| Literature DB >> 34284630 |
Kathryn V Walter1, Daniel Conroy-Beam1, David M Buss2, Kelly Asao3, Agnieszka Sorokowska4,5, Piotr Sorokowski4, Toivo Aavik6, Grace Akello7, Mohammad Madallh Alhabahba8, Charlotte Alm9, Naumana Amjad10, Afifa Anjum11, Chiemezie S Atama12, Derya Atamtürk Duyar13, Richard Ayebare14, Carlota Batres15, Mons Bendixen16, Aicha Bensafia17, Boris Bizumic18, Mahmoud Boussena19, Marina Butovskaya20,21, Seda Can22, Katarzyna Cantarero23, Antonin Carrier24, Hakan Cetinkaya25, Ilona Croy26, Rosa María Cueto27, Marcin Czub3, Daria Dronova20, Seda Dural22, Izzet Duyar13, Berna Ertugrul13, Agustín Espinosa27, Ignacio Estevan28, Carla Sofia Esteves29, Luxi Fang30, Tomasz Frackowiak4, Jorge Contreras Garduño31, Karina Ugalde González32, Farida Guemaz33, Petra Gyuris34, Mária Halamová35, Iskra Herak36, Marina Horvat37, Ivana Hromatko38, Chin-Ming Hui31, Jas Laile Jaafar39, Feng Jiang40, Konstantinos Kafetsios41, Tina Kavčič42, Leif Edward Ottesen Kennair16, Nicolas Kervyn36, Truong Thi Khanh Ha43, Imran Ahmed Khilji44, Nils C Köbis45, Hoang Moc Lan43, András Láng34, Georgina R Lennard18, Ernesto León27, Torun Lindholm9, Trinh Thi Linh43, Giulia Lopez46, Nguyen Van Luot43, Alvaro Mailhos28, Zoi Manesi47, Rocio Martinez48, Sarah L McKerchar18, Norbert Meskó34, Girishwar Misra49, Conal Monaghan18, Emanuel C Mora50, Alba Moya-Garófano44, Bojan Musil37, Jean Carlos Natividade51, Agnieszka Niemczyk4, George Nizharadze52, Elisabeth Oberzaucher53, Anna Oleszkiewicz4,5, Mohd Sofian Omar-Fauzee54, Ike E Onyishi55, Baris Özener13, Ariela Francesca Pagani46, Vilmante Pakalniskiene56, Miriam Parise46, Farid Pazhoohi57, Annette Pisanski50, Katarzyna Pisanski3,58,59, Edna Ponciano60,61, Camelia Popa62, Pavol Prokop63,64, Muhammad Rizwan65, Mario Sainz66, Svjetlana Salkičević38, Ruta Sargautyte56, Ivan Sarmány-Schuller67, Susanne Schmehl53, Shivantika Sharad68, Razi Sultan Siddiqui69, Franco Simonetti70, Stanislava Yordanova Stoyanova71, Meri Tadinac38, Marco Antonio Correa Varella72, Christin-Melanie Vauclair73, Luis Diego Vega33, Dwi Ajeng Widarini74, Gyesook Yoo75, Marta Marta Zaťková33, Maja Zupančič76.
Abstract
A wide range of literature connects sex ratio and mating behaviours in non-human animals. However, research examining sex ratio and human mating is limited in scope. Prior work has examined the relationship between sex ratio and desire for short-term, uncommitted mating as well as outcomes such as marriage and divorce rates. Less empirical attention has been directed towards the relationship between sex ratio and mate preferences, despite the importance of mate preferences in the human mating literature. To address this gap, we examined sex ratio's relationship to the variation in preferences for attractiveness, resources, kindness, intelligence and health in a long-term mate across 45 countries (n = 14 487). We predicted that mate preferences would vary according to relative power of choice on the mating market, with increased power derived from having relatively few competitors and numerous potential mates. We found that each sex tended to report more demanding preferences for attractiveness and resources where the opposite sex was abundant, compared to where the opposite sex was scarce. This pattern dovetails with those found for mating strategies in humans and mate preferences across species, highlighting the importance of sex ratio for understanding variation in human mate preferences.Entities:
Keywords: cross-cultural; mate preferences; mating market; sex differences; sex ratio
Mesh:
Year: 2021 PMID: 34284630 PMCID: PMC8292757 DOI: 10.1098/rspb.2021.1115
Source DB: PubMed Journal: Proc Biol Sci ISSN: 0962-8452 Impact factor: 5.349
The interaction between sex and sex ratio predicting absolute and relative mate preferences.
| preference | sex ratio measure | s.e. | ||
|---|---|---|---|---|
| good financial prospects | birth | −0.088 (−0.099) | 0.025 (0.043) | 0.001**a (0.027*) |
| adult | −0.040 (−0.092) | 0.029 (0.043) | 0.174 (0.037*) | |
| 15–49 | −0.061 (−0.114) | 0.026 (0.037) | 0.025* (0.004**a) | |
| 15–64 | −0.048 (−0.103) | 0.028 (0.040) | 0.087 (0.013*) | |
| city | −0.044 (−0.084) | 0.027 (0.037) | 0.108 (0.028*) | |
| physical attractiveness | birth | −0.095 (−0.082) | 0.025 (0.040) | <0.001***a (0.049*) |
| adult | −0.084 (−0.118) | 0.027 (0.038) | 0.004**a (0.003**a) | |
| 15–49 | −0.115 (−0.131) | 0.023 (0.033) | <0.001***a (<0.001***a) | |
| 15–64 | −0.108 (−0.122) | 0.024 (0.035) | <0.001***a (0.001**a) | |
| city | −0.083 (−0.123) | 0.026 (0.033) | 0.002**a (<0.001***a) | |
| intelligence | birth | −0.076 (−0.025) | 0.023 (0.043) | 0.002**a (0.568) |
| adult | −0.014 (−0.011) | 0.026 (0.042) | 0.604 (0.789) | |
| 15–49 | −0.031 (−0.033) | 0.025 (0.038) | 0.212 (0.383) | |
| 15–64 | −0.018 (−0.011) | 0.025 (0.039) | 0.486 (0.784) | |
| city | −0.004 (−0.006) | 0.026 (0.039) | 0.885 (0.870) | |
| kindness | birth | −0.011 (−0.002) | 0.024 (0.038) | 0.668 (0.965) |
| adult | −0.013 (−0.021) | 0.025 (0.037) | 0.598 (0.578) | |
| 15–49 | −0.004 (−0.032) | 0.024 (0.033) | 0.856 (0.330) | |
| 15–64 | −0.016 (−0.036) | 0.024 (0.034) | 0.497 (0.295) | |
| city | −0.021 (−0.037) | 0.023 (0.034) | 0.362 (0.288) | |
| health | birth | −0.085 (−0.081) | 0.023 (0.039) | <0.001***a (0.044*) |
| adult | −0.023 (−0.048) | 0.027 (0.039) | 0.401 (0.226) | |
| 15–49 | −0.038 (−0.069) | 0.025 (0.034) | 0.134 (0.051) | |
| 15–64 | −0.034 (−0.074) | 0.025 (0.036) | 0.183 (0.045*) | |
| city | −0.021 (−0.056) | 0.024 (0.036) | 0.391 (0.123) |
*p < 0.05; **p < 0.01; ***p < 0.001. Results for relative mate preferences shown in parentheses.
aRemained significant after Holm–Bonferroni correction.
Figure 1Participant mate preferences across sex ratios. Data are jittered to reduce overplotting. Regression lines, separated by sex, shown with shaded areas indicating 95% confidence intervals. The specific preference (absolute preference for physical attractiveness; relative preference for physical attractiveness; relative preference for good financial prospects) and specific sex ratio (sex ratio ages 15–49; sex ratio ages 15–64; city sex ratio) can be identified in each plot label. Sex ratio is the number of males per 100 females. (Online version in colour.)