Abstract：
:Subterranean termites live in large colonies under the ground where they build an elaborate network of tunnels for foraging. In this study, we explored how the termite population size can be estimated using partial information on tunnel patterns. To achieve this, we used an agent-based model to create tunnel patterns that were characterized by three variables: the number of simulated termites (N), passing probability of two termites encountering one another (P), and distance that termites move soil particles (D). To explore whether the N value could be estimated using a partial termite tunnel pattern, we generated four tunnel pattern groups by partially obscuring different areas in an image of a complete tunnel pattern, where: (1) the outer area of the tunnel pattern was obscured (I-pattern); (2) half of the tunnel pattern was obscured (H-pattern); (3) the inner region of the tunnel pattern was obscured (O-pattern); and (4) I- and O-patterns (IO-pattern) were combined. For each group, 80% of the tunnel patterns were used to train a convolutional neural network while the remaining 20% were used for estimating the N value. Estimation results showed that the N estimates for IO-patterns were the most accurate, followed by I-, H-, and O-patterns. This indicates that termite population size can be estimated based on tunnel information near the center of a colony. We briefly discuss the advantages and disadvantages of this method for estimating termite population size.
journal_name
Math Bioscijournal_title
Mathematical biosciencesauthors
Seo JK,Baik S,Lee SHdoi
10.1016/j.mbs.2019.108218subject
Has Abstractpub_date
2019-09-01 00:00:00pages
108218eissn
0025-5564issn
1879-3134pii
S0025-5564(18)30744-2journal_volume
315pub_type
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