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Restricted gene flow in the endangered Capricorn Yellow Chat Epthianura crocea macgregori: consequences for conservation management

Published online by Cambridge University Press:  07 November 2017

WAYNE A. HOUSTON*
Affiliation:
Central Queensland University, School of Health, Medical and Applied Sciences, Bruce Highway, Rockhampton, Queensland, Australia 4702.
WILLIAM J. ASPDEN
Affiliation:
Central Queensland University, School of Health, Medical and Applied Sciences, Bruce Highway, Rockhampton, Queensland, Australia 4702.
ROD ELDER
Affiliation:
Central Queensland University, School of Health, Medical and Applied Sciences, Bruce Highway, Rockhampton, Queensland, Australia 4702.
ROBERT L. BLACK
Affiliation:
Central Queensland University, School of Health, Medical and Applied Sciences, Bruce Highway, Rockhampton, Queensland, Australia 4702.
LINDA E. NEAVES
Affiliation:
Australian Museum Research Institute, Australian Museum, 1 William St, Sydney NSW 2010, Australia. Royal Botanic Garden Edinburgh, 20A Inverleith Row, Edinburgh, EH35LR, United Kingdom.
ANDREW G. KING
Affiliation:
Australian Museum Research Institute, Australian Museum, 1 William St, Sydney NSW 2010, Australia.
RICHARD E. MAJOR
Affiliation:
Australian Museum Research Institute, Australian Museum, 1 William St, Sydney NSW 2010, Australia.
*
*Author for correspondence; e-mail: w.houston@cqu.edu.au
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Summary

The Yellow Chat Epthianura crocea is comprised of three disjunct subspecies. Subspecies E. c. macgregori (Capricorn Yellow Chat) is listed as Critically Endangered under the EPBC Act and has a distribution that also appears to be disjunct, with a limited geographic area of less than 7,000 ha. Some populations are threatened by rapid industrial development, and it is important for conservation of the subspecies to determine the extent to which the putative populations are connected. We used 14 microsatellite markers to measure genetic diversity and to determine the extent of gene flow between two disjunct populations at the northern and southern extremes of the subspecies’ range. No significant differences in genetic diversity (number of alleles and heterozygosity) were observed, but clear population structuring was apparent, with obvious differentiation between the northern and southern populations. The most likely explanation for reduced gene flow between the two populations is either the development of a geographic barrier as a consequence of shrinkage of the marine plains associated with the rise in sea levels following the last glacial maxima, or reduced connectivity across the largely unsuitable pasture and forest habitat that now separates the two populations, exacerbated by declining population size and fewer potential emigrants. Regardless of the mechanism, restricted gene flow between these two populations has important consequences for their ongoing conservation. The relative isolation of the smaller southern groups (the Fitzroy River delta and Curtis Island) from the much larger northern group (both sides of the Broad Sound) makes the southern population more vulnerable to local extinction. Conservation efforts should focus on nature refuge agreements with land owners agreeing to maintain favourable grazing management practices in perpetuity, particularly in the northern area where most chats occur. Supplemental exchanges of individuals from northern and southern populations should be explored as a way of increasing genetic diversity and reducing inbreeding.

Information

Type
Research Article
Copyright
Copyright © BirdLife International 2017 
Figure 0

Figure 1. Present distribution of Capricorn Yellow Chats (locations from Houston et al. (2013) indicated by +) and the two sampling sites (large X).

Figure 1

Table 1. Allelic frequencies of 14 microsatellite loci in two populations (southern and northern) of Capricorn Yellow Chats. Data shown are the Number of alleles (Na), Number of Effective alleles (Ne), Heterozygosity observed (Ho) and Heterozygosity expected (He). Significant deviations from Hardy Weinberg equilibrium are denoted by * (P < 0.05). Two individuals were excluded from the full dataset because they were siblings of a third individual.

Figure 2

Figure 2. Convergence of log likelihood values (mean ± SD) of 10 simulations of two model population structures ranging between one and four genetic populations (K). Models using non-informative locality priors are indicated by filled circles and solid lines. Models using informative locality priors are indicated by filled squares and dashed lines. The “best” models (i.e. K = 2) are those with highest (least negative) log likelihood values.

Figure 3

Figure 3. Population structuring as identified from Monte-Carlo Markov chain simulation using the program Structure, with (a) non-informative locality priors, and (b) informative locality priors. Black and grey shadings represent the percentage of ancestry of each of 16 individuals derived from two genetically distinct populations identified from MCMC simulations.