A comparison of pedigree, genetic, and genomic estimates of relatedness for informing pairing decisions in two critically endangered birds

Conservation management strategies for many highly threatened species include conservation breeding to prevent extinction and enhance recovery. Pairing decisions for these conservation breeding programmes can be informed by pedigree data to minimise relatedness between individuals in an effort to avoid inbreeding, maximise diversity, and maintain evolutionary potential. However, conservation breeding programmes struggle to use this approach when pedigrees are shallow or incomplete. While genetic data (i.e., microsatellites) can be used to estimate relatedness to inform pairing decisions, emerging evidence indicates this approach lacks precision in genetically depauperate species, and more effective estimates will likely be obtained from genomic data (i.e., thousands of genome-wide single nucleotide polymorphisms, or SNPs). Here, we compare relatedness estimates using pedigree-, genetic-, and genomic-based approaches for making pairing decisions in two critically endangered birds endemic to New Zealand: kakī/black stilt (Himantopus novaezelandiae) and kākāriki karaka/orange-fronted parakeet (Cyanoramphus malherbi). Our findings indicate genetic-based estimates of relatedness are indeed the least precise when assessing known parent-offspring and full sibling relationships. Furthermore, our results show that relatedness estimates and subsequent pairing recommendations using PMx are most similar between pedigree- and genomic-based approaches. These combined results indicate that in lieu of robust pedigrees, SNPs are an effective tool for informing pairing decisions, which has exciting implications for many poorly pedigreed conservation breeding programmes worldwide.

While pedigrees are still considered the 'gold standard' for estimating relatedness in conservation breeding programmes (Hammerly, de la Cerda, Bailey, & Johnson, 2016;Jiménez-Mena, Schad, Hanna, & Lacy, 2016), there are inherent assumptions that, when violated, hinder pedigree accuracy. For example, pedigrees assume that all founders are unrelated (Ballou, 1983), which is unlikely for many highly threatened wild populations, given most will have experienced one or more historical population bottlenecks and founders sourced from these remnant wild populations are likely related (Bergner, Jamieson, & Robertson, 2014;Hogg et al., 2018). Simulation studies suggest that complete pedigrees with substantial depth (> 5 generations recorded) are robust enough to reflect true relatedness and inbreeding estimates despite violating this assumption (Balloux, Amos, & Coulson, 2004;J. Pemberton, 2004;Rudnick & Lacy, 2008). However, in many conservation breeding programmes, unknown founders are routinely sourced from wild populations to supplement captive populations (e.g., kākāriki karaka, Cyanoramphus malherbi, this manuscript) and to reduce the risk associated with adaptation to captivity (Frankham, 2008).
Under these circumstances, pedigree depth may remain perpetually shallow and therefore susceptible to violations of the assumption of founder relatedness. This is captured by Hogg et al. (2018), who demonstrate how violating this assumption led to a significant underestimation of both relatedness and inbreeding in a conservation breeding programme routinely augmented by wild individuals. In addition to these caveats, many intensively managed populations are poorly pedigreed, meaning these pedigrees contain missing information (i.e., unknown parentage due to matings that include unidentified individuals or extra-pair parentage; Bérénos, Ellis, Pilkington, & Pemberton, 2014;Lacy, 2009;Pemberton, 2008;Putnam & Ivy, 2014) or record keeping errors (e.g., Hammerly et al., 2016).
Even when pedigrees are of high depth, have no missing information, and contain no errors, expected relatedness between individuals can differ from realised relatedness, as pedigrees are based on probabilities as opposed to empirical estimates of genome sharing (Hill & Weir, 2012;Kardos, Luikart, & Allendorf, 2015;Speed & Balding, 2015;Willoughby et al., 2015). Based on Mendelian inheritance, pedigrees estimate the probability that two alleles, one chosen at random from each of two individuals, are identical by descent (IBD) from a parent or common ancestor (Ballou, 1983;Lacy, 1995). When using a pedigree, the relatedness coefficient (R) for parents and offspring, as well as for full siblings, is 0.5 when inbreeding is not present, indicating each pair shares 50% of their genomic information.
While parents do contribute roughly 50% of their genomic information to their gametes, the combined effects of recombination, independent assortment, and random fertilisation can lead to a larger range of realised relatedness between full siblings (Hill & Weir, 2011, 2012Speed & Balding, 2015). For example, a simulation study in humans revealed that realised relatedness between full siblings could range anywhere from 0.37-0.61 (Visscher et al., 2006), however this range can vary depending on the genome architecture of the species in question (e.g., number and size of chromosomes and the frequency and location of recombination events; Hill & Weir, 2011;Kardos et al., 2015;Ulrich Knief, Kempenaers, & Forstmeier, 2017).
To our knowledge, no study has compared pedigree-, genetic-, and genomic-based approaches for estimating relatedness to inform pairing decisions for conservation breeding programmes, despite there being over 350 vertebrates worldwide that are captive bred for release to the wild (Smith et al., 2011). Here, we evaluate the effectiveness of these three approaches using two critically endangered birds endemic birds to Aotearoa New Zealandthe kakī/black stilt (Himantopus novaezelandiae) and kākāriki karaka/orange-fronted parakeet (Cyanoramphus malherbi) -as Proof-of-Concept. Kakī and kākāriki karaka are excellent candidates for this research as both have active conservation breeding programmes, as well as available multigenerational pedigrees, microsatellites panels (Andrews, Hale, & Steeves, 2013;Steeves, Hale, & Gemmell, 2008) and genomic resources including species-specific reference genomes and whole genome resequencing data (Galla et al., 2019; this study).
Once found on both main islands of Aotearoa, kakī experienced significant population declines throughout the 20th century due to introduced mammalian predators (e.g., cats, stoats, and hedgehogs) along with braided river habitat loss and degradation (Sanders & Maloney, 2002). Today, an estimated 129 kakī are largely restricted to braided rivers of Te Manahuna/The Mackenzie Basin (Department of Conservation, personal comm.; Figure 1A) and recovery efforts include a conservation breeding programme that was initiated in the early 1980's (Reed, 1998). In addition to breeding birds in captivity, the kakī recovery programme also collects eggs from intensively monitored wild nests and rears them in captivity before wild release. Similar to kakī, kākāriki karaka were also once found on both main islands of New Zealand and experienced population declines in the 19th and 20th centuries due to introduced mammalian predators (e.g., brush-tailed possums and stoats) and habitat loss (Kearvell & Legault, 2017). Today, breeding populations of an Eggs from these pairs are harvested, brought into captivity, and fostered under surrogate birds, with hatchlings incorporated into the conservation breeding programme.
Because captive breeding pairs for both species are kept in separate enclosures with offspring intensively managed, kakī and kākāriki karaka present an opportunity to examine relatedness in known family groups. Specifically, we compare relatedness estimates from pedigree, microsatellites, and genome-wide SNPs using known parent-offspring and full sibling relationships. We then compare pairing recommendations among these three approaches to assess how each translates to effective conservation management. While kakī and kākāriki karaka have comparative data sets available, they represent two taxonomically distinct bird species with different life history strategies. Thus, we anticipate the results of our research may be applicable to the wider conservation breeding community.   Known parent-offspring relationships were verified through an allele mismatch exclusion analysis (Jones & Ardren, 2003) using microsatellite panels previously developed for kakī (Steeves et al., 2008) and kākāriki karaka (Andrews, 2013), with a maximum allowed mismatch of one allele at one locus (see Microsatellite data below).
Due to the short distance between P2/P8 alleles on the Z and W chromosomes in kākāriki karaka (Robertson & Gemmell, 2006), 2µL of PCR products using a tagged forward primer were combined with 11.7µL formamide and 0.3µL of Genescan™ LIZ® 500 size standard (Applied Biosystems) and genotyped on an ABI 3739xl (Applied Biosystems), with alleles manually scored using GeneMarker v. 2.2 (State College, PA, USA). Inconsistencies in pedigrees were identified using the validation tool in PopLink and corrected using observations by DOC and the Isaac Conservation and Wildlife Trust. Pedigree depth for kakī is deeper (1-8 generations, 3.35 average) than for kākāriki karaka (1-5 generations, 2.48 average). Pairwise estimates of kinship and inbreeding were produced using the programme PMx v. 1.6.20190628 (Lacy, 2012). Pairwise coefficients of relatedness (R) were calculated from kinship data using R(xy) = 2* f(xy) / √{(1+Fx)(1+Fy)}. In this formula, f(xy) is the kinship between two individuals (x and y) and Fx and Fy are the inbreeding coefficients of individuals x and y (Crow & Kimura, 1970 (Steeves et al., 2008). Microsatellite loci (n = 17) designed for kākāriki karaka and one locus (Cfor0809) for Forbe's parakeet (C. forbesi) that crossamplified in kākāriki karaka were amplified using PCR protocols by Andrews et al. (2013) and Chan et al. (2005), respectively. Samples were prepared for genotyping by adding 0.5 µl of PCR product to 11.7µl formamide and 0.3µl Genescan™ LIZ® 500 size standard (Applied Biosystems) and were genotyped on either a 3130xl or 3730xl Genetic Analyser (Applied Biosystems). Chromatograms were visualised using GeneMarker v. 2.2 (State College, PA, USA). To avoid bias by potential dye shifts (Sutton, Robertson, & Jamieson, 2011), peaks were scored manually. The number of alleles and standard estimates of per locus diversity -including expected heterozygosity (HO) and observed heterozygosity (HE) -were produced using GenAlEx v. 6.5 (Peakall & Smouse, 2006;Smouse & Peakall, 2012). Tests for deviations from Hardy-Weinberg and linkage disequilibrium for these loci using samples that are representative of larger kakī and kākāriki karaka populations can be found in Steeves et al. (2008; and Andrews (2013) respectively. For kākāriki karaka, only eight of the 18 microsatellite markers previously described were polymorphic in this study and these eight loci were used in all downstream analyses.
Genetic-based R estimates were produced in the programme COANCESTRY v. 1.0.1.9 (Wang, 2011). COANCESTRY offers seven different estimators of relatedness, and to choose the most appropriate estimator for the kakī and kākāriki karaka microsatellite data sets, we employed the simulation module within COANCESTRY using allele frequencies, missing data, and error rates from our microsatellite data sets. To produce dyads that represent the relationships and degree of inbreeding found within kakī and kākāriki karaka, we used R package 'identity' (Li, 2010) to generate 10,879 dyads for kakī and 1,484 dyads for kākāriki karaka based on the pedigrees of both species. The frequency of each unique dyad in the kakī and kākāriki karaka data sets were scaled to create 1,000 dyads for each set that are representative of relationships between individuals used in this study. The COANCESTRY simulations were conducted using allele frequencies, error rates, and missing data rates from each microsatellite data set, with settings changed to account for inbreeding. The triadic likelihood approach (Wang, 2007) was selected given it had the highest Pearson's correlation with 'true' relatedness for both data sets (see Supplemental Materials for details). This approach is also preferred, as it is one of the few estimators that accounts for instances of inbreeding (Wang, 2007).
To estimate R with our genetic data set, COANCESTRY programme parameters were set to account for inbreeding, with the number of reference individuals and bootstrapping samples set to 100.

Genomic data
To produce genome-wide SNPs for this study, a reference-guided low coverage resequencing approach was used for kakī and kākāriki karaka.

Reference genomes
A reference genome for kakī has already been assembled (Galla et al., 2019) and was used in this study. To assemble a de novo reference genome for kākāriki karaka, a pairedend library was prepared at the Institute of Clinical Molecular Biology (IKMB) at Kiel University using the Nextera™DNA Flex Library Prep Kit according to manufacturer specifications and sequenced on an Illumina NovaSeq™6000 with 2 x 150 bp reads at a depth of approximately 70x.
FastQC v. 0.11.8 (Andrews, 2010) was used to evaluate the quality of the raw Illumina data and assess potential sample contamination. Initial read trimming was performed using TrimGalore v. 0.6.2 (Krueger, 2019)  FastQC v. 0.11.4 and 0.11.8 (S. Andrews, 2010) were used to evaluate the quality of the raw Illumina data for kakī and kākāriki karaka, respectively. Kakī resequencing reads were subsequently trimmed for the Illumina barcode, a minimum Phred quality score of 20, and a minimum length of 50bp using Trimmomatic v. 0.38 (Bolger, Lohse, & Usadel, 2014).
Because kākāriki karaka libraries were produced using different library preparation protocols and nextera chemistry, reads were trimmed using TrimGalore v. 0.6.2 (Krueger, 2019) for nextera barcodes and two-colour chemistry, using a median Phred score of 20, end trim quality of 30, and a minimum length of 54. Prior to mapping, the kakī reference genome was concatenated to a single chromosome using the custom perl script 'concatenate_genome.pl' (Moraga, 2018a) for use in an aligned project that used both resequencing and genotypingby-sequencing reads (see Galla et al., 2019 To produce estimates of R using whole-genome SNPs, the programme KGD (Dodds et al., 2015) was used, as it was designed to estimate relatedness using reduced-representation and resequencing data while taking into account read depth. Furthermore, KGD approximates parent-offspring relatedness estimates closer to 0.5 compared to the triadic likelihood approach method (Wang, 2007), which underestimates relatedness, while still providing estimates that significantly correlate with this traditional estimator in both kakī

Comparison of relatedness
Pedigree, microsatellite, and SNP-based R between all individuals (n = 630) were compared among the three methods using Pearson's correlation coefficient (r). While our relatedness data sets are non-parametric, Pearson's was used over non-parametric tests, such as rank correlations, as our pedigree and microsatellite data sets have an excess of tied values.

Pairing recommendations
We used mate suitability index (MSI) scores in PMx v. 1.6.20190628 (Lacy, 2012) to show how pairing recommendations may change using pedigree-, microsatellite-, and SNPbased approaches for estimating R. MSI scores indicate how valuable offspring of a potential pair would be to the population on a scale from 1-6, with 1 being "very beneficial", and 6 being "very detrimental". An additional category, usually denoted with a "-" indicates that a pairing is "very highly detrimental" based on a high degree of kinship, and therefore the pairing should be avoided; for these analyses, this category has been put on the numerical MSI scale as a 7. MSI score takes into account four factors: deltaGD (i.e., the net positive or negative genetic diversity provided to the population), the difference of mean kinship values of the pair, the inbreeding coefficients of resulting offspring, and unknown ancestry (Ballou et al. 2001;Ballou et al. 2011). Pairing recommendations using the pedigree were made treating all individuals with unknown parentage in the pedigree as founders. To produce pairing recommendations using genetic and genomic markers, empirical R estimates were uploaded to PMx and weighted to 1 to produce MSI scores relying only on empirical data.
Pearson's correlation (r) was used to evaluate whether pairwise MSI scores between the approaches were concordant. To test whether the distribution of MSI scores were statistically different from one another, we used a non-parametric Kruskal-Wallis test with Bonferroni correction and a Tukey Honest Significant Difference (HSD) test.

Pedigree-based relatedness
This study has produced the first functional multigenerational pedigrees for two critically endangered endemic birds from Aotearoa. The kakī pedigree includes 2,680 wild and captive individuals recorded from 1977-present. Pedigree-based R between all kakī used in this study ranged from 0 to 0.56, with an average R of 0.13 ± SD 0.13. The average coefficient of relatedness between all known kakī parent-offspring was higher than the expected 0.5 contribution from each parent (0.52 ± SD 0.02), with averaged full sibling R of 0.52 ± SD 0.02 (Figure 2). The kākāriki karaka pedigree includes 624 captive individuals from 2003-present. Pedigree-based R for all individuals in the pedigree ranged from 0 to 0.67, with an average R of 0.19 ± SD 0.18. Average R between all parent-offspring was 0.52 ± SD 0.03, with averaged full sibling R being 0.51 ± SD 0.02 ( Figure 2).

Microsatellite diversity and relatedness
All eight microsatellite markers for kakī successfully amplified in all individuals used in this study. The number of alleles present across kakī loci ranged from 2-4 (average 3.13 ± SD 0.64; Table 2), with overall fewer alleles found here than reported in previous studies utilising these loci with more individuals (Hagen, Hale, Maloney, & Steeves, 2011;Steeves et al., 2010). While eighteen microsatellite markers were amplified in kākāriki karaka, one was removed from this study for not successfully amplifying in more than 50% of individuals (locus OFK56) and nine were removed for being monomorphic ( Table 2). The number of alleles among polymorphic loci ranged from 2-4 (average 3.0 ± SD 0.93), with overall fewer alleles found here than reported in previous studies (Andrews, 2013;Andrews et al., 2013).
Microsatellite-based R between all kakī used in this study ranged from 0 to 0.85, with an average R of 0.16 ± SD 0.19. Average R between all known kakī parent-offspring (0.44 ± SD 0.13) was below the expected relatedness value of 0.5, with a larger standard deviation of R values compared to pedigree-based estimates. Averaged full sibling R (0.41 ± SD 0.20) also had a larger deviation around the mean compared to the microsatellite-based parentoffspring estimates (Figure 2).
Microsatellite-based R between all kākāriki karaka used in this study ranged from 0 to 0.84, with an average R of 0.18 ± SD 0.22. Similar to kakī, average R between all known kākāriki karaka parent-offspring relationships (0.47 ± SD 0.19) was below the expected R value of 0.5, with a larger standard deviation of R values compared to pedigree-based estimates. Averaged full sibling R (0.49 ± SD 0.21) also had a larger deviation around the mean compared to microsatellite-based parent-offspring estimates (Figure 2). See Data Availability section for access information.

SNP discovery and diversity
Library preparation and Illumina sequencing resulted in 6.07 billion total reads for kakī (168.69 ± SD 65.32 million reads). In addition to the individual used for the reference assembly, 3.64 billion total reads (average = 103.92 ± SD 29.76 million reads) were produced for the additional 35 kākāriki karaka in this study. More SNPs were discovered during initial SNP discovery using kākāriki karaka than kakī, and more remained post filtering (Table 3).
These filtered SNPs were used for all downstream analyses. Average missingness was low for both data sets (Table 3), but lower for kākāriki karaka than kakī, as kākāriki karaka had a hard minimum cut-off for depth during filtering that resulted in no missing data. Average depth for both data sets was relatively high (Table 3), with kakī having slightly higher average depth. Average diversity statistics (nucleotide diversity, and the average observed and expected SNP heterozygosity per individual post filtering) were similar in both species, with diversity in kakī being slightly higher. SNP density using the kakī data set was higher than the kākāriki karaka data set, indicating that discovered SNPs are closer in proximity in the kakī data set (Table 3).

SNP-based relatedness
SNP-based R between all kakī used in this study ranged from 0.13-0.61, with an average R of 0.27 ± SD 0.09. Similar to pedigree-based estimates, average R between all known kakī parent-offspring were slightly higher than the expected relatedness value of 0.5 with a small standard deviation relative to microsatellite-based estimates (0.54 ± SD 0.03).
Averaged full sibling R also had a larger deviation around the mean (0.52 ± SD 0.05) than parent-offspring relationships (Figure 2).
SNP-based R between all kākāriki karaka used in this study ranged from 0.08-0.67, with an average R of 0.30 ± SD 0.12. Similar to pedigree-based estimates, average R between all known kākāriki karaka parent-offspring was slightly above the expected R value of 0.5 with a small standard deviation relative to genetic-based estimates (0.53 ± SD 0.03).
Averaged full sibling relatedness also had a larger deviation around the mean (0.52 ± SD 0.05) compared to the pedigree-based estimates ( Figure 2).

Comparison of relatedness estimates and pairing recommendations
All kakī and kākāriki karaka R estimates using pedigree-, microsatellite-, and SNPbased approaches correlated with one another with high statistical significance (Pearson's Correlation, p < 0.001; Table 4, Figure 3). Of all the approaches, the correlation coefficient between pedigree-and SNP-based approaches was markedly higher than between other approaches, indicating that they are the most concordant (Figure 3).  providing the highest frequency of category 7 (i.e., highly detrimental) pairings (Figure 4).
While the distributions of MSI scores between each approach were different, each approach produced scores that correlated significantly with one another (Pearson's correlation, p < 0.01-0.001). Similar to correlations between R estimates, correlation coefficients between pedigree and SNP-based MSI scores were highest (Table 4). Of all possible pairings represented by MSI scores, 38% did not experience a change in score value between pedigree-and SNP-based approaches; however, 20% of pairings experienced an MSI score change that was 3+ categories different. In 2% of pairings, pedigree-based MSI scores were categorised as a 1 (i.e., preferred pairing) while SNP-based MSI scores were categorised as a 7 (i.e., highly detrimental).
Of all possible pairings represented by MSI scores, 59% did not experience a change in score value between pedigree-and SNP-based approaches; however, 9% of pairings experienced an MSI score change that was 3+ categories different. In 2% of pairings, pedigree-based MSI scores were categorised as a 1 (i.e., very beneficial) while SNP-based MSI scores were categorised as a 7 (i.e., highly detrimental).

Discussion
This study is the first to compare pedigree-, microsatellite-, and SNP-based estimates of relatedness and subsequent pairing recommendations for conservation breeding programmes. The results indicate that microsatellites provide the least precision when estimating relatedness in known family groups, with pedigree-and SNP-based estimates providing higher precision and a much closer approximation of parent-offspring and full sibling relatedness. Further, estimates of relatedness and downstream pairing recommendations are both more similar when using pedigree-and SNP-based data sets compared to microsatellite-based data sets. Despite this, there were important differences in pairing recommendations between the two approaches, with SNP-based mate suitability index (MSI) scores being statistically higher than pedigree-based scores, and some substantial disagreements existing between the two sets of MSI scores. Together, this study provides insight into the differences between pedigree-, microsatellite-, and SNP-based approaches for making pairing recommendations and a pathway for estimating relatedness using genome-wide SNPs to inform pairing decisions in poorly pedigreed conservation breeding programmes worldwide.

Relatedness comparisons
Pedigree-based estimates of parent-offspring and full sibling relatedness approximated 0.5 for both kakī and kākāriki karaka (Figure 2), with some measures being slightly higher, which likely reflects intergenerational inbreeding. These results are consistent with expectations, as pedigrees are based on the probability of Mendelian inheritance, which postulates that first-order relationships (i.e., parents and offspring, and siblings) share 50% of their genomic information (Lacy, 1995;Wright, 1922). We expect realised (i.e., empirical) parent-offspring relationships to also approximate 0.5, but a broader range of realised relatedness estimates among full siblings, as they may receive different genetic material from each parent due to recombination and independent assortment during meiosis, and random fertilisation (Hill & Weir, 2011, 2012Speed & Balding, 2015).
Thus, even when pedigrees are robust, an unavoidable shortcoming is that they do not adequately capture true relatedness between full siblings. We anticipate this uncaptured diversity may prove useful for maximising existing diversity, especially in conservation breeding programmes with relatively few founders (Ballou & Lacy, 1995).
When examining our empirical data sets (i.e., microsatellites and SNPs), more variation is captured between siblings than parents and offspring, albeit with different levels of precision between empirical approaches used ( Figure 2). A broad range of microsatellitebased relatedness estimates were observed in both parent-offspring and sibling relationships. In some instances, even parent-offspring pairings appeared relatively unrelated using microsatellites (e.g., minimum parent-offspring R = 0.14 in kakī and R = 0 in kākāriki karaka), which underscores the lack of precision in this approach and how it could inadvertently lead to poorly informed pairing recommendations. These large ranges of relatedness values using microsatellites can be explained because genetic-based relatedness values between parent-offspring and full siblings are based on allele frequencies, and relatedness between individuals that share common alleles will be substantially lower than individuals that share rare alleles (Speed & Balding, 2015;Wang, 2011). This bias in relatedness values can be exacerbated when samples sizes are small (Wang, 2017), which is typical of conservation breeding programmes. Furthermore, the lack of precision using microsatellites shown here is consistent with studies that suggest relatively few markers with low allelic diversity are insufficient for estimating relatedness and inbreeding, especially in genetically depauperate species (e.g., Attard et al., 2018;Escoda, González-Esteban, Gómez, & Castresana, 2017;Hellmann, Sovic, Gibbs, Reddon, O'Connor, Ligocki, Marsh-Rollo, et al., 2016;Taylor, 2015;Taylor et al., 2015). While the number of microsatellites used here was small and allelic diversity was relatively low, the use of a few microsatellite markers to estimate relatedness and other diversity measures (e.g., inbreeding) is not uncommon (e.g., Hammerly et al., 2013;2016;Hellmann et al., 2016;Tzika et al., 2009) and low allelic diversity is expected in endangered species (Taylor, 2015;Taylor et al., 2015).
Compared to microsatellite-based relatedness, SNP-based relatedness showed a relatively small range with parent-offspring and full sibling relatedness estimates approximating 0.5, and full siblings showing a wider range of values than parent-offspring relationships ( Figure 2). Not only is this pattern consistent with expectations given the behaviour of chromosomes during meiosis and random fertilisation, but it also shows more precision than the microsatellite data sets. Other researchers have found similar results in a diverse range of wild taxa, indicating that thousands of genome-wide SNPs show more precision than microsatellites when measuring relatedness and inbreeding (e.g., Attard et al., 2018;Hellmann, Sovic, Gibbs, Reddon, O'Connor, Ligocki, Marsh-Rollo, et al., 2016;Hoffman et al., 2014;Lemopoulos et al., 2019;Thrasher, Butcher, Campagna, Webster, & Lovette, 2018).
Beyond parent-offspring and full sibling relationships, pedigree and SNP-based relatedness estimates showed the highest concordance with one another among the three approaches used (Figure 3). In kakī, the data sets used here include non-captive bred individuals with intensively monitored wild parents. These results provide more credibility to the semi-wild kakī pedigree, where socially monogamous wild pairs of kakī are assumed to be the genetic parents of offspring at nests (but see also Overbeek et al., 2017), as it shows that the kakī pedigree generally concurs with empirical relatedness estimates. Still, it should be noted that many pairs with pedigree-based relatedness values of 0 had pairwise relatedness values ranging upwards of 0.40 in kakī 0.33 and in kākāriki karaka using a SNPbased approach, which approximates first and second order relationships in both species ( Figure 2). This indicates that pedigree-based R between these individuals may be downwardly biased by unknown founders, missing information, and/or low pedigree depth (Balloux et al., 2004;Bérénos et al., 2014;Hammerly et al., 2016;Hogg et al., 2018;Lacy, 1995;Pemberton, 2008;Rudnick & Lacy, 2008;Tzika et al., 2009).

Pairing recommendations
When these relatedness values are translated into pairing recommendations using MSI scores, there is a high concordance between pedigree and SNP-based approaches, despite the SNP-based MSI scores being significantly higher. This latter result is somewhat expected, given that average relatedness estimates using SNPs was highest among the approaches used here, and empirical estimates of relatedness and inbreeding are usually higher than pedigrees as they more effectively capture relatedness between founders or mis-assigned individuals (Hammerly et al., 2016;Hogg et al., 2018). With that said, when making pairing recommendations using kinship-based pairing decisions (e.g., Ballou & Lacy, 1995), it is often the relative kinships between individuals that are more important than absolute values (Galla et al., 2019;McLennan et al., 2019). This suggests that pedigree and SNP-based approaches both yield similar results for pairing recommendations, with some important differences. For example, while correlations between these two sets of MSI scores are high relative to other comparisons, there are still some instances here where pairings are considered 'highly beneficial' (i.e., MSI category 1) when using the pedigree and 'very highly detrimental' (i.e., MSI category 7) when using SNPs. We expect some differences in MSI scores using pedigree and empirical approaches, given pedigrees MSI scores use kinship values based on Mendelian inheritance while SNP-based estimates are based on the proportion of observed alleles that are identical by state and inferred to be identical by descent given allele frequency information. However, these discrepancies are noteworthy, as they may be indicative of errors in the pedigree (e.g., Hammerly et al., 2016) or affects from founders that are assumed to be unrelated (e.g., Hogg et al. 2018).

Management Implications
Pairing recommendations take into account logistical, demographic, and genetic considerations to maximise population recovery. From these combined results shown here, we recommend that when conservation breeding programmes are poorly pedigreed, SNPs should be used to provide a precise indicator of relatedness to genetically inform pairing decisions. We anticipate SNPs will be particularly applicable in circumstances when pedigrees are the least reliable. For instance, when the founders of a conservation breeding population have no ancestry data available and are likely to be related, SNP-based relatedness estimates between individuals can be used to avoid highly related matings (Hogg et al., 2018). This situation may not only coincide with the original founding event of a captive population, but iteratively when individuals are sourced from wild or translocated populations to augment the captive population, as suggested in Frankham (2008) andHogg et al (2018). For example, in kākāriki karaka, whole genome resequencing has been made available for all current breeding individuals in the conservation breeding programme, including individuals who are founders themselves. Because birds of unknown ancestry are being routinely sourced from highly endangered wild populations, and will also be founders, we anticipate the need for resequencing these birds as they are incorporated into the breeding programme to assess their relatedness to other individuals.
While we expect SNPs will be important for pairing recommendations moving forward, there are relatively few studies to date that effectively combine existing pedigree data with genomic estimates of relatedness to inform pairing recommendations (but see Hogg et al., 2018;Ivy, Putnam, Navarro, Gurr, & Ryder, 2016). To date, these studies are the inclusion of empirical data, this approach requires caution, as the calculation of pedigree-based identity by descent for subsequent generations -including kinship and gene diversity -will be affected by the addition of empirical data (Hogg et al. 2018). We acknowledge this approach requires further investigation and validation, particularly for species that receive periodic influx of wild individuals of unknown ancestry in their conservation breeding programme.

Future Directions and Concluding Remarks
Although beyond the scope of this study, we hypothesize that SNPs will provide more useful estimates of relatedness over pedigrees, given they capture genetic variation among full siblings (Hill & Weir, 2011;Kardos et al., 2015;Ulrich Knief et al., 2017;Speed & Balding, 2015). Simulation studies have shown that estimates of identity by descent are more precise when using thousands of genome-wide SNPs than pedigrees (e.g., Kardos et al., 2015;Wang, 2016). Further, a recent study in zebra finch (Taeniopygia guttata) by Knief et al. (2017) shows that the amount of Mendelian 'noise' resulting from meiotic recombination will be exacerbated in species where crossing over events are non-uniformly distributed, such as birds (Ellegren, 2013). We expect that simulations using pedigree-and SNP-based pairing recommendations over several generations will determine whether a pedigree-or SNPbased approach best maximises genome-wide diversity in the long-term for conservation breeding programmes.
With that said, for poorly pedigreed populations, we recommend a SNP-based approach to estimating relatedness for subsequent pairing recommendations. Given that SNPs have been successfully used to estimate relatedness for different purposes across a wide diversity of taxonomic groups outside of this study (as reviewed in Attard et al., 2018), we anticipate a SNP-based approach for estimating relatedness and making subsequent pairing recommendations will be applicable beyond birds. It should be noted that many approaches used to date have used de novo reduced representation approaches (e.g., genotyping-by-sequencing, RADseq; Narum, Buerkle, Davey, Miller, & Hohenlohe, 2013) for SNP discovery, which typically have more missing data, lower depth, and fewer resulting SNPs than the reference-guided whole genome resequencing approach used here. While these factors may contribute to bias in relatedness estimates (but see Dodds et al., 2015), research still indicates that they can provide more precision than microsatellites (Attard et al., 2018). We expect reduced-representation approaches will persist in the short-term, especially for species with massive and complex genomes (e.g., some fish, amphibians, and invertebrates) that otherwise cannot yet be affordably resequenced across entire captive populations. We look forward to seeing more taxonomically diverse species use a SNP-based approach for estimating relatedness, as the results from this paper suggest it can be applied For Māori (the indigenous people of Aotearoa), all genomic data obtained from taonga species have whakapapa (genealogy that includes people, plants and animals, mountains, rivers and winds) and are therefore taonga in their own right. Thus, these data are tapu (sacred) and tikanga (customary practices, protocols, and ethics) determine how people interact with it. To this end, the passwords for the genomic data in this manuscript will be made available to researchers on the recommendation of the kaitiaki (guardians) for the iwi (tribes) that affiliate with kakī and kākāriki karaka.

AUTHOR CONTRIBUTIONS
This research was led by SJG, AWS, and TES, with the research concept developed in collaboration with all authors. All DNA extractions and microsatellite lab work was completed by SJG. The kakī pedigree was developed by SJG in collaboration with LB and SC.
The kākāriki karaka pedigree was developed by AR with edits by SJG. The kākāriki karaka genome was assembled by RM. SJG and RM conducted SNP discovery. All statistical analyses were conducted by SJG. The manuscript was written by SJG in collaboration with all authors.