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

Yong-Chao Su, Cheng-Yu Wu, Cheng-Hong Yang, Bo-Sheng Li, Sin-Hua Moi and Yu-Da Lin
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Yong-Chao Su: Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
Cheng-Yu Wu: Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
Cheng-Hong Yang: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Bo-Sheng Li: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
Sin-Hua Moi: Center of Cancer Program Development, E-Da Cancer Hospital, I-Shou University, Kaohsiung 82445, Taiwan
Yu-Da Lin: Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Penghu 880011, Taiwan

Mathematics, 2021, vol. 9, issue 4, 1-16

Abstract: 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 Keywords: machine learning; data imputation; group foraging; PLS-PM; ideal free distribution; kleptoparasitism; resource allocation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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