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Monitor individual health and improve breeding success in Crested ibis (Nipponia nippon)

He, Y.; Zhang, X.; Xi, X.; Yang, S.; Cai, D.; Zhan, X.; Wang, D.

2025-12-04 zoology
10.64898/2025.12.02.691777 bioRxiv
Show abstract

Biodiversity loss has become a pressing issue, requiring effective conservation measures. Drawing lessons from successful examples is essential. The Crested ibis (Nipponia nippon), once critically endangered but now recovering through intensive conservation programs, provides an informative model for evaluating and improving conservation practices. Using 17 years of monitoring data from a captive population spanning eight generations, we applied quantitative genetic tools (Animal Model) to characterize individual growth and improve breeding success of this endangered species. We found that body weight from Day 0 to Day 42 exhibited significant heritability (h2 = 0.195, 95% HPD 0.139 [~] 0.250). As for growth curve parameters, growth time-scale parameter a (h2 = 0.33, 95% HPD: 0.14 [~] 0.55) and growth-rate coefficient b (h2 = 0.22, 95% HPD: 0.10 [~] 0.34) were also significantly heritable, while the heritability of potential maximum weight M was relatively low (h2 = 0.023, 95% HPD: 0.00 [~] 0.16). We further estimated the breeding values of these phenotypical traits to inform future breeding and selections. Intergenerational analyses showed the estimated breeding values for body weight exhibited a tendency to increase, accompanied by a slower growth rate, though trends did not differ significantly from expectations under genetic drift. Comparison of parental and offspring growth trajectories revealed that healthy offspring closely follow parental growth trajectories, whereas unhealthy individuals display reduced growth. Given the multiple breeding times of this long-lived species, this approach enables effective monitoring of individual growth and health, allowing timely veterinary interventions so to enhance conservation efficiency.

Published in Current Zoology · not in our set (fewer than 10 published preprints to learn from) · training set

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