Selective pressure for reduced rates of growth and maturity in primates

Selective pressure for reduced rates of growth and maturity in primates

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Humans are somewhat notable for the slow rate at which they grow and reach physical maturity. Some related primates also take over a decade to reach sexual maturity.

However, many mammals - including ones like horses that grow quite larger than any primates - reach their maximum size in only a few years, so the slow rate of primate growth is not due to simple biologic or metabolic constraints on mammalian growth rates.

Are there any hypothesized selective pressures that might explain the slow physical growth rates of higher primates?

Growth rates in a wild primate population: ecological influences and maternal effects

Growth rate is a life-history trait often linked to various fitness components, including survival, age of first reproduction, and fecundity. Here we present an analysis of growth-rate variability in a wild population of savannah baboons (Papio cynocephalus). We found that relative juvenile size was a stable individual trait during the juvenile period: individuals generally remained consistently large-for-age or small-for-age throughout development. Resource availability, which varied greatly in the study population (between completely wild-foraging and partially food-enhanced social groups), had major effects on growth. Sexual maturity was accelerated for animals in the food-enhanced foraging condition, and the extent and ontogeny of sexual dimorphism differed with resource availability. Maternal characteristics also had significant effects on growth. Under both foraging conditions, females of high dominance rank and multiparous females had relatively large-for-age juveniles. Large relative juvenile size predicted earlier age of sexual maturation for both males and females in the wild-feeding condition. This confirmed that maternal effects were pervasive and contributed to differences among individuals in fitness components.

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Fishing directly selects on growth rate via behaviour: implications of growth-selection that is independent of size

Size-selective harvest of fish and crustacean populations has reduced stock numbers, and led to reduced growth rates and earlier maturation. In contrast to the focus on size-selective effects of harvest, here, we test the hypothesis that fishing may select on life-history traits (here, growth rate) via behaviour, even in the absence of size selection. If true, then traditional size-limits used to protect segments of a population cannot fully protect fast growers, because at any given size, fast-growers will be more vulnerable owing to bolder behaviour. We repeatedly measured individual behaviour and growth of 86 crayfish and found that fast-growing individuals were consistently bold and voracious over time, and were subsequently more likely to be harvested in single- and group-trapping trials. In addition, there was some indication that sex had independent effects on behaviour and trappability, whereby females tended to be less active, shyer, slower-growing and less likely to be harvested, but not all these effects were significant. This study represents, to our knowledge, the first across-individual support for this hypothesis, and suggests that behaviour is an important mechanism for fishing selectivity that could potentially lead to evolution of reduced intrinsic growth rates.

1. Introduction

Few would argue with the suggestion that humans are a major selective pressure on animals. Of the many pressures that humans impose on animals, the persistent selective harvesting of fish and ‘wildlife’ species is thought to have major impacts, extending beyond numerical effects [1,2]. Animal harvest is a particularly strong selective pressure, because larger individuals are usually intensively and selectively targeted, leading to reductions in body size, growth rate, horn size and age at maturity over successive generations [2–5].

In contrast to the literature on size-selective effects of fishing harvest [6], the role of behaviour in mediating selection on life-history traits in fisheries remains under-studied despite the long-known links between behaviour and catchability [reviewed by 7,8]. However, one recent study highlighted how fishing may select on life-history traits via a behavioural mechanism, even in the absence of any size selection [9]. Selection on growth rate via behaviour can occur, because fast-growing individuals tend to be more active, bold and voracious [10,11], and so, in turn, we should expect fast-growers to be more vulnerable to harvest owing to greater encounter rates with fishing gear, and lower gear avoidance. Indeed, at the group level, faster growth is associated with greater catchability, even in the absence of size selection [9,12]. In addition, several recent studies have shown biases towards more active and bold individuals in the catch [7,9,13–15], but see [16]. Nonetheless, a demonstration of selection on growth rate via a behavioural mechanism has not yet been made at the individual level. That is, no study has, to our knowledge, yet assessed vulnerability to harvest of a group of individual animals with known behavioural tendencies and life-history trajectories from within a single population (see also Discussion).

A behavioural mechanism for selection on life history emphasizes the importance of distinguishing between selection on growth rate via size selection (indirect growth selection) from direct selection on growth rate via behaviour which is independent of size [9]. In many instances, behaviour should represent the immediate mechanism for catchability, because by definition inactive or shy individuals are less likely to approach or contact fishing gear, and more likely to avoid approaching fishing gear. By extension, direct selection on growth can occur if a strong correlation exists between intrinsic growth rates and behaviour. In other words, any selection on life-history traits (and the proximate physiology underpinning it, such as metabolism and hunger) may, in general, be through behaviour. If widespread, this mechanism has important management implications, because the common management practice of imposing size-limits to protect segments of the population [6] may not be sufficient to protect fast-growing and productive individuals. Why? Because fast-growers will be always be more vulnerable at any given size owing to greater levels of activity and boldness. However, fast growers can more quickly grow through vulnerable size ranges in a slot-limit, which may compensate for such effects, at least in part.

In this study, we test this mechanism using a freshwater crayfish as a model. We chose the Australian common yabby, Cherax destructor, because it is harvested both recreationally and commercially. More generally, crustaceans are intensively harvested around the world, and declines in size have been documented in shrimp and lobster [17,18], but, to date, none have studied links between behaviour and fishing harvest. We repeatedly assayed the behaviour and growth of 86 yabbies over five months, while fed ad libitum bold and voracious individuals were also consistently faster-growing in this population [19]. Here, we used those individuals with known behavioural types and intrinsic growth rates, and assessed their vulnerability to harvest in individual trapping trials, and in group trapping trials that contained a mix of behavioural types—importantly, we used traps that were not size-selective. Because behaviour and growth rate are labile traits, and individual means are estimated with considerable uncertainty, we used multivariate mixed models to quantify correlations between behaviour, growth and catchability, using behavioural and growth data collected for one month prior to trapping trials.

2. Methods

(a) Subjects and behavioural assays

We used 86 yabbies that had been housed individually since ‘birth’, and fed ad libitum for nearly five months, before being assessed for trappability. Six berried females were sourced from a single population from a commercial supplier and held until they released their young, at which time 15 individuals from each female were randomly chosen for observations (see Biro et al. [19]). Given these females were trapped, the variation in behaviour and growth rate may be reduced relative to that present in the source population owing to behavioural sampling bias [20,21]. Nonetheless, substantial variation existed in both traits (see Results).

Yabbies were housed individually with an artificial burrow, and were repeatedly and concurrently assayed for ‘boldness', ‘voracity’ and growth rate across an interval of about five months. There was no effect of female identity on behaviour or growth of the young, and there was no detectable individual variation in initial mass at the start of the experiment. However, this does not mean growth rate is not heritable, because any given female may carry offspring fertilized by several fathers. In addition, yabby growth rate is known to be heritable [22]. Animals were held in a constant temperature room, and water was changed every 3 days to ensure water quality. All details of the set-up and husbandry, including detailed descriptions of the behavioural assays, are outlined elsewhere ([18], but see below).

We used the many repeated measures of behaviour and mass taken during one month prior to trapping trials (termed ‘protocol 2’ in Biro et al. [19]) to relate to individual trappability. These data were comprised 13 daytime and five night observations of boldness for each individual (total number of scores taken, n = 1548) voracity was measured eight times for each individual (total scores, n = 688 Biro et al. [19]) and mass was measured three times (no significant differences in individual mass were observed at the start of the experiment). Across the entire dataset, all traits displayed consistency over time, and repeatability values ranged from 0.44 to 0.50 for behavioural traits, and 0.85 to 0.99 for mass changes over time [18]. Consistent individual differences (repeatability) were also confirmed for data collected one month prior to trapping (see Results).

Briefly, boldness was assessed as the tendency to use open areas away from its burrow, whereby we repeatedly scored the position of the individual during a series of 30 point (scan) observations. In each scan, the yabby was given a score reflecting the relative risk of their position: zero for hiding within the refuge, one for fully emerged from the refuge but still in the half of the aquarium with sand on the bottom (nearest the refuge) and two when positioned in the open half of the aquaria where they were least camouflaged the sum of scores across the 30 scans in a single trial represented a boldness index. Voracity was assessed as the latency to reach bloodworms, introduced in the open area away from its burrow a shorter latency was inferred to represent greater voracity. Boldness was assessed during day and at night, and voracity only during the day (additional details in Biro et al. [19]).

(b) Individual trapping trials

Each individual was first measured for its trappability in its own home tank. Thirty traps were made for the individual trapping stage of the experiment which was carried out over four nights following the end of all behavioural observations. Each trap was a white PVC tube which was 5 cm long with a diameter of 3 cm. Both ends were covered with mesh, but with an opening at the top on one end which was ca 1 × 2 cm wide for the yabby to enter through. The size of this opening was more than large enough to accommodate the largest individual at that time. The juveniles were not fed the day of trapping. The trap was placed in the open half of their aquaria at 17.00 with the open end facing towards the shelter. Five bloodworms were pippetted into the middle of the trap, and it was checked at 8.00 the following morning when the water temperature was taken. Because temperature varied slightly across the bench in the constant temperature room, we rotated into the same positions the next 30 tanks (and individuals) to be tested to help maintain relatively constant temperature (20–21°C) nonetheless, we tested for temperature effects in our models but found none (see Results).

(c) Group trapping trials

Crayfish were sorted into six groups, with 15 individuals in each group. Each group had a mixture of behavioural types, and were chosen semi-randomly to avoid any group-related differences in growth or behavioural scores. There were no systematic differences in average predicted behavioural scores or growth rates among groups (ANOVA, all p > 0.7), but we nonetheless tested for a group effect in analyses. Each yabby was numbered using waterproof paper glued to their carapace. Three large tanks (2 m long, 1 m wide) were set up with sand covering the bottom. Each had a heater set to 21°C and an air stone at both ends to circulate and aerate the water (15 cm deep). Three groups of 15 individuals each were placed into the tanks with 22 tubes each for shelter. The tubes were translucent, so that the individual's number could still be viewed while they refuged within. Overhead fluorescent lights were set to be on from 4.30 to 16.30 with two small red lamps on during night.

Crayfish were left to acclimate for two nights, and then trapping was run on the third and fourth nights. Two Plexiglass shrimp/snail traps with a cube of bloodworms inside were placed into each tank each night at 17.30, for a total of two nights of trapping. These traps are entirely enclosed with entry only possible by pushing through flaps which close behind. This trap can catch the smallest, as well as the largest of crayfish we had in our sample (dimensions: 140 × 70 × 70 mm see One trap was placed by each air stone to spread the scent. The following day at 8.00, the traps were removed, and any trapped individuals placed back into their individual home tanks. The traps were set again that night as before. Trapping was then repeated for the remaining three groups of 15.

(d) Statistical analyses

We tested for across-individual correlations between behaviour, growth rate and harvest (captured, not captured) using multivariate mixed models which assumed normal errors for the behaviours and growth rate, and binomial errors on capture probability (Proc Glimmix, SAS Institute, Cary, NC). We used the individual repeated measures data collected in the final month prior to trapping (see above). Multivariate mixed models allow one to rigorously estimate correlations between traits in a set of individuals that take account of the repeated measures and the effect of variability in the data when calculating parameter estimates and measures of their uncertainty [23]. Individual identity was specified as a random (intercept) effect in the model, sex was specified as a trait-specific fixed effect and we fitted a separate residual variance parameter for each trait (binomial trapping data residual variance set to zero). All variables (except harvest) were standardized to mean zero and variance of one (z-transformed) in order to help with model convergence. We tested for the significance of trait variances (which estimate the variation in individual predicted mean values, i.e. the random intercept effect), and covariances (across-individual correlations between traits), using the ‘covtest’ option. We used the ‘gcorr’ option to output the across-individual correlations between traits, which are calculated as the covariance divided by the square root of the product of the two trait variances [23].

3. Results

(a) Individual trapping trials

Multivariate analyses, using the binomial trapping data and all the individual assays of behaviour and growth during the month preceding trapping revealed that there were consistent individual differences in daytime boldness, night-time boldness, voracity and growth (each variance parameter p < 0.0001). As expected, all four traits were significantly correlated with one another across individuals, whereby bold individuals (day or night) tended to be voracious (shorter latency to feed) and fast growing (each covariance p < 0.0001 table 1). Probability of harvest was not significantly correlated with daytime boldness (r = 0.11, p = 0.45), but was higher for individuals that tended to be bolder at night (r = 0.32, p = 0.063), and was higher for more voracious and faster-growing individuals (r = 0.37 and 0.34, both p < 0.03 table 1 and figure 1). As expected, there was a significant effect of sex that differed among the traits (trait × sex interaction: F5,335 = 2.70, p < 0.025) females had lower day and night-time activity (both coefficients, p < 0.015) and lower growth rates (coefficient, p < 0.01). Females may also have had higher latencies (shyer) and lower trapping probability, but these were not significant (both coefficients, p > 0.16).

Table 1. Correlation matrix obtained from multivariate mixed models that examine across-individual correlations between traits (also known as the G-correlation matrix). (Individual identity is specified as a random intercept effect which characterizes individual differences in trait values over time and accounts for multiple repeated measures per individual (except harvest, which is measured once per individual).)

Figure 1. Raw data for individual mean values for boldness (averaged across day versus night contexts), voracity (here, latency to feed) and growth rate, in relation to whether it was harvested or not, both in individual trapping trials and in groups containing a mixture of behavioural types (‘group’ trials). (Shown are the means (±s.e.) of 86 individuals in total, calculated on a log scale (see Methods for units and details).)

(b) Group trapping trials

Multivariate analyses using the binomial group trapping data were congruent with the individual trapping data. All trait variances and covariances among behavioural and growth measures were significant and near-identical to the preceding analysis (all p < 0.0001). Probability of harvest was higher for individuals with higher daytime boldness (r = 0.19, p < 0.025), higher night-time boldness (r = 0.25, p < 0.005) and faster-growth rate (r = 0.33, p < 0.001 table 1 and figure 1). However, probability of harvest was not related to voracity (r = 0.02, p > 0.8 table 1 and figure 1). Again, there was a significant effect of sex that differed among the traits (trait × sex interaction: F5,335 = 2.58, p < 0.03) females had lower day and night-time activity (both coefficients, p < 0.02) and lower growth rates (coefficient, p < 0.01). Females may also have had higher latencies (shyer) and lower trapping probability, but these were not significant (both coefficients, p > 0.13).

4. Discussion

We hypothesized that if fishing directly selects on growth rate via consistent individual differences in behaviour, we should observe that harvested individuals are generally bold, voracious and fast-growing. The weight of evidence here suggests this hypothesis is supported—we observed consistent individual differences in daytime boldness, night-time boldness, voracity and growth rate across 86 individuals during the month preceding our trapping trials. In both multivariate analyses, one for individual trapping and one for group trapping, all of the behavioural and growth traits were strongly and positively correlated with one another and, most importantly, faster-growing individuals and those that were bold at night were always more likely to be harvested. However, daytime boldness was not related to individual trappability, and voracity (measured during the day) was not related to trappability in a group setting. Given that traps were set overnight as is typically done in fisheries, and crayfish are largely nocturnal, it is perhaps not surprising that night-time behavioural measures were consistently related to the probability of trapping whereas daytime measures were not.

Overall, this study indicates for the first time to the best of our knowledge that consistent across-individual differences in behaviour are a probable mechanism explaining why fishing harvest can select against fast-growing individuals, even when using gear that is not size selective. Our study distinguishes itself from previous similar work which showed that across-group differences in behaviour (wild and domesticated genotypes) in fishes resulted in differential harvest when gear was not size selective [9]. Our study also distinguishes itself from a selection experiment which demonstrated changes over time in life-history and behavioural traits owing to fishing [24], but did not control for size selection. Here, we build upon this previous work, and find support for the hypothesis regarding the direct role of behaviour in catchability, by conducting a similar experiment but at the individual level (as opposed to group level), using a large number of individuals (albeit from few females) that were repeatedly tested to establish consistent behavioural differences. It is crucial to perform such a test, because selection acts at the individual level, and the hypothesis centres on individuals that consistently differ in behaviour within a given population, and its link to growth rate.

The strongest correlations we observed were between intrinsic growth rate and trappability, rather than were behavioural traits and trappability. We might expect that if behaviour is the immediate determinant of whether individuals are captured or not, then correlations should be highest between behaviour and catchability (all else being equal). However, behaviour is inherently a more labile trait than is growth rate, and repeatability of behavioural traits reached a maximum of 0.50 in comparison with growth rate which reached a maximum of 0.99 at the end of the experiment (see Methods [25]). Thus, we can a priori expect that behaviour–catchability correlations will be lower [25]. Nonetheless, behaviour must be the immediate cause of catchability in this study because it must choose to move and choose to enter the trap, even if other more proximate factors like hunger are motivating behaviour in the first instance. Given that behavioural variation cannot affect differences in foraging success and impact upon growth under ad libitum food conditions, it would seem that intrinsic differences in growth rates (life-history strategy) are what affect differences in behaviour which, in turn, affect catchability.

Given that our observations are based on fishing gear that was not size selective, an important implication of our results is that size-limits may not by themselves sufficiently protect fast-growing individuals. They are unlikely to fully protect fast-growers, because bold/fast-growing phenotypes will always be harvested at a higher rate relative to other individuals at any given size. So while we might try to protect larger individuals in a fishery using size-limits, which may help in the short term, we will nonetheless continue to differentially remove the fast-growing individuals among the smaller size classes. This would also apply to minimum size-limits as well, which give fishes a chance to attain maturity, but, thereafter, fast-growing individuals would be harvested at higher rates. However, the extent to which this is true will depend upon other factors, particularly on how quickly fast-growers move through vulnerable size ranges in a slot-limit which will be compensatory. Protecting fast-growers is important as these individuals reach larger sizes more quickly, tend to be more fecund and probably contribute more to overall population productivity than slow growers.

Given the significant correlations between behaviour, voracity, growth rate and catchability, we might speculate that the directional selection we observed on growth rate could extend beyond numerical effects, and lead to evolution of decreased growth rates over time, at least in the absence of compensatory processes [8]. Indeed, a realistic recent study on fishes showed that fishing harvest can lead to changes over time [24], but that study did not exclude size-selection as a factor. The very high repeatability of growth rate observed (reaching 0.99) suggests a heritable component to intrinsic growth rate in our yabbies. What remains unknown is whether size-independent selection owing to behaviour can lead to evolutionary change. This is now the focus of our current experiments.

Our results suggest that intrinsically fast-growing individuals are hungrier on average, and therefore spend more time outside of their burrow in search of food, because in nature, greater boldness would increase feeding rates. Indeed, the fastest-growers rarely resided in their burrows, whereas some others hardly ever left their burrow, did not approach the food item in voracity trials, and hardly grew at all relative to others (see figures in Biro et al. [19]). In the light of this, we might speculate that not only are slow-growers not vulnerable to fishing harvest in the short term, but they may also rarely if ever approach traps, and might therefore effectively represent an ‘insurance policy’ that could prevent local extinction.

A caveat to be noted regarding the extent to which our findings are general is that this was a laboratory study, where traps and crayfish were necessarily in close proximity, and that our trials involved yabbies of a small size (several grams in mass) that would not normally be retained by a fishery (but fishers targeting animals for bait would). At the same time, an advantage of the laboratory setting that is not easily duplicated in the field is the ability to have every individual assayed for its behavioural and life-history type, obtained through multiple repeated assays of mass and behaviour. Given the relatively low repeatability of behaviour in this study (0.4–0.5), it would indeed be difficult to obtain reliable estimates of an individuals' behavioural type without several repeated measures in a laboratory setting, and low repeatability and low sample size would together contribute to lower power to detect correlations across individuals between any two traits of interest [25]. Finally, an unavoidable consequence of the correlation between behaviour and growth rate in this experiment is that we could not control for the effect of size on trappability. Consequently, it is possible that size per se may play a role in catchability that interacts with (or is in addition to) the effects of behaviour, and this would represent an interesting topic for future research.


Male-biased sexual dimorphism in body mass is widespread in mammals, with the most dimorphic taxa including members of the orders Pinnipedia, Proboscidea, Carnivora, Artiodactyla and Primates (Ralls, 1977 Moors, 1980 Abouheif & Fairbairn, 1997 Weckerly, 1998 Mysterud, 2000 ). Ultimate explanations for high levels of dimorphism in these taxa are founded in sexual selection theory (Darwin, 1871 ). Because female mammals have lower potential rates of reproduction than do males, males often compete intensely over access to females (Trivers, 1972 Clutton-Brock & Parker, 1992 Kvarnemo & Ahnesjo, 1996 ). Sexual selection theory predicts that sexual dimorphism is a consequence of intrasexual competition, which favours larger, more competitive males (Isaac, 2005 ).

The extreme variation in sexual size dimorphism among members of certain taxa is often explained by differences in the strength of sexual selection. For example, much of the variation in degree of sexual dimorphism in body mass among haplorhine primate species, which ranges from extreme male bias in some old-world monkey species to completely lacking in some new-world monkeys (Ford, 1994 Smith & Jungers, 1997 ), can be explained by degree of polygyny, operational sex ratio and levels of intermale competition, all of which are thought to reflect the strength of intrasexual selection (Clutton-Brock et al., 1977 Plavcan & van Schaik, 1992 , 1997 Mitani et al., 1996 Lindenfors & Tullberg, 1998 ). Lemurs are unique among primates in that they have been shown to generally lack sexual size dimorphism (Kappeler, 1991 ) despite often having polygynous mating systems (Wimmer & Kappeler, 2002 Kappeler & Schaeffler, 2008 ) and intense male–male competition during the breeding season (Pereira & Weiss, 1991 Sauther, 1991 Kappeler, 1997a , b Cavigelli & Pereira, 2000 Gould & Ziegler, 2007 Ostner et al., 2008 ), both of which should result in strong intrasexual selection favouring large male body size. Furthermore, in contrast to expectations, the low levels of sexual size dimorphism that have been detected in some lemur species tend to be female-biased (Glander et al., 1992 ).

Explanations for the causes of sexual size monomorphism in lemurs despite circumstances favouring large male body size have been proposed. The fecundity selection hypothesis states that because lemur females have unusually high costs associated with reproduction, selection has favoured an increase in female size, which reduces these costs and results in larger, healthier infants (Kappeler, 1990 ). Alternatively, selective pressures on large male body size may be relaxed in lemur males as passive mate-guarding strategies reduce the need for physical competition (Dunham & Rudolf, 2009 ). It has also been proposed that intersexual selection promotes sexual size monomorphism in lemurs through female preference for smaller, more compliant males who will not challenge females or their infants for food (Richard, 1992 ) or through female preference for more agile males (Kappeler, 1991 ). Rensch's rule, which attempts to explain variation in sexual size dimorphism among primates as a correlated response to body size (Leutenegger, 1978 Leutenegger & Cheverud, 1982 ), fails to explain variation in dimorphism among strepsirhines (Lindenfors & Tullberg, 1998 Smith & Cheverud, 2002 Gordon, 2006 ), many of which are relatively large but still lack dimorphism (Kappeler, 1991 Godfrey et al., 1993 ). Finally, sexual size monomorphism in lemurs has been attributed to phylogenetic inertia (Cheverud et al., 1985 ) however, this explanation does not address the cause of the origins of sexual size monomorphism (Lindenfors & Tullberg, 1998 ).

By contrast, the duration of growth in strepsirhine primates, such as lemurs, tends to be short (Leigh & Terranova, 1998 ). As a result, unlike many primate clades in which interspecific variation in body size results from differences in a combination of growth rates and duration (Leigh, 1992 Leigh & Shea, 1995 O'Mara et al., 2012 ), variation in size among lemurid (Leigh & Terranova, 1998 O'Mara et al., 2012 ) and indriid (Ravosa et al., 1993 ) species appears to result primarily from variation in growth rates, indicating that growth durations may be constrained in lemurs. Selective factors restricting the length of the growth period in lemurs are therefore thought to have limited the evolution of sexual size dimorphism by restricting bimaturism.

Several factors may target lemur growth durations. Strong seasonal fluctuations in food availability may selectively favour the attainment of foraging independence prior to an individual's first dry season (Pereira, 1993 Leigh & Terranova, 1998 ). However, many other primate species inhabit highly seasonal environments (Kamilar, 2009 Dunham et al., 2013 ), and it has been demonstrated that environmental factors are not associated with sexual size dimorphism across primate species (Dunham et al., 2013 ). Thus, the fact that lemurs respond with shortened growth durations while haplorhine primates respond by adopting a risk aversion strategy that lengthens the growth period requires consideration. Life-history theory predicts that when infant mortality rate is so extreme that there is a low probability of survival to reproductive age, fast growth is expected (Case, 1978 Stearns & Koella, 1986 Stearns, 1992 Berrigan & Koella, 1994 ). Reportedly high infant mortality in lemurs compared to haplorhine species (Wright, 1999 ) may indicate that lemurs are particularly sensitive to seasonality and that extended growth periods are simply not viable in lemurs. Alternatively, the energetic demands of rapid growth in early life may lead to high infant mortality, in accordance with the metabolic risk aversion hypothesis (Janson & van Schaik, 1993 Leigh, 2001 ). Breeding seasonality in lemurs is another factor that can affect growth and sexual size dimorphism. Most lemurs exhibit strong breeding seasonality (Rasmussen, 1985 van Schaik & Kappeler, 1993 ), likely as a response to seasonal availability of food resources (Pereira, 1993 ). Selection may favour reduced growth durations in lemurs because extension of the period of growth could cause seasonal breeders to forego a breeding season (Leigh, 1992 ), which would reduce lifetime reproductive output.

It has been argued that lemurs are subject to unusually high levels of energetic stress due to severe droughts, cyclones, poor soil and seasonal food scarcity in Madagascar (Wright, 1999 ). In particular, the predictable stress brought on by the harsh winter season is cited as a strong driver of lemur life-history traits (Pereira, 1993 ). By contrast, Dunham et al. ( 2013 ) showed that lemurs do not experience stronger seasonality than other primate species, but rather that interannual variability in seasonality is unusually high for lemurs. Indeed, although there is variation in the degree to which lemur populations experience seasonal fluctuations (Lehman et al., 2005 Dewar & Richard, 2007 Gordon et al., 2013 ), extreme interannual variability in seasonality within a single habitat leads to unpredictable phenological cycles (Wright et al., 2005 ). For example, a single site in Madagascar is reported as lacking a dry season in one year (Hemingway, 1998 ) and again as having distinct wet and dry seasons in another (Tecot, 2010 ). Consequently, even those lemur species traditionally considered to inhabit environments with low resource seasonality are frequently faced with stresses associated with challenging environments.

Potential ontogenetic constraints on sexual size dimorphism in lemurs have previously received little attention in the literature due to availability of small ontogenetic data sets for a limited number of species. Recently compiled large data sets on many species across the lemur clade provide the opportunity to test the relationships among life-history variables, growth parameters, and sex and species differences in body size in lemurs. First, I examine the prevalence of sexual size monomorphism and dimorphism in 18 species of lemur. Next, I test the hypothesis that limited growth durations reduce the opportunity for bimaturism and constrain sexual size dimorphism in lemurs. If short phases of growth preclude bimaturism in lemurs, durations of growth are expected to exhibit reduced variability among species and between the sexes. Reduced variability in growth durations may be balanced by high interspecific variability in growth rates. If lemurs respond to reduced resource availability by limiting growth durations, ultimately leading to a lack of sexual size dimorphism, strong associations between the life-history variables that are likely to be constrained by resource availability (e.g. weaning age and age of first reproduction) and growth durations are expected.


Ceará, the northward location, presented higher temperatures and potential evapotranspiration than the southward location, Alagoas, for the period of study in each location (Table 2). On the other hand, rainfall, actual evapotranspiration, and relative humidity did not differ between these locations during the study periods—although it seems Ceará tends to have less rainfall and humidity than Alagoas given the marginal significant p values (Table 2). Temperature and rainfall historical data from Ceará (30 years) and Alagoas (12 years) suggest that Ceará is warmer and has less rainfall than Alagoas, supporting the annual pattern which was found during the period of study in each location as commented above. Moreover, previous data indicates that Ceará has higher evaporation rates than Alagoas (Table 1).

Both males and females from Ceará were larger than their counterparts from Alagoas (males—Alagoas: 28.66 ± 6.72 mm, Ceará: 31.19 ± 8.23 mm, t (978) = −5.30, p < 0.001 females—Alagoas: 27.71 ± 5.92 mm, Ceará: 29.89 ± 6.75 mm, t (942) = −5.26, p < 0.001) (Fig. 2). Moreover, both sexes reached larger maximum sizes in Ceará than their counterparts from Alagoas (males—Alagoas: 45.35 mm, Ceará: 48.50 mm females—Alagoas: 43.45 mm, Ceará: 44.30 mm).

Goniopsis cruentata. Mean body sizes of males and females for Alagoas and Ceará. White dots represent the mean values, the boxes the mean ± standard error, and the bars represent mean ± 2*standard error. These data refer to the total number of males and females sampled in each location (males: 494 in Alagoas and 409 in Ceará females: 373 in Alagoas and 392 in Ceará)

Size at morphometric maturity was larger in males (28.15 mmCW) and females (30.0 mmCW) from Ceará in comparison with males (22.17 mmCW) and females (24.8 mmCW) from Alagoas (Figs. 3, 4). The relative growth of the gonopod length and the abdomen width on body size differed between juveniles and adults within each sex but not in females from Alagoas (Alagoas—males: F 1490 = 423.53, p < 0.001, females: F 1369 = 1.26, p = 0.26 Ceará—males: F 1405 = 91.01, p < 0.001, females: F 1388 = 44.30, p < 0.001). We then considered size at maturity with regard to the intercept of the regression lines given that the intercept differed between juveniles (−4.643 ± 1.146, intercept ± SE) and adults (−7.129 ± 0.785), we then considered the size at maturity of females from Alagoas as a reliable estimation.

Goniopsis cruentata. Size at sexual maturity of males from Alagoas and Ceará estimated through the relative growth of gonopod length (mm) on body size (mm). The lines indicate the breakpoint of such relationship, which indicates the size at sexual maturity. The white circles represent juveniles whereas black squares represent adults

Goniopsis cruentata. Size at sexual maturity of females from Alagoas and Ceará estimated through the growth of the abdomen width (mm) relative to body size (mm). The lines indicate the breakpoint of such relationship, which indicates the size at sexual maturity. The white circles represent juvenile individuals whereas black dots represent adult individuals

The relative growth of the gonopod length and abdomen width was positive allometric for the majority of cases, except for the gonopod length of adult males from Ceará, which showed a negative allometric growth (Table 3). The gonopod growth on body size differed between juveniles and also between adults from Alagoas and Ceará (juveniles: F 1231 = 40.12, p < 0.001 adults: F 1664 = 40. 36, p < 0.001 ANCOVA, interaction carapace*location), in which a steeper positive allometric growth was observed in males from Alagoas. This was even more pronounced for juveniles, which presented a gonopod length growing 30 % faster than that of their counterparts from Ceará (difference of 30 % between slopes, Table 3). In females, the abdomen growth also differed between juveniles and between adults (juveniles—F 1331 = 6.33, p < 0.001 adults—F 1454 = 4. 65, p < 0.031 ANCOVA, interaction carapace*location). However, a clear pattern of growth as the one found in males was not observed: The abdomen grew markedly more relative to body size in juvenile females from Ceará than in juveniles from Alagoas and grew more in adults from Alagoas than in adults from Ceará (Table 3).


Opposite psychopathy, an established fast life history strategist, is obsessive personality, herein depicted as a slow life history strategist as demonstrated by several classes of correlates: anxiety, harm avoidance, risk aversion, loss aversion, future-oriented thought, executive control, compulsivity, conventionality, conscientiousness, capital accrual, fidelity, and parental effort. As has been shown, there is a suggestive connection within the empirical literature between these many variables and slow life history, and through them, between slow life history and obsessive-compulsive personality.

Beyond connecting correlates in this way, it is important to discuss etiology. A life history framework has etiological implications because, to claim that obsessive personality is an extreme life history variant, is to implicitly claim that obsessive personality has been evolved, and has an evolutionary history. Though traditionally explained psychoanalytically (Hertler 2014b), obsessive character was recently described as an evolved strategy (Hertler 2014a 2015c). Specifically, obsessive character was hypothesized to be a product of post dispersal evolution out of Africa and into the temperate latitudes of Eurasia. This thesis explained obsessive cognition and behavior as arising from a two-part shift in the selective regime to which upper Paleolithic and early Neolithic migrants were exposed. As designated by the term ecological opportunity (Schluter 2000 Yoder et al. 2010), northward migration into Eurasia reduced biotic, density dependent selective pressures, including equatorial parasites (Bar-Yosef and Belfer-Cohen 2000, 2001 Phillips et al. 2010 Harcourt 2012) intraspecific competition (Cavalli-Sforza et al. 1994 Mellars 2006 Reich and Goldstein 1998) and conflict (Shea 2007). Second, as designated by the term migration load (Perin 2009), northward migration into Eurasia augmented abiotic, density-independent selective pressures, including cold and seasonal scarcity. Migration thus altered the selective regime, which then selected for obsessive psychology. With this two-part shift in the environmental selective regime, obsessive traits, such as extreme conscientiousness, reflexively future-oriented thought, and habitual parsimoniousness became at once practicable and adaptive practicable because futurity was less uncertain, and adaptive because hardship was more certain (Hertler 2015c).

Beyond its simply being evolutionary, this etiology has several parallels with life history theory, as described by Rushton and later theorists. Both Rushton’s research and this evolutionary etiology are fundamentally ecological in that they feature change in latitude as an important function of migration. As in this evolutionary etiology, Rushton emphasized the cold of higher latitudes as causal of evolutionary change. Furthermore, the evolutionary model in question (Hertler 2015c) is also consistent with more recent life history research, which situates stressors such as seasonal cold more explicitly within the context of mortality regime (Griskevicius et al. 2011a, b). Mortality regime is now commonly understood to calibrate the life history of the organism (Promislow and Harvey 1990 Franco and Silvertown 1997 Chisholm 1999a, b Vindenes et al. 2012 McDonald et al. 2012 van Schaik and Isler 2012). As described by Sherman et al. (2013), unstable, uncontrollable, and unpredictable selective regimes impose high extrinsic mortality, shorten lifespans irrespective of the efforts of the organism, and thereby select for fast life histories. Alternatively, stable, controllable, and predictable selective regimes impose high intrinsic mortality, encouraging somatic investment, parental effort, and thereby select for slow life histories (Sherman et al. 2013 Ellis et al. 2009). So as intrinsic mortality waxes and extrinsic mortality wanes, life history slows. These are precisely the conditions specified as causal in the evolution of obsessive psychology: Northward-dispersing humans assumed a predictable, environmentally imposed hardship while being partially relieved of random, biotically imposed mortality (Hertler 2015c).

Future Research

In part then, because of associations between migration, latitude, and mortality regime, validating the present theory that obsessive character is a slow life history strategy, will come from obtaining support for the above-described evolutionary etiology. First, future research should reexamine latitude as it affects mortality regime and life history. Consistent with the hypothesized equatorial concentration of biotically imposed extrinsic mortality (Hertler 2015c), 99 % of under-five childhood deaths are concentrated within India, Nigeria, Democratic Republic of the Congo, Pakistan, and China (Wertheim et al. 2012). The distribution of vectors, parasites and pathogens also show a strong latitudinal gradient (Wertheim et al. 2012 Low 1988). In addition to examining how latitude presently affects life history through mortality regime, the past must be considered. This line of research necessitates descending into the complexities of ancestral migration with its study of bottlenecks, drift, and founder effects, as well as of paleoclimate with its study of Milankovic Cycles, Heinrich Events, and oceanic oscillations. Such knowledge remains uncertain (Hetherington and Reid 2010) and moreover what is known is not yet sufficiently applied. Studying latitudinal effects on mortality regime will, of course, not only relate to obsessive etiology, but extends more broadly to the study of life history evolution. Second, and more specifically, as predicted by the ecological nature of this evolutionary theory, obsessives should be concentrated among ancestral inhabitants of higher latitudes. Obsessives should, for instance, be concentrated in those of European extraction as compared with those of Sub-Saharan African extraction (Hertler 2015c). Direct studies are limited. Nevertheless, there have been some comparisons of African-Americans to Caucasian Americans, which show no systematic differences in OCPD incidence (Chavira et al. 2003 Grant et al. 2012). However, significant genetic admixture (Bryc et al. 2015) partially undermines the utility of American samples. Studies more broadly focusing on the obsessive spectrum show mixed results, and anyway are of little applicability to OCPD per se (Matsunaga and Seedat 2007 Frost and Steketee 2002 Swets et al. 2014). On the other hand, the obsessive traits of time urgency and future-oriented thought, as well as parsimony and conservation of resources, appear to positively correlate with latitude (Hertler 2015c). Cross-national comparisons of self-reported conscientiousness correlates negatively with latitude, though, as reviewed, this data is open to several explanations, ranging from negation (Heine et al. 2008 van de Vijver and Leung 2001 Cohen 2007) to inverse interpretation (Hertler 2015c). Clearly, more data is wanting. As detailed previously (Hertler 2015c), however, cross-national obsessive distribution may not be amenable to survey research, as per the ceiling effect (Haigler and Widiger 2001) and frame of reference effect (Schmit et al. 1995 Peng et al. 1997 Heine et al. 2002). Obsessive character might instead have to be measured using a combination of structured interview and demographic correlates of obsessive character such as SES, homeownership, time orientation, and several other variables previously outlined (Hertler 2015c).

Beyond corroborating an evolutionary account of obsessive etiology, future research must be directed towards explicitly locating K-selected traits within obsessive samples. Herein, evidence that obsessive character is an extremely K-selected variant has been culled indirectly from clinical research and psychoanalytic description. The former assumes obsessive character to be disordered while the latter finds parental harshness to be the source of that disorder. Under these assumptions, and without expressly holding a life history perspective, existing literature on obsessive character furnishes data largely relevant to sociometric and psychometric life history markers, which continue to be actively validated. The sociometric and psychometric markers herein reviewed should be confirmed within obsessive samples using the Arizona Life History Battery or analogous measures. More than this, future research should remedy the want of core biometric data. Longitudinal research should measure obsessive character’s developmental pace, including growth rate, dental eruption, pubertal timing and age of naturally caused mortality. Slight slowing should be observed, consistent with the K-selected spectrum of developmental variance. Also, a life history perspective must compass mating, reproductive timing, and parental effort all of which were logically neglected from a psychoanalytic perspective that scrutinized how obsessives were treated as children by their parents, not how they themselves parent. It is hypothesized that, when controlling for other causal variables, obsessives will (1) marry more often, (2) divorce less frequently, (3) delay reproduction longer, (4) have fewer offspring with less partners, (5) and invest more in those offspring, especially via indirect parental care, such as provisioning. This is to say at length that the reproductive behaviors of obsessives should map onto that of a slow life history strategy. Again, personality traits like conscientiousness imply pair-bonding, fidelity, and parental investment, but direct studies will provide direct data.


Being future oriented, over-controlled, risk averse, loss averse, harm avoidant, and excessively conscientious, OCPD appears to seamlessly map onto all dimensions of the slow life history. However, there are two domains to the contrary. First, consistent with diagnostic descriptions of limited openness and flexibility (American Psychiatric Association 2000), trait studies provide evidence that four of six facets of openness to experience are inversely associated with obsessive character (Lynam and Widiger 2001). Yet, openness to experience is, for instance, part of the General Factor of Personality, an aggregate personality measure comprised of many slow life history traits (Rushton et al. 2009). Still, in some studies, openness is only marginally related to life history metrics (Dunkel and Decker 2010 Figueredo et al. 2007 Gladden et al. 2009), while in others (Rushton et al. 2008) only modest correlations (0.14) have been demonstrated. More importantly than openness, agreeableness displays a consistent and moderate correlation, which is positive for slow life history (Rushton et al. 2008 Manson 2015), and negative for obsessive personality (Widiger and Costa 1994 Furnham and Crump 2005 Samuel and Widiger 2010).

Obsessive personality, rather than being a slow life history exemplar, may be a distinct morph of the routinized and insular variety, explaining why obsessives are low on openness and agreeableness. In the context of the obsessive personality, low agreeableness, on one hand, might not have been overly impairing as per its purported evolutionary history of occupying less dense northerly regions and low agreeableness, on the other hand, might have been co-evolved as a safeguard against exploitation of conscientious labor and hoarded resources (Hertler 2015b). Even as one can place a person or type on a life history continuum, it does not follow that the person or type should differ from those higher and lower on the continuum only in quantitative position. Instead, one must understand life history variation as a function of, or correlative to, personality variation and vice versa. Consistent with the coral reef model (Sherman et al. 2013), it then follows that variants, morphs, or types should evolve, which are nonetheless distributed along a background of quantitative change on a life history continuum. In other words, understanding life history-based personality styles to evolve via the process of adaptive diversification (Hertler 2015d) created by physical, temporal, and social environmental heterogeneity, and regulated by negative frequency dependent balancing selection (Penke 2007 Penke et al. 2007 Hertler 2015d), one would expect continuous variation yes, but also distinctiveness of variants. So it follows that any particular variant or morph will imperfectly align with comprehensive life history variables and measures. It may be that variables like the K factor and covitality (Figueredo et al. 2005), and measures like the Mini-K (Figueredo et al. 2006) and the Arizona Life History Battery (Gladden et al. 2009) represent Platonic ideals against which actual variants and morphs deviate. In this case, however, deviation is not a matter of imperfection, but of adaptive variation on a life history theme. Encapsulating this viewpoint is Réale et al. (2010) who caution against simplification of life history theory, noting that varying selective pressures would promote the diversity of types. More directly, and speaking of humans, Figueredo et al. (2014b) explain that there is more specialization at the slow end of the life history spectrum resulting in the development of distinct morphs, of which OCPD seems to be one (Hertler 2014a).

Diagnostic Implications

As described, heretofore, the psychopathic personality has dominated the life history literature with resultant focus on extremes generated at the fast end of the spectrum, as illustrated by the work of Jonason and colleagues (Jonason and Webster 2010 Jonason et al. 2010 Jonason et al. 2009). Making it ever easier to pathologize r strategists (Brüne et al. 2010), fast life history behaviors correlate with deviance, while also being antithetical to contemporary middle-class North American standards (Figueredo et al. 2006) and social norms of modern industrialized societies (Brumbach et al. 2009). Discordantly, however, as Del Guidice rightly concludes, OCPD is pathologized precisely because it is an extreme K strategist. In consequence, obsessive personality can serve as the necessary archetypal slow life history counterpoint to the psychopathic personality, recalling the point that life history strategies can be more or less valued, but cannot be objectively ranked. Life histories are not better when slow or when fast (Wenner et al. 2013), but rather are properly understood as tradeoffs between risk and reward that have some contextually relevant outcome (Biro and Stamps 2008 Wolf and Figueredo 2011). In this way, the juxtaposition of the obsessive and psychopathic personalities within a life history perspective forces a distinction between culturally relativistic notions of pathology and objective evolutionary markers of dysfunction. Though sheltered under a century of Freudian explanation (Freud 1908/1959) and wedded to psychoanalysis through ties of ideology, history, and precedent (McCann 2009 Hertler 2014b), as this most prevalent of personality patterns (American Psychiatric Association 2013 Hertler 2015e) becomes increasingly accepted as an evolved slow life history variant, the validity of its diagnostic classification recedes. When jointly viewed within a life history framework, obsessive character and psychopathy may erode the practice and purpose of personality disorder diagnosis.


The authors thank Chris Conroy (University of California Berkeley Museum of Vertebrate Zoology), Esther Langan and Darrin Lunde (Smithsonian National Museum of Natural History), and Jack Dumbacher and Moe Flannery (California Academy of Sciences) for assistance accessing specimens. We would like to thank Marianne Brasil, Josh Carlson, Peter Kloess, Catherine Taylor, and Madeleine Zuercher for providing helpful feedback and discussion, Andrew Weitz for assistance with photography, and Meagan Oldfather and Matthew Kling for assistance with phylogenetic statistical methods. We would also like to thank Timothy D. Smith and three anonymous reviewers for their thoughtful comments and suggestions that greatly improved this manuscript. Funding for this study was generously provided by the Human Evolution Research Center, the Museum of Vertebrate Zoology, the Museum of Paleontology, and the Department of Integrative Biology, University of California Berkeley. TAM wrote the manuscript and took the photographs used in analysis. JLC collected the craniodental data as part of an undergraduate honors program at UC Berkeley and contributed to writing and editing the manuscript. LJH directed the larger project in which this work was done and edited the manuscript. All authors contributed to the intellectual content, context, and interpretation. All data are available upon request from the corresponding author.


Figure 6.18a Deforestation of Bornean rainforest for conversion to palm oil plantations. Figure 6.18b Men in Madagascar hunt and kill a white-fronted brown lemur for bushmeat.

There are over 600 species and subspecies of primates on the planet today, and almost half of them live under the threat of extinction. While there are many threats to primates, habitat destruction and hunting are the leading causes of population decline (Figure 6.18a–b). Primate populations have withstood small-scale forest clearing and low levels of hunting by local human groups for hundreds of years. However, the recent, intense pressure of expanding human populations on many primate habitats is resulting in rapid population declines for many species. The majority of primates live in tropical habitats, and the loss of tropical forest, whether due to logging or farming, is the single greatest factor contributing to the decline of primate populations across the planet. Between 1973 and 2010, almost 100,000 km2 of orangutan habitat was cleared for palm oil plantations in Borneo (Figure 6.18a). During this same time, the orangutan population decreased from almost 300,000 to 100,000, an average loss of more than 5,000 orangutans every year. As of 2017, that number may be as low as 60,000 (Schwitzer et al. 2017). If this rate of loss is not curtailed, the Bornean orangutan will go extinct in less than 15 years. Hunting, whether for bushmeat (Figure 6.18b), trophies, or the pet trade, has had devastating effects on many primate populations. Even though Grauer’s gorillas are legally protected, they are highly prized for bushmeat because they are relatively easy to track and shoot, and their large body size yields significant amounts of meat. Survey work has revealed that the Grauer gorilla population has declined significantly since the 1990s, due almost entirely to illegal hunting. The gorilla population in Kahuzi-Biega National Park, in Democratic Republic of Congo (DRC), is estimated to have declined 87% since 1994 (Schwitzer et al. 2017).

As consumers and concerned citizens, all of us are learning how to use our wallets to combat habitat and species loss. We do not buy palm oil or products made with palm oil in an effort to save orangutans. We donate to conservation organizations doing important on-the-ground work in Democratic Republic of Congo and other conservation hot-spots. We educate ourselves as well as our friends, families, and communities about the plight of endangered primates. Primatologists, too, contribute to conservation efforts. No longer is primatology research restricted to the “ivory tower” of academia. Current and future primatologists have the opportunity to affect real change in primate conservation (Chapman and Peres 2001). Whether understanding the mechanisms that determine species abundance, predicting the effects of human activity on species survival, documenting patterns of environmental change, understanding the effects of species removal in broader contexts, or evaluating different approaches to conservation, information gained from primate studies offers some of the best hope we have for a future that continues to include our closest living relatives. You can learn more about primate conservation in Appendix B.


Growing evidence suggests that an individual at the end of adolescence cannot be considered to be an adult when using physical, physiological, intellectual, social, emotional, and behavioral measures. When adolescents in developed societies mature and achieve adult body size, their behavior often remains immature. Specialists in adolescent medicine have recognized this incongruity, and have redefined adolescence to include young adults up to age 24 years, of whom many have not yet assumed adult roles (1, 2). Reproduction in contemporary forager societies also begins several years after adolescence and post-adolescent individuals are often limited in their gathering and/or hunting skills (3𠄵). Compared to other mammals, primates produce few offspring. Humans have an even slower growth rate than that of non-human primates of comparable size, but human growth may be even more prolonged than is generally realized.

Arnett proposed emerging adulthood as a phase of life between adolescence and full-fledged adulthood, with distinctive demographic, social, and subjective psychological features (6, 7). This life- history stage applies to individuals aged between 18 and 25 years, the period during which they become more economically independent by training and/or education. Previously, the psychodynamic theorist Erik Erikson identified a stage that he called a prolonged adolescence or psychosocial moratorium in young people in developed societies (8, 9). Much more recently, Hopwood and colleagues explored genetic and environmental influences on personality development during the transition to adulthood in same-sex male and female monozygotic and dizygotic twins assessed in late adolescence (approximately age 17 years), emerging adulthood (

24 years), and young adulthood (

29 years) (10). Their genetically-informed results support a life-course perspective on personality development during the transition to adulthood. In addition, the United Nations has identified youth, defined as the period from 15 to 24 years of age, as a period of vulnerability worldwide and has made it a priority for multiple interventions (11).

Here, we use an evolutionary approach in order to understand emerging adulthood, arguing that it is not just a sociological transition period but a biological life-history phase. Trait variability, whether it is molecular, cellular, physiological, morphological, or behavioral, is the leading edge of evolution. Together with genetic evolution, plasticity in developmental programming has evolved to provide the organism with traits that can secure its survival and reproductive success (12). Life-history theory is a powerful tool for understanding child growth and development from an evolutionary perspective (2, 13, 14). We provide evidence that emerging adulthood exists in some other mammals, which implies genetic evolution, and we discuss emerging adulthood in foraging as well as developed societies, which implies the occurrence of adaptive plasticity and cultural influences. We propose that genetic and cultural evolution have interacted to produce the emerging adulthood stage in human life history.


Holometabolous insects including mosquitoes, with distinct larval, pupal and adult stages, show clearly the difference between growth and development. Growth describes increases in size or mass, while development describes changes in which physiological and morphological characteristics progress toward reproductive maturity (Butler,1984). These two processes are related, as pupation typically does not occur until the larvae attain a certain minimum size or, more importantly,a critical level of nutrient stores(Clements, 2000). In mosquitoes, as in many other insects, adult fecundity and reproductive success are influenced by size. Size in insects has a strong genetic component but can also be influenced to a variable degree by environmental conditions. Because adults are enclosed in an exoskeleton and do not molt, adult size and dry mass are determined in large part by the dry mass of the larvae at the time of pupation. This is in turn determined by the ability of the larvae to acquire and conserve nutrients and the length of the larval stage. Any stress that increases energy expenditure or reduces nutrient assimilation efficiency will necessarily increase feeding rates, reduce adult size and nutrient stores,lengthen the larval stage, or cause a combination of these effects.

The different physiological mechanisms used by these two species to deal with ionic loads are expected to lead to differences in the effects of salinity on the balance between nutrient assimilation and nutrient expenditure during development. This is expected to lead in turn to differences in responses of growth and developmental parameters to salinity in the two species. We originally hypothesized that the two species would react in a similar way at low salinities, and differences would emerge and increase in magnitude as the salinity departed from values normally experienced by freshwater larvae. What we found instead is that the developmental programs of the two species respond to salinity in fundamentally different ways. The effects of salinity on growth and development of O. taeniorhynchusare due to positive direct influences of salinity on assimilation of dry mass,and on larval stage duration, both of which positively influence mass at pupation (Fig. 5). Although the increase in larval stage duration in response to increased salinity is actually larger than the increase in dry growth rate (Figs 2, 4), because the latter so strongly influences pupal mass, it is via its effect on growth rate that salinity most strongly influences final dry mass(Fig. 5). In O. taeniorhynchus, pupal mass is a function of larval stage duration, which is positively related to salinity (see Figs 3, 5). Thus, it appears that in O. taeniorhynchus the decision to pupate is uncoupled from information about pupal mass, so that increased larval stage duration and growth rate results in greater mass as salinity increases. In A. aegypti, a very different pattern emerges. Salinity influences mass through a curvilinear effect on larval duration, which lengthens the time to pupation at high salinities (Table 1, Figs 2, 5). However increased salinity also decreases larval growth rate in A. aegypti(Table 1, Figs 4, 5). As a result, despite the positive correlation between increased larval duration and mass at pupation,the increase in this trait at high salinity cannot fully compensate for negative effects of salinity via growth rate, resulting in a net negative effect of salinity on pupal dry mass(Table 1, Figs 2, 5). As salinity increases above 7 g l –1 , A. aegypti may partially compensate for salinity-induced changes in growth rates by adjusting developmental time,delaying pupation until the animal has acquired the critical mass for pupation and thereby maintaining pupal mass. The overall negative slope of this relationship shows that larvae in more saline water delay pupation but still pupate before they attain the mass that they would have attained in less saline conditions. This pattern suggests that A. aegypti assesses both larval stage duration and mass, reaching a compromise between rapid completion of the larval stage and maintaining ideal mass. Salinity thus influences developmental programs in fundamentally different ways in the two species investigated.

The increment in salinity that most dramatically delays development of A. aegypti is that over which the hemolymph osmotic pressure approaches the osmotic pressure of the environment. In larval mosquitoes,feeding leads to ingestion of the medium and may thus contribute to ionic loads under saline conditions. The decrease in growth rate of A. aegypti at greater salinities (Table 1, Fig. 4) may be due to decreased feeding rates to avoid ingestion of ions at greater rates than can be eliminated by the excretory system, and/or by decreased assimilation of nutrient stores due to increased metabolic demands of osmo-and iono-regulation at elevated salinities. This pattern could be explained by relatively slow growth rates at the time that they reach the critical mass for pupation, so that pupation occurs before significant additional mass has been accumulated. These data also suggest that a significant portion of the energy budget of larval A. aegypti is used for ionoregulation at higher salinities within the tolerable range.

In situations where the commitment to pupate occurs at a given mass, larger size together with delayed development, as occurs in O. taeniorhynchus (Table 1, Fig. 2), should occur if environmental conditions delay early growth until physiological adjustments have been made. If growth is especially rapid following these adjustments,then given fixed times between reaching critical mass and pupation, the mass of the insect would overshoot the target. Intriguingly, these data resemble those describing the relationship between adult size and developmental temperatures, in which larvae develop more slowly at lower temperatures but reach greater size (Clements,2000).

The trade-off that exists between completing development quickly and attaining large size appears to lead to selection for increased growth rates in O. taeniorhynchus relative to A. aegypti, and in females of both species relative to males. It is tempting to speculate that the differences between the growth rates of A. aegypti and O. taeniorhynchus (Figs 2, 3, 4) are evolved traits driven by selective pressures. This is supported by the observation that the differences in growth rates of the two species are paralleled by differences in the behavior of their larvae and pupae. Larvae and pupae of O. taeniorhynchus swim quite rapidly, darting around the container in rapid bursts. A. aegypti larvae on the other hand swim slowly, and have a much greater tendency to mass together in the darkest corner of the rearing dish (T. M. Clark, unpublished observation). Ochlerotatus taeniorhynchus also completes the pupal stage within 48 h at 26°C,whereas the pupal stage of A. aegypti typically lasts more than 48 h at this temperature (T. M. Clark, unpublished observation). The reason for greater pupal mass of O. taeniorhynchus is likely to involve selective pressures acting on adults, such as selection for increased fecundity, possibly driven by increased mortality of larvae in their less-protected larval habitats. Similarly, the differences in growth rates and swimming speeds are likely to be driven by different selective pressures experienced by larvae of the two species in their natural habitats. Ochlerotatus taeniorhynchus larvae live in salt marshes, where predators such as fishes are common and rapid evaporation of temporary pools occurs. Larval A. aegypti, on the other hand, live in very small bodies of water such as tin cans and discarded tires, habitats unlikely to contain such predators. The difference in growth rates between the species increases with increasing salinity reflecting their different mechanisms of iono- and osmo-regulation.

A large number of environmental parameters influence growth and development in larval mosquitoes. These sources of variability may explain some of the discrepancies between the results of the current study and those of Nayar(1969), who observed that size,dry mass and percent lipid of O. taeniorhynchus decrease with increasing salinity. It is possible, but unlikely, that the differences are due to evolution of the laboratory strain used in the two studies during the 30+ years since the studies of Nayar(1969). The differences between the results of that study and the present one more probably result from different rearing conditions, such as feeding regimens. Similarly, the significance of comparisons between the present work and the work of McGinnis and Brust (1983) is not clear. In their study, the euryhaline Aedes togoi showed a υ-shaped response of developmental time to salinity, similar to the response of A. aegypti. However, unlike both A. aegypti and O. taeniorhynchus (this study), A. togoi showed the slowest development in the most dilute water. It is possible that this species exhibits yet a third pattern of response although once again we suspect that environmental conditions contribute to the observed differences.

This is the first study to directly compare growth and development of two species under the same environmental conditions in order to avoid such artifacts. Patrick et al.(2002a,b)have documented surprisingly diverse mechanisms of ionoregulation among freshwater species, and even among populations within a species. The present study shows that fundamental differences in mechanisms by which growth and development respond to environmental influences can also occur among closely related species.

Watch the video: Growth of Biological Thought: Where do we stand- Dr. Himender Bharti (May 2022).