analysis in the R environment (v. 2.4.1 R Development Core Team 2006). All p-values are two-tailed. It is worth noting that a 95% CI which includes zero is equivalent to a p-value greater than 0.05. One potential problem is that our results might be modified by women who died before they reached menopause, if such women produced few, low-quality children owing to dying young. We therefore repeated each analysis outlined below without women who died before age 50, which was the latest age of reproduction in the dataset (reduced dataset size of 363 women). Similarly, women whose husbands died before those women reached menopause might also modify the results. This is because husband's death could reduce both offspring number and offspring quality due to decreased family income. Therefore, we also repeated each analysis using only women who both survived to age 50 and whose first husbands were alive when they reached this age (reduced dataset size of 240 women). However, we found that the results using these reduced datasets were qualitatively similar to those from the full dataset of 437 women.

Preliminary to the main analyses outlined in §2a,b, we initially described the differences in the numbers of offspring, recruited offspring and grand-offspring between the landowning and landless socio-economic groups. To do this, we used three separate linear models with Box–Cox power-transformed response variables (table 1). The Box–Cox powers for offspring (0.68), recruited offspring (0.59) and grand-offspring (0.43) were identified using the function boxcox in the R package MASS. These descriptive analyses were used to highlight the general effects of socio-economic differences before contrasting the relationship between maternal fecundity and offspring quality or maternal fitness between the socio-economic groups.

(a) Maternal fecundity, offspring recruitment and grand-offspring number

We first investigated the influence of socio-economic conditions on the way maternal fecundity and the proportion of offspring recruited combine to predict total grand-offspring number. This was done using a generalized linear mixed model (GLMM) with a Poisson error structure and a log link function that was fitted using the function lmer in the R package lme4 (Bates & Sarkar 2007). To the model, we added random intercepts for parish (four levels) and maternal birth cohort (10-year periods) to correct for geographical and temporal variation in fecundity and recruitment. However, the addition of maternal birth cohort to the model explained only a very small amount of additional variation. The model was fitted to the full dataset of 437 women using a Laplace approximation to maximum likelihood. Despite the quality of the church registers, it was occasionally not possible to determine reliably the total number of grand-offspring because some offspring (9%) were not followed for their entire life history. We accounted for this by including a vector of weights in the model, according to the proportion of a mother's offspring with complete follow-up.

Initial data inspection indicated that both maternal fecundity and the proportion of offspring recruited had curvilinear (quadratic) relationships with grand-offspring number (figure 1). Therefore, we also added quadratic terms for each of these explanatory variables to the model. To visualize the three-dimensional surface created by the quadratic relationships of these two explanatory variables with grand-offspring number, we used a second-order response surface model. This is a standard method for describing three-dimensional surfaces where there might be maxima, minima or ridges in the response variable (in this case, grand-offspring number subject to a log link function). The equation for this model has the form y=x1+x1 2 +x2+x2 2 +x1x2 (Draper & John 1988). To investigate the influence of socio-economic status, we compared a model, where x1 is the maternal fecundity and x2 the proportion of offspring recruited, with an expanded model that incorporated socio-economic status as a two-level factor (landowners versus landless) and its interaction with each term in the smaller model (Draper & John 1988). Comparison between these two models was made based on standard likelihood-ratio tests as well as the difference in Akaike's information criterion (AIC table 2).

Figure 1 Grand-offspring number shows a significantly curvilinear (quadratic) relationship with both (a) maternal fecundity and (b) the proportion of offspring recruited (survived to breed). Solid lines represent non-parametric smoothing functions (on a standardized y-axis scale) of grand-offspring number, with dashed lines showing the standard errors. Vertical dotted lines show the population means of maternal fecundity (6.61) and the proportion of offspring recruited (0.47). Rug plots on the x-axis give an indication of the distribution of raw data points.

Table 2 Socio-economic status significantly changed the way maternal fecundity and the proportion of offspring recruited combine to predict grand-offspring number. (This was shown by the reduction in model AIC when socio-economic status was added as a two-level factor, and this was further confirmed with a likelihood-ratio test. The likelihood-ratio tests presented compare each model with that in the row above it. The model is a GLMM with a Poisson error structure and a log link function that had random intercepts for each parish and 10-year maternal birth cohort. d.f. 1 are the total degrees of freedom taken up by the terms in the model. d.f. 2 are the degrees of freedom for the Χ 2 statistic in the likelihood-ratio test. The minimal adequate model (MAM) was obtained by the removal of the three-way and 2 two-way interaction terms. * indicates the terms deleted to reach the MAM. Eligible terms were removed one at a time and their contribution to model explanatory power assessed using likelihood-ratio tests and by comparing the AIC of models with and without each term. The term that caused the biggest reduction in model AIC was deleted and the process repeated until the MAM was reached.)

To allow the assessment of uncertainty in the estimates of individual parameters in the model that included socio-economic status, we reduced this model to its minimal adequate model (MAM). This was done by single-term deletion, starting with the highest-order interactions. We discarded terms if their presence did not improve the explanatory power of the model. This was tested using both model AIC and standard likelihood-ratio tests (table 2). The use of whole-model deviance in this way is equivalent to assessing the deviance explained by individual terms in the model, e.g. using F-ratios in a traditional ANOVA. However, F-ratios are currently unobtainable for the type of model used here. The predicted values from the resulting MAM were then interpolated and used to generate contour plots showing the relationships of maternal fecundity and offspring recruitment with grand-offspring number in each socio-economic group.

Uncertainty in the MAM parameter estimates was assessed primarily using 95% confidence limits based on the underlying Poisson distribution of the model residuals. To do this, we first generated a Markov Chain Monte Carlo (MCMC) sample from the posterior distribution of each parameter estimate. This used the function mcmcsamp in the R package lme4 (Bates & Sarkar 2007). Second, we computed the Bayesian highest posterior density (HPD) 95% CIs of the MCMC sample for each parameter estimate using the function HPDinterval in the R package coda (Plummer et al. 2006). Although this method does not generate p-values, it is currently the most reliable way to assess the uncertainty in the parameter estimates for this type of GLMM (Baayen et al. submitted). However, despite the potential bias, for ease of interpretation, we have also provided p-values based on the normal (z) distribution.

(b) Offspring quantity versus quality

To investigate the relationship between maternal fecundity and offspring quality (rather than overall maternal fitness) in each socio-economic group, we used three different measures of offspring quality. (i) As a measure of overall offspring quality, we used the average number of grand-offspring that each birth contributed to maternal fitness (i.e. grand-offspring per birth). This overall measure of offspring quality combined both the proportion of offspring recruited and their subsequent lifetime fecundity. Then, in order to understand the individual contributions of these two variables to offspring quality in each socio-economic group, we analysed the data separately for (ii) the proportion of offspring recruited and (iii) the subsequent fecundity of recruited offspring.

As offspring quality is ultimately a measure of the average contribution of each offspring to population growth, we needed to select an appropriate method to quantify offspring quality for each offspring number in each socio-economic group. One potential problem was that the high degree of temporal and spatial variation in our dataset (women were born over 106 years, 1709–1815, across Finland) leads to a large amount of variation in the average offspring quality of mothers with the same fecundity. This might bias any conclusions because increases in variation can decrease the long-term fitness associated with each maternal fecundity (Roff 2002). In order to account for any effect of this nature, we compared the geometric means of average offspring quality between women with each offspring number in each socio-economic group. The application of geometric means where there is random temporal or spatial variation can have a ‘variance discounting’ effect, which has been shown to increase the reliability of fitness estimates (Frank & Slatkin 1990). Geometric means have previously been used in theoretical investigations of offspring quantity versus quality because they can also assess the fitness outcomes of possible bet-hedging strategies where environmental conditions are variable (see Simons 2007). Finally, we compared the regression slopes of geometric mean offspring quality against maternal fecundity for the full dataset of 437 women between socio-economic groups, using an ANCOVA design in three separate linear models. This allowed the comparison of the effects of increased maternal fecundity on offspring quality in different socio-economic conditions. The response variables in these linear models met the assumptions of normality.

3. Results

The initial descriptive analyses of the differences between socio-economic groups showed that women from landowning families had higher numbers of offspring (i.e. maternal fecundity), more recruited offspring and more grand-offspring (i.e. maternal fitness) than women from landless families (table 1).

(a) Maternal fecundity, offspring recruitment and grand-offspring number

Socio-economic status had a significant influence on the way maternal fecundity and the proportion of offspring recruited combined to predict grand-offspring number (table 2). Improved socio-economic conditions increased the number of grand-offspring gained from increased maternal fecundity (table 3). As the model in table 3 accounts for the effects of offspring recruitment, this suggests that the subsequent fecundity of offspring in landowning families remained higher than in landless families as maternal fecundity increased. This can be seen in figure 2a,b by following the horizontal dashed line, which indicates the population mean of the proportion of offspring recruited. For landowning but not landless families, the number of grand-offspring continues to increase with increasing maternal fecundity. Progressive exclusion of women with the highest fecundity confirmed that the relationship between maternal fecundity and grand-offspring number was not significantly curvilinear below seven births for landless families. Notably, this is remarkably close to the population mean maternal fecundity (6.61, s.e.±0.13). This suggests that for landless families, grand-offspring returns begin to diminish at maternal fecundities beyond the population mean. These results were qualitatively the same when we excluded from the analysis women who died before age 50, or those whose husbands died before the women reached age 50.

Table 3 Parameter estimates from the MAM of the effects of maternal fecundity and the proportion of offspring recruited on grand-offspring number in landowning and landless families. (The relative differences in the parameter estimates for the landowning compared with the landless socio-economic group are given in the row below the parameter estimates for the landless socio-economic group. The model is a GLMM with a Poisson error structure and a log link function that had random intercepts for each parish and 10-year maternal birth cohort. The Poisson parameter estimates were obtained using a Laplace approximation to maximum likelihood. 95% CIs are the Bayesian HPD 95% CIs of the MCMC sample for each parameter. The p-values based on the normal (z) distribution show the same pattern as the 95% CIs, which are based on the underlying Poisson distribution of the model residuals.)

Figure 2 Increased maternal fecundity leads to lower grand-offspring returns for (a) women from landless families, compared with (b) women from landowning families. In addition, increases in the proportion of offspring recruited lead to lower grand-offspring returns in both socio-economic groups. Shown are the predicted values from the minimal adequate Poisson GLMM, with a log link function and random intercepts for each parish and 10-year maternal birth cohort. Contour lines represent different numbers of grand-offspring, with the number of grand-offspring shown on the contour line. The grey shaded area gives an indication of the range of raw data points. The population mean maternal fecundity (6.61) is shown by the vertical dashed lines and the proportion of offspring recruited (0.47) by the horizontal dashed lines.

After accounting for the effects of maternal fecundity, grand-offspring number showed a significantly curvilinear relationship with the proportion of offspring recruited, which did not differ significantly between socio-economic groups (table 2). The presence of the same curvilinear relationship in both socio-economic groups suggests that increased offspring recruitment leads to increased competition among recruited offspring. This might lead to the observed diminished grand-offspring returns due to the association between socio-economic conditions and fecundity (see §4). This is supported by the finding that the relationship between offspring recruitment and grand-offspring number (figure 1b) does not appear significantly curvilinear below the population mean of the proportion of offspring recruited (0.47, s.e.±0.01). In addition, it is worth noting that the linear relationships of maternal fecundity and offspring recruitment with grand-offspring number were significantly steeper for women from landless families (table 3). This is likely to be because the highest numbers of grand-offspring in the landless socio-economic group are associated with higher levels of offspring recruitment at lower maternal fecundities than in the landowning group (figure 2).

(b) Offspring quantity versus quality

To investigate the relationship between maternal fecundity and offspring quality in each socio-economic group, we used three different measures of offspring quality (figure 3 table 4). (i) As a measure of overall offspring quality, we used the average number of grand-offspring that each birth contributed to maternal fitness (i.e. grand-offspring per birth). Then, we analysed the data separately for (ii) the proportion of offspring recruited and (iii) the subsequent fecundity of recruited offspring.

Figure 3 The linear model showed that (a) the number of grand-offspring per birth decreased with increasing maternal fecundity for women from landless families (slope estimate −0.12±0.044). (b) However, grand-offspring per birth remained constant with increasing maternal fecundity for women from landowning families (slope estimate 0.0043±0.040). Underlying these relationships were the following measures. (c,d) A decrease in the proportion of offspring recruited with increasing maternal fecundity in both socio-economic groups (slope estimate −0.028±0.0048). (e) Subsequent offspring fecundity remained constant with increasing maternal fecundity for women from landless families (slope estimate 0.024±0.044). (f) However, there was an increase in subsequent offspring fecundity with increased maternal fecundity for women from landowning families (slope estimate 0.31±0.040). The raw data points appear in the background. Vertical dashed lines show the population mean maternal fecundity (6.61). Horizontal dashed lines show the population geometric means for (a,b) grand-offspring number per birth (1.93), (c,d) the proportion of offspring recruited (0.45) and (e,f) average subsequent offspring fecundity (4.29). Parameter estimates are derived from analyses presented in table 4.

Table 4 The linear model parameter estimates for the effects of maternal fecundity on the geometric means of three measures of offspring quality: (i) grand-offspring per birth (overall offspring quality), (ii) the proportion of offspring recruited, and (iii) average subsequent fecundity of recruited offspring.

First, women from landowning families with high maternal fecundity were able to maintain high overall offspring quality. However, this was not the case for women from landless families, which showed declines in overall offspring quality with increasing maternal fecundity (figure 3a,b table 4). Notably, the regression line of overall offspring quality in landless families dropped below the population mean overall offspring quality at the population mean maternal fecundity. This indicates that for landless families, above-average maternal fecundity does indeed lead to below-average overall offspring quality. These results were qualitatively the same when we repeated the analysis without women who died, or whose husbands died before the women reached age 50.

Second, increased maternal fecundity caused the proportion of offspring recruited to decrease and this was not influenced by socio-economic grouping (figure 3c,d table 4). However, as with previous studies, there is some ambiguity of cause and effect between maternal fecundity and early offspring survival (see §4), which is a major component of offspring recruitment. Even so, it is worth noting that the regression line of the proportion of offspring recruited dropped below the population mean of the proportion of offspring recruited at the population mean maternal fecundity. This suggests that producing more than the mean number of offspring leads to below-average recruitment of those offspring in both socio-economic groups.

Third, the subsequent offspring fecundity of women from landless families remained constant with increasing maternal fecundity. In contrast, the subsequent offspring fecundity of women from landowning families increased with increasing maternal fecundity (figure 3e,f table 4). Thus, for women from landowning families, high maternal fecundity also led to subsequently high offspring fecundity, despite the correspondingly low offspring recruitment described above. The high subsequent offspring fecundity in landowning families appeared to ‘compensate’ for the potential decrease in overall offspring quality caused by decreased offspring recruitment. However, women from landless families appeared to exhibit a trade-off between the production of sufficient numbers of offspring and the achievement of sufficiently high recruitment of those offspring to maximize grand-offspring number.

4. Discussion

We took advantage of a multigenerational dataset of humans from pre-industrial Finland to test how maternal fecundity affects offspring quality and maternal fitness in landowning versus landless families. For women from landless families, subsequent offspring fecundity was constrained by increasing maternal fecundity, leading to lower grand-offspring returns compared with women from landowning families. We then specifically investigated two main aspects of offspring quality: the proportion of offspring recruited and the subsequent fecundity of recruited offspring. The proportion of offspring recruited decreased with increasing maternal fecundity and this was unaffected by socio-economic status. However, subsequent offspring fecundity increased with increasing maternal fecundity in landowning families, but remained constant with increasing maternal fecundity in landless families. Thus, maternal fitness showed diminishing returns with increasing maternal fecundity for women from landless but not from landowning families. These results are therefore consistent with Lack's (1947) hypothesis of a trade-off between offspring quantity and quality, and also with the expectation that such trade-offs should only be observed when resources are limited (van Noordwijk & de Jong 1986). In particular, the results suggest that the failure to observe an offspring quantity versus quality trade-off in some contemporary hunter-gatherer populations (e.g. Hill & Hurtado 1996) could be because the expected trade-off is masked by variation in socio-economic conditions not accounted for.

Few previous studies of offspring quantity versus quality in wild populations have been able to link maternal fecundity directly with subsequent offspring reproductive success and maternal fitness, which is fundamental to understanding the trade-off between offspring quantity and quality. Our results suggest that the use of more complete long-term data on offspring reproductive success could improve the consistency between the observed offspring numbers and the offspring numbers predicted to maximize maternal fitness. However, differences between observed and expected offspring numbers might arise for a number of adaptive reasons (see Godfray et al. 1991), rather than simply the inability to measure offspring fitness reliably. Nevertheless, our results suggest that the difference between the observed and expected offspring numbers could also be dependent on resource availability. For example, subsequent offspring fecundity increased with increasing maternal fecundity only for women from ‘resource-rich’ landowning families. This could be due to the association of high socio-economic status with lower age at first reproduction and increased fecundity among women in our study population (Pettay et al. 2007). Women from landowning families could thereby not only increase maternal fecundity, but also increase the socio-economic resources available to their offspring, and consequently increase subsequent offspring fecundity. Such a relaxation of socio-economic constraints on reproduction may be connected with the social transmission of reproductive behaviour leading to higher offspring fecundity (Anderton et al. 1987), and might also allow the realization of genetically inherited reproductive potential between mothers and daughters (Pettay et al. 2005).

Even so, in both socio-economic groups, we observed diminished grand-offspring returns beyond the population mean offspring recruitment. This indicates that regardless of socio-economic status, there is still competition among offspring for parental resources and investment. As such, our results are consistent with a study of an agro-pastoral community in Kenya, which showed lower grand-offspring returns for women with more than six or seven offspring surviving to 5 years of age, with this effect being strongest in the poorest families (Borgerhoff Mulder 2000). These results suggest that the trade-off between offspring quantity and quality is not necessarily concerned with the number of offspring born, but rather with the number of offspring that share parental resources over their entire juvenile period and perhaps beyond. Thus, the decrease in offspring recruitment with increasing maternal fecundity could be a result of compensation for early offspring mortality to optimize the number of offspring raised to independence. This idea that the degree of sibling competition for parental resources can be an important determinant of subsequent offspring reproductive success is consistent with research on the effects of early conditions in animals and humans (Lindström 1999 Lummaa & Clutton-Brock 2002). It is worth noting that whereas for many bird and mammal species early conditions are restricted to a short period before independence, the extended period of juvenile development in humans could prolong sensitivity to early conditions for many years (Gurven & Walker 2006). In addition, we have previously shown that high previous maternal reproductive investment (production of twins or more expensive sons) may decrease the mother's likelihood of re-breeding (Lummaa 2001) as well as the LRS of her subsequent offspring (Rickard et al. 2007). Finally, the costs to mothers of giving birth many times should not be forgotten, especially if these reduce maternal longevity (e.g. Helle et al. 2002), which in turn might reduce the amount of parental care available to surviving offspring (see Lahdenperä et al. 2004).

In conclusion, our study provides convincing evidence for the existence of a fundamental trade-off between offspring quantity and quality in human lifetime reproduction, which has no doubt shaped the evolution of human reproductive physiology and psychology. However, modern human societies that have undergone a demographic transition are somewhat enigmatic because women no longer give birth to the number of offspring that will maximize maternal fitness (Borgerhoff Mulder 1998). Even so, this need not contradict our conclusions when the modern labour market is considered (Kaplan 1996). For example, increasing delays and reductions in childbearing due to educational or career requirements appear consistent with a wish to acquire sufficient resources to give offspring ‘the best start in life’. This was illustrated by a study of the offspring quantity versus quality trade-off in New Mexican men between 1990 and 1993, which found no evidence for optimization based on fitness maximization but did find evidence for a trade-off between parental fecundity and subsequent offspring education and income (Kaplan et al. 1995). Thus, even though humans might still show adaptive reproductive behaviour, future investigators of this in terms of maternal fitness should be careful to take into account the particular socio-economic context of their study.

We thank Aino Siitonen, Kimmo Pokkinen and Timo Verho for collection of the demographic dataset and the White Rose University Consortium (D.O.S.G.), Academy of Finland (V.L.) and the Royal Society (V.L. and A.F.R.) for funding the study. We also thank Andrew Beckerman, Loeske Kruuk, William Hill, Peter Mayhew, Ian Rickard, Stewart Plaistow, Josh Goldstein, Raj Whitlock, Jessica Stapley, Rowan Martin, Ewan Harney, Kay Gillespie and three anonymous referees for their advice on the analysis and earlier versions of the manuscript.


New research demands rethink on Darwin's theory of 'fecundity selection'

A key concept in Darwin's theory of evolution which suggests nature favours larger females that can produce greater numbers of off-spring must be redefined according to scientists behind ground-breaking research published today (3rd November 2015).

The study, published in the scientific journal Biological Reviews, concludes that the theory of 'fecundity selection' - one of Charles Darwin's three main evolutionary principles, also known as 'fertility selection' - should be redefined so that it no longer rests on the idea that more fertile females are more successful in evolutionary terms. The research highlights that too many offspring can have severe implications for mothers and the success of their descendants, and that that males can also affect the evolutionary success of a brood.

Darwin's theory of fecundity selection was postulated in 1874 and, together with the principles of natural selection and sexual selection, remains a fundamental component of modern evolutionary theory. It describes the process of reproductive success among organisms, defined by the number of successful offspring which reach breeding age.

After years of research, an evolutionary biologist from the University of Lincoln, UK, has proposed a revised version of the theory of fecundity selection which recommends an updated definition, adjusts its traditional predictions and incorporates important new biological terms.

The research indicates that rather than aiding survival, too many offspring can be extremely costly, and can in fact reduce the lifetime reproductive success of females. It highlights that in many species, mothers who produce fewer offspring tend to raise them more efficiently, and that in some cases fathers can take the lead in nurturing young by evolving 'male pregnancy'.

The study also concludes that nature will favour all physical traits that influence 'optimal' fertility in either sex, and that climate and food availability also influence the evolution of reproductive processes – factors which Darwin originally overlooked.

The research, led by Dr Daniel Pincheira-Donoso from the University of Lincoln's School of Life Sciences, reveals that phenomena such as climate change could therefore play a significant role in the fertility of species around the world.

Dr Pincheira-Donoso said: "Evolutionary theory is all about reproductive success, or the number of 'successful' offspring an individual can produce. The more successful offspring, the more genes encoding successful traits are passed on to the next generation.

"However, advances in fecundity selection theory reveal that a higher number of successful descendants can actually result from the production of fewer offspring which can be looked after more efficiently. We therefore need to acknowledge that fertility should be more efficient, not necessarily higher, and that males can have a substantial role in influencing the production of efficient broods.

"Also, a stream of evidence shows that climate and food availability play very important roles in the evolution of fecundity among species. This opens up opportunities for the development of theories involving major natural phenomena, such as rapid changes in the climate. We must explore how these climatic changes can affect the reproductive strategies which evolution has been shaping for thousands or millions of years"

Based on previous studies of the life-history, physical and ecological aspects of fecundity, Dr Pincheira-Donoso's work also concludes that the theory should distinguish between fertility during an animal's lifetime and during one particular breeding season, rather than grouping all time periods together. This is because some animals may have one extremely large brood per breeding season, while others produce one offspring on a more regular basis, which can have enormous implications for the overall reproductive success, and hence evolutionary potential, of species.


Ageing Throughout History: The Evolution of Human Lifespan

It is not surprising that one of the most complex phenomena in nature is that of ageing. It does not only bear biological interest, but it is also associated with cultural, psychological, social and even philosophical issues. It is therefore to be expected that a great deal of research is being performed in order to study the evolution of ageing and, more specifically, the evolution of human ageing. Historical aspects of this evolution will be discussed. Evidence from a variety of sources shows that the human lifespan is increasing, and may well continue to increase to levels that are difficult to predict. In addition, the most important theories about ageing based on evolutionary principles will be examined. Examples are mutation accumulation, antagonistic pleiotropy and the disposable soma theory. Finally, a section about future evolution of human ageing, based upon newly emerging research, will shed some light and provide speculative–provocative ideas about the future of ageing in humans.

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Historical Human Fecundity info? - History

The U.S. Department of Health and Human Services (HHS) is the nation's principal agency for protecting the health of all Americans and providing essential human services.

Below is a list of major events in HHS history and a list of the Secretaries of HHS/HEW.

The Affordable Care Act was signed into law, putting in place comprehensive U.S. health insurance reforms.

The Medicare Prescription Drug Improvement and Modernization Act of 2003 was enacted - the most significant expansion of Medicare since its enactment. It included a prescription drug benefit.

The Office of Public Health Emergency Preparedness (now the Office of the Assistant Secretary for Preparedness and Response) was created to coordinate efforts against bioterrorism and other emergency health threats.

The Centers for Medicare & Medicaid was created, replacing the Health Care Financing Administration.HHS responds to the nation's first bioterrorism attack - delivery of anthrax through the mail.

Publication of human genome sequencing.

The Ticket to Work and Work Incentives Improvement Act of 1999 was signed, making it possible for millions of Americans with disabilities to join the workforce without fear of losing their Medicaid and Medicare coverage. It also modernized the employment services system for people with disabilities.

Initiative to combat bioterrorism was launched.

The State Children's Health Insurance Program (SCHIP) was created, enabling states to extend health coverage to more uninsured children.

Welfare reform under the Personal Responsibility and Work Opportunity Reconciliation Act was enacted.

The Health Insurance Portability and Accountability Act (HIPAA) was enacted.

The Social Security Administration became an independent agency.

Vaccines for Children Program was established, providing free immunizations to all children in low-income families.

Human Genome Project was established.

Nutrition Labeling and Education Act was passed, authorizing the food label.

Ryan White Comprehensive AIDS Resource Emergency (CARE) Act began providing support for people with HIV/AIDS

The Agency for Health Care Policy and Research (now the Agency for Healthcare Research and Quality) was created.

JOBS program and federal support for child care was created.

McKinney Act was passed to provide health care to the homeless.

National Organ Transplantation Act was signed into law.

Identification of AIDS - In 1984, the HIV virus was identified by the Public Health Service and French scientists. In 1985, a blood test to detect HIV was licensed.

Federal funding was provided to states for foster care and adoption assistance.

The Department of Education Organization Act was signed into law, providing for a separate Department of Education. The Department of Health, Education, and Welfare (HEW) became the Department of Health and Human Services (HHS) on May 4, 1980.

The Health Care Financing Administration was created to manage Medicare and Medicaid separately from the Social Security Administration.

Worldwide eradication of smallpox, led by the U.S. Public Health Service.

Child Support Enforcement and Paternity Establishment Program was established.

National Cancer Act was signed into law.

National Health Service Corps was created.

International Smallpox Eradication program was established.

Community Health Center and Migrant Health Center programs were launched.

Medicare and Medicaid programs were created, making comprehensive health care available to millions of Americans.

Older Americans Act created the nutritional and social programs administered by HHS' Administration on Aging.

Head Start program was created.

Release of the first Surgeon General's Report on Smoking and Health.

Migrant Health Act was passed, providing support for clinics serving agricultural workers.

First White House Conference on Aging.

Licensing of the Salk polio vaccine.

Indian Health Service was transferred to HHS from the Department of Interior.

The Cabinet-level Department of Health, Education, and Welfare (HEW) was created under President Eisenhower, officially coming into existence April 11, 1953. In 1979, the Department of Education Organization Act was signed into law, providing for a separate Department of Education. HEW became the Department of Health and Human Services, officially arriving on May 4, 1980.

Communicable Disease Center was established, forerunner of the Centers for Disease Control and Prevention.

The Federal Security Agency was created, bringing together related federal activities in the fields of health, education, and social insurance.

Federal Food, Drug, and Cosmetic Act was passed.

Social Security Act was passed.

The National Institute (later Institutes) of Health was created out of the Public Health Service's Hygienic Laboratory.

The Bureau of Indian Affairs Health Division, forerunner to the Indian Health Service, was created.

President Theodore Roosevelt's first White House Conference urged creation of the Children's Bureau to combat exploitation of children.

The Pure Food and Drugs Act was passed, authorizing the government to monitor the purity of foods and the safety of medicines, now a responsibility of the FDA.

Conversion of the Marine Hospital Service into the Public Health and Marine Hospital Service in recognition of its expanding activities in the field of public health. In 1912, the name was shortened to the Public Health Service.

Immigration legislation was passed, assigning the Marine Hospital Service the responsibility for medical examination of arriving immigrants.

The federal government opened a one-room laboratory on Staten Island for research on disease, a very early precursor to the National Institutes of Health.

The National Quarantine Act was passed, beginning the transfer of quarantine functions from the states to the federal Marine Hospital Service.

Appointment of the first Supervising Surgeon (later called the Surgeon General) for the Marine Hospital Service, which had been organized the prior year.

President Lincoln appointed a chemist, Charles M. Wetherill, to serve in the new Department of Agriculture. This was the beginning of the Bureau of Chemistry, forerunner to the Food and Drug Administration.


1. Data and Main Variables

Our empirical investigation exploits information on individuals from a sample of 26 English parishes. The locations of the parishes are illustrated in Figure B1 in online Appendix B. The information was originally recorded in English church books for the period 1541–871. It was later transcribed by the Cambridge Group for the History of Population and Social Structure as documented in Wrigley et al. (1997). 8 The parishes were selected by the Cambridge Group on merit of data quality and have been shown to represent England as whole rather well ( Wrigley et al., 1997, 41ff). In addition to documenting the dates of baptisms, marriages, burials as well as the genealogy of individuals, the data frequently contains information on occupation and literacy status, which we explore below. 9

1.1. Sample Limitation

We focus on families in which the first child was conceived after the wedding, i.e. in which the protogenesic interval was at least 40 weeks long. 10 In order to avoid confounding the effects of parental mortality on family size and offspring human capital investments, we follow standard demographic procedures by restricting the sample to completed marriages in which both parents survived until the wife reached the age of 50 ( Wrigley et al., 1997, p. 359). 11 Because a missing birth or death date implies that the individual likely migrated between parishes ( Souden, 1984), the restriction to completed marriages, which requires the wife's birth and death dates, also mitigates the possibility of births occurring outside the sampled parishes. Families with unobserved birth and death dates of the husband are excluded for the same reason. We further restrict the sample to offspring with known occupation or literacy status. These restrictions leave us with 1,517 individuals born between 1596 and 1843 and coming from 729 families. 12 The summary statistics are reported in Table C3 in online Appendix C. Figure 1 shows the distributions of offspring with known occupation and literacy status by their year of birth. Nine out of 10 individuals were born between 1684 and 1814, which includes the majority of the classic years of the industrial revolution.

Histograms of Birth Dates for the Observations on Literacy and Occupation in the Total Regression Sample. (a) Observations in Literacy Sample. (b) Observations in Occupation Sample


Watch the video: 2 Ταυτότητα του χάρτη (January 2022).