Evolutionary drivers of group foraging: A new framework for investigating variance in food intake and reproduction

Boldness suppresses hoarding behavior in food hoarding season and reduces over-wintering survival in a social rodent

The "pace-of-life" syndrome (POLS) framework can encompass multiple personality axes that drive important functional behaviors (e.g., foraging behavior) and that co-vary with multiple life history traits. Food hoarding is an adaptive behavior important for an animal's ability to adapt to seasonal fluctuations in food availability. However, the empirical evidence for the relationships between animal personality and hoarding behavior remains unclear, including its fitness consequences in the POLS framework. In this study, the Mongolian gerbil (Meriones unguiculatus), a social rodent, was used as a model system to investigate how boldness or shyness is associated with food hoarding strategies during the food hoarding season and their impact on over-winter survival and reproduction at both individual and group levels. The results of this study showed that, compared with shy gerbils, bold gerbils had a lower effort foraging strategy during the food hoarding season and exhibited lower over-winter survival rates. However, bold-shy personality differences had no effect on over-winter reproduction. These findings suggest that the personality is a crucial factor influencing the foraging strategy during the food hoarding season in Mongolian gerbils. Personality may be related to energy states or the reaction to environmental changes (e.g., predation risk and food availability) in bold or shy social animals. These results reflect animal life history trade-offs between current versus future reproduction and reproduction versus self-maintenance, thereby helping Mongolian gerbils in adapting to seasonal fluctuations in their habitat.

Group-Living Spider Cyrtophora citricola as a Potential Novel Biological Control Agent of the Tomato Pest Tuta absoluta

Group-living spiders may be uniquely suited for controlling flying insect pests, as their high tolerance for conspecifics and low levels of cannibalism result in large, predator dense capture webs. In laboratory settings, we tested the ability of the facultatively communal spider, Cyrtophora citricola, to control the tomato leafminer, Tuta absoluta; a major pest of tomato crops worldwide. We tested whether prey capture success was affected by spider body size, and whether prey capture differed among T. absoluta, flightless fruit flies (Drosophila hydei), and larger black soldier flies (Hermetia illucens). We found that larger spiders generally caught more prey, and that prey capture success was similar for T. absoluta and easily caught fruit flies, while black soldier flies were rarely caught. We further investigated the seasonal variations in web sizes in southern Spain, and found that pest control would be most effective in the tomato planting and growing season. Finally, we show that C. citricola in Spain have >50% infection rates of an egg predatory wasp, Philolema palanichamyi, which may need controlling to maintain pest control efficacy. These results suggest that using C. citricola as a biological control agent in an integrated pest management system could potentially facilitate a reduction of pesticide reliance in the future.

Long-Term Colony Dynamics and Fitness in a Colonial Tent-Web Spider Cyrtophora citricola

Social animals are expected to experience a positive effect of conspecific number or density on fitness (an Allee effect) because of the benefits of group living. However, social animals also often disperse to live either solitarily or in small groups, so to understand why social animals leave their groups it is necessary to understand how group size affects both average fitness and the expected fitness outcomes of individuals. We examined the relationships between group size and fitness in the colonial spider Cyrtophora citricola using long-term observations of colony demographics. We censused colonies, recording the number of juveniles, large females, and egg sacs, approximately every 2 months for 2 years. We also recorded the substrates supporting colony webs, including plant species and size, and the azimuth the colony occupied on the plant. Colonies in all regions showed cyclical patterns of growth and decline; however, regions were not synchronized, and seasonal effects differed between years. Colonies with fewer individuals at the initial observation were less likely to survive over the course of observations, and extinction rates were also influenced by an interaction between region and plant substrate. Small colonies were more likely to be extinct by the next census, but if they survived, they were more likely to have high growth rates compared to larger colonies. Despite the potential for high growth rates, high extinction rates depressed the average fitness of small colonies so that population growth rates peaked at intermediate colony sizes. Variance in egg sac production also peaked at intermediate colony sizes, suggesting that competitive interactions may increase the uneven distribution of resources in larger groups. Even if average fitness is high, if spiders can anticipate poor outcomes in large colonies, they may disperse to live solitarily or in smaller, less competitive groups.

Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider

Cost–benefit analysis is widely used to elucidate the association between foraging group size and resource size. Despite advances in the development of theoretical frameworks, however, the empirical systems used for testing are hindered by the vagaries of field surveys and incomplete data. This study developed the three approaches to data imputation based on machine learning (ML) algorithms with the aim of rescuing valuable field data. Using 163 host spider webs (132 complete data and 31 incomplete data), our results indicated that the data imputation based on random forest algorithm outperformed classification and regression trees, the k-nearest neighbor, and other conventional approaches (Wilcoxon signed-rank test and correlation difference have p-value from < 0.001–0.030). We then used rescued data based on a natural system involving kleptoparasitic spiders from Taiwan and Vietnam (Argyrodes miniaceus, Theridiidae) to test the occurrence and group size of kleptoparasites in natural populations. Our partial least-squares path modelling (PLS-PM) results demonstrated that the size of the host web (T = 6.890, p = 0.000) is a significant feature affecting group size. The resource size (T = 2.590, p = 0.010) and the microclimate (T = 3.230, p = 0.001) are significant features affecting the presence of kleptoparasites. The test of conformation of group size distribution to the ideal free distribution (IFD) model revealed that predictions pertaining to per-capita resource size were underestimated (bootstrap resampling mean slopes <IFD predicted slopes, p < 0.001). These findings highlight the importance of applying appropriate ML methods to the handling of missing field data.