Indirect detection of genetic dispersal (movement and breeding events) through pedigree analysis of dugong populations in southern Queensland, Australia
Introduction
The genetic analysis of populations, population structure and genetic dispersal is of significant interest in a wide variety of biological applications. In wildlife populations, genetic parameters are crucial for conservation, as efficient management of any species requires at least a basic understanding of their population dynamics (Hampton et al., 2004). Patterns of genetic diversity within populations and genetic differentiation amongst populations are important for the assessment of the spatial extent of endangered species (Blouin et al., 2010) and development of appropriate conservation strategies (Excoffier and Heckel, 2006).
The F statistics developed independently by Wright, 1949, Malěcot, 1948 provide convenient measures of genetic differentiation among and within populations, and have long been used to infer demographic history, estimate movement rates and identify regions of the genome under selection (Holsinger and Weir, 2009). Population structure provides insight into the levels of dispersal and connectivity between wildlife populations, because populations that are genetically distinct have likely had little exchange or interbreeding, whilst populations with little structuring have had significant immigration/emigration and interbreeding. Coalescent methods are an alternative method of determining models for population structure and for the direct estimation of movement rates between populations, such as those implemented in the software Ima2 (Hey and Nielsen, 2007) or Migrate-n (Beerli and Palczewski, 2010). Assignment testing, i.e., the assignment of individuals to populations based on their genetic composition, can provide insight not only into the long-term differentiation between populations, but into movement on an individual level in the short term. Movement can be inferred, for example, where an individual found in one location is assigned genetically to a population found in a different location. A relevant method of assignment testing is via Bayesian clustering, which infers population structure without assuming predefined populations (Chen et al., 2007), with the program STRUCTURE (Pritchard et al., 2000) being the most influential system to implement these techniques (François and Durand, 2010).
Whilst long-term population structure parameters are valuable and assignment testing provides some insight into movements (e.g., Nater et al., 2012), information on recent movements and effects on population structure of wildlife is critical for population management. Such data are used to address significant questions in conservation such as determining immediate causes of fluctuations and particularly declines in population size, and hence distinguishing movement events from mortality events, particularly in relation to anthropogenic or natural disturbance. Typically, short term movement patterns have been understood through direct observation, such as the tracking of individuals using telemetry (e.g., Maxwell et al., 2011), or through identification of individual animals in multiple locations, either by natural discriminatory marks or physical or genetic tags, e.g., Wells et al. (2008). While they can be effective in many situations depending on the question being asked, these methods come at considerable expense, require significant sampling effort over the duration of a study to recapture or re-observe individuals in multiple locations (particularly difficult for cryptic species), can be disrupted by loss or changes in discriminatory markings such as scar patterns, and can only detect those movements that occur during the study. In contrast, the use of genetic methods to detect contemporary movements, i.e., movements occurring within the lifetimes of extant individuals, can overcome some of these challenges, in particular the necessity of observing the same individual in multiple locations, and the limitation to movements that occur within the duration of the study. The use of genetic assignment testing, however, requires sufficiently genetically distinct populations, and may not provide significant insight into the timing of movement events nor indicate if these were accompanied by subsequent breeding and hence gene flow into the new location. Recently, assignment of parent-offspring relationships between individuals has been used to infer contemporary dispersal in wildlife, with Waser and Hadfield (2011) finding similar rates of dispersal though recapture and parentage analysis methods informed by spatial data for banner-tailed kangaroo rats (Dipodomys spectabilis). Here, we suggest that constructing a pedigree based primarily on genetic data will provide insight into contemporary movements and breeding events, by identifying individuals observed in different locations to their parents, siblings or offspring. We demonstrate this technique by considering the dugong (Dugong dugon) populations of southern Queensland, Australia.
The dugong is a large marine mammal inhabiting tropical and subtropical regions of the western Pacific and Indian oceans, its range covering the territories of over 37 countries (Marsh, 2002). Dugong are classified by the IUCN as vulnerable to extinction (Marsh, 2008), and aerial surveys over the past few decades indicate that significant population declines have occurred throughout their range and that many populations are currently under threat (Marsh et al., 2001a, Marsh and Lawler, 2001c, Sobtzick et al., 2012). Their long lifespans, protracted breeding cycles, and specialised seagrass diets make dugongs vulnerable to human impact, particularly where their habitats are close to large population centres (Marsh, 2002). In particular, dugong populations along the urban coast of southern Queensland, Australia, are found close to developed population centres featuring significant industrial and coastal activity. Modelling of long term trends in dugong bycatch in a government shark control program have indicated that significant declines in dugong populations may have occurred along the entire southern Queensland coast, to approximately 3% of 1960 population levels (Marsh et al., 2001a, Marsh et al., 2005). Within south-east Queensland, the majority of dugongs are found in two spatially distinct foraging areas (300 km apart): in Moreton Bay (MB) and the Hervey Bay-Great Sandy Straits (HB-GSS) region, each of which includes designated sanctuary areas. Aerial surveys conducted over the past two decades have indicated short-term fluctuations in the HB-GSS region; a population of 2206 420 in 1988 (Lee Long et al., 1993) declined to 807 151 in 1994 (Marsh et al., 1996) after flood-associated loss of seagrass in 1992 (Preen and Marsh, 1995), and was then documented as 1654 248 in 1999 (Marsh and Lawler, 2001b), 2547 410 in 2005 (Marsh et al., 2006), and 2116 108 in 2011 (Sobtzick et al., 2012), i.e., population estimates varied between 36 and 115 of the 1988 estimate. In MB, population estimates have ranged from 442 69 in 1988 (Preen, 1992), 968 44 in 1995 (Lanyon et al., 2003), 454 41 in 2005 (Marsh et al., 2006) to 883 63 in 2011 (Sobtzick et al., 2012), i.e., population estimates varying up to 219 of the 1988 estimate. However, because survey methodology in MB has not been consistent, population trends are doubtful, likely reflecting changes in survey technique rather than actual changes in population (Marsh, 2002, Lanyon et al., 2003). All estimates are confidence intervals.
In order to appropriately manage threats to the south-east Queensland dugong populations, it is important to understand population connectedness and determine if population fluctuations in the region have been due to local mortality and/or large-scale movements between locations. Telemetry-based studies have previously indicated that dugongs are capable of large-scale movements of up to a maximum observed journey of 560 km by one individual, with a further 14 of 70 tagged individuals making movements of over 100 km (Sheppard et al., 2006). On a larger scale, gene-flow based studies on dugongs suggest significant dispersal between populations Australia-wide, and indicate that population-genetic structure exists on large geographic scales (Blair et al., 2013). Comparisons between population structure suggested by mitochondrial DNA and nuclear DNA indicated that gene-flow has been primarily male-mediated (McDonald, 2006). More recently, population genetic analysis has indicated low but significant population differentiation within southern Queensland (e.g., between MB and GSS), and a Bayesian clustering analysis (via STRUCTURE) suggested two clusters, primarily distinguishing MB dugongs from those in the more northern populations (Seddon et al., 2014).
When large-scale movement is likely to be occurring, insight into the extent of movements between foraging areas is required, particularly to determine if these are routine or occur only in response to major disturbances or environmental stressors. In 1992, the combination of a cyclone and flooding events caused significant seagrass death in the HB area (Preen and Marsh, 1995), corresponding to an apparent decline in the size of the local dugong populations, as well as to an unusually high number of recovered dugong carcasses, i.e., high mortality. It was suggested that the adjacent MB population may have increased by approximately 100 dugongs at this time (Preen and Marsh, 1995). Such large scale movement has been proposed as an explanation for fluctuations in dugong populations elsewhere, i.e., in Western Australia (Gales et al., 2004) and in the Torres Strait (Marsh et al., 2004). It is difficult to detect trends in abundance in marine mammals because they are typically hard to observe and identify (Marsh, 1995), and Wade (1998) suggests that it is likely easier to detect circumstances which might lead to population decline than to detect the decline itself. Having knowledge of the patterns of movement and breeding between dugong populations along a coastal strip would allow us to better understand the apparent fluctuations in population size that have been observed, and permit forecasting of changes in abundance in the face of future threats, so that appropriate risk-management strategies can be established.
In 2001, a population capture-mark-recapture program was initiated for dugongs in MB, southern Queensland, Australia (Lanyon et al., 2002), with individuals genotyped for identity using a panel of 24 microsatellite markers (Broderick et al., 2007). More than 600 live individuals were sampled over this period ( of 2011 population estimates; Sobtzick et al. (2012)), providing a large amount of biological and genetic recapture data. This thorough survey of a single population has provided insight into the distribution of individuals in the region (Lanyon et al., 2003, Lanyon et al., 2005) and to aspects of their life histories (Lanyon et al., 2009a, Burgess et al., 2012a, Burgess et al., 2012b). Individuals in other locations in southern Queensland have been sampled for genetic data: approximately 60 live individuals from HB and more than 400 from the GSS. A small number (approx. 30) of dugongs from Shoalwater Bay (SB), a dugong foraging location ∼500 km north of HB, has also been sampled and these are included in this study.
Reconstruction of a pedigree for dugong populations is challenging for a number of reasons. Dugongs are long-lived, breed infrequently and have only one offspring at a time, at irregular intervals: as a result, sibling groups are small and not directly observable (Marsh et al., 1984). Dugongs are understood to undertake promiscuous mating, either via scramble promiscuity or lek mating (Marsh and O’Shea, 2012), assumed here to be effectively random. Generational structure is neither clear nor distinct, as once maturity has been reached, offspring are indistinguishable from their parents in terms of age, and thus relative age data is only available for individuals first sampled as calves/juveniles or sub-adults. A pedigree reconstruction system PR-genie (Cope et al., 2014) has been developed specifically for difficult circumstances such as these, taking into account genetic and ancillary biological information such as sex and size/maturity class to reconstruct complex, multigenerational pedigrees. The aim of this study was to use these field and genetic data to demonstrate the use of a large reconstructed pedigree to infer contemporary genetic dispersal for wildlife, in this case, the dugong populations of southern Queensland.
Section snippets
Sample collection
Dugongs in Moreton Bay (MB), Queensland, Australia ( S) were sampled as part of a decade-long ongoing year-round capture-mark-recapture program (Lanyon et al., 2002) that began in 2001, with small numbers of dugongs sampled in 1998–99 as part of a pilot study. Dugongs in the southern and central Great Sandy Straits (GSS, S) were sampled on annual trips since 2006, and in the Burrum Heads region of Hervey Bay (HB, S) in 2010–2011. HB and GSS have previously been considered a
Overall population statistics
A total of 1969 tissue samples from dugongs sampled in Moreton Bay (MB, n = 1369), the Great Sandy Straits (GSS, 488), Hervey Bay (HB, 83) and Shoalwater Bay (SB, 29), were genetically analysed. After genotype matching and biological verification, 1002 individual and unique dugongs were identified unambiguously, i.e., with no recaptures or with consistent genetic and biological data across recaptures (Table 1). This represents approximately of the most recent total dugong population estimate
Discussion
We have demonstrated that the analysis of pedigree relationships in a wildlife population can provide indications of movement and breeding events at an individual level. Pedigree reconstruction is a novel means of detecting contemporary movements in wildlife populations, capable of detecting movements outside the duration of the study, and without the need for physical tracking or the direct observation of individuals in multiple locations. Moreover, pedigree reconstruction provides information
Acknowledgements
Thanks to Helen Peereboom (Sneath) for collation of data and assistance with validation of genotype matches against biological data. Many thanks to The University of Queensland Dugong Research Team, in particular Helen Peereboom, Rob Slade, Erin Neal, Ben Schemel, Liz Burgess, John Kirkwood, Paul Sprecher, Merrick Ekins, and Damien Broderick. This research was funded by an Australian Research Council (ARC) Linkage Grant and associated Australian Postgraduate Award (Industry) (APAI) scholarship
References (58)
A simulated annealing algorithm for maximum likelihood pedigree reconstruction
Theor. Popul. Biol.
(2003)- et al.
Diagnosing pregnancy in free-ranging dugongs using fecal progesterone metabolite concentrations and body morphometrics: a population application
Gen. Comp. Endocrinol.
(2012) - et al.
Movement heterogeneity of dugongs, Dugon dugon (Müller), over large spatial scales
J. Exp. Mar. Biol. Ecol.
(2006) - et al.
Unified framework to evaluate panmixia and migration direction among multiple sampling locations
Genetics
(2010) - et al.
Pleistocene sea level uctuations and the phylogeography of the dugong in Australian waters
Marine Mammal Sci.
(2013) - et al.
Population structure and conservation genetics of the Oregon spotted frog, Rana pretiosa
Conserv. Genet.
(2010) - et al.
Characterization of 26 new microsatellite loci in the dugong (Dugong dugon)
Mol. Ecol. Notes
(2007) - et al.
Testosterone and tusks: maturation and seasonal reproductive patterns of live, free-ranging male dugongs (Dugong dugon) in a subtropical population
Reproduction
(2012) - Burgess, E.A., Brown, J.L., Lanyon, J.M., (2013). Sex, scarring, and stress: understanding seasonal costs in a cryptic...
- et al.
Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study
Mol. Ecol. Notes
(2007)
Development and testing of a genetic marker based pedigree reconstruction system PR-genie incorporating size-class data
Mol. Ecol. Resour.
Computer programs for population genetics data analysis: a survival guide
Nat. Rev. Genet.
Spatially explicit bayesian clustering models in population genetics
Mol. Ecol. Resour.
Change in abundance of dugongs in Shark Bay, Ningaloo and Exmouth Gulf, Western Australia: evidence for large-scale migration
Wildlife Res.
Molecular techniques, wildlife management and the importance of genetic population structure and dispersal: a case study with feral pigs
J. Appl. Ecol.
Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics
PNAS
Genetics in geographically structured populations: defining, estimating and interpreting
Nat. Rev. Genet.
Circos: an information aesthetic for comparative genomics
Genome Res.
Distribution and abundance of dugongs in Moreton Bay, Queensland, Australia
Wildlife Res.
A method for capturing dugongs (Dugong dugon) in open water
Aquat. Mammals
Reproductive steroids are detectable in the faeces of dugongs
Aust. Zool.
Establishing a mark-recapture program for dugongs in Moreton Bay, south-east Queensland
Aust. Mammal.
Three skin sampling methods for molecular characterisation of free-ranging dugong (Dugong dugon) populations
Aquat. Mammals
Physiological response of wild dugongs (Dugong dugon) to out-of-water sampling for health assessment
Aquat. Mammals
Sexing Sirenians: Validation of visual and molecular sex determination in both wild dugongs (Dugong dugon) and Florida manatees (Trichechus manatus latirostris)
Aquat. Mammals
Seagrasses between Cape York and Hervey Bay, Queensland, Australia
Aust. J. Mar. Freshw. Res.
Cited by (10)
Management of Megafauna in Estuaries and Coastal Waters: Moreton Bay as a Case Study
2019, Coasts and Estuaries: The FutureDugong: Dugong dugon
2017, Encyclopedia of Marine Mammals, Third EditionCryptic marine barriers to gene flow in a vulnerable coastal species, the dugong (Dugong dugon)
2023, Marine Mammal SciencePopulation genetics and microevolutionary theory
2021, Population Genetics and Microevolutionary Theory