Andy Gardner

Social semantics: altruism, cooperation, mutualism, strong reciprocity and group selection

From an evolutionary perspective, social behaviours are those which have fitness consequences for both the individual that performs the behaviour, and another individual. Over the last 43 years, a huge theoretical and empirical literature has developed on this topic. However, progress is often hindered by poor communication between scientists, with different people using the same term to mean different things, or different terms to mean the same thing. This can obscure what is biologically important, and what is not. The potential for such semantic confusion is greatest with interdisciplinary research. Our aim here is to address issues of semantic confusion that have arisen with research on the problem of cooperation. In particular, we: (i) discuss confusion over the terms kin selection, mutualism, mutual benefit, cooperation, altruism, reciprocal altruism, weak altruism, altruistic punishment, strong reciprocity, group selection and direct fitness; (ii) emphasize the need to distinguish between proximate (mechanism) and ultimate (survival value) explanations of behaviours. We draw examples from all areas, but especially recent work on humans and microbes.

Social evolution theory for microorganisms

Microorganisms communicate and cooperate to perform a wide range of multicellular behaviours, such as dispersal, nutrient acquisition, biofilm formation and quorum sensing. Microbiologists are rapidly gaining a greater understanding of the molecular mechanisms involved in these behaviours, and the underlying genetic regulation. Such behaviours are also interesting from the perspective of social evolution ? why do microorganisms engage in these behaviours given that cooperative individuals can be exploited by selfish cheaters, who gain the benefit of cooperation without paying their share of the cost? There is great potential for interdisciplinary research in this fledgling field of sociomicrobiology, but a limiting factor is the lack of effective communication of social evolution theory to microbiologists. Here, we provide a conceptual overview of the different mechanisms through which cooperative behaviours can be stabilized, emphasizing the aspects most relevant to microorganisms, the novel problems that microorganisms pose and the new insights that can be gained from applying evolutionary theory to microorganisms.

Evolutionary Explanations for Cooperation

Natural selection favours genes that increase an organisms ability to survive and reproduce. This would appear to lead to a world dominated by selfish behaviour. However, cooperation can be found at all levels of biological organisation: genes cooperate in genomes, organelles cooperate to form eukaryotic cells, cells cooperate to make multicellular organisms, bacterial parasites cooperate to overcome host defences, animals breed cooperatively, and humans and insects cooperate to build societies. Over the last 40 years, biologists have developed a theoretical framework that can explain cooperation at all these levels. Here, we summarise this theory, illustrate how it may be applied to real organisms and discuss future directions.

Changes in energy balance and body composition at menopause: a controlled longitudinal study.

THIS ARTICLE HAS BEEN RETRACTED Menopause heralds the onset of physiologic changes that increase the risk for cardiovascular disease [1-3]. Although many women report increased body weight during menopause, no longitudinal studies have examined energy intake and energy expenditure and their association with changes in body composition and fat distribution. Several cross-sectional studies have suggested that decreases in resting metabolic rate and physical activity may be accelerated in the postmenopausal years [4-6]. The decrease in energy expenditure during rest and physical activity may be related to the decreased fat-free mass that has been seen in postmenopausal women [7-9]. More-over, the decrease in energy expenditure may result in increased fat mass if daily energy intake is not reduced accordingly. Other researchers, however, have not found that menopause independently affects body weight gain [10, 11]. These studies [4-11] have provided incomplete information on menopause-related changes in energy expenditure and body composition. Cross-sectional designs do not allow a robust examination of the effects of menopause on metabolic and cardiovascular risk factors, and no longitudinal studies have included measures of resting metabolic rate and body composition. To this end, we compared longitudinal changes in resting metabolic rate, body composition, and physical activity in a cohort of healthy premenopausal women who experienced menopause with changes in these variables in a cohort of women who remained premenopausal. Methods THIS ARTICLE HAS BEEN RETRACTED Patients Thirty-eight healthy, nonsmoking, premenopausal white women (age range, 44 to 48 years) were tested for baseline metabolic characteristics. Women were recruited from the surrounding communities through advertisements in the local newspaper and radio and were then screened by a telephone interview. None had coronary heart disease (for example, ST-segment depression greater than 1 mm at rest or during a modified Balke exercise test), cardiomyopathy, or hypertension (resting blood pressure greater than 140/90 mm Hg); were receiving medications that could affect cardiovascular function or metabolic rate; or had a medical history of diabetes. All premenopausal women were tested between days 5 and 12 of the follicular phase of their menstrual cycle. Menopause status was determined by interview. Six years later, patients participated in an identical series of metabolic tests, for which identical testing equipment was used. The reproducibility of the metabolic tests over this time period has previously been reported [5]. After follow-up, 18 women had spontaneously stopped menstruating for at least 12 months (2 1 years after menopause began), and 17 reported normal menstrual function. We excluded three perimenopausal women from the analyses. None of the women had received hormone replacement therapy. The Committee on Human Research for the Medical Sciences approved the study, and each volunteer provided written informed consent before the study began. Timing and Description of Metabolic Tests The study methods have been previously described [4]. Briefly, resting metabolic rate was measured for 45 minutes on the morning (0730 hours) after a 12-hour overnight fast. Each volunteer was placed in a supine position, and a clear plastic hood was placed over her head. Room air was continuously drawn through the hood, and the flow rate was measured by a pneumotachograph. Oxygen consumption (Vo 2) and CO2 production were continuously measured and analyzed during this period and were converted to a caloric equivalent (kcal/d) [12]. Peak Vo 2 was assessed by a progressive, symptom-limited treadmill exercise test. Body fat was estimated from body density by underwater weighing [13], and fat-free mass was calculated as total body mass minus fat mass. Waists were measured at the minimal circumference between the xiphoid process and the superior anterior iliac crest, and hips were measured at the maximal protrusion of the buttocks. The physical activity level during leisure time was measured using a structured interview [14]. Plasma glucose levels were determined using a YSI glucose analyzer (Yellow Springs Instruments, Yellow Springs, Ohio). Plasma immunoreactive insulin levels were determined by radioimmunoassay. Food intake was measured from 3-day food intake diaries (2 weekdays and 1 weekend day). We used a Student t-test to compare changes in the outcome measures between baseline and the end of follow-up within each subgroup. Values are expressed as the mean SD. Results THIS ARTICLE HAS BEEN RETRACTED Baseline physical characteristics of the cohort of premenopausal women are shown in Table 1. No differences were seen between the baseline characteristics of women who experienced natural menopause and those of women who remained premenopausal (Table 1). When women were retested after 6 years of follow-up, 18 were classified as postmenopausal and 17 remained premenopausal. Although total body weight did not differ between groups after follow-up, the composition of body weight showed distinct differences. Postmenopausal women lost more fat-free mass and gained more fat mass than women who remained premenopausal (Table 1). Women who experienced natural menopause had a greater decrease in resting metabolic rate and physical activity during leisure time. Fasting insulin levels and the waist-to-hip ratio were also increased more in women who became postmenopausal. Menopause did not affect body weight, fasting glucose levels, energy intake, or peak Vo 2. Table 1. Longitudinal Changes in Women Who Experienced Menopause (n = 18) and in Women Who Remained Premenopausal (n = 17)* Discussion THIS ARTICLE HAS BEEN RETRACTED Women in the United States now live an average of 75 to 80 years [15]. Those who experience menopause can expect to live approximately 30 years beyond this natural event, a time period that is approximately equivalent to their reproductive life span. The onset of menopause may be a risk factor for cardiovascular disease in women [1-3]. We used a longitudinal design to compare cardiovascular and metabolic changes in women who spontaneously stopped menstruating with changes in age-matched women who remained premenopausal. Women who stopped menstruating had distinct changes in energy expenditure during rest and physical activity, body composition, and fat distribution that could increase the risk for cardiovascular and metabolic disorders. Although our study could not establish cause and effect, our results suggest that natural menopause is associated with a worsening cardiovascular and metabolic risk profile. Resting metabolic rate, the largest component of daily energy expenditure, regulates body weight, body composition, and daily energy needs [16]. Resting metabolic rate decreases with age in women, a process that is associated with the loss of fat-free mass [4]; however, the degree to which normal menopause accelerates this decrease is unclear. Cross-sectional data suggest that the decrease in resting metabolic rate and fat-free mass may be accelerated in postmenopausal women not receiving hormone replacement therapy [4, 17]. However, few previous longitudinal studies have been done on the effect of menopause on metabolic rate and body composition. In our study, fat-free mass decreased by 3 kg and resting metabolic rate declined by approximately 100 kcal/d in postmenopausal women; no marked changes were noted in women who remained premenopausal. Thus, it is likely that resting energy requirements are lower in postmenopausal women because of the associated loss of metabolically active tissue. We used a peak Vo 2 test to evaluate cardiovascular fitness and used an activity questionnaire to assess physical activity during leisure time. Postmenopausal women reported lower levels of leisure time physical activity than did premenopausal women, despite a similar decrease in peak Vo 2 in both groups. The decline in physical activity during leisure time may have been related to the increase in body fat and decline in fat-free mass seen in postmenopausal women. Although Wing and colleagues [11] did not measure body composition, they found greater weight gain in women who reported the largest decline in exercise-related behaviors over 3 years. Taken together, the decline in energy expenditure during rest and leisure (approximately 230 kcal/d), without a proportional reduction in energy intake, supports a period of positive energy imbalance that may be related to the increased fat mass seen in women who became postmenopausal. Cross-sectional studies have reported discrepant results on the independent effects of menopause on body fat and fat distribution. Some investigators found an accelerated accumulation of fat in the intra-abdominal region [8, 9], whereas others found no independent menopause-related effect [17]. Our results support an increase in total body fat, which, as evidenced by the increase in the waist-to-hip ratio, is probably stored in central regions of the body. An increase in central body fat increases the risk for many chronic disorders, including atherosclerosis, hypertension, hypercholesterolemia, and insulin resistance [18]. Fasting insulin levels increased in women who experienced menopause, a finding consistent with those of previous longitudinal investigations [3]. This increased insulin level could be predicted because increased levels of total and intra-abdominal body fat are associated with higher levels of fasting insulin [17]. Our findings cannot establish cause and effect and cannot be extrapolated to other racial and ethnic groups. Larger sample sizes consisting of women receiving and not receiving hormone replacement therapy are needed to confirm and extend our preliminary observations regarding the energy imbalance of menopause and the possible therapeutic benefits of hormone replacement. Nevertheless, our longitudinal findings sugge

PROGRESSIVE VS SINGLE-STAGE TREADMILL TESTS FOR EVALUATION OF CLAUDICATION

The reliability of claudication pain and the metabolic and hemodynamic measurements of the lower limbs of patients with stable peripheral vascular occlusive disease (PVOD) were compared during and following single-stage (S) and progressive (P) treadmill tests. Ten patients (69.8 +/- 1.8 yr; X +/- SE) walked to maximal claudication pain twice a month for 4 months. Patients walked at 1.5 mph up a 7.5% grade (S test) and at 2 mph on a 0% grade, increasing by 2% every 2 min (P test). Distance walked to the onset of claudication pain (CPD) and maximal walking distance (MWD) were recorded. Foot transcutaneous oxygen tension (TcPO2) was measured before, during, and after exercise, while ankle systolic blood pressure (SBP) and the ankle-to-brachial SBP index (ABI) were measured before and after exercise. Intraclass correlation coefficients (R) of CPD and MWD during S tests were R = 0.53 and R = 0.55, respectively. In contrast, the respective R values during P tests were R = 0.89 and R = 0.93. Higher R values of foot TcPO2 were also obtained during and following P tests, while ankle SBP and ABI were highly reliable following both tests. It is concluded that the severity of PVOD is better assessed by P treadmill tests because clinical measurements are more reliable during exercise and recovery.

How many steps/day are enough? For older adults and special populations

Older adults and special populations (living with disability and/or chronic illness that may limit mobility and/or physical endurance) can benefit from practicing a more physically active lifestyle, typically by increasing ambulatory activity. Step counting devices (accelerometers and pedometers) offer an opportunity to monitor daily ambulatory activity; however, an appropriate translation of public health guidelines in terms of steps/day is unknown. Therefore this review was conducted to translate public health recommendations in terms of steps/day. Normative data indicates that 1) healthy older adults average 2,000-9,000 steps/day, and 2) special populations average 1,200-8,800 steps/day. Pedometer-based interventions in older adults and special populations elicit a weighted increase of approximately 775 steps/day (or an effect size of 0.26) and 2,215 steps/day (or an effect size of 0.67), respectively. There is no evidence to inform a moderate intensity cadence (i.e., steps/minute) in older adults at this time. However, using the adult cadence of 100 steps/minute to demark the lower end of an absolutely-defined moderate intensity (i.e., 3 METs), and multiplying this by 30 minutes produces a reasonable heuristic (i.e., guiding) value of 3,000 steps. However, this cadence may be unattainable in some frail/diseased populations. Regardless, to truly translate public health guidelines, these steps should be taken over and above activities performed in the course of daily living, be of at least moderate intensity accumulated in minimally 10 minute bouts, and add up to at least 150 minutes over the week. Considering a daily background of 5,000 steps/day (which may actually be too high for some older adults and/or special populations), a computed translation approximates 8,000 steps on days that include a target of achieving 30 minutes of moderate-to-vigorous physical activity (MVPA), and approximately 7,100 steps/day if averaged over a week. Measured directly and including these background activities, the evidence suggests that 30 minutes of daily MVPA accumulated in addition to habitual daily activities in healthy older adults is equivalent to taking approximately 7,000-10,000 steps/day. Those living with disability and/or chronic illness (that limits mobility and or/physical endurance) display lower levels of background daily activity, and this will affect whole-day estimates of recommended physical activity.

The genetical theory of kin selection

Natural selection operates both directly, via the impact of a trait upon the individual’s own fitness, and indirectly, via the impact of the trait upon the fitness of the individual’s genetically related social partners. These effects are often framed in terms of Hamilton’s rule, rb − c > 0, which provides the central result of social‐evolution theory. However, a number of studies have questioned the generality of Hamilton’s rule, suggesting that it requires restrictive assumptions. Here, we use Fisher’s genetical paradigm to demonstrate the generality of Hamilton’s rule and to clarify links between different studies. We show that confusion has arisen owing to researchers misidentifying model parameters with the b and c terms in Hamilton’s rule, and misidentifying measures of genotypic similarity or genealogical relationship with the coefficient of genetic relatedness, r. More generally, we emphasize the need to distinguish between general kin‐selection theory that forms the foundations of social evolution, and streamlined kin‐selection methodology that is used to solve specific problems.

Physical activity is related to quality of life in older adults

BackgroundPhysical activity is associated with health-related quality of life (HRQL) in clinical populations, but less is known whether this relationship exists in older men and women who are healthy. Thus, this study determined if physical activity was related to HRQL in apparently healthy, older subjects.MethodsMeasures were obtained from 112 male and female volunteers (70 ± 8 years, mean ± SD) recruited from media advertisements and flyers around the Norman, Oklahoma area. Data was collected using a medical history questionnaire, HRQL from the Medical Outcomes Survey short form-36 questionnaire, and physical activity level from the Johnson Space Center physical activity scale. Subjects were separated into either a higher physically active group (n = 62) or a lower physically active group (n = 50) according to the physical activity scale.ResultsThe HRQL scores in all eight domains were significantly higher (p < 0.05) in the group reporting higher physical activity. Additionally, the more active group had fewer females (44% vs. 72%, p = 0.033), and lower prevalence of hypertension (39% vs. 60%, p = 0.041) than the low active group. After adjusting for gender and hypertension, the more active group had higher values in the following five HRQL domains: physical function (82 ± 20 vs. 68 ± 21, p = 0.029), role-physical (83 ± 34 vs. 61 ± 36, p = 0.022), bodily pain (83 ± 22 vs. 66 ± 23, p = 0.001), vitality (74 ± 15 vs. 59 ± 16, p = 0.001), and social functioning (92 ± 18 vs. 83 ± 19, p = 0.040). General health, role-emotional, and mental health were not significantly different (p > 0.05) between the two groups.ConclusionHealthy older adults who regularly participated in physical activity of at least moderate intensity for more than one hour per week had higher HRQL measures in both physical and mental domains than those who were less physically active. Therefore, incorporating more physical activity into the lifestyles of sedentary or slightly active older individuals may improve their HRQL.

Capturing the superorganism: a formal theory of group adaptation

Adaptation is conventionally regarded as occurring at the level of the individual organism. However, in recent years there has been a revival of interest in the possibility for group adaptations and superorganisms. Here, we provide the first formal theory of group adaptation. In particular: (1) we clarify the distinction between group selection and group adaptation, framing the former in terms of gene frequency change and the latter in terms of optimization; (2) we capture the superorganism in the form of a ‘group as maximizing agent’ analogy that links an optimization program to a model of a group‐structured population; (3) we demonstrate that between‐group selection can lead to group adaptation, but only in rather special circumstances; (4) we provide formal support for the view that between‐group selection is the best definition for ‘group selection’; and (5) we reveal that mechanisms of conflict resolution such as policing cannot be regarded as group adaptations.

Bacteriocins, spite and virulence

There has been much interest in using social evolution theory to predict the damage to a host from parasite infection, termed parasite virulence. Most of this work has focused on how high kinship between the parasites infecting a host can select for more prudent exploitation of the host, leading to a negative relationship between virulence and parasite kinship. However, it has also been shown that if parasites can cooperate to overcome the host, then high parasite kinship within hosts can select for greater cooperation and higher growth rates, hence leading to a positive relationship between virulence and parasite kinship. We examine the impact of a spiteful behaviour, chemical (bacteriocin) warfare between microbes, on the evolution of virulence, and find a new relationship: virulence is maximized when the frequency of kin among parasites; social partners is low or high, and is minimized at intermediate values. This emphasizes how biological details can fundamentally alter the qualitative nature of theoretical predictions made by models of parasite virulence.