Figures and data in A meta-analysis of the association between male dimorphism and fitness outcomes in humans

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Tables
Additional files

6 figures, 4 tables and 8 additional files

Figures

Figure 1

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Figure 2

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Forest plot of the association between body masculinity and the mating domain.

Effect sizes are shown as Z-transformed r, with 95% confidence intervals in brackets. The width of the diamond corresponds to the confidence interval for the overall effect.

Figure 3

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Forest plot of the association between voice pitch and the mating domain.

Effect sizes are shown as Z-transformed r, with 95% confidence intervals in brackets. The width of the diamond corresponds to the confidence interval for the overall effect.

Figure 4

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Forest plot of the association between testosterone levels and the mating domain.

Effect sizes are shown as Z-transformed r, with 95% confidence intervals in brackets. The width of the diamond corresponds to the confidence interval for the overall effect.

Figure 5

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Forest plot of the association between height and the mating domain.

Effect sizes are shown as Z-transformed r, with 95% confidence intervals in brackets. The width of the diamond corresponds to the confidence interval for the overall effect.

Figure 6

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Forest plot of the association between body masculinity and the reproductive domain.

Effect sizes are shown as Z-transformed r, with 95% confidence intervals in brackets. The width of the diamond corresponds to the confidence interval for the overall effect.

Tables

Table 1

All studies included in the meta-analysis.

Authors	Year	Predictor	Outcome	Sample	Sample location	Low or high fert.	N
Alvergne et al., 2009	2009	T	REP	Rural villagers	Senegal	High	53
Apicella, 2014	2014	Body masc	MAT, REP, OM	Hadza	Tanzania	High	51
Apicella et al., 2007	2007	Body masc, voice pitch, height	REP, OM	Hadza	Tanzania	High	44-52
Arnocky et al., 2018	2018	Facial masc	MAT	Students	Canada	Low	135
Aronoff, 2017	2017	T	MAT	Students	US	Low	99
Atkinson, 2012	2012	Body masc	MAT	Students	US	Low	66
Atkinson et al., 2012	2012	Body masc, voice pitch, height	REP	Himba (Ovahimba)	Namibia	High	36
Bogaert and Fisher, 1995	1995	T	MAT	Students	Canada	Low	195-196
Booth et al., 1999	1999	T	MAT	Army veterans and non-veterans	US	Low	4393
Boothroyd et al., 2008	2008	Facial masc	MAT	Students	UK	Low	18-19
Boothroyd et al., 2011	2011	Facial masc	MAT	Students	UK	Low	36
Boothroyd et al., 2017	2017	Facial masc	REP, OM	Agta	Philippines	High	65
		Facial masc	MAT, REP, OM	Maya	Belize	High	23-35
Charles and Alexander, 2011	2011	2D:4D, T	MAT	Students	US	Low	25-42
Chaudhary et al., 2015	2015	Body masc, height	MAT, REP, OM	Mbendjele BaYaka	Democratic Republic of the Congo	High	55-73
Edelstein et al., 2011	2011	T	MAT	Students	US	Low	134
Falcon, 2016	2016	2D:4D	MAT	Students	US	Low	137
Farrelly et al., 2015	2015	T	MAT	Students	UK	Low	75-78
Frederick, 2010	2010	Body masc, 2D:4D, height	MAT	Students	US	Low	61
Frederick and Haselton, 2007	2007	Body masc	MAT	Students	US	Low	56-121
Frederick and Jenkins, 2015	2015	Height	MAT	Online	Worldwide	Low	28759-31418
Gallup et al., 2007	2007	Body masc, 2D:4D	MAT	Students	US	Low	71-75
Genovese, 2008	2008	Body masc	REP	Former teenage delinquents	US	High	181
Gettler et al., 2019	2019	T	MAT	Cebu Longitudinal Health and Nutrition Survey	Philippines	High	288
Gildner, 2018	2018	Body masc, 2D:4D, height	REP	Shuar Health and Life History Project	Ecuador	High	48
Gómez-Valdés et al., 2013	2013	Facial masc	REP	Hallstatt skulls	Austria	High	179
Hartl et al., 1982	1982	Body masc, height	MAT, REP	Former teenage delinquents	US	High	180-185
Hill et al., 2013	2013	Facial masc, body masc,	MAT	Students	US	Low	63
		voice pitch, height
Hoppler et al., 2018	2018	T	REP	Men’s health 40+ study	Switzerland	Low	268
Hughes and Gallup, 2003	2003	Body masc	MAT	Students	US	Low	50-59
Honekopp et al., 2006	2006	2D:4D, height	MAT	Students and non-students	Germany	Low	79-99
Honekopp et al., 2007	2007	Facial masc, body masc, height, T	MAT	Students and non-students	Germany	Low	77
Kirchengast, 2000	2000	Height	REP, OM	!Kung San	Namibia	High	103
Kirchengast and Winkler, 1995	1995	Height	REP, OM	Urban and rural Kavango people	Namibia	High	59-78
Klimas et al., 2019	2019	T	MAT	Men’s health 40+ study	Switzerland	Low	159
Klimek et al., 2014	2014	2D:4D, height	REP	Mogielica Human Ecology Study Site	Poland	High	238
Kordsmeyer et al., 2018	2018	Body masc, voice pitch, height, T	MAT	Students and non-students	Germany	Low	103-164
Kordsmeyer and Penke, 2017	2017	2D:4D, height	MAT	Students and non-students	Germany	Low	141
Krzyżanowska et al., 2015	2015	Height	REP	National Child Development Study	UK	Low	6535
Kurzban and Weeden, 2005	2005	Height	MAT, REP	Speed daters	US	Low	1503-1501
Lassek and Gaulin, 2009	2009	Body masc, height	MAT	NHANES III	US	Low	4167-5159
Little et al., 1989	1989	Height	REP, OM	Rural; growth stunted	Mexico	High	103
Loehr and O’Hara, 2013	2013	Facial masc	REP	WWII soldiers	Finland	High	795
Longman et al., 2018	2018	T	MAT	Students	UK	Low	38
Luevano et al., 2018	2018	Facial masc, height	MAT	Students	US	Low	35-66
Lukaszewski et al., 2014	2014	Body masc	MAT	Students	US	Low	48-174
Maestripieri et al., 2014	2014	T	MAT	Students	US	Low	41-61
Manning and Fink, 2008	2008	2D:4D	MAT, REP	Online	Worldwide	Low	26872-83681
Manning et al., 2003	2003	2D:4D	REP	Community	England	Low	189
		2D:4D	REP	Sugali and Yanadi tribal groups	India	High	80
		2D:4D	REP	Zulus from townships near Durban	South Africa	High	66
Marczak et al., 2018	2018	2D:4D	REP	Yali	Indonesia	High	47
McIntyre et al., 2006	2006	T	MAT	Students	US	Low	68-81
Međedović and Bulut, 2019	2019	Height	MAT	Students	Serbia	Low	39
Mosing et al., 2015	2015	Height	MAT, REP	Study of Twin Adults: Genes and Environment	Sweden	Low	2310-2549
Muller and Mazur, 1997	1997	Facial masc	REP	West Point class of 1950	US	High	337
Nagelkerke et al., 2006	2006	Height	MAT	NHANES 99–00	US	Low	798-809
Nettle, 2002	2002	Height	REP	National Child Development Study	UK	Low	4474
Pawlowski et al., 2008	2008	Height	REP	Rural	Poland	High	46
Pawlowski et al., 2000	2000	Height	REP	Urban and rural	Poland	High	3201
Peters et al., 2008	2008	Facial masc, body masc, T	MAT	Students	Australia	Low	100-113
Pollet et al., 2011	2011	T	MAT	National Social Life, Health, and Aging Project	US	Low	749
Polo et al., 2019	2019	Facial masc, body masc, height	MAT	Students and non-students	Chile	Low	198-206
Price et al., 2013	2013	Body masc, height	MAT	Mainly students	UK	Low	55
Prokop and Fedor, 2011	2011	Height	REP	Friends and family of students	Slovakia	Low	499
Prokop and Fedor, 2013	2013	Height	MAT	Students	Slovakia	Low	105-150
Puts et al., 2006	2006	Voice pitch	MAT	Students	US	Low	103
Puts et al., 2015	2015	T	MAT	Students	US	Low	59-61
Putz et al., 2004	2004	2D:4D	MAT	Students	US	Low	207-219
Rahman et al., 2005	2005	2D:4D, height	MAT	Students and non-students	UK	Low	78-150
Rhodes et al., 2005	2005	Facial masc, body masc, height	MAT	Mainly students	Australia	Low	142-166
Rosenfield et al., 2020	2020	Body masc, voice pitch, height	MAT, REP, OM	Tsimané	Bolivia	High	55-62
Schwarz et al., 2011	2011	2D:4D	MAT	Students	Germany	Low	52-89
Scott and Bajema, 1982	1982	Height	REP	Third Harvard Growth Study	US	High	606
Shoup and Gallup, 2008	2008	Body masc, 2D:4D	MAT	Students	US	Low	28-38
Sim and Chun, 2016	2016	Body masc, 2D:4D	MAT	Students	US	Low	90
Simmons and Roney, 2011	2011	Body masc, T	MAT	Students	US	Low	138
Smith et al., 2017	2017	Body masc	REP	Hadza	Tanzania	High	51
Sneade and Furnham, 2016	2016	Body masc	MAT	Students	UK	Low	145
Sorokowski et al., 2013	2013	Height	REP, OM	Yali	Indonesia	High	49-52
Steiner, 2011	2011	2D:4D, T	REP	Students and non-students	US	Low	30
Stern et al., 2020	2020	T	MAT	Students	UK	Low	61
Strong, 2014	2014	Body masc	MAT	Students	US	Low	31
Strong and Luevano, 2014	2014	Body masc, 2D:4D, height	MAT	Students	US	Low	51-66
Subramanian et al., 2009	2009	Height	OM	2005-2006 National Family Health Survey	India	Low	21120
Suire et al., 2018	2018	Voice pitch	MAT	Mainly students	France	Low	57-58
Tao and Yin, 2016	2016	Height	REP	The Panel Study of Family Dynamics	Taiwan	Low	1409
van Anders et al., 2007	2007	T	MAT	Non-students	US	Low	31
Van Dongen and Sprengers, 2012	2012	Facial masc, body masc, 2D:4D	MAT	Not specified	Not specified	Low	52
Varella et al., 2014	2014	Body masc, 2D:4D, height	MAT	Students	Brazil, Czech Republic	Low	69-80
von Rueden et al., 2011	2011	Body masc, height	REP, OM	Tsimané	Bolivia	High	162-197
Voracek et al., 2010	2010	2D:4D, height	REP	Firefighters	Austria	Low	134
Walther et al., 2016	2016	Body masc	REP	Men’s health 40+ study	Switzerland	Low	271
Walther et al., 2017a	2017a	Body masc	MAT	Men’s health 40+ study	Switzerland	Low	226
Walther et al., 2017b	2017b	Height	REP	Men’s health 40+ study	Switzerland	Low	271
Walther et al., 2017c	2017c	Height	MAT	Men’s health 40+ study	Switzerland	Low	226
Waynforth, 1998	1998	2D:4D, height	MAT, REP, OM	Villagers	Belize	High	35-56
Weeden and Sabini, 2007	2007	Body masc, 2D:4D, height	MAT	Students	US	Low	188-212
Winkler and Kirchengast, 1994	1994	Height	REP, OM	!Kung San	Namibia	High	31-114

Table 2

Masculine traits predicting mating: main analyses and subgroup analyses of mating attitudes vs mating behaviors and low vs high fertility samples.

Pearson’s r (95% CI); p value for meta-analytic effect, q-value (correcting for multiple comparisons); number of observations (k), samples (s), and unique participants (n); test for heterogeneity (Q), p value for heterogeneity. Statistically significant meta-analytic associations are bolded if still significant after controlling for multiple comparisons.

	Mating
Outcome: Sample	Facial masculinity	Body masculinity	2D:4D	Voice pitch	Height	T levels
Mating domain: All samples	r = 0.080 (-0.003, 0.164), p = 0.060, q = 0.117	r = 0.133 (0.091, 0.176), p < 0.001, q = 0.001	r = 0.034 (0.000, 0.069), p = 0.049, q = 0.102	r = 0.132 (0.061, 0.204), p < 0.001, q = 0.002	r = 0.057 (0.027, 0.087), p < 0.001, q = 0.002	r = 0.093 (0.066, 0.121), p < 0.001, q = 0.001
	k = 30, s = 11, n = 948	k = 121, s = 32, n = 7939	k = 84, s = 23, n = 66,807	k = 8, s = 5, n = 443	k = 62, s = 25, n = 43,686	k = 66, s = 21, n = 7083
	Q(df = 29) = 54.834, p = 0.003	Q(df = 120) = 297.472, p < 0.001	Q(df = 83) = 101.994, p = 0.077	Q(df = 7) = 2.334, p = 0.939	Q(df = 61) = 263.247, p < 0.001	Q(df = 65) = 66.090, p = 0.439
Mating attitudes: All samples	r = .095 (-0.072, 0.263), p = 0.263, q = 0.304	r = .078 (0.002, 0.155), p = 0.045, q = 0.098	r = 0.035 (-0.061, 0.132), p = 0.474, q = 0.385	s = 0	r = 0 .028 (-0.013, 0.068), p = 0.179, q = 0.253	r = 0.099 (0.026, 0.173), p = 0.008, q = 0.032
	k = 5, s = 4, n = 407	k = 20, s = 9, n = 922	k = 19, s = 7, n = 504		k = 9, s = 6, n = 4232	k = 21, s = 11, n = 1039
	Q(df = 4) = 8.684, p = 0.070	Q(df = 19) = 17.606, p = 0.549	Q(df = 18) = 24.141, p = 0.151		Q(df = 8) = 5.137, p = 0.743	Q(df = 20) = 25.379, p = 0.187
Mating behaviors: All samples	r = .025 (-0.059, 0.109), p = 0.554, q = 0.424	r = .142 (0.099, 0.187), p < 0.001, q = 0.001	r = 0.038 (-0.002, 0.078), p = 0.061, q = 0.117	r = 0.124 (0.043, 0.206), p = 0.003, q = 0.016	r = 0.054 (0.021, 0.087), p = 0.001, q = 0.008	r = 0.084 (0.058, 0.110), p < 0.001, q = 0.001
	k = 22, s = 8, n = 755	k = 91, s = 31, n = 7738	k = 51, s = 19, n = 1607	k = 7, s = 5, n = 443	k = 48, s = 24, n = 42,179	k = 32, s = 17, n = 6765
	Q(df = 21) = 37.044, p = 0.017	Q(df = 90) = 267.876, p < 0.001	Q(df = 50) = 64.049, p = 0.087	Q(df = 6) = 2.162, p = 0.904	Q(df = 47) = 247.032, p < 0.001	Q(df = 31) = 28.558, p = 0.592
Mating domain: Low fert. samples	r = 0.089 (-0.001, 0.179), p = 0.053, q = 0.109	r = 0.135 (0.091, 0.180), p < 0.001, q = 0.001	r = 0.038 (0.002, 0.073), p = 0.037, q = 0.086	r = 0.129 (0.055, 0.204), p < 0.001, q = 0.005	r = 0.055 (0.024, 0.086), p < 0.001, q = 0.004	r = 0.099 (0.069, 0.129), p < 0.001, q = 0.001
	k = 28, s = 10, n = 913	k = 117, s = 28, n = 7572	k = 82, s = 22, n = 66,751	k = 7, s = 4, n = 388	k = 58, s = 21, n = 43,310	k = 58, s = 20, n = 6795
	Q(df = 27) = 54.287, p = 0.001	Q(df = 116) = 289.080, p < 0.001	Q(df = 81) = 101.369, p = 0.063	Q(df = 6) = 2.234, p = 0.897	Q(df = 57) = 259.576, p < 0.001	Q(df = 57) = 61.443, p = 0.320
Mating attitudes: Low fert. samples	r = 0.095 (-0.072, 0.262), p = 0.263, q = 0.304	r = 0.078 (0.002, 0.155), p = 0.045, q = 0.098	r = 0.035 (-0.061, 0.132), p = 0.474, q = 0.385	s = 0	r = 0.028 (-0.013, 0.068), p = 0.179, q = 0.253	r = 0.108 (0.021, 0.195), p = 0.015, q = 0.047
	k = 5, s = 4, n = 407	k = 20, s = 9, n = 922	k = 19, s = 7, n = 504		k = 9, s = 6, n = 4,232	k = 17, s = 10, n = 751
	Q(df = 4) = 8.684, p = 0.070	Q(df = 19) = 17.606, p = 0.549	Q(df = 18) = 24.141, p = .151		Q(df = 8) = 5.137, p = 0.743	Q(df = 16) = 20.017, p = 0.220
Mating behaviors: Low fert. samples	r = 0.028 (-0.063, 0.119), p = 0.543, q = 0.420	r = 0.145 (0.100, 0.193), p< 0.001, q = 0.001	r = 0.042 (0.001, 0.083), p = 0.045, q = 0.098	r = .119 (0.034, 0.205), p = 0.006, q = 0.025	r = .051 (0.017, 0.086), p = 0.004, q = 0.019	r = .088 (0.058, 0.119), p < 0.001, q = 0.001
	k = 20, s = 7, n = 720	k = 87, s = 27, n = 7371	k = 49, s = 19, n = 1551	k = 6, s = 4, n = 388	k = 44, s = 20, n = 41,803	k = 30, s = 16, n = 6477
	Q(df = 19) = 36.610, p = 0.009	Q(df = 86) = 259.448, p < 0.001	Q(df = 48) = 62.941, p = 0.073	Q(df = 5) = 2.017, p = 0.847	Q(df = 43) = 243.392, p < 0.001	Q(df = 29) = 27.793, p = 0.529
Mating domain: High fert. samples	s = 1	r = 0.105 (-0.069, 0.280), p = 0.235, q = 0.285	s = 1	s = 1	r = 0.089 (-0.016, 0.193), p = 0.096, q = 0.157	s = 1
		k = 4, s = 4, n = 367			k = 4, s = 4, n = 376
		Q(df = 3) = 7.282, p = 0.063			Q(df = 3) = 3.388, p = 0.336
Mating attitudes: High fert. samples	s = 0	s = 0	s = 0	s = 0	s = 0	s = 1

Mating behaviors: High fert. samples	s = 1	r = 0.105 (-0.069, 0.280), p = 0.235, q = 0.285	s = 1	s = 1	r = 0.089 (-0.016, 0.193), p = 0.096, q = 0.157	s = 1
		k = 4, s = 4, n = 367			k = 4, s = 4, n = 376
		Q(df = 3) = 7.282, p = 0.063			Q(df = 3) = 3.388, p = 0.336

Note. Fert. = fertility; k = number of observations; n = number of unique participants; Q = Cochran’s Q test of heterogeneity; q = q-value; s = number of samples; T = testosterone.

Table 3

Masculine traits predicting reproduction: main analyses and subgroup analyses of mating attitudes vs mating behaviors and low vs high fertility samples.

	Reproduction
Outcome: Sample	Facial masculinity	Body masculinity	2D:4D	Voice pitch	Height	T levels
Reproductive domain: All samples	r = 0.099 (-0.012, 0.211), p = 0.081, q = 0.140	r = 0.143 (0.076, 0.212), p < 0.001, q = 0.001	r = 0.074 (-0.006, 0.154), p = 0.070, q = 0.131	r = 0.136 (-0.053, 0.328), p = 0.158, q = 0.228	r = 0.006 (-0.049, 0.062), p = 0.819, q = 0.491	r = 0.039 (-0.067, 0.145), p = 0.474, q = 0.385
	k = 5, s = 5, n = 1411	k = 14, s = 8, n = 897	k = 19, s = 10, n = 84,558	k = 5, s = 3, n = 143	k = 35, s = 25, n = 22,326	k = 3, s = 3, n = 351
	Q(df = 4) = 8.799, p = 0.066	Q(df = 13) = 16.356, p = 0.230	Q(df = 18) = 31.704, p = 0.024	Q(df = 4) = 5.378, p = 0.251	Q(df = 34) = 433.359, p < 0.001	Q(df = 2) = 0.387, p = 0.824
Fertility: All samples	r = 0.003 (-0.253, 0.260), p = 0.980, q = 0.543	r = 0.130 (0.060, 0.201), p < 0.001, q = 0.002	r = 0.032 (-0.065, 0.130), p = 0.514, q = 0.406	s = 2	r = 0.011 (-0.039, 0.062), p = 0.660, q = 0.451	s = 2
	k = 3, s = 3, n = 437	k = 8, s = 6, n = 813	k = 13, s = 5, n = 84,128		k = 26, s = 23, n = 22,242
	Q(df = 2) = 5.416, p = 0.067	Q(df = 7) = 4.840, p = 0.679	Q(df = 12) = 17.757, p = 0.123		Q(df = 25) = 400.038, p < 0.001
RS: All samples	s = 2	r = 0.192 (-0.052, 0.441), p = 0.122, q = 0.189	r = 0.174 (0.085, 0.267), p < 0.001, q = 0.002	s = 2	r = −0.044 (-0.201, 0.113), p = 0.584, q = 0.430	s = 1
		k = 6, s = 4, n = 205	k = 6, s = 5, n = 430		k = 9, s = 9, n = 603
		Q(df = 5) = 11.344, p = 0.045	Q(df = 5) = 0.976, p = 0.965		Q(df = 8) = 33.311, p < 0.001
Reproductive domain: Low fert. samples	s = 0	s = 1	r = 0.083 (-0.023, 0.190), p = 0.126, q = 0.191	s = 0	r = −0.037 (-0.112, 0.038), pp = 0.337, q = .347	s = 2
			k = 8, s = 4, n = 84,034		k = 8, s = 8, n = 17,135
			Q(df = 7) = 13.988, p = 0.051		Q(df = 7) = 244.970, p < 0.001
Fertility: Low fert. samples	s = 0	s = 1	r = 0.052 (-0.065, 0.169), p = 0.386, q = 0.369	s = 0	r = −0.037 (-0.112, 0.038), p = 0.337, q = 0.347	s = 2
			k = 7, s = 3, n = 83,845		k = 8, s = 8, n = 17,135
			Q(df = 6) = 8.335, p = 0.215		Q(df = 7) = 244.970, p < 0.001
RS: Low fert. samples	s = 0	s = 0	s = 1	s = 0	s = 0	s = 0

Reproductive domain: High fert. samples	r = 0.099 (-0.012, 0.211), p = 0.081, q = 0.140	r = 0.163 (0.104, 0.225), p < 0.001, q = 0.001	r = 0.083 (-0.039, 0.205), p = 0.184, q = 0.257	r = 0.136 (-0.053, 0.327), p = 0.158, q = 0.228	r = 0.034 (-0.041, 0.109), p = 0.377, q = 0.367	s = 1
	k = 5, s = 5, n = 1411	k = 13, s = 7, n = 626	k = 11, s = 6, n = 524	k = 5, s = 3, n = 143	k = 27, s = 17, n = 5191
	Q(df = 4) = 8.799, p = 0.066	Q(df = 12) = 12.347, p = 0.418	Q(df = 10) = 12.595, p = 0.247	Q(df = 4) = 5.378, p = 0.251	Q(df = 26) = 70.216, p < 0.001
Fertility: High fert. samples	r = 0.003 (-0.253, 0.260), p = 0.980, q = 0.543	r = 0.165 (0.095, 0.237), p < 0.001, q = 0.001	s = 2	s = 2	r = 0.059 (0.007, 0.111), p = 0.025, q = 0.068	s = 0
	k = 3, s = 3, n = 437	k = 7, s = 5, n = 542			k = 18, s = 15, n = 5,107
	Q(df = 2) = 5.416, p = 0.067	Q(df = 6) = 0.988, p = 0.986			Q(df = 17) = 26.458, p = 0.067
RS: High fert. samples	s = 2	r = 0.192 (-0.052, 0.441), p = 0.122, q = 0.189	r = 0.170 (0.053, 0.291), p = 0.005, q = 0.022	s = 2	r = -0.044 (-0.201, 0.113), p = 0.584, q = 0.430	s = 1
		k = 6, s = 4, n = 205	k = 5, s = 4, n = 241		k = 9, s = 9, n = 603
		Q(df = 5) = 11.344, p = 0.045	Q(df = 4) = 0.965, p = 0.915		Q(df = 8) = 33.311, p < 0.001

Note. fert. = fertility; k = number of observations; n = number of unique participants; Q = Cochran’s Q test of heterogeneity; q = q-value; RS = reproductive success; s = number of samples; T = testosterone.

Table 4

Overview of moderation analyses for the mating vs reproductive domains.

Significant associations are indicated by+ and – signs, showing the direction of the moderator relative to the reference category (stated first in the moderator column); crosses indicate no significant moderation; and ‘na’ indicates that power was too low to run that specific analysis. Only associations that remained significant after controlling for multiple comparisons are indicated here. Note that this table only shows general moderators shared by all masculine traits; for trait-specific moderation analyses, see Supplementary file 4. Likewise, for moderation analyses of the two mating domain measures attitudes and behaviors, and the two reproductive domain measures fertility and reproductive success, we also refer to Supplementary file 4.

Moderator	Facial masc.		Body masc.		2D:4D		Voice pitch		Height		T levels
Moderator	MAT	REP	MAT	REP	MAT	REP	MAT	REP	MAT	REP	MAT	REP
Mating vs reproductive domain	✗		✗		✗		✗		✗		✗
Mating attitudes vs behaviors	✗	na	✗	na	✗	na	na	na	✗	na	✗	na
Fertility vs reproductive success	na	na	na	✗	na	✗	na	na	na	✗	na	na
Low vs high fertility sample	na	na	✗	na	na	✗	na	na	✗	✗	na	na
Low fertility: student vs non-student sample	na	na	✗	na	✗	na	na	na	✗	na	✗	na
High fertility: traditional vs industrialized sample	na	na	na	na	na	✗	na	na	na	✗	na	na
Predominantly white vs mixed/other/unknown ethnicity sample	✗	na	✗	na	–	✗	na	na	✗	✗	✗	na
Monogamous vs non-monogamous marriage system	na	na	✗	na	na	✗	na	na	na	✗	na	na
Published vs non-published results	✗	na	✗	✗	✗	✗	na	na	✗	✗	✗	na
Peer reviewed vs not peer reviewed study	na	na	✗	na	✗	na	na	na	✗	na	na	na
Heterosexual vs gay/mixed/unknown sample	✗	na	✗	na	✗	✗	na	na	✗	+	–	na
Non-normality-transformed vs transformed variables	na	na	✗	✗	+	✗	na	na	✗	✗	+	na
Non-converted vs converted effect sizes	na	na	✗	+	na	na	na	na	✗	✗	✗	na
Age controlled for vs not controlled for	✗	na	+	na	✗	✗	na	na	✗	✗	✗	na
Inclusion of non-relevant control variables vs not	na	na	na	✗	na	na	na	na	na	✗	✗	na

Note. Masc = masculinity; MAT = mating; REP = reproduction; T = testosterone.

Additional files

Supplementary file 1 Effect size conversion formulas.: https://cdn.elifesciences.org/articles/65031/elife-65031-supp1-v2.docx
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Supplementary file 2 Study coding decisions.: https://cdn.elifesciences.org/articles/65031/elife-65031-supp2-v2.docx
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Supplementary file 3 Description of moderators.: https://cdn.elifesciences.org/articles/65031/elife-65031-supp3-v2.docx
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Supplementary file 4 Moderation analyses.: https://cdn.elifesciences.org/articles/65031/elife-65031-supp4-v2.docx
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Supplementary file 5 Global masculinity analyses.: https://cdn.elifesciences.org/articles/65031/elife-65031-supp5-v2.docx
Download elife-65031-supp5-v2.docx
Supplementary file 6 Funnel plots of effect sizes for mating measures.: https://cdn.elifesciences.org/articles/65031/elife-65031-supp6-v2.docx
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Supplementary file 7 Output for q-value computation for all analyses.: https://cdn.elifesciences.org/articles/65031/elife-65031-supp7-v2.docx
Download elife-65031-supp7-v2.docx
Transparent reporting form: https://cdn.elifesciences.org/articles/65031/elife-65031-transrepform1-v2.pdf
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Linda H Lidborg
Catharine Penelope Cross
Lynda G Boothroyd

(2022)

A meta-analysis of the association between male dimorphism and fitness outcomes in humans

eLife 11:e65031.

https://doi.org/10.7554/eLife.65031

Figures

Overall analysis structure.

Forest plot of the association between body masculinity and the mating domain.

Forest plot of the association between voice pitch and the mating domain.

Forest plot of the association between testosterone levels and the mating domain.

Forest plot of the association between height and the mating domain.

Forest plot of the association between body masculinity and the reproductive domain.

Tables

All studies included in the meta-analysis.

Masculine traits predicting mating: main analyses and subgroup analyses of mating attitudes vs mating behaviors and low vs high fertility samples.

Masculine traits predicting reproduction: main analyses and subgroup analyses of mating attitudes vs mating behaviors and low vs high fertility samples.

Overview of moderation analyses for the mating vs reproductive domains.

Additional files

Supplementary file 1

Supplementary file 2

Supplementary file 3

Supplementary file 4

Supplementary file 5

Supplementary file 6

Supplementary file 7

Transparent reporting form

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Overall analysis structure.

Forest plot of the association between body masculinity and the mating domain.

Forest plot of the association between voice pitch and the mating domain.

Forest plot of the association between testosterone levels and the mating domain.

Forest plot of the association between height and the mating domain.

Forest plot of the association between body masculinity and the reproductive domain.

All studies included in the meta-analysis.

Masculine traits predicting mating: main analyses and subgroup analyses of mating attitudes vs mating behaviors and low vs high fertility samples.

Masculine traits predicting reproduction: main analyses and subgroup analyses of mating attitudes vs mating behaviors and low vs high fertility samples.

Overview of moderation analyses for the mating vs reproductive domains.

Supplementary file 1

Supplementary file 2

Supplementary file 3

Supplementary file 4

Supplementary file 5

Supplementary file 6

Supplementary file 7

Transparent reporting form

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)