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Worldwide Variation in Life-Span Sexual Dimorphism and Sex-Specific Environmental Mortality Rates
ANATOLY T. TERIOKHIN, FRANCOIS GUEGAN1
1,2

ELENA V. BUDILOVA,2 FREDERIC THOMAS,1 AND JEAN-

Abstract In all human populations mean life span of women generally exceeds that of men, but the extent of this sexual dimorphism varies across different regions of the world. Our purpose here is to study, using global demographic and environmental data, the general tendency of this variation and local deviations from it. We used data on male and female life histor y traits and environmental conditions for 227 countries and autonomous territories; for each countr y or territor y the life-span dimorphism was defined as the difference between mean life spans of women and men. The general tendency is an increase of life-span dimorphism with increasing average male­female life span; this tendency can be explained using a demographic model based on the Makeham­Gomper tz equation. Roughly, the life-span dimorphism increases with the average life span because of an increase in the duration of expressing sex- and age-dependent mor tality described by the second (exponential) term of the Makeham­Gomper tz equation. Thus we investigated the differences in male and female environmental mor tality described by the first term of the Makeham­Gomper tz equation fitted to the data. The general pattern that resulted was an increase in male mor tality at the highest and lowest latitudes. One plausible explanation is that specific factors tied to extreme latitudes influence males more strongly than females. In par ticular, alcohol consumption increases with increasing latitude and, on the contrar y, infection pressures increase with decreasing latitude. This finding agrees with other obser vations, such as an increase in male mor tality excess in Europe and Christian countries and an increase in female mor tality excess in Asia and Muslim countries. An increase in the excess of female mor tality may be also due to increased maternal mor tality caused by an increase in fer tility. However, this relation is not linear: In regions with the highest fer tility (e.g., in Africa) the excess of female mor tality is smaller than in regions with relatively lower fer tility (e.g., in Asia). A possible explanation of this phenomenon is an evolutionar y adaptation of women to the pressures of extremely high fer tility by means of some reduction of their maternal mor tality.
1 Centre d'Etudes sur le Polymorphisme des Micro-Organismes, CEPM/UMR CNRS-IRD 9926, Institut de ´ ´ Recherches pour le Developpement, 911 Avenue Agropolis, B.P. 64501, 34394 Montpellier Cedex 5, France. 2 Chair of General Ecology, Faculty of Biology, Moscow State University, Moscow 119899, Russia.

Human Biology, August 2004, v. 76, no. 4, pp. 000 ­ 000 Copyright 2004 Wayne State University Press, Detroit, Michigan 48201-1309 KEY WORDS: SEXUAL DIMORPHISM, LIFE SPAN, MORTALITY RATE.

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624 / teriokhin et al. The existence of life-span sexual dimorphism in humans, characterized by a longer female life expectancy, is commonly recognized and empirically confirmed (e.g., Lopez and Ruzicka 1983; Gavrilov and Gavrilova 1991; Trovato and Lalu 1998; Mathers et al. 2001; Kraemer 2000; Lobmayer and Wilkinson 2000; Kirkwood 2001; Luy 2002). However, there is no established consensus concerning the general worldwide pattern and regional deviations in differences between female and male life spans. Here, we address this question by analyzing global demographic and environmental data. Evolutionar y hypotheses explaining the emergence of female life-span predominance are mainly based on the differences in ecological roles between males and females. Males are expected to maximize their fitness by increasing their mating success, whereas females need to increase their longevity for obtaining maximal reproductive output (Bateman 1948; Williams and Williams 1957; Rolff 2002). An evolutionar y optimization model, based on the similar assumption that males should preferentially spend large amounts of energy in shor t times (mating, hunting) and females should accumulate energy over long periods (gestation and rearing children), results in the emergence of greater female life spans (Teriokhin and Budilova 2000). This ``maternal'' hypothesis can be completed by the ``grandmaternal'' hypothesis, which explains both extended female longevity and limited reproductive period (menopause) by advantages of grandmaternal care over maternal care for older females (Hamilton 1964; Trivers 1972; Alvarez 2000; Peccei 2001), and is par tly confirmed by the analysis of obser ved data (Jamison et al. 2002; Sear et al. 2002; Voland and Beise 2002). Although children can inherit up to twice as many of the female's genes as grandchildren, the risks for an old female not to have enough vital resources and time to gestate and bring up her own child would overcome the advantages of giving a new bir th (Teriokhin and Budilova 2000). A quasi-universal predominance of female life expectancy and especially its persistence in highly developed countries, where differences in ecological roles of males and females are attenuated and environmental mor tality risks are reduced, suggest that a substantial component of the sex difference in life expectancy is under genetic control (Wells 2000). We refer to this as life-span sexual dimorphism. However, nongenetic external causes of mor tality that affect males and females differently, which we call sex-specific environmental mor tality, undoubtedly exist. For example, the consumption of alcohol, usually higher in males, is known to reduce the life span of males (Lunetta et al. 1998; Nolte et al. 2003). Some studies argue that males are more vulnerable to infections (Franceschi et al. 2000; Wells 2000). In contrast, environmental conditions, primarily social ones, might reduce the longevity of females (Klasen 1998; Lavoyin 2001). We used the Gomper tz­Makeham model (Gomper tz 1825; Makeham 1860) to divide total mor tality into two components: one that reflects the general tendency of age- and sex-dependent mor tality and one that takes into account regional deviations (which, in addition, might be sex-specific) from this general tendency.

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Life-Span Sexual Dimorphism / 625

Materials and Methods
The global demographic and environmental data used in the analyses were collected for 227 countries and autonomous territories (see Appendix) using mainly international electronic databases accessible on the Internet, such as those provided by the World Health Organization (http://www.who.int), the Centers for Disease Control and Prevention in the United States (http://www.cdc.gov), the United Nations Statistical Division (http://un.stats.un.org), the World Bank Group (http://www.worldbank.org), and the World Sites Atlas (http://www.sitesatlas.com). These data were par tly completed by information from other sources (e.g., scientific journals and repor ts from ministries of health). Disease occurrences in the different countries were compiled for a set of 324 categories of human parasitic and infectious diseases affecting human survival (see more information at http://www.cyinfo.com), and the disease load was calculated as the total number of diseases for each countr y. The consumption of alcohol per individual was measured in liters per capita per year. Life expectancy at bir th and infant mor tality were considered separately for each sex. The maternal mor tality ratio was defined as the number of maternal deaths caused by deliveries and complications of pregnancy and childbir th divided by the number of live bir ths for a given year; it is expressed per 100,000 live bir ths. The fer tility indicates the number of offspring born to a woman per lifetime passing through the child-bearing age. The nutritional conditions were evaluated by the calorie consumption per average inhabitant per day. Mean latitude and mean longitude refer to the value measured at the geographic center of each countr y. Instead of life span at bir th L0, which is presented in our source data and which includes infant mor tality of the first year of life, we use the life-span estimate L1, which is calculated under the assumption of having sur vived the first year. L1 can be obtained from the equation representing L0 as a weighted sum of L 1 (the life span of those who have not sur vived the first year) and L1 (the life span of those who did sur vive the first year): L0 p1L
1

(1

p1)L1,

(1)

where p1 is the probability of dying during the first year, which is also present in our data. Taking into account that L 1 is equal to 1 p1 (the probability of surviving the first year), we obtain the following formula for L1: L1 L0 (1 p1 ) P1. (2)

The values of L1 were calculated separately for women and men using the values of L0 and p1 (known for each sex). Only values of L1 will be used fur ther and will be referred to as female and male life spans.

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Figure 1.

Scatterplots and regression lines of female (upper) and male (lower) life spans on average life span (female­male mean life-span half-sum).

Regression and variance analyses were performed using the S-Plus statistical package (Venables and Ripley 1994). General Tendency of Life-Span Dimorphism. The general tendency of the global pattern of life-span dimorphism is that dimorphism increases with the average life span (half-sum of female and male mean life spans). This appears clearly in Figure 1, where the dependencies of female and male life spans, Lf and Lm, on their half-sum L are approximated by the linear regressions L
f

1.822

1.0641L,

R

0.996, p

0.0000001,

(3)

and L
m

1.822

0.9349L,

R

0.996, p

0.0000001.

(4)

In more detail, this tendency is shown in Figure 2, where the dependence of lifespan dimorphism, defined as female minus male life span, d Lf Lm, on L is approximated by a statistically significant linear regression:

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Life-Span Sexual Dimorphism / 627

Figure 2.

Scatterplots and regression line of female minus male life span on average life span (female­male mean life-span half-sum).

d

3.644

0.128L,

R

0.564, p

0.0000001

(5)

(countr y names are designated by their two-letters codes, given in the Appendix). Alternatively, the significance of increasing life-span dimorphism with increasing life span can be detected using an approach proposed by Mosimann (Mosimann 1970; Mosimann and Darroch 1985). According to this approach, we should regress the logarithms of Lf on the averages of the logarithms of Lf and Lm and compare the slope of this regression with 1.0. In our case we obtained a value of slope equal to 1.039, which is significantly greater than 1.0 (p 0.0000001), thus indicating that life-span dimorphism does increase with increasing life span. This tendency can be explained by using the Gomper tz­Makeham law (Gomper tz 1825; Makeham 1860), which presents the age dynamics of the individual rate of mor tality m(t) as the sum of two terms, according to the following equation: m(
t)

A

BeCt.

(6)

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628 / teriokhin et al. The first term, A, is independent of age and reflects the action of environmental causes of death, whereas the second term increases exponentially with age t. The accelerated increase of mor tality with age can be explained by a progressive reduction of an organism's resources allocated to its repair, as is predicted by evolutionar y optimization models (Abrams and Ludwig 1995; Cichon 1997; Teriokhin 1998). The estimations of the parameters A, B, and C from demographic data for different human populations (e.g., Gavrilov and Gavrilova 1991) show that parameters B and C are relatively stable in geographic space and historical time compared to parameter A and that parameter C is more stable with respect to sex. We therefore assume the following model to describe the age dynamics of the mor tality rate m(r, s, t) for an individual of sex s (f, female; m, male) living in a region r: m(r,s,t) A
r

BseCt.

(7)

When the age dynamics of mor tality are known, the individual's expected mean life span can be computed using the equation
T

L

s

1
t 1

exp

Ar t

Bs (e C

Ct

1) ,

(8)

which approximates the exact integral equation L 1
t

s

exp
0

Ar t

Bs (e C

Ct

1) dx.

(9)

The maximum life span T in the approximated equation must be a sufficiently large age for which the probability to sur vive up to it is small. We used the value T 120, for which this probability is less than 0.0000001, even in the absence of environmental mor tality. To find the best estimates for the parameters Bf, Bm, and C (i.e., minimizing the sum of squares of differences between obser ved and estimated life spans through all the countries and both sexes), we assumed that on the global scale the regional sex differences in the parameter Ar are mutually balanced (i.e., that the values of the parameter Ar for each region r were equal for both sexes). Thus we had to estimate N 3 parameters on the basis of 2N obser vations (life spans for males and females for N countries). The estimates obtained for the parameters were Bf 0.0000078, Bm 0.000017, and C 0.101. In turn, the estimates of life spans computed using the Gomper tz­Makeham equation with these parameter estimates (plus corresponding estimates of Ar) do not differ practically (not greater than onetenth of a year) from the estimates obtained using the linear regressions. Hence

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Life-Span Sexual Dimorphism / 629 the obser ved linear trend of life-span dimorphism associated with increasing average life span can be explained by sex differences in the parameter Bs in the Gomper tz­Makeham equation. Regional Deviations from the General Tendency. We then tried to explain the deviations from the general linear trend by using the regional differences in the first term of this equation: m(r,s,t) Ar
,s

Bs eCt.

(10)

In this second stage of the analysis, values of parameters Bf, Bm, and C were fixed at their estimated values and parameter A was allowed to depend both on region and sex. Fitting this model to the data allowed us to estimate values of environmental mor tality rate for each countr y and for each sex (see Appendix). We then tried to relate mor tality sex-specific differences, expressed by Ar,s, to the environmental conditions in different countries. To attenuate the role of outlying differences between male and female environmental mor tality rates, we did not analyze row differences but their logarithmically transformed values dA, obtained using the equation d
A

(Ar

,m

Ar, f )log[1

10,000 (Ar

,m

Ar, f ) ],

(11)

which we call male environmental mor tality rate excess, or simply male mor tality excess. To evaluate the environmental influence on sexual differences in environmental mor tality, we estimated the dependencies of dA on different environmental factors using regression and dispersion analyses. These analyses identified several environmental factors significantly related to dA ,some of which were nonlinear. In par ticular, the excess of male environmental mor tality is obser ved at lower and higher latitudes (lesser than 10 and greater than 45 ; see Figure 3). The dependence of dA on latitude x is described by a second-order polynomial function with a statistically significant quadratic term: d
A

0.4574

0.07736 x

0.001499x2, R 0.225, px

0.0037, p

x

2

0.00048.

(12)

We suggest that such a nonlinear dependence can be explained by opposite linear dependences of dr with different environmental factors. Factors significantly (at the 5% level) related to the male mor tality excess are shown in Tables 1 and 2. From Table 1 we see that the factor that is the most incontestably correlated with dA (R 0.27, p 0.00024) is the annual per capita consumption of alcohol. This factor is also positively correlated with latitude (R 0.50, p 0.0000001), so that the excess of male environmental mor tality at higher latitudes can, at least

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Figure 3.

Scatterplots and quadratic regression line of excess of male environmental mor tality dA (see text) on latitude. Names of regions (countries and autonomous territories) are indicated by their two-letter codes (see Appendix).

Table 1. Quantitative Environmental Factors Significantly Correlated to the Excess of Male Environmental Mor tality, dA
Environmental Factor Alcohol Infections Physicians Correlation with d 0.27 0.19 0.22
A

p Level for Testing H0: R 0.00024 0.013 0.0034

0

in par t, be explained by a negative influence of excessive consumption of alcohol, which affects primarily men (Lunetta et al. 1998; Nolte et al. 2003). On the contrar y, another environmental factor, the number of infections (corrected for the logarithm of population number), which also correlated positively with dA (R 0.19, p 0.013) (see Table 1), increases with decreasing latitude (R 0.53, p 0.0000001). This may explain, at least in par t, the increase in male mor tality excess at lower latitudes, because, in general, infections affect men more strongly than women (Franceschi et al. 2000; Wells 2000). We might

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Life-Span Sexual Dimorphism / 631
Table 2. Qualitative Environmental Factors Significantly Related to the Excess of Male Environmental Mor tality, dA
Environmental Factor Europe Asia Muslims Christians Island Mean Values of dA in Presence and Absence of Factor 0.50 0.49 1.07 0.16 0.48 vs. 028 vs. 0.05 vs. 0.19 vs. 0.74 vs. 0.05 p Level for Testing the Absence of Difference 0.011 0.044 0.0088 0.048 0.044

generalize these two obser vations by proposing that stressful factors, in par ticular, those manifested at extreme higher and lower latitudes, influence primarily men negatively, thus increasing the excess of male environmental mor tality (Wells 2000). The same line of thinking can be applied to the negative correlation of insular situation of region with dA (mean value of dA is 0.48 on islands versus 0.05 on continents, p 0.044) (see Table 2). We suggest that stressful factors on islands are less expressed than on continental territories. The correlation of dA with the number of physicians may simply be due to its correlation with other environmental factors, in par ticular, with alcohol (R 0.57, p 0.0000001). Direct interpretation of this correlation (i.e., that women are more sensitive to an increase or decrease in the number of physicians) is never theless also possible. The significant effect of continent and religion (dA is higher in European and Christian countries and lower in Asian and Muslim countries; see Table 2) can also be explained by the influence of some environmental factors. Indeed, the consumption of alcohol is significantly greater in Europe than in Asia (11.1 versus 2.8 l, p 0.0000001) and in Christian countries than in Muslim countries (7.3 versus 0.9 l, p 0.0000001). An additional factor that lowers the excess of male environmental mor tality (or rather, increases the excess of female environmental mor tality) in Muslim countries compared with Christian countries is higher fer tility (4.4 versus 2.8 children, p 0.000014). Higher fer tility may decrease dA because of increasing maternal mor tality, which is strongly correlated with fer tility (R 0.80, p 0.0000001). However, the relation of excess male environmental mor tality with fer tility is not linear. We see in Figure 4 that, although male mor tality excess decreases with increasing fer tility from lowest to middle values (from 1 to 4.5 children), dA increases with increasing fer tility from middle to highest values (from 4.5 to 8 children). The dependence of dA on fer tility f is well described by a second-order polynomial function with a statistically significant quadratic term: d
A

2.139

1.278f

0.1467f 2, R 0.258, p
f

0.00049, p

f

2

0.0014.

(13)

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Figure 4.

Scatterplots and quadratic regression line of excess of male environmental mor tality dA (see text) on fer tility. Names of regions (countries and autonomous territories) are indicated by their two-letter codes (see Appendix).

One interpretation of a relative increase in dA at low latitudes is based on the fact that fer tility, like infections, significantly increases with decreasing latitude (R 0.62, p 0.0000001). However, we have already noted a negative influence of the infection pressure on males. If this pressure overcomes the negative effect of high fer tility, which acts predominantly negatively on females, it may explain the obser ved increase in dA in regions with highest fer tility. The increase in dA with increasing fer tility (in parallel with increasing infections) to its highest value can also be explained by evolutionar y adaptation of women to the necessity of having a considerable increase in fer tility in relation ´ ´ to a high parasitic pressure (Guegan and Teriokhin 2000; Guegan et al. 2000). In suppor t of this, the highest fer tility values are mainly obser ved in Africa (5.1 children in Africa versus 3.3 in Asia and 1.4 in Europe), where the average life span is extremely low (53.1 years in Africa versus 69.2 in Asia and 76.1 in Europe). Thus a relative reduction in female environmental mor tality (by means

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Life-Span Sexual Dimorphism / 633 of genetic selection or cultural adaptation) may indeed be vitally impor tant for population sur vival.

Conclusion
The two goals of this study were (1) to identify and explain the general tendency of human life-span sexual dimorphism and (2) to identify and relate the deviations from the general tendency to environmental conditions. The general tendency consists in an increase of life-span dimorphism with improved environmental conditions and an increase in mean life span. This tendency is obser ved empirically and can be obtained theoretically if we assume that the age-dependent exponential component of human mor tality in the Gomper tz­Makeham equation is more conser vative than the age-independent (but environment-dependent) component. On the intuitive level this increase in life-span dimorphism with increasing average male­female life span is due to the fact that the longer the life span of men and women, the longer the period for expressing the difference in their age-dependent mor talities. With regard to deviations from the general trend, the general pattern indicates an excess of male mor tality at the highest and lowest latitudes. One explanation is that the stressful factors linked to extreme latitudes affect males more strongly than females. In par ticular, alcohol consumption increases with increasing latitude and infection pressures increase with decreasing latitude. This pattern agrees with obser vations that male environmental mor tality increases in European and Christian countries and that female mor tality increases in Asian and Muslim countries. An increase in the excess of female mor tality might also be caused by increased maternal mor tality associated with increasing fer tility, although this relation is not linear. However, in the regions with highest fer tility, notably in Africa, the excess of female mor tality is lower than in Asia. A possible explanation may be that African populations have adapted to their highly stressful environment by means of female mor tality reduction.
Acknowledgments We thank B. Lafay for helpful comments and an anonymous referee for numerous valuable suggestions. The research was suppor ted by the Centre National de la Recherche Scientifique (CNRS) through a Senior Research Fellowship awarded to A. Teriokhin and by funds from the Russian Foundation for Basic Research (RFBR) (grant 01-04-48384) awarded to E. Budilova and A. Teriokhin. Received 23 March 2003; revision received 29 October 2003.

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Appendix. Regions (Countries and Autonomous Territories)
Female Environmental Mortality, Ar,f 0.0166 0.0037 0.0048 0.0005 0.0205 0.0024 0.0043 0.003 0.0026 0.005 0.0017 0.0015 0.0019 0.0062 0.0043 0.0035 0.0089 0.0035 0.0039 0.0018 0.0041 0.0138 0.0025 0.0126 0.0064 0.0038 0.025 0.0062 0.0033 0.0037 0.0161 0.0162 0.0095 0.0115 0.0014 0.0044 0.0018 0.0172 0.0024 0.0041 0.0038 0.0079 0.0135 0.0135 Male Environmental Mortality, Ar,m 0.0142 0.0038 0.0039 0.0001 0.0215 0.0023 0.004 0.0025 0.0028 0.0066 0.0017 0.0011 0.0019 0.0081 0.00 0.0032 0.0071 0.0033 0.0067 0.0019 0.0039 0.0134 0.0018 0.0107 0.0067 0.0038 0.0244 0.0079 0.003 0.0043 0.0156 0.0159 0.0101 0.0108 0.0014 0.0049 0.0014 0.018 0.0026 0.0035 0.0047 0.0082 0.0164 0.0144 Difference of Male and Female Mortalities 0.0024 0.0001 0.0009 0.0004 0.0010 0.0001 0.0003 0.0005 0.0002 0.0016 0.0000 0.0004 0.0000 0.0019 50.0007 0.0003 0.0018 0.0002 0.0028 0.0001 0.0002 0.0004 0.0007 0.0019 0.0003 0.0000 0.0006 0.0017 0.0003 0.0006 0.0005 0.0003 0.0006 0.0007 0.0000 0.0005 0.0004 0.0008 0.0002 0.0006 0.0009 0.0003 0.0029 0.0009

Region (Country, Territory), r Code Afghanistan Albania Algeria Andorra Angola Anguilla (United Kingdom) Antigua and Barbuda Antilles (Netherlands) Argentina Armenia Aruba (Netherlands) Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda (United Kingdom) Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands (United Kingdom) Central African Republic Chile China Colombia Comoros Congo, Brazzaville Congo Democratic Republic af al dz ad ao ai ag an ar am aw au at az bs bh bd bb by be bz bj bm bt bo ba bw br bn bg bf bi kh cm ca cv ky cf cl cn co km cg zr

Female Life Span, Lf 46.49 75.41 71.93 86.61 40.89 79.63 73.57 77.54 79.15 71.38 82.23 83.04 81.34 68.07 73.60 76.08 61.14 76.20 74.65 81.65 74.02 51.03 79.33 53.40 67.46 75.08 35.64 68.13 76.64 75.31 47.24 47.12 59.84 55.58 83.29 73.24 81.66 45.56 79.69 74.08 74.97 63.54 51.71 51.58

Male Life Span, Lm 48.02 69.55 69.15 80.62 38.38 73.83 68.90 73.05 72.23 62.55 75.37 77.19 74.88 59.29 66.45 71.21 61.50 70.99 62.40 74.84 69.36 49.26 75.29 54.09 62.24 69.48 35.38 59.63 71.81 68.09 45.95 45.42 55.19 53.90 76.34 66.61 76.47 42.54 72.90 70.19 67.18 59.09 44.72 47.69

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Region (Country, Territory), r Code Cook Islands (New Zealand) Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Faeroe Islands (Denmark) Fiji Islands Finland France Gabon Gambia Gaza Strip Georgia Germany Ghana Gibraltar (United Kingdom) Greece Greenland (Denmark) Grenada Guadeloupe (France) Guam (United States) Guatemala Guernsey (United Kingdom) Guinea Guinea-Bissau Guyana Guyana (France) Haiti Honduras Hong Kong (China) Hungary Iceland ck cr ci hr cu cy cz dk dj dm do ec eg sv gq er ee et fo fj fi fr ga gm gz ge de gh gi gr gl gd gp gu gt gg gn gw gy gf ht hn hk hu is

Female Life Span, Lf 73.25 78.97 46.41 78.01 79.20 79.54 78.69 79.71 54.01 76.96 76.14 74.77 66.61 74.29 56.97 59.52 76.39 45.50 82.25 71.20 81.55 83.17 50.66 56.39 72.69 68.63 81.12 58.79 82.29 81.53 72.43 66.41 80.72 80.77 69.94 83.05 49.38 52.70 65.56 80.09 51.72 70.71 82.74 76.61 82.10

Male Life Span, L 69.39 73.77 43.88 70.58 74.26 74.84 71.50 74.34 50.26 71.13 71.83 69.05 62.33 66.92 52.76 54.52 64.12 43.81 75.34 66.23 74.13 75.21 48.50 52.45 70.13 61.54 74.68 56.01 76.42 76.22 65.25 62.83 74.27 75.86 64.47 76.95 44.41 48.03 60.21 73.26 48.36 67.33 77.14 67.62 77.45

m

Female Environmental Mortality, Ar,f 0.0044 0.0026 0.0166 0.0029 0.0025 0.0024 0.0027 0.0024 0.0123 0.0032 0.0035 0.0039 0.0067 0.0041 0.0108 0.0096 0.0034 0.0172 0.0017 0.0051 0.0019 0.0014 0.0141 0.0111 0.0046 0.006 0.002 0.01 0.0017 0.0019 0.0047 0.0068 0.0021 0.0021 0.0055 0.0015 0.0148 0.0129 0.0071 0.0023 0.0135 0.0053 0.0015 0.0033 0.0017

Male Environmental Mortality, Ar,m 0.0038 0.0023 0.017 0.0034 0.0021 0.0019 0.0031 0.0021 0.0128 0.0032 0.003 0.004 0.0067 0.0048 0.0114 0.0104 0.0059 0.0171 0.0017 0.0051 0.0022 0.0018 0.0139 0.0116 0.0036 0.007 0.002 0.0097 0.0014 0.0015 0.0055 0.0065 0.0021 0.0016 0.0058 0.0012 0.0167 0.0142 0.0076 0.0025 0.014 0.0046 0.0012 0.0045 0.0011

Difference of Male and Female Mortalities 0.0006 0.0003 0.0004 0.0005 0.0004 0.0005 0.0004 0.0003 0.0005 0.0000 0.0005 0.0001 0.0000 0.0007 0.0006 0.0008 0.0025 0.0001 0.0000 0.0000 0.0003 0.0004 0.0002 0.0005 0.0010 0.0010 0.0000 0.0003 0.0003 0.0004 0.0008 0.0003 0.0000 0.0005 0.0003 0.0003 0.0019 0.0013 0.0005 0.0002 0.0005 0.0007 0.0003 0.0012 0.0006

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638 / teriokhin et al.
Appendix. (Continued)
Female Environmental Mortality, Ar,f 0.0076 0.005 0.0049 0.0059 0.0023 0.002 0.0016 0.003 0.0011 0.0019 0.0022 0.0057 0.0155 0.0078 0.0031 0.0061 0.0111 0.0038 0.004 0.0155 0.0123 0.0028 0.0015 0.0036 0.002 0.001 0.0033 0.0101 0.0233 0.004 0.0075 0.0149 0.002 0.0019 0.0061 0.0029 0.0122 0.0036 0.0083 0.0037 0.0052 0.0057 0.0014 0.0065 0.0022 Male Environmental Mortality, Ar,m 0.0064 0.005 0.004 0.0049 0.002 0.0012 0.0015 0.0023 0.001 0.0014 0.0017 0.0085 0.0152 0.0087 0.0016 0.0078 0.0115 0.0063 0.0038 0.0151 0.0124 0.0022 0.0017 0.0061 0.0021 0.0006 0.0029 0.0107 0.0233 0.004 0.0068 0.015 0.0016 0.002 0.0057 0.0005 0.0131 0.0045 0.0084 0.0039 0.0048 0.0074 0.0018 0.0065 0.0029 Difference of Male and Female Mortalities 0.0012 0.0000 0.0009 0.0010 0.0003 0.0008 0.0001 0.0007 0.0001 0.0005 0.0005 0.0028 0.0003 0.0009 0.0015 0.0017 0.0004 0.0025 0.0002 0.0004 0.0001 0.0006 0.0002 0.0025 0.0001 0.0004 0.0004 0.0006 0.0000 0.0000 0.0007 0.0001 0.0004 0.0001 0.0004 0.0024 0.0009 0.0009 0.0001 0.0002 0.0004 0.0017 0.0004 0.0000 0.0007

Region (Country, Territory), r India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey (United Kingdom) Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao (China) Macedonia Madagascar Malawi Malaysia Maldives Mali Malta Man, Isle of (United Kingdom) Marshall Islands Martinique (France) Mauritania Mauritius Mayotte (France) Mexico Micronesia Moldova Monaco Mongolia Montenegro (Yugoslavia)

Code in id ir iq ie il it jm jp je jo kz ke ki kw kg la lv lb ls lr ly li lt lu mo mk mg mw my mv ml mt im mh mq mr mu yt mx fm md mc mn me

Female Life Span, Lf 64.31 71.33 71.88 68.85 80.16 81.06 82.67 77.83 84.28 81.44 80.42 69.38 48.15 63.92 77.46 68.42 56.31 75.26 74.50 48.16 53.98 78.31 82.77 75.69 81.01 84.77 76.77 58.53 37.57 74.33 64.60 49.18 81.00 81.40 68.34 78.00 54.09 75.68 62.75 75.37 70.82 69.57 83.29 67.19 80.27

Male Life Span, L 62.93 66.50 69.07 66.73 74.45 76.88 76.13 73.76 77.76 76.38 75.43 58.39 46.52 57.94 75.65 59.85 52.47 63.24 69.59 46.70 51.02 73.93 75.52 63.64 74.24 79.00 72.11 53.93 36.50 68.90 62.09 46.76 75.82 74.49 64.61 79.23 49.80 67.67 58.55 69.18 66.92 60.66 75.26 62.81 71.98

m

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Life-Span Sexual Dimorphism / 639

Region (Country, Territory), r Code Montserrat Island (United Kingdom) Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Caledonia (France) New Zealand Nicaragua Niger Nigeria North Korea Northern Mariana Islands (United States) Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Polynesia (France) Portugal Puerto Rico (United States) Qatar Reunion Island (France) Romania Russia Rwanda Samoa (United States) San Marino Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovakia Slovenia ms ma mz mm na nr np nl nc nz ni ne ng kp mp no om pk pw pa pg py pe ph pl pf pt pr qa re ro ru rw as sm st sa sn sc sl sg sk si

Female Life Span, Lf 80.45 72.38 35.10 57.44 37.32 65.31 58.63 81.62 76.42 81.31 71.64 42.26 50.95 74.60 79.26 82.10 74.71 63.22 72.60 78.88 66.37 76.95 73.36 71.29 78.11 77.75 79.91 80.72 75.61 76.80 74.51 73.10 39.62 80.27 85.23 67.75 70.53 64.94 76.72 49.63 83.50 78.47 79.40

Male Life Span, L 76.17 67.83 36.76 54.27 41.11 58.13 59.42 75.74 70.38 75.22 67.63 42.57 50.96 68.47 72.90 76.04 70.32 61.44 66.19 73.30 62.10 71.91 68.47 65.46 69.59 72.95 72.70 71.57 70.57 69.84 66.76 62.42 38.61 71.20 77.84 64.79 67.04 61.65 65.62 43.70 77.37 70.26 71.46

m

Female Environmental Mortality, Ar,f 0.0022 0.0047 0.025 0.0106 0.0236 0.0072 0.01 0.0018 0.0034 0.0019 0.0049 0.0195 0.0139 0.004 0.0025 0.0017 0.0039 0.0081 0.0046 0.0026 0.0068 0.0032 0.0044 0.0051 0.0029 0.003 0.0023 0.0021 0.0036 0.0033 0.004 0.0044 0.0216 0.0022 0.0009 0.0063 0.0053 0.0074 0.0033 0.0146 0.0013 0.0028 0.0025

Male Environmental Mortality, Ar,m 0.0015 0.0044 0.023 0.0106 0.0192 0.0086 0.008 0.0016 0.0035 0.0018 0.0045 0.018 0.0124 0.0042 0.0026 0.0015 0.0035 0.0071 0.0051 0.0024 0.0068 0.0029 0.0042 0.0054 0.0038 0.0026 0.0026 0.003 0.0034 0.0037 0.0049 0.0066 0.0213 0.0032 0.0009 0.0056 0.0047 0.007 0.0053 0.0172 0.0011 0.0035 0.0031

Difference of Male and Female Mortalities 0.0007 0.0003 0.0020 0.0000 0.0044 0.0014 0.0020 0.0002 0.0001 0.0001 0.0004 0.0015 0.0015 0.0002 0.0001 0.0002 0.0004 0.0010 0.0005 0.0002 0.0000 0.0003 0.0002 0.0003 0.0009 0.0004 0.0003 0.0009 0.0002 0.0004 0.0009 0.0022 0.0003 0.0010 0.0000 0.0007 0.0006 0.0004 0.0020 0.0026 0.0002 0.0007 0.0006

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640 / teriokhin et al.
Appendix. (Continued)
Female Environmental Mortality, Ar,f 0.004 0.0149 0.0169 0.0026 0.0015 0.0038 0.0022 0.004 0.0033 0.0022 0.0039 0.0099 0.0039 0.0229 0.0016 0.0015 0.0053 0.0024 0.0062 0.0127 0.0123 0.0046 0.0111 0.0051 0.005 0.0035 0.0041 0.0073 0.0035 0.0057 0.0175 0.0047 0.0031 0.0021 0.0022 0.0025 0.0062 0.0081 0.0032 0.0046 0.0032 Male Environmental Mortality, Ar,m 0.0038 0.0156 0.0159 0.0032 0.0016 0.0036 0.002 0.0041 0.0039 0.0016 0.0032 0.0094 0.0038 0.023 0.0011 0.0012 0.0043 0.0022 0.0068 0.0123 0.0131 0.0051 0.0116 0.0051 0.0051 0.0026 0.0038 0.0087 0.003 0.0056 0.0174 0.0073 0.0028 0.0017 0.002 0.0027 0.0074 0.0076 0.0034 0.0045 0.0018 Difference of Male and Female Mortalities 0.0002 0.0007 0.0010 0.0006 0.0001 0.0002 0.0002 0.0001 0.0006 0.0006 0.0007 0.0005 0.0001 0.0001 0.0005 0.0003 0.0010 0.0002 0.0006 0.0004 0.0008 0.0005 0.0005 0.0000 0.0001 0.0009 0.0003 0.0014 0.0005 0.0001 0.0001 0.0026 0.0003 0.0004 0.0002 0.0002 0.0012 0.0005 0.0002 0.0001 0.0014

Region (Country, Territory), r Code Solomon Islands Somalia South Africa South Korea Spain Sri Lanka St. Helena Island (United Kingdom) St. Kitts and Nevis St. Lucia St. Pierre and Miquelon (France) St. Vincent and Grenadines Sudan Suriname Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Tchad Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Island (United Kingdom) Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela Vietnam Virgin Islands (United Kingdom) sb so za kr es lk sh kn lc pm vc sd sr sz se ch sy tw tj tz td th tg to tt tn tr tm tc tv ug ua ae uk us uy uz vu ve vn vg

Female Life Span, Lf 74.54 49.19 45.94 79.00 82.80 75.11 80.37 74.36 76.74 80.37 74.74 58.88 74.84 38.02 82.66 82.93 70.55 79.76 68.13 53.05 53.85 72.71 56.41 71.20 71.40 76.08 74.32 65.25 76.14 69.36 45.03 72.20 77.20 80.88 80.25 79.27 68.05 63.15 76.97 72.71 76.96

Male Life Span, Lm 69.56 45.92 45.48 71.26 75.67 69.95 74.50 68.61 69.37 75.73 71.19 56.60 69.42 36.78 77.22 77.01 68.12 74.04 62.03 51.18 49.72 66.21 52.43 66.23 66.21 72.78 69.49 58.01 71.73 64.99 43.38 61.00 72.19 75.34 74.55 72.44 60.83 60.30 70.72 67.60 75.07

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Region (Country, Territory), r Code Virgin Islands (United States) Wallis and Futuna Islands (France) West Bank Western Sahara Western Samoa Yemen Yugoslavia Zambia Zimbabwe vi wf wb eh ws ye yu zm zw

Female Life Span, Lf 82.60 75.54 74.43 51.98 72.87 62.84 76.83 37.97 35.31

Male Life Span, Lm 74.63 74.46 70.92 49.32 67.30 59.23 70.86 37.40 38.12

Female Environmental Mortality, Ar,f 0.0016 0.0037 0.004 0.0133 0.0045 0.0082 0.0033 0.023 0.025

Male Environmental Mortality, Ar,m 0.002 0.002 0.0033 0.0134 0.0046 0.0081 0.0033 0.0224 0.0218

Difference of Male and Female Mortalities 0.0004 0.0017 0.0007 0.0001 0.0001 0.0001 0.0000 0.0006 0.0032

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