  This study introduces markerless retro-identification of animals, a novel
concept and practical technique to identify past occurrences of organisms in
archived data, that complements traditional forward-looking chronological
re-identification methods in longitudinal behavioural research. Identification
of a key individual among multiple subjects may occur late in an experiment if
it reveals itself through interesting behaviour after a period of
undifferentiated performance. Often, longitudinal studies also encounter
subject attrition during experiments. Effort invested in training software
models to recognise and track such individuals is wasted if they fail to
complete the experiment. Ideally, we would be able to select individuals who
both complete an experiment and/or differentiate themselves via interesting
behaviour, prior to investing computational resources in training image
classification software to recognise them. We propose retro-identification for
model training to achieve this aim. This reduces manual annotation effort and
computational resources by identifying subjects only after they differentiate
themselves late, or at an experiment's conclusion. Our study dataset comprises
observations made of morphologically similar reed bees (\textit{Exoneura
robusta}) over five days. We evaluated model performance by training on final
day five data, testing on the sequence of preceding days, and comparing results
to the usual chronological evaluation from day one. Results indicate no
significant accuracy difference between models. This underscores
retro-identification's value in improving resource efficiency in longitudinal
animal studies.
