In:
Oryx, Cambridge University Press (CUP), Vol. 57, No. 5 ( 2023-09), p. 659-669
Abstract:
Estimating the population parameters, performance and factors that influence reproduction from long-term, individual-based monitoring data is the gold standard for effective wildlife management and conservation. Yet this information is often difficult and costly to collect or inaccessible to managers. We synthesized a 20-year set of individual-based monitoring data from a subset of black rhinoceros Diceros bicornis subpopulations across a range of environmental conditions in Namibia. Our findings demonstrate that despite the relatively arid landscape in Namibia, the black rhinoceros metapopulation is performing well, measured by age at first reproduction, inter-birth interval, population growth and survivorship. Information-theoretic modelling revealed that a univariate model including normalized differential vegetative index had a greater influence upon age at first reproduction than population density. The inter-birth interval model set identified cumulative rainfall during the 15 months prior to the birth month as the top model, although the mean normalized differential vegetative index during the inter-birth interval was comparable. There was little evidence for density-dependence effects on reproduction. These findings suggest that although browse quality could have a greater impact on parameters spanning multiple years, shorter-term parameters could be more influenced by rainfall. Our analysis also revealed a synchronous pattern of conceptions occurring in the rainy season. Our study provides a set of population parameter estimates for Namibian black rhinoceros subpopulations and preliminary insights on factors driving their reproduction. These expand our collective knowledge of global black rhinoceros population dynamics and improve our confidence and capability to adaptively manage the black rhinoceros metapopulation of Namibia.
Type of Medium:
Online Resource
ISSN:
0030-6053
,
1365-3008
DOI:
10.1017/S0030605322001065
Language:
English
Publisher:
Cambridge University Press (CUP)
Publication Date:
2023
detail.hit.zdb_id:
2020801-7
SSG:
12
SSG:
23
