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Perceived mate availability does not influence variation in egg-laying patterns in the Hawaiian Pacific field cricket (Orthoptera: Gryllidae)

Published online by Cambridge University Press:  26 August 2025

Aarcha Thadi*
Affiliation:
Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, Minnesota, 55108, United States of America
Ruby Ales
Affiliation:
Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, Minnesota, 55108, United States of America
Marlene Zuk
Affiliation:
Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, Minnesota, 55108, United States of America
*
Corresponding author: Aarcha Thadi; Email: thadi003@umn.edu

Abstract

Females may adjust how many eggs they lay over the course of their lifetime (i.e., their egg-laying pattern) to bias their investment into either current or future reproduction. Using mate availability cues to bias reproductive investment could ensure that females obtain the benefits of multiple mating when future mate availability is high or low. We studied whether perceived mate availability influenced egg-laying patterns in Teleogryllus oceanicus Le Guillou (Orthoptera: Gryllidae), the Pacific field cricket, and whether variation in those patterns affected females’ future egg-laying or total reproductive output. On hearing the male calling song to simulate high mate availability, females did not alter their egg-laying patterns relative to females that did not hear the song. The lack of influence of perceived mate availability on egg-laying patterns is noteworthy because this treatment affects many other aspects of this species’ reproductive investment. Neither investing highly in current versus future egg-laying nor having a highly variable egg-laying pattern appeared to be costly in this species. Despite consistent conditions and sufficient resources for females during the experiment, our fine-scale study of egg-laying patterns highlights the variability that exists in these patterns, and we speculate on some factors that may drive this variation.

Information

Type
Research Paper
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Entomological Society of Canada
Figure 0

Table 1. Results of the repeated-measures multivariate analysis of covariance testing of the effect of song or silent treatment on the egg-laying patterns of Teleogryllus oceanicus females. Covariates are genotype, female pronotum width, male pronotum width, and female age. Significant effects (P < 0.05) are noted in bold.

Figure 1

Table 2. Linear models testing the effect of song or silent treatment on the total number of eggs laid and the fraction of eggs laid in the first week by Teleogryllus oceanicus females. Covariates are genotype, female pronotum width, male pronotum width, and female age. Significant effects (P < 0.05) are noted in bold.

Figure 2

Figure 1. Egg-laying patterns among females that were reared under song (dark grey box and band, open circles) and silent treatments (light grey box and band, filled circles) to mimic high and low mate availability: A, egg-laying patterns over two weeks – grey bands represent 95% confidence intervals around the linear fit for each treatment; B, proportion of eggs laid in the first week relative to the total number of eggs; C, total number of eggs laid over two weeks. In B and C, the edges of the boxplot represent the first and third quartiles (Q1 and Q3) of the data, and the thick solid line in the middle represents the median. The whiskers extend to the largest and smallest values within 1.5× the interquartile range (IQR = Q3 – Q1).

Figure 3

Table 3. Partial correlations exploring the possibility of trade-offs between number of eggs laid in subsequent timepoints and variability in egg-laying patterns and total number of eggs laid. Significant relationships (P < 0.05) are noted in bold.

Figure 4

Figure 2. A–E, Average number of eggs laid per day at one timepoint plotted against the subsequent timepoint, from timepoints 1 to 6; F, female’s average z-scores against the total number of eggs she laid. Average z-scores are calculated from the absolute values of the z-scores for the number of eggs laid at each timepoint compared to the average number of eggs laid across all timepoints for each female. The grey band represents the 95% fit for the linear fit between these variables.

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