Dataset of Moura et al. (2026): "The unusual food provisioning by males beyond mating in the spider Manogea porracea (Araneidae)"
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GENERAL INFORMATION 1. Paper Citation Moura, R.R., Valentim, A.M., Vasconcellos-Neto, J., Gonzaga, M.O. (2026). The unusual food provisioning by males beyond mating in the spider Manogea porracea (Araneidae). Behavioral Ecology, in press. 2. Abstract The transfer of resources between mating pairs, beyond obligatory gametes, influences their fitness. In spiders, prey offerings generally occur from the male to the female and may increase male acceptance or female fecundity, but they are restricted to courtship and mating contexts. In the spider Manogea porracea (Araneidae), males offer prey to females both before and after copulation while guarding the mate and caring for offspring. Here, we first described this unusual behavior through different experiments in four populations from southeastern Brazil and analyzed adaptative consequences of its occurrence. We recorded the feeding duration of both sexes for the same prey, described male-female interactions, and assessed correlates of prey offerings (e.g., body condition and egg sac number). In all populations, males consistently offered prey to females without mating attempts; instead, they vibrated females’ webs to attract partners’ attention to the prey and then returned to their own web. Females eventually stole prey from males, but prey offerings were common. Males threw small prey from their webs to the females, but carried large prey through the supporting threads, often breaking some of them, directly to their mates. They partially consumed prey before offering the remnants to females, and the duration of female feeding decreased as the duration of male feeding increased. Males provisioned more often when females had laid more egg sacs but not based on the body condition of partners or their own condition. We discuss how male provisioning behavior may influence future egg production, female lifespan and parental care. 3. Originators Rafael Rios Moura. 4. Contact information Rafael Rios Moura, Núcleo de Extensão e Pesquisa em Ecologia e Evolução (NEPEE), Departamento de Ciências Agrárias e Naturais, Universidade do Estado de Minas Gerais – UEMG, R. Ver. Geraldo Moisés da Silva, s/n - Universitário, CEP 38302-192, Ituiutaba, MG, Brazil. E-mail: rafael.moura@uemg.br. 5. Date of data collection Data was collected in four populations between August 1991 and December 2024. 6. Geographic location(s) of data collection Population 1: A protected area of Atlantic Forest in the “Reserva Natural Vale”, located in Linhares, Espírito Santos, Brazil (19°06′54″S, 39°56′20″W).Population 2: A Eucalyptus plantation located in Agudos, São Paulo, Brazil (22°26′35″S, 48°57′38″W).Population 3: A protected area of Brazilian savanna (Cerrado) at “Estação Ecológica de Pirapitinga”, located in Três Marias, Minas Gerais, Brazil (18°21′18″S, 45°19′19″W).Population 4: Another Eucalyptus plantation at Fazenda Nova Monte Carmelo, located in Estrela do Sul, Minas Gerais, Brazil (18°49′27″S, 47°51′47″W). 7. Information about funding sources that supported the collection and curation of the data We thank Thiago M. Rezende for his help during the data collection at the “Estação Ecológica Pirapitinga”. We thank Vale S.A. for providing access to the “Reserva Natural da Vale”, Dexco S.A. for providing access to the “Fazenda de Agudos”, and LD Celulose S.A. for providing financial support and access to the “Fazenda Nova Monte Carmelo”. This project was also supported by Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG: APQ-04815-17, APQ-02009-21, APQ-03316-22, APQ-02950-25, BIP-00193-24), Universidade do Estado de Minas Gerais (Pesquisador de Produtividade – PQ/UEMG), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES: Finance Code 001), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq: Proc. 441225/2016-0, 314518/2023-1) and Programa de Pesquisas Ecológicas de Longa Duração (CAPES-CNPq-PELD: Proc. 88887.136318/2017). All spiders are stored in the collection of the Laboratório de Aracnologia, at the Universidade Federal de Uberlândia (M. O. Gonzaga, curator). ACCESS INFORMATION 1. Licenses/restrictions placed on the data Cite the original article published in the Behavioral Ecology journal. 2. Data derived from other sources Not applicable. DATA FILES AND VARIABLES 1. feeding_duration.csv • obs_ID: observation identification • web_ID: web identification • male_duration: male feeding duration (min) • female_duration: female feeding duration (min) 2. prey_offering.csv • obs_ID: observation identification • date: date of sampling • web_ID: web identification • experimenter: experimenter identification • male_mass: male body mass (mg) • female_mass: female body mass (mg) • male_carapace_width: male carapace width (mm) • female_carapace_width: female carapace width (mm) • distance_webs: distance between male and female webs (cm) • eggsacs: number of egg sacs • offer: occurrence of prey offering (0 = no offer, 1 = offer) CODE SCRIPTS AND WORKFLOW 1. script_Moura_et_al.R The R script reproduces all statistical analyses presented in the article. SOFTWARE VERSIONS R version 4.5.1 (2025-06-13 ucrt). loaded packages: • car package: version 3.1.5 • lme4 package: version 1.1.37 • MASS package: version 7.3.65 • DHARMa package: version 0.4.7 • MuMIn package: version 1.48.11 • effects package: version 4.2.2 REFERENCES Bartoń K (2025). _MuMIn: Multi-Model Inference_. doi:10.32614/CRAN.package.MuMIn <https://doi.org/10.32614/CRAN.package.MuMIn>, R package version 1.48.11, <https://CRAN.R-project.org/package=MuMIn>.Bates D, Maechler M, Bolker B, Walker S (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. doi:10.18637/jss.v067.i01.Fox J, Hong J (2009). Effect Displays in R for Multinomial and Proportional-Odds Logit Models: Extensions to the effects Package. Journal of Statistical Software, 32(1), 1-24. doi: 10.18637/jss.v032.i01.Fox J, Weisberg S (2019). An R Companion to Applied Regression, Third edition. Sage, Thousand Oaks CA.Hartig F (2024). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. <https://doi.org/10.32614/CRAN.package.DHARMa>, R package version 0.4.7, <https://CRAN.R-project.org/package=DHARMa>.Venables WN, Ripley BD (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York.



