1. Evolutionary Biology

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

  1. Linda H Lidborg  Is a corresponding author
  2. Catharine Penelope Cross
  3. Lynda G Boothroyd
  1. Department of Psychology, Durham University, United Kingdom
  2. School of Psychology and Neuroscience, University of St Andrews, United Kingdom
https://doi.org/10.7554/eLife.65031
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Cite this article
  1. Linda H Lidborg
  2. Catharine Penelope Cross
  3. 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
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6 figures, 4 tables and 8 additional files

Figures

Figure 1
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Overall analysis structure.
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.
AuthorsYearPredictorOutcomeSampleSample locationLow or high fert.N
Alvergne et al., 20092009TREPRural villagersSenegalHigh53
Apicella, 20142014Body mascMAT, REP, OMHadzaTanzaniaHigh51
Apicella et al., 20072007Body masc, voice pitch, heightREP, OMHadzaTanzaniaHigh44-52
Arnocky et al., 20182018Facial mascMATStudentsCanadaLow135
Aronoff, 20172017TMATStudentsUSLow99
Atkinson, 20122012Body mascMATStudentsUSLow66
Atkinson et al., 20122012Body masc, voice pitch, heightREPHimba (Ovahimba)NamibiaHigh36
Bogaert and Fisher, 19951995TMATStudentsCanadaLow195-196
Booth et al., 19991999TMATArmy veterans and non-veteransUSLow4393
Boothroyd et al., 20082008Facial mascMATStudentsUKLow18-19
Boothroyd et al., 20112011Facial mascMATStudentsUKLow36
Boothroyd et al., 20172017Facial mascREP, OMAgtaPhilippinesHigh65
Facial mascMAT, REP, OMMayaBelizeHigh23-35
Charles and Alexander, 201120112D:4D, TMATStudentsUSLow25-42
Chaudhary et al., 20152015Body masc, heightMAT, REP, OMMbendjele BaYakaDemocratic Republic of the CongoHigh55-73
Edelstein et al., 20112011TMATStudentsUSLow134
Falcon, 201620162D:4DMATStudentsUSLow137
Farrelly et al., 20152015TMATStudentsUKLow75-78
Frederick, 20102010Body masc, 2D:4D, heightMATStudentsUSLow61
Frederick and Haselton, 20072007Body mascMATStudentsUSLow56-121
Frederick and Jenkins, 20152015HeightMATOnlineWorldwideLow28759-31418
Gallup et al., 20072007Body masc, 2D:4DMATStudentsUSLow71-75
Genovese, 20082008Body mascREPFormer teenage delinquentsUSHigh181
Gettler et al., 20192019TMATCebu Longitudinal Health and Nutrition SurveyPhilippinesHigh288
Gildner, 20182018Body masc, 2D:4D, heightREPShuar Health and Life History ProjectEcuadorHigh48
Gómez-Valdés et al., 20132013Facial mascREPHallstatt skullsAustriaHigh179
Hartl et al., 19821982Body masc, heightMAT, REPFormer teenage delinquentsUSHigh180-185
Hill et al., 20132013Facial masc, body masc,MATStudentsUSLow63
voice pitch, height
Hoppler et al., 20182018TREPMen’s health 40+ studySwitzerlandLow268
Hughes and Gallup, 20032003Body mascMATStudentsUSLow50-59
Honekopp et al., 200620062D:4D, heightMATStudents and non-studentsGermanyLow79-99
Honekopp et al., 20072007Facial masc, body masc, height, TMATStudents and non-studentsGermanyLow77
Kirchengast, 20002000HeightREP, OM!Kung SanNamibiaHigh103
Kirchengast and Winkler, 19951995HeightREP, OMUrban and rural Kavango peopleNamibiaHigh59-78
Klimas et al., 20192019TMATMen’s health 40+ studySwitzerlandLow159
Klimek et al., 201420142D:4D, heightREPMogielica Human Ecology Study SitePolandHigh238
Kordsmeyer et al., 20182018Body masc, voice pitch, height, TMATStudents and non-studentsGermanyLow103-164
Kordsmeyer and Penke, 201720172D:4D, heightMATStudents and non-studentsGermanyLow141
Krzyżanowska et al., 20152015HeightREPNational Child Development StudyUKLow6535
Kurzban and Weeden, 20052005HeightMAT, REPSpeed datersUSLow1503-1501
Lassek and Gaulin, 20092009Body masc, heightMATNHANES IIIUSLow4167-5159
Little et al., 19891989HeightREP, OMRural; growth stuntedMexicoHigh103
Loehr and O’Hara, 20132013Facial mascREPWWII soldiersFinlandHigh795
Longman et al., 20182018TMATStudentsUKLow38
Luevano et al., 20182018Facial masc, heightMATStudentsUSLow35-66
Lukaszewski et al., 20142014Body mascMATStudentsUSLow48-174
Maestripieri et al., 20142014TMATStudentsUSLow41-61
Manning and Fink, 200820082D:4DMAT, REPOnlineWorldwideLow26872-83681
Manning et al., 200320032D:4DREPCommunityEnglandLow189
2D:4DREPSugali and Yanadi tribal groupsIndiaHigh80
2D:4DREPZulus from townships near DurbanSouth AfricaHigh66
Marczak et al., 201820182D:4DREPYaliIndonesiaHigh47
McIntyre et al., 20062006TMATStudentsUSLow68-81
Međedović and Bulut, 20192019HeightMATStudentsSerbiaLow39
Mosing et al., 20152015HeightMAT, REPStudy of Twin Adults: Genes and EnvironmentSwedenLow2310-2549
Muller and Mazur, 19971997Facial mascREPWest Point class of 1950USHigh337
Nagelkerke et al., 20062006HeightMATNHANES 99–00USLow798-809
Nettle, 20022002HeightREPNational Child Development StudyUKLow4474
Pawlowski et al., 20082008HeightREPRuralPolandHigh46
Pawlowski et al., 20002000HeightREPUrban and ruralPolandHigh3201
Peters et al., 20082008Facial masc, body masc, TMATStudentsAustraliaLow100-113
Pollet et al., 20112011TMATNational Social Life, Health, and Aging ProjectUSLow749
Polo et al., 20192019Facial masc, body masc, heightMATStudents and non-studentsChileLow198-206
Price et al., 20132013Body masc, heightMATMainly studentsUKLow55
Prokop and Fedor, 20112011HeightREPFriends and family of studentsSlovakiaLow499
Prokop and Fedor, 20132013HeightMATStudentsSlovakiaLow105-150
Puts et al., 20062006Voice pitchMATStudentsUSLow103
Puts et al., 20152015TMATStudentsUSLow59-61
Putz et al., 200420042D:4DMATStudentsUSLow207-219
Rahman et al., 200520052D:4D, heightMATStudents and non-studentsUKLow78-150
Rhodes et al., 20052005Facial masc, body masc, heightMATMainly studentsAustraliaLow142-166
Rosenfield et al., 20202020Body masc, voice pitch, heightMAT, REP, OMTsimanéBoliviaHigh55-62
Schwarz et al., 201120112D:4DMATStudentsGermanyLow52-89
Scott and Bajema, 19821982HeightREPThird Harvard Growth StudyUSHigh606
Shoup and Gallup, 20082008Body masc, 2D:4DMATStudentsUSLow28-38
Sim and Chun, 20162016Body masc, 2D:4DMATStudentsUSLow90
Simmons and Roney, 20112011Body masc, TMATStudentsUSLow138
Smith et al., 20172017Body mascREPHadzaTanzaniaHigh51
Sneade and Furnham, 20162016Body mascMATStudentsUKLow145
Sorokowski et al., 20132013HeightREP, OMYaliIndonesiaHigh49-52
Steiner, 201120112D:4D, TREPStudents and non-studentsUSLow30
Stern et al., 20202020TMATStudentsUKLow61
Strong, 20142014Body mascMATStudentsUSLow31
Strong and Luevano, 20142014Body masc, 2D:4D, heightMATStudentsUSLow51-66
Subramanian et al., 20092009HeightOM2005-2006 National Family Health SurveyIndiaLow21120
Suire et al., 20182018Voice pitchMATMainly studentsFranceLow57-58
Tao and Yin, 20162016HeightREPThe Panel Study of Family DynamicsTaiwanLow1409
van Anders et al., 20072007TMATNon-studentsUSLow31
Van Dongen and Sprengers, 20122012Facial masc, body masc, 2D:4DMATNot specifiedNot specifiedLow52
Varella et al., 20142014Body masc, 2D:4D, heightMATStudentsBrazil, Czech RepublicLow69-80
von Rueden et al., 20112011Body masc, heightREP, OMTsimanéBoliviaHigh162-197
Voracek et al., 201020102D:4D, heightREPFirefightersAustriaLow134
Walther et al., 20162016Body mascREPMen’s health 40+ studySwitzerlandLow271
Walther et al., 2017a2017aBody mascMATMen’s health 40+ studySwitzerlandLow226
Walther et al., 2017b2017bHeightREPMen’s health 40+ studySwitzerlandLow271
Walther et al., 2017c2017cHeightMATMen’s health 40+ studySwitzerlandLow226
Waynforth, 199819982D:4D, heightMAT, REP, OMVillagersBelizeHigh35-56
Weeden and Sabini, 20072007Body masc, 2D:4D, heightMATStudentsUSLow188-212
Winkler and Kirchengast, 19941994HeightREP, OM!Kung SanNamibiaHigh31-114
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: SampleFacial masculinityBody masculinity2D:4DVoice pitchHeightT levels
Mating domain:
All samples		r = 0.080 (-0.003, 0.164), p = 0.060, q = 0.117r = 0.133 (0.091, 0.176), p < 0.001, q = 0.001r = 0.034 (0.000, 0.069), p = 0.049, q = 0.102r = 0.132 (0.061, 0.204), p < 0.001, q = 0.002r = 0.057 (0.027, 0.087), p < 0.001, q = 0.002r = 0.093 (0.066, 0.121), p < 0.001, q = 0.001
 k = 30, s = 11, n = 948k = 121, s = 32,
n = 7939		k = 84, s = 23,
n = 66,807		k = 8, s = 5, n = 443k = 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 = 0.304r = .078 (0.002, 0.155), p = 0.045, q = 0.098r = 0.035 (-0.061, 0.132), p = 0.474, q = 0.385s = 0r = 0 .028 (-0.013, 0.068), p = 0.179, q = 0.253r = 0.099 (0.026, 0.173), p = 0.008, q = 0.032
 k = 5, s = 4, n = 407k = 20, s = 9, n = 922k = 19, s = 7, n = 504 k = 9, s = 6, n = 4232k = 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 = 0.424r = .142 (0.099, 0.187), p < 0.001, q = 0.001r = 0.038 (-0.002, 0.078), p = 0.061, q = 0.117r = 0.124 (0.043, 0.206), p = 0.003, q = 0.016r = 0.054 (0.021, 0.087), p = 0.001, q = 0.008r = 0.084 (0.058, 0.110), p < 0.001, q = 0.001
 k = 22, s = 8, n = 755k = 91, s = 31, n = 7738k = 51, s = 19, n = 1607k = 7, s = 5, n = 443k = 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 = 0.109r = 0.135 (0.091, 0.180), p < 0.001, q = 0.001r = 0.038 (0.002, 0.073), p = 0.037, q = 0.086r = 0.129 (0.055, 0.204), p < 0.001, q = 0.005r = 0.055 (0.024, 0.086), p < 0.001, q = 0.004r = 0.099 (0.069, 0.129), p < 0.001, q = 0.001
 k = 28, s = 10, n = 913k = 117, s = 28,
n = 7572		k = 82, s = 22,
n = 66,751		k = 7, s = 4, n = 388k = 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 = 0.304r = 0.078 (0.002, 0.155), p = 0.045, q = 0.098r = 0.035 (-0.061, 0.132), p = 0.474, q = 0.385s = 0r = 0.028 (-0.013, 0.068), p = 0.179, q = 0.253r = 0.108 (0.021, 0.195), p = 0.015, q = 0.047
 k = 5, s = 4, n = 407k = 20, s = 9, n = 922k = 19, s = 7, n = 504 k = 9, s = 6, n = 4,232k = 17, s = 10, n = 751
 Q(df = 4) = 8.684, p = 0.070Q(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 = 0.420r = 0.145 (0.100, 0.193), p< 0.001, q = 0.001r = 0.042 (0.001, 0.083), p = 0.045, q = 0.098r = .119 (0.034, 0.205), p = 0.006, q = 0.025r = .051 (0.017, 0.086), p = 0.004, q = 0.019r = .088 (0.058, 0.119), p < 0.001, q = 0.001
 k = 20, s = 7, n = 720k = 87, s = 27, n = 7371k = 49, s = 19, n = 1551k = 6, s = 4, n = 388k = 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 = 1r = 0.105 (-0.069, 0.280), p = 0.235, q = 0.285s = 1s = 1r = 0.089 (-0.016, 0.193), p = 0.096, q = 0.157s = 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 = 0s = 0s = 0s = 0s = 0s = 1
       
Mating behaviors:
High fert. samples		s = 1r = 0.105 (-0.069, 0.280), p = 0.235, q = 0.285s = 1s = 1r = 0.089 (-0.016, 0.193), p = 0.096, q = 0.157s = 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		 

  1. 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.

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.

 Reproduction
Outcome: SampleFacial masculinityBody masculinity2D:4DVoice pitchHeightT levels
Reproductive domain:
All samples		r = 0.099 (-0.012, 0.211), p = 0.081, q = 0.140r = 0.143 (0.076, 0.212), p < 0.001, q = 0.001r = 0.074 (-0.006, 0.154), p = 0.070, q = 0.131r = 0.136 (-0.053, 0.328), p = 0.158, q = 0.228r = 0.006 (-0.049, 0.062), p = 0.819, q = 0.491r = 0.039 (-0.067, 0.145), p = 0.474, q = 0.385
 k = 5, s = 5, n = 1411k = 14, s = 8, n = 897k = 19, s = 10,
n = 84,558		k = 5, s = 3, n = 143k = 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 = 0.543r = 0.130 (0.060, 0.201), p < 0.001, q = 0.002r = 0.032 (-0.065, 0.130), p = 0.514, q = 0.406s = 2r = 0.011 (-0.039, 0.062), p = 0.660, q = 0.451s = 2
 k = 3, s = 3, n = 437k = 8, s = 6, n = 813k = 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 = 2r = 0.192 (-0.052, 0.441), p = 0.122, q = 0.189r = 0.174 (0.085, 0.267), p < 0.001, q = 0.002s = 2r = −0.044 (-0.201, 0.113), p = 0.584,
q = 0.430		s = 1
  k = 6, s = 4, n = 205k = 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 = 0s = 1r = 0.083 (-0.023, 0.190), p = 0.126, q = 0.191s = 0r = −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 = 0s = 1r = 0.052 (-0.065, 0.169), p = 0.386, q = 0.369s = 0r = −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 = 0s = 0s = 1s = 0s = 0s = 0
       
Reproductive domain:
High fert. samples		r = 0.099 (-0.012, 0.211), p = 0.081, q = 0.140r = 0.163 (0.104, 0.225), p < 0.001, q = 0.001r = 0.083 (-0.039, 0.205), p = 0.184, q = 0.257r = 0.136 (-0.053, 0.327), p = 0.158, q = 0.228r = 0.034 (-0.041, 0.109), p = 0.377, q = 0.367s = 1
 k = 5, s = 5, n = 1411k = 13, s = 7, n = 626k = 11, s = 6, n = 524k = 5, s = 3, n = 143k = 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 = 0.543r = 0.165 (0.095, 0.237), p < 0.001, q = 0.001s = 2s = 2r = 0.059 (0.007, 0.111), p = 0.025, q = 0.068s = 0
 k = 3, s = 3, n = 437k = 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 = 2r = 0.192 (-0.052, 0.441), p = 0.122, q = 0.189r = 0.170 (0.053, 0.291), p = 0.005, q = 0.022s = 2r = -0.044 (-0.201, 0.113), p = 0.584,
q = 0.430		s = 1
  k = 6, s = 4, n = 205k = 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		 

  1. 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.

ModeratorFacial masc.Body masc.2D:4DVoice pitchHeightT levels
MATREPMATREPMATREPMATREPMATREPMATREP
Mating vs reproductive domain✗✗✗✗✗✗
Mating attitudes vs behaviors✗na✗na✗nanana✗na✗na
Fertility vs reproductive successnanana✗na✗nanana✗nana
Low vs high fertility samplenana✗nana✗nana✗✗nana
Low fertility: student vs non-student samplenana✗na✗nanana✗na✗na
High fertility: traditional vs industrialized samplenanananana✗nanana✗nana
Predominantly white vs mixed/other/unknown ethnicity sample✗na✗na–✗nana✗✗✗na
Monogamous vs non-monogamous marriage systemnana✗nana✗nanana✗nana
Published vs non-published results✗na✗✗✗✗nana✗✗✗na
Peer reviewed vs not peer reviewed studynana✗na✗nanana✗nanana
Heterosexual vs gay/mixed/unknown sample✗na✗na✗✗nana✗+–na
Non-normality-transformed vs transformed variablesnana✗✗+✗nana✗✗+na
Non-converted vs converted effect sizesnana✗+nananana✗✗✗na
Age controlled for vs not controlled for✗na+na✗✗nana✗✗✗na
Inclusion of non-relevant control variables vs notnanana✗nanananana✗✗na

  1. 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
Supplementary file 2

Study coding decisions.

https://cdn.elifesciences.org/articles/65031/elife-65031-supp2-v2.docx
Supplementary file 3

Description of moderators.

https://cdn.elifesciences.org/articles/65031/elife-65031-supp3-v2.docx
Supplementary file 4

Moderation analyses.

https://cdn.elifesciences.org/articles/65031/elife-65031-supp4-v2.docx
Supplementary file 5

Global masculinity analyses.

https://cdn.elifesciences.org/articles/65031/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
Supplementary file 7

Output for q-value computation for all analyses.

https://cdn.elifesciences.org/articles/65031/elife-65031-supp7-v2.docx
Transparent reporting form
https://cdn.elifesciences.org/articles/65031/elife-65031-transrepform1-v2.pdf

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  1. Linda H Lidborg
  2. Catharine Penelope Cross
  3. 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