Estrus monitoring in giant pandas revisited: The importance of assay selection.

Authors

Tom Cools, Kirsten S Wilson, Catherine Vancsok, Baptiste Mulot, Antoine Leclerc, Marko Haapakoski, José Kok, W Colin Duncan, Simon Girling, Yingmin Zhou, Rengui Li, Desheng Li, Lynn Vanhaecke, Jella Wauters

Published in

Animal reproduction science. Volume 292. Pages 108275. Jun 18, 2026. Epub Jun 18, 2026.

Abstract

Accurate and early prediction of ovulation is critical in captive breeding of giant pandas (Ailuropoda melanoleuca) as both natural mating and the use of artificial insemination require major logistical preparations, and the narrow fertile window limits breeding opportunities. Estrus monitoring traditionally relies on parallel measurement of urinary estrogens and progestogens to identify the estrogen-progestogen cross-over point, which marks the onset of the follicular phase 1-2 weeks before ovulation. To increase this window as much as possible, this study aimed to investigate whether differences in estrogen assays and standard selection may affect hormone concentration measurements and the timing of cross-over detection. We evaluated the suitability of estrone-based enzyme immunoassays, targeting estrone (E1), estrone-3-glucuronide (E1G), and estrone-3-sulfate (E1S), combined with E1, E1G, and E1S standards for urinary estrus monitoring in four giant pandas (n = 5 cycles). All nine assay-standard combinations yielded highly correlated estrogen profiles (r > 0.98, p < 0.001), but significantly higher concentrations were observed with the E1S standard, regardless of assay type. Comparative testing of the most cost-effective high-sensitivity approach (E1 assay, E1S standard) against a commonly used method (E1G assay, E1G standard) in an extended dataset (n = 5 pandas, 9 cycles) revealed that the E1-E1S approach detected the cross-over point 1-3 days earlier (mean ± SD: 2.11 ± 0.93 days, p < 0.01). This earlier detection offers significant logistical advantages for breeding management without increasing analytical workload or costs and highlights the importance of optimizing assay-standard selection in reproductive monitoring.

PMID:
42320122
Bibliographic data and abstract were imported from PubMed on 20 Jun 2026.

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