Cross-amplification in Falcons and Their Hybrids: A New Implemented Multi-locus Panel for Conservation and Forensic Purposes

Anna Padula^1*, Vincenzo Buono², Patrizia Giangregorio¹, Chiara Mengoni¹ and Nadia Mucci^1
*Correspondence: Anna Padula, Department for the Monitoring and Protection of the Environment and for Biodiversity Conservation, Italian Institute for Environmental Protection and Research (ISPRA), Unit for Conservation Genetics (BIO-CGE), 40064 Bologna, Italy, Email:

Keywords

Falco • Falcon hybrids • Cross-amplification • Microsatellites • Forensic • Illegal trade • Kinship • Genetic differentiation • Hybrid detection

Introduction

The global loss of biodiversity is mediated by a synergistic combination of several important factors such as habitat destruction, overexploitation of wild populations, pollution and climate changes [1], driven ultimately by human-induced demographic, economic and societal factors [2]. According to the International Union for the Conservation of Nature’s (IUCN) Red List of Threatened Species, 13% of bird species are now threatened with extinction [3] and their trade still represents one of the main drivers of extinction globally [4-7]; indeed, the demand on international trade for bird species remains high, increasing the pressure on their conservation [8].

Particularly, the international trafficking of raptors is a highly profitable industry [9]. Birds of prey have been since ever subject to human persecution in the forms of killing, trapping, or “laundering” into the captive- bred population to meet the international demand [10,11]. Furthermore, in the last fourth decades, it has been also recorded a significant increase in the legal raptor market [12], mainly fuelled by pet trade and socio-cultural reasons. The trafficking of raptors for the pet trade is indeed an emerging problem; for example, the owl trade increased in the last decades due to the growing popularity as pets [13]. Falco spp. and their hybrids are among the most traded raptor species, with the United Arab Emirates who import the largest number of captive-bred raptors for falconry [12]. Indeed, this practice is one of the oldest known human activities [14] and remains commonly in use as a sport and art form, especially in the Middle East [15] where it is preserved as part of cultural and sporting heritage [16]. Because of a soaring global market, a persistent decline in some falcon populations has been documented, impacting both population numbers and geographic distribution [10-17]. The population trend of F. biarmicus, F. cherrug and F. tinnunculus is decreasing and, particularly, Saker Falcon has most recently been assessed for the IUCN Red List of Threatened Species and listed as endangered [3].

As a result, all Falco spp. have been listed in Appendices I and II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) that played a fundamental role in regulating and monitoring wildlife species international trade and, presently, represents a valuable tool for biodiversity conservation preventing overexploitation of wild species for market purposes [18]. The European Union (EU) joined CITES, whose directives have been applied in all the Member States through Council (EU) Regulations.

Species subjected to maximum protection are listed in Annex A, that includes all CITES Appendix I-listed species and some species listed in Appendices II and III for which the EU has adopted stricter domestic measures. As included in Annex A, Falco spp. and their hybrids enjoy the maximum protection, and the trade is allowed only if strict conditions are respected and the captivity-bred is proved [19,20].

Biomolecular investigations represent a reliable tool to support actions finalized in preventing species overexploitation and in tracking any wildlife trade. Contextually, they can inform about wild populations, particularly on any changes in genetic variability or structure that could affect their viability. Microsatellites loci (STRs) are amongst the most popular markers in molecular ecology [21]. They allow the description of genetic variability [22], the absence of gene flow in the presence of barriers hampering the movements of individuals [23], the admixture and hybridization between different populations or species [24,25] the description of genetic dynamics during the genetic monitoring of carnivores [26]. Expertise in their use is widespread and the possibility of sharing information across different research groups is possible by the construction of an allele ladder [27,28]. Moreover, forensic genetics permits the identification of species and individuals [29], family clusters [30] illegal hybridization [31] thus making their use reliable in several crime resolutions.

In the past decades, many species-specific panels of STRs have been developed in some Falco species, such as F. colombarius [32], F. naumanni [33], F. peregrinus [34,35], F. rusticolus [36], F. vespertinus [37]. However, only five panels focused on more than one species and were tested across a restricted range of falcon species, sometimes resulting non performant in all of them [32-37]. The lack of a validated STR panel across a wide range of falcon species makes unfeasible a deep comparison of variability indices for monitoring and conservation projects, and the identification of hybrid individuals for forensic purposes. Such evidence calls for a newly defined panel permitting the genetic variability analysis across the mostly traded Falco spp. and their hybrids. In this study, we tested thirty-two polymorphic microsatellite loci selected from literature in six species of falcons (F. biarmicus, F. cherrug, F. pelegrinoides, F. peregrinus, F. rusticolus and F. tinnunculus) and three hybrids (F. cherrug x F. peregrinus, F. peregrinus x F. rusticolus and F. cherrug x F. rusticolus) with the aim of evaluating the reliability of a unique panel for the most critical species of falcons. Specifically, the present study is focused on using such a panel for conservation and forensic purposes i) to monitor genetic variability and differentiation, ii) to perform individual identification and track illegal trades through paternity tests; iii) to identify Falco hybrids.

Materials and Methods

Sampling

Samples included in this work were selected from the CITES sample database, managed since 1995 by the Italian Institute of Environmental Protection and Research (ISPRA) on behalf of the Italian Ministry of the Environment and Energy Security (MASE).

A total of 163 captive-bred individuals was used for the analysis (Table 1), belonging to the following species: F. biarmicus (n=24), F. cherrug (n=24), F. pelegrinoides (n=24), F. peregrinus (n=24), F. rusticolus (n=24), F. tinnunculus (n=24), F. cherrug x F. peregrinus (n=5), F. peregrinus x F. rusticolus (n=4), and F. cherrug x F. rusticolus (n=10).

Marker choice

Twenty-five out of the 32 PCR primers used in this work were isolated from Peregrine falcon (F. peregrinus) [34,35] thirteen out of them have been originally tested in Gyrfalcon F. rusticolus and Saker F. cherrug too [35]. We also included seven microsatellite loci described in Merlin (F. columbarius) and characterized for cross-species amplification in Gyrfalcon and Peregrine falcon [32] and one marker isolated from northern goshawk (A. gentilis) [38] proved to be effective for cross-amplification in F. biarmicus [39].

Experimental design

We used the following experimental strategy: i) all thirty-two microsatellite loci were tested on two unrelated individuals of six Falco species (F. biarmicus, F. cherrug, F. pelegrinoides, F. peregrinus, F. rusticolus, F. tinnunculus), and amplification reactions were performed in singleplex to test for the right allelic range and specific amplification; ii) microsatellite loci giving positive PCR were tested in multiplex on additional four individuals for each species; iii) the set-up multiplexed microsatellite loci panel was used for genotyping 24 individuals for each pure falcon species and 19 F1 hybrid individuals from three species (five, four and ten specimens of F. cherrug x F. peregrinus, F. peregrinus x F. rusticolus and F. cherrug x F. rusticolus, respectively); eventually, 163 individuals were included in the study. We identified two sample sets to be used in the following analysis: sample set #1 (n=144) including individuals from pure species belonging to 46 parental groups tested for genetic variability and differentiation and parentage analysis; sample set #2 (n=91) enclosing individuals from pure species F. cherrug, F. peregrinus and F. rusticolus (n=72) and their hybrids (n=19) tested in the analysis of hybrid detection (Table 1).

DNA extraction, amplification and fragment detection

DNA was isolated from feathers using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany), according to the manufacturer’s instructions. After digestion in 180 μL ATL buffer, 20 μL proteinase K and 20 μL DTT and incubation overnight at 56°C, the lysate was loaded in a QIAcube HT robotic station (Qiagen, Hilden, Germany) for further purification steps. DNA amplification was performed in a total volume of 8 μl with 20 ng of DNA as the template, 0.025 U of HotStarTaq^® (Qiagen, Hilden, Germany), 0.8 μl of 10X PCR Buffer, 0.8 μl of 0.2% BSA, 0.48 μl of 25 mM MgCl₂, 0.4 μl of 2.5 mM dNTP mix, 0.1 μl of 10 μM of each primer. Each forward primer was labelled with fluorescent ABI dyes. Negative PCR controls were included in the amplification to monitor the performance of the process. DNA was amplified in a Veriti™ 96-Well Thermal Cycler (Thermo Fisher Scientific, Waltham, MA, USA). According to melting temperature and reference bibliography, the following PCR thermal profile was used: initial denaturation at 94°C for 15 min; 35 cycles at 94°C for 90 s, annealing at 50 or 55°C (Table 2) for 40 s, extension at 72°C for 40 s; final extension at 72°C for 10 min. Amplicons were separated through capillary electrophoresis in an ABI 3130xl genetic Analyzer (Thermo Fisher Scientific, Waltham, MA, USA); alleles were scored in GeneMapper 5.0 using GeneScan 500 ROX size standard (Thermo Fisher Scientific, Waltham, MA, USA).

Genetic variability and differentiation

Genetic variability and differentiation analyses were conducted on Sample Set #1 (Table 1). Allele number (NA), observed and unbiased expected heterozygosity (Ho, uHe), number of Private Alleles (PA) were computed using GenAlEx 6.41 [40]. Allelic Richness (AR) was estimated using FSTAT [41]. Species differentiation was estimated with pairwise Fst in Genetix 4.05 using Weir and Cockerham method and 1000 permutations [42]. The significance of differences in genetic diversity between species was tested using PAST software [43]. The distribution of genetic variation was quantified through Analysis of Molecular Variance (AMOVA) in GenAlEx 6.41 [40]; the significance of the variance components was based on 999 permutations.

The genetic structure was investigated using a Bayesian model-based clustering method implemented in the software Structure 2.3.4 [44]. The analysis was conducted using: a burn-in period at 10,000 followed by 100,000 replicates of the MCMC; five independent runs for each cluster (range 1-6 K); “admixture” model with “independent allele frequencies”. Structure Harvester v0.6.92 [45] was used to determine the optimal number of genetic clusters according to the Evanno’s delta-K method and the likelihood distribution. The software Clumpp 1.1.2 [46] was used to combine admixture values from multiple runs and results were visualized using Distruct v1.1 [47]. Factorial Correspondence Analysis (FCA) was carried out in Genetix 4.05 to check genetic structure and distances between species [42].

Probability of identity and parentage analysis

Probability of Identity for unrelated (PID) and related samples (PID_sib, sibling) were computed using GenAlEx 6.41 [40]. Parentage assignment was performed using CERVUS 3.0 [48] and COLONY 2.0 [49]. In CERVUS, we simulated 10,000 offspring produced by 46 candidate fathers and 48 candidate mothers, with 100% parents sampled, 1% genotyping error rate and confidence levels assessed by Delta and LOD distribution (relaxed >80%, strict >95%). The minimum typed loci were set to ≥ 90% of the whole number of loci. In COLONY, we used non-inbreeding data, monogamy models and set the genotyping error rate value at 0.0001. All the analyses were conducted in Sample Set #1 (Table 1).

Hybrid detection

Hybrid detection analyses were conducted on Sample Set #2 (Table 1). Each hybrid with relative pure species was investigated using a Bayesian method (range 1-3 K) and a factorial correspondence analysis as described above.

Results

Screening of microsatellite loci

Thirty-two microsatellite markers (Fco001, Fco003, Fco005, Fco012, Fco014, Fco015, Fco016, Fpeu1, Fpeu26_1, Fpeu25_1, Fpeu33_1, Fpeu46_2, Fpeu56_1, Fpeu208_1, Fpeu248_1, Fpeu298_1, Fpeu342_1, Fpeu353_1, Fpeu98_2, Fpeu145_1, fp5, fp13, fp31, fp54, fp79_1, fp74_4, fp82_2, fp86_2, fp89, fp92_1, fp107, Age5) were chosen to evaluate their cross-amplification power? on six species of falcons (F. biarmicus, F. cherrug, F. pelegrinoides, F. peregrinus, F. rusticolus, F. tinnunculus). Data obtained revealed that twentysix out of 32 markers gave amplicons in the two unrelated individuals from all the six tested species. The remaining 6 microsatellite loci (fp5, fp79_1, Fco005, Fco015, Fpeμ98_2, Fpeμ145_1) were discarded because they either did not give any amplification product or led to unreliable PCR amplicons. The twenty-six microsatellite markers selected in the first step were tested in multiplex PCRs in other four individuals for each pure species; five out of them (Fco001, Fco003, Fco012, Fco014, Fco016) were discarded because they lead to sub-optimal results or complete failure when multiplexed. One marker was selected (Fpeμ26_1) although not multiplexable because characterized by a high number of alleles. Eventually, a twenty-one multiplexed microsatellite loci panel was used for genotyping 163 individuals (Table 2).

Genetic variability and differentiation

All the 163 samples were successfully genotyped at 21 microsatellite loci. The mean Allele Number (NA) over species was 5.76 (± 0.21), with the highest values recorded respectively in F. tinnunculus (7.00 ± 0.63) and the lowest one in F. pelegrinoides (4.71 ± 0.36). Allelic richness (AR) ranged from 2.80 (± 0.27) and 5.49 (± 0.38) in F. pelegrinoides and F. tinnunculus, respectively. Mean observed Heterozygosity (Ho) ranged from 0.51 (± 0.06) in F. rusticolus and 0.64 (± 0.05) in F. tinnunculus, with an average value of 0.60 (± 0.02); while the unbiased expected heterozygosity ranged from 0.56 (± 0.06) to 0.75 (± 0.03) in F. rusticolus and F. tinnunculus, with an average value of 0.59 (± 0.02). Mean number of private alleles (PA) ranged from 0.38 (± 0.11) in F. pelegrinoides to 2.38 (± 0.51) in F. tinnunculus (Table 3). All analysed loci were polymorphic in all the tested species.

Estimates of genetic divergence using Fst for pairs of populations indicated significant genetic differentiation between all pairs of species (Table 4). Furthermore, the AMOVA showed that genetic variation among species was 25% while 68% was within individuals and 8% was partitioned among individuals (Table 5). Factorial Correspondence Analysis (FCA) revealed that F. peregrinus and F. pelegrinoides overlapped in the FCA graphic, both consistently diverging from other species. F. cherrug and F. rusticolus showed a low distance from each other and resulted not very distant from F. biarmicus. F. tinnunculus exhibited the greatest genetic distance from the other species. Evanno’s method supported K=3 (Mean LnP(K)= -10020.58 ± 2.73 SD) as the optimal number of genetic clusters, clearly separating F. tinnunculus, biarmicus F. cherrug/F. rusticolus and F. peregrinus/F. pelegrinoides in the STRUCTURE output (Figure 1a). FCA conducted on the cluster F. biarmicus /F. cherrug/F. rusticolus showed a sub-structure in which the three species slightly diverged, and the result was confirmed by Bayesian analysis (K=3; Mean LnP(K)= -4340.98 ± 0.66) (Figure 1b).

Individual identification and parentage analysis

The probability of identity resulted in different thresholds when estimated for unrelated or related individuals and depending on the species. A PID value lower than 0.001 was reached using an average of 5 markers, while the same value was obtained with PID_sib using an average of 11 loci (Figure 2). In parentage analysis performed by Colony the correct parent pair was assigned with the maximum probability value (1.000) but decreased in F_ru_6 (0.9999), F_ru_9 (0.9874) and F_ti_15 (0.9053); F_pl_24 was associated only with the correct mother (Table 6). In Cervus, all the individuals were associated with the correct parent pair. Both Delta and LOD distribution produced comparable results (Table 7).

Hybrid detection

FCA carried out on hybrid falcons showed that F. cherrug x F. peregrinus and F. peregrinus x F. rusticolus were distributed in a mid-range position with respect to relative pure species; conversely, F. rusticolus x F. cherrug mainly placed in a mid-range position and partially overlapped to F. cherrug. Bayesian analysis supported K=2 (F. cherrug x F. peregrinus mean LnP(K)= -3303.92 ± 0.78 SD; F. peregrinus x F. rusticolus mean LnP(K)= - 2989.32 ± 1.15 SD; F. rusticolus x F. cherrug, mean LnP(K) = -3368.26 ± 0.33 SD) as the optimal number of genetic clusters for all the three Falco hybrids (Figures 1c-1e).

Discussion

In this study we proposed a new microsatellite loci panel for cross amplification in Falco species with a wide range of applications, from genetic variability and differentiation analysis to forensic investigation purposes. The effectiveness of such an implemented panel in individual identification, parentage analysis and hybrid detection could make it a practical tool to contrast illegal trafficking. Wildlife trade has been detected as a global threat to the sustainability of biodiversity [50], leading to destruction of habitats and overexploitation of many animal and plant species with consequent negative socio-economic effects at local and global scales, such as the extinction of species and the loss of biodiversity and ecosystem services [51,52]. In the past 30 years, demanding wildlife for commercial purposes has played negative effects on biodiversity [53] due to the removal of a large number of individuals from natural populations that, in turn, caused a loss of genetic variability in a short timescale [8,51-54]. Wildlife trade represents one of the major threats to the survival of bird populations [7]. One-fifth of wildlife trade records are found to be demanded for pets or entertainment purposes [55] and these requests are particularly high for birds used as pets or display, hunted for food and employed in sport [56]. Overexploitation affects over one-third of all birds [8] and it is the second most significant threat to migratory species [57,58]. Particularly, a major concern has been arisen about the international trade in falcons, a significant proportion of which involving Arabian countries where there is a strong falconry tradition. In the last decades, the trend in legal trade of falcons is strongly increased [12] and, even though all species of falcons have been included in the CITES Appendices, the illegal international trade has been continuing [59].

Forensics science has been successfully applied to investigation of wildlife crime supporting the implementation of wildlife protection programs and molecular analysis was proven as the most effective method for dealing with this issue [60]. Currently, microsatellite loci are amongst the most popular markers in genotyping assays being a rapid, informative, and low-cost approach to produce evidence of crime in forensic investigations. Microsatellites are easily analyzed and highly polymorphic; expertise in their use is widespread and their usefulness in individual identification, assignment of individuals to specific populations, and relatedness testing have been largely proven [27-30,61-63]. Given the high performances in the use of microsatellite-based approach to address questions related to forensic investigation in wildlife crimes, it could be advantageous to investing resources in the characterization of new and practical STR-panel for species particularly threatened by trade and illegal trafficking, such as Falco spp.

Genetic variability and differentiation

The first aim of this study was to set up a unique panel of microsatellite loci for the most traded Falco spp. and assessing its reliability for both conservation and forensic purposes. Species-specific microsatellite loci have been identified in various Falco species and, in some cases, their cross-amplification power has been tested in a restricted number of related species; however, a single panel of microsatellite loci for different Falco species has not been validated yet.

Cross-amplification is a widely used time- and cost-effective approach reducing the need to design additional STR markers. Furthermore, it can significantly facilitate studies across related species, thus making the results directly comparable and the research more cost-effective. Cross-amplification is expected to work better for phylogenetically close species, even more, when target species are congeneric. Differences among species were described by genetic components at in microsatellite loci. The analysis revealed high level of allelic richness and private alleles were detected in at least six loci for each analyzed species. As confirmed by ANOVA using intralocus heterozygosity values for each population, observed heterozygosity did not differ statistically among species as well as unbiased expected heterozygosity (except for the pair F. pelegrinoides/F. tinnunculus). Despite the similar heterozygosities, the number of private alleles differed, suggesting an inter-population differentiation.

The results of FCA and Bayesian analysis reflect the current knowledge about taxonomy and systematics of Falconidae family. Falcons can be broadly divided into three major monophyletic groups [64] large and mid- sized falcons consisting of the F. peregrinus and the subgenus Hierofalco; the Old-World kestrels, including F. tinnunculus; and finally, the hobbies (subgenus Hypotriorchis, not represented in this study). Coherently with this classification, the common kestrel F. tinnunculus showed the greatest genetic distance and clustered separately from the other studied species. Bayesian analysis clearly identified another cluster (both using K=3 and K=4) including Peregrine falcon (F. peregrinus) and the barbery falcon (F. pelegrinoides), which graphically overlapped in FCA too. Indeed, controversy exists around the placement of these two species. The Peregrine falcon is differentiated into various subspecies, which are only slightly distinct, and therefore Barbery falcon is sometimes listed as a subspecies of Peregrine falcon (F. peregrinus pelegrinoides) [65] despite molecular data suggested it could be recognized as an independent species [64]. The hierofalcon group, a term commonly used for the so-called “desert falcons”, consists of four ecologically and morphologically similar species - the Saker falcon (F. cherrug), the Gyrfalcon (F. rusticolus), the Lanner falcon (F. biarmicus) and the Laggar falcon (F. jugger) [66,67] that constituted another cluster in our Bayesian analysis. Evanno’s method supported the inclusion of F. cherrug, F. biarmicus and F. rusticolus in the same cluster (K=3). Indeed, divergence time within hierofalcons is extremely recent and the group members are closely related to each other [11]. However, in FCA analysis F. cherrug and F. rusticolus overlapped and slightly diverged from F. biarmicus showing the presence of a sub-structure (K=4), in which the latter formed a separated cluster.

Conversely, FCA conducted on hierofalcon species only showed that F. cherrug and F. rusticolus were separated and were split into two clusters, according to the results previously obtained by principal component analysis [66], although divergence between Saker, Lanner and Gyrfalcon remains very small. Genetic structure analyses were consistent with Fst values confirming that the analysed species are not genetically homogeneous but show various differentiation levels reflecting the pattern of the known phylogenetic relationships. Furthermore, AMOVA revealed a high genetic differentiation among the studied species, and this could confirm the expected low gene flow among the populations observed in the present study since individuals are captive-bred specimens.

Individual identification and paternity test

Our results permitted to define a unique panel of 21 highly polymorphic microsatellite loci, able to identify individuals and family groups in six species belonging to genus Falco. The results indicate that the tested microsatellite panel ensures a high power to discriminate individuals. Five loci were enough to distinguish with high confidence between unrelated individuals, whereas eleven loci allowed differentiating among siblings, reaching a PID and a PID_ sib lower than 0.001 which should be sufficiently low for forensic application [68]. The power of microsatellite-based assignment techniques is dependent on several factors, including usefulness of markers, genotyping errors, and test procedure itself [69]. In Colony, all offspring but one was associated with the correct parent pair, and the maximum probability value was reached in almost all of offspring individuals. Instead, the totality of family groups was correctly identified in Cervus with a confidence level higher than 95%. Such results revealed that the presented STR panel was highly informative in parentage and sibship inference, identifying the correct parent pair in all the tested species.

Hybridization

The third aim of this study was to test the use of the proposed panel for the identification of Falco hybrids. Hybridization among falcon species is rare in nature, although some observations of natural hybridization among hierofalcons exist [66-70]. These falcons can easily hybridize in captivity and can also hybridize with the only slightly more divergent Peregrine falcon. Indeed, captive-bred hybrids of Saker and Gyrfalcons are amongst the most traded raptor species, representing a remarkable percentage of falconry birds. Falcon hybrids possess phenotypic and behavioural characteristics desirable by falconers, such as agility, aesthetical appeal, and robustness [70,71]. For instance, F. cherrug and F. rusticolus were crossed to unify the strength of Saker with the robust physique of Gyrfalcon, obtaining a specimen more tolerant to heat and disease; F. peregrinus was crossed for its speed and endurance, generating hybrids with superior hunting skills.

The set-up microsatellite panel allowed us to discriminate F1 hybrids from pure parental species. Due to the increasing volume in Falcon hybrids trade, having a tool to genetically identify hybrid species would be helpful. Hybrid specimens of Peregrine falcon showed clear signs of admixture with respective parental species, whereas a variable level of admixture has been found in the hierofalcon hybrids. This latter pattern could be explained by low genetic divergence or by shared ancestral polymorphisms among species belonging to this group. Anyway, Bayesian analysis revealed that all parental and F1 individuals could be correctly identified using the proposed microsatellite loci panel. The Saker, the Gyrfalcon and their hybrids could be unambiguously distinguished, although closely related, thereby highlighting the reliability of these markers to identify hybridization events.

Hybridization could have direct conservational risk. In Europe and in the Middle East every year several specimens of hybrid falcons escape from their owner or are released into the wild [10]. These hybrids may interbreed with wild individuals, as it occasionally happened in Scandinavia and Germany between F. peregrinus and F. rusticolus x F. peregrinus hybrids [72]. While hierofalcons and Peregrine Falcon generate viable offsprings but rarely capable to sire because females are often sterile, hierofalcon hybrids preserve their reproductive potential for several generations [70]. Indeed, long-term hybrid lineages of hierofalcon hybrids represent the standard stocks of falcons commonly bred in the United Arab Emirates. Therefore, hybrid specimens might represent a potential threat for natural populations due to genetic introgression and gene pool erosion.

Conclusion

The proposed microsatellite loci panel may be of high utility, being suitable for a range of applications requiring polymorphic markers. According to its high cross-species amplification potential, it could be useful in research projects on wild populations to describe genetic diversity, genetic structure, mating system and gene flow. Moreover, it may provide a tool in forensic genetics for individual identification and parentage analysis to investigate parental claims, illegal transfer or suspected smuggling in Falco species and their hybrids.

Acknowledgement

The authors gratefully acknowledge the Italian Ministry of the Environment and Energy Security (MASE), particularly the Italian CITES Management Authority, which since 1997 granted support to the Unit for Conservation Genetics (BIO-CGE) at Italian Institute of Environmental Protection and Research (ISPRA) and allowed us to develop molecular procedures for improving wildlife forensics.

V.B. is funded by the European Union - NextGenerationEU.

Conflict of Interest

The authors declare no conflict of interest.

References

1. Caro, Tim, Zeke Rowe, Joel Berger and Philippa Wholey, et al. "An inconvenient misconception: Climate change is not the principal driver of biodiversity loss." Conserv Lett 15 (2022): e12868.

Google Scholar, Crossref, Indexed at

2. Díaz, Sandra, Josef Settele, Eduardo S. Brondízio and Hien T. Ngo, et al. "Pervasive human-driven decline of life on earth points to the need for transformative change." Science 366 (2019): eaax3100.

Google Scholar, Crossref, Indexed at

3. Green, Jennah, Jan Schmidt-Burbach and Angie Elwin. "Commercial trade of wild animals: Examining the use of the IUCN red list and cites appendices as the basis for corporate trade policies." Front Conserve Sci 3 (2022): 902074.

Google Scholar, Crossref, Indexed at

4. Chan, David Tsz Chung, Emily Shui Kei Poon, Anson Tsz Chun Wong and Simon Yung Wa Sin. "Global trade in parrots–Influential factors of trade and implications for conservation." Glob Ecol Conserv 30 (2021): e01784.

Google Scholar, Crossref, Indexed at

5. Wang, Qingqing, Jianbin Shi, Xinchen Shen and Tian Zhao. "Characteristics and patterns of international trade in cites-listed live birds in China from 2010 to 2019." Glob Ecol Conserv 30 (2021): e01786.

Google Scholar, Crossref, Indexed at

6. Butchart, Stuart H. M., Alison J. Stattersfield, Leon A. Bennun and Sue M. Shutes, et al. "Measuring global trends in the status of biodiversity: Red List Indices for birds." PLoS Biol 2 (2004): e383.

Google Scholar, Crossref, Indexed at

7. Szabo, Judit K., Nyil Khwaja, Stephen T. Garnett and Stuart HM Butchart. "Global patterns and drivers of avian extinctions at the species and subspecies level." (2012): e47080.

Google Scholar, Crossref, Indexed at

8. Harris, J. Berton C., Jonathan MH Green, Dewi M. Prawiradilaga and Xingli Giam, et al. "Using market data and expert opinion to identify overexploited species in the wild bird trade." Biol Conserv 187 (2015): 51-60.

Google Scholar, Crossref, Indexed at

9. Madden, Kristin K., Genevieve C. Rozhon and James F. Dwyer. "Conservation letter: Raptor persecution." J Raptor Res 53 (2019): 230-233.

Google Scholar, Crossref, Indexed at

10. Fleming, L. Vincent, Andrew F. Douse and Nick P. Williams. "Captive breeding of peregrine and other falcons in great britain and implications for conservation of wild populations." Endanger Species Res 14 (2011): 243-257.

Google Scholar, Crossref, Indexed at

11. Wilcox, Justin JS, Stéphane Boissinot and Youssef Idaghdour. "Falcon genomics in the context of conservation, speciation, and human culture." Ecol Evol 9 (2019): 14523-14537.

Google Scholar, Crossref, Indexed at

12. Panter, Connor, Eleanor Atkinson and Rachel White. "Quantifying the global legal trade in live CITES-listed raptors and owls for commercial purposes over a 40-year period." Avocetta 43 (2019): 23-36.

Google Scholar, Indexed at

13. Nijman, Vincent and K. Anne-Isola Nekaris. "The harry potter effect: The rise in trade of owls as pets in java and Bali, Indonesia." Glob Ecol Conserv 11 (2017): 84-94.

Google Scholar, Crossref, Indexed at

14. https://www.academia.edu/39511995/Zooarchaeological_evidence_for_falconry_in_England_up_t%20o_AD_1500?auto=download&email_work_card=download-paper%20(accessed%20on%2011%20July%202022)
15. Koch, Natalie. "Gulf nationalism and the geopolitics of constructing falconry as a ‘heritage sport’." Stud Ethn Nationalism 15 (2015): 522-539.

Google Scholar, Crossref, Indexed at

16. Wakefield, Sarina. "Falconry as heritage in the United Arab Emirates." World Archaeol 442012): 280-290.

Google Scholar, Crossref, Indexed at

17. Stretesky, Paul B., Ruth E. McKie, Michael J. Lynch and Michael A. Long, et al. "Where have all the falcons gone? saker falcon (falco cherrug) exports in a global economy." Glob Ecol Conserv 13 (2018): e00372.

Google Scholar, Crossref, Indexed at

18. Harfoot, Michael, Satu AM Glaser, Derek P. Tittensor and Gregory L. Britten, et al. "Unveiling the patterns and trends in 40 years of global trade in cites-listed wildlife." Biol Conserv 223 (2018): 47-57.

Google Scholar, Crossref, Indexed at

19. Regulation, Council. "No 338/97 of 9 December 1996 on the protection of species of wild fauna and flora by regulating trade therein." OJL 61 (1997).
20. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A12016P%2FTXT
21. Abdul-Muneer, P. M. "Application of microsatellite markers in conservation genetics and fisheries management: recent advances in population structure analysis and conservation strategies." Genet Res Int 2014 (2014).

Google Scholar, Crossref, Indexed at

22. Bruford, Michael W., Neil Davies, Mohammad Ehsan Dulloo and Daniel P. Faith, et al. "Monitoring changes in genetic diversity." The GEO Handbook on Biodiversity Observation Networks (2017): 107-128.

Google Scholar, Crossref, Indexed at

23. Bonnin, Noémie, Alex K. Piel, Richard P. Brown and Yingying Li, et al. "Barriers to chimpanzee gene flow at the south‐east edge of their distribution." Mol Ecol (2023).

Google Scholar, Crossref, Indexed at

24. Huang, Long, Lishi Zhang, Dan Li and Rongfei Yan, et al. "Molecular evidence of introgressive hybridization between related species jankowski's bunting (E. jankowskii) and meadow bunting (E. cioides) (Aves: Passeriformes)." Avian Res 13 (2022): 100035.

Google Scholar, Crossref, Indexed at

25. Harrison, Richard G. and Erica L. Larson. "Hybridization, introgression and the nature of species boundaries." J Hered 105 (2014): 795-809.

Google Scholar, Crossref, Indexed at

26. Caniglia, Romolo, Elena Fabbri, Marco Galaverni and Pietro Milanesi, et al. "Noninvasive sampling and genetic variability, pack structure, and dynamics in an expanding wolf population." J Mammal 95 (2014): 41-59.

Google Scholar

27. Giangregorio, Patrizia, Lorenzo Naldi, Chiara Mengoni and Claudia Greco, et al. "Cross-amplification in strigiformes: A new str panel for forensic purposes." Genes 12 (2021): 1721.

Google Scholar, Crossref, Indexed at

28. Biello, Roberto, Mauro Zampiglia, Claudia Corti and Gianluca Deli, et al. "Mapping the geographic origin of captive and confiscated Hermann’s tortoises: A genetic toolkit for conservation and forensic analyses." Forensic Sci Int Genet 51 (2021): 102447.

Google Scholar, Crossref, Indexed at

29. Linacre, Adrian. "Animal forensic genetics." Genes 12 (2021): 515.

Google Scholar, Crossref, Indexed at

30. Jan, Catherine and Luca Fumagalli. "Polymorphic DNA microsatellite markers for forensic individual identification and parentage analyses of seven threatened species of parrots (family psittacidae)." Peer J 4 (2016): e2416.

Google Scholar, Crossref, Indexed at

31. Lorenzini, Rita, Rita Fanelli, Francesco Tancredi and Antonino Siclari, et al. "Matching STR and SNP genotyping to discriminate between wild boar, domestic pigs and their recent hybrids for forensic purposes." Sci Rep (2020): 3188.

Google Scholar, Crossref, Indexed at

32. Hull, Joshua M., George K. Sage, Sarah A. Sonsthagen and Megan C. Gravley, et al. "Isolation and characterization of microsatellite loci in merlins (F. columbarius) and cross-species amplification in gyrfalcons (F. rusticolus) and peregrine falcons (F. peregrinus)." Mol Biol Rep 47 (2020): 8377-8383.

Google Scholar, Crossref, Indexed at

33. Padilla, J. A., J. C. Parejo, J. Salazar and M. Martínez-Trancón, et al. "Isolation and characterization of polymorphic microsatellite markers in lesser kestrel (F. naumanni) and cross-amplification in common kestrel (F. tinnunculus)." Conserv Genet 10 (2009): 1357-1360.

Google Scholar, Crossref, Indexed at

34. Nesje, Marit, K. H. Røed, J. T. Lifjeld and P. Lindberg, et al. "Genetic relationships in the peregrine falcon (F. peregrinus) analysed by microsatellite DNA markers." Mol Ecol 9 (2000): 53-60.

Google Scholar, Crossref, Indexed at

35. Beasley, Jordan, Guy Shorrock, Rita Neumann and Celia A. May, et al. "Massively parallel sequencing and capillary electrophoresis of a novel panel of falcon STRs: Concordance with minisatellite DNA profiles from historical wildlife crime." Forensic Sci Int Genet 54 (2021): 102550.

Google Scholar, Crossref, Indexed at

36. Nesje, Marit and K. H. Røed. "Microsatellite DNA markers from the gyrfalcon (F. rusticolus) and their use in other raptor species." Mol Ecol 9 (2000): 1438-1440.

Google Scholar, Crossref, Indexed at

37. Magonyi, Nóra M., Róbert Mátics, Krisztián Szabó and Péter Fehérvári, et al. "Characterization of novel microsatellite markers for the red-footed falcon (F. vespertinus) and cross-species amplification in other falco species." Eur J Wildl Res 65 (2019): 1-5.

Google Scholar, Crossref, Indexed at

38. Topinka, J. Rick and Bernie May. "Development of polymorphic microsatellite loci in the Northern Goshawk (A. gentilis) and cross-amplification in other raptor species." Conserv Genet 5 (2004): 861-864.

Google Scholar, Crossref, Indexed at

39. Sarà, Maurizio, Chiara Mengoni, Nadia Mucci and Enrico Guzzo, et al. "Genetic variability and population structuring in the european lanner falcon falco biarmicus feldeggii." Avocetta 43 (2019).

Google Scholar, Crossref, Indexed at

40. Peakall, R. O. D and Peter E. Smouse. "GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research." Mol Ecol Notes 6 (2006): 288-295.

Google Scholar, Crossref, Indexed at

41. Goudet, J. F. S. T. A. T. "FSTAT (version 1.2): A computer program to calculate F-statistics." J Hered 86 (1995): 485-486.

Google Scholar, Indexed at

42. Belkhir, K. "GENETIX 4.05, logiciel sous windows tm pour la génétique des populations." (2004).

Google Scholar, Indexed at

43. Hammer, Øyvind and David AT Harper. "Past: Paleontological statistics software package for education and data anlysis." Palaeontol Electron 4 (2001): 1.

Google Scholar, Indexed at

44. Pritchard, Jonathan K., Matthew Stephens and Peter Donnelly. "Inference of population structure using multilocus genotype data." Genetics 155 (2000): 945-959.

Google Scholar

45. Earl, Dent A. and Bridgett M. VonHoldt. "Structure harvester: A website and program for visualizing structure output and implementing the evanno method." Conserv Genet Resour 4 (2012): 359-361.

Google Scholar, Crossref, Indexed at

46. Porras-Hurtado, Liliana, Yarimar Ruiz, Carla Santos and Christopher Phillips, et al. "An overview of structure: applications, parameter settings, and supporting software." Front Genet 4 (2013): 98.

Google Scholar, Crossref, Indexed at

47. Rosenberg, Noah A. "Distruct: A program for the graphical display of population structure." Mol Ecol Notes (2004): 137-138.

Google Scholar, Crossref, Indexed at

48. Pagnotta, Mario A. "Comparison among methods and statistical software packages to analyze germplasm genetic diversity by means of codominant markers." J 1 (2018): 197-215.

Google Scholar, Crossref, Indexed at

49. Jones, Owen R. and Jinliang Wang. "Colony: A program for parentage and sibship inference from multilocus genotype data." Mol Ecol Resour 10 (2010): 551-555.

Google Scholar, Crossref, Indexed at

50. Cardoso, Pedro, Kofi Amponsah-Mensah, Joao P. Barreiros and Jamie Bouhuys, et al. "Scientists' warning to humanity on illegal or unsustainable wildlife trade." Biol Conserv 263 (2021): 109341.

Google Scholar, Crossref, Indexed at

51. Rosser, Alison M. and Sue A. Mainka. "Overexploitation and species extinctions." Conserv Biol 16 (2002): 584-586.

Google Scholar, Crossref, Indexed at

52. Adla, Kahrić, Kulijer Dejan, Dedić Neira and Šnjegota Dragana. "Degradation of ecosystems and loss of ecosystem services." In One Health (2022): 281-327.

Google Scholar, Crossref, Indexed at

53. Gluszek, Sarah, Daniel Ariano-Sánchez, P. A. T. R. I. C. I. A. Cremona and Alejandra Goyenechea, et al. "Emerging trends of the illegal wildlife trade in mesoamerica." Oryx 55 (2021): 708-716.

Google Scholar, Crossref, Indexed at

54. Tingley, Morgan W., J. Berton C. Harris, Fangyuan Hua and David S. Wilcove, et al. "The pet trade's role in defaunation." Sci 356 (2017): 916-916.

Google Scholar, Crossref, Indexed at

55. Baker, Sandra E., Russ Cain, Freya Van Kesteren and Zinta A. Zommers, et al. "Rough trade: Animal welfare in the global wildlife trade." BioScience 63 (2013): 928-938.

Google Scholar, Crossref, Indexed at

56. Butchart, Stuart HM. "Red List Indices to measure the sustainability of species use and impacts of invasive alien species." Bird Conserv Int 18 (2008): S245-S262.

Google Scholar, Crossref, Indexed at

57. Kirby, Jeff S., Alison J. Stattersfield, Stuart HM Butchart and Michael I. Evans, et al. "Key conservation issues for migratory land-and waterbird species on the world's major flyways." Bird Conserv Int 18 (2008): S49-S73.

Google Scholar, Crossref, Indexed at

58. Brochet, Anne-Laure, Willem Van den Bossche, Sharif Jbour and P. Kariuki ndang’ang’a, et al. "Preliminary assessment of the scope and scale of illegal killing and taking of birds in the mediterranean." Bird Conserv Int 26 (2016): 1-28.

Google Scholar, Crossref, Indexed at

59. https://cites.org/eng/news/meetings/falcon.shtml
60. Gupta, Sandeep Kumar. "Application of DNA fingerprinting and wildlife forensics." DNA Fingerprinting: Advancements and Future Endeavors (2018): 77-87.

Google Scholar, Indexed at

61. Mucci, Nadia, Patrizia Giangregorio, Letizia Cirasella and Gloria Isani, et al. "A new STR panel for parentage analysis in endangered tortoises." Conserv Genet Resour 12 (2020): 67-75.

Google Scholar, Crossref, Indexed at

62. Potoczniak, Margret J., Michael Chermak, Lawrence Quarino and Shanan S. Tobe, et al. "Development of a multiplex, pcr-based genotyping assay for african and asian elephants for forensic purposes." Int J Legal Med 134 (2020): 55-62.

Google Scholar, Crossref, Indexed at

63. FitzSimmons, NANCY N., S. U. S. A. N. Tanksley, Michael RJ Forstner and Edward E. Louis, et al. "Microsatellite markers for crocodylus: New genetic tools for population genetics, mating system studies and forensics." Crocodilian Biology and Evolution (2001): 51-57.

Google Scholar

64. Fuchs, Jérôme, Jeff A. Johnson and David P. Mindell. "Rapid diversification of falcons (Aves: falconidae) due to expansion of open habitats in the late miocene." Mol Phylogenetics Evol 82 (2015): 166-182.

Google Scholar, Crossref, Indexed at

65. Wink, Michael. "Phylogeny of falconidae and phylogeography of peregrine falcons." Ornis Hung 26 (2018): 27-37.

Google Scholar, Crossref, Indexed at

66. Nittinger, Franziska, Anita Gamauf, Wilhelm Pinsker and Michael Wink, et al. "Phylogeography and population structure of the saker falcon (Falco cherrug) and the influence of hybridization: Mitochondrial and microsatellite data." Mol Ecol 16 (2007): 1497-1517.

Google Scholar, Crossref, Indexed at

67. Nittinger, F., E. Haring, W. Pinsker and Michael Wink, e al. "Out of Africa? phylogenetic relationships between F. biarmicus and the other hierofalcons (Aves: Falconidae)." Zoolog Syst Evol Res 43 (2005): 321-331.

Google Scholar, Crossref, Indexed at

68. Waits, Lisette P., Gordon Luikart and Pierre Taberlet. "Estimating the probability of identity among genotypes in natural populations: Cautions and guidelines." Mol Ecol 10 (2001): 249-256.

Google Scholar, Crossref, Indexed at

69. Wang, Jinliang and Anna W. Santure. "Parentage and sibship inference from multilocus genotype data under polygamy." Genet 181 (2009): 1579-1594.

Google Scholar, Crossref, Indexed at

70. Pomichal, Krisztián, Balázs Vági and Tibor Csörgő. "A case study on the phylogeny and conservation of saker falcon." Ornis Hung 22 (2014): 1-14.

Google Scholar, Crossref, Indexed at

71. Dixon, Andrew. "Conservation of the saker falcon falco cherrug and the use of hybrids for falconry." Aquila 119 (2012): 9-19.

Google Scholar, Indexed at

72. Lindberg, Peter and Marit Nesje. "Lost falconers birds and hybrid falcons–do they have an impact on european peregrine falcon (F. peregrinus) populations? A case study of lost falconers birds breeding in Sweden." In Raptors in the new millenium. Proceedings of the world conference on birds of prey and owls (2002): 107.

Google Scholar, Crossref, Indexed at