\articletype Original Articles Improving non-invasive sampling for the genetic survey of the rock ptarmigan (Lagopus muta)

\articletype Original Articles Improving non-invasive sampling for the genetic survey of the rock ptarmigan (Lagopus muta) | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 January 2026 V1 Latest version Share on \articletype Original Articles Improving non-invasive sampling for the genetic survey of the rock ptarmigan (Lagopus muta) Authors : Emeline Charbonnel 0000-0002-8561-7610 [email protected] , Estelle Lauer , Sébastien Zimmermann , Pascal Lapebie , and Gaël Aleix-Mata Authors Info & Affiliations https://doi.org/10.22541/au.176796579.99736867/v1 167 views 66 downloads Contents Abstract Introduction Materials and Methods Results Discussion Supplementary Material References Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract \articletype Original Articles Non-invasive genetic sampling is particularly useful for the genetic monitoring of rare, protected, or elusive species that require accurate evaluation of their conservation status. However, non-invasive samples often contain degraded DNA, leading to important data losses and high genotyping error rates. These samples have already been validated as a suitable source of DNA for genetic studies on a very discreet species, the rock ptarmigan (Lagopus muta). But, there is currently no specific protocol for producing high-quality data with minimal losses and errors for this species. Thus, this study aimed to improve non-invasive sampling and laboratory protocols for genetic monitoring of mountain Galliformes populations. To this end, we collected 338 fecal samples over two years in the French Alps and analyzed the impact of sample freshness and weather conditions on genotyping success and genotyping error rates for 19 microsatellites. Our study shows that the use of non-invasive samples can be optimized from the field phase onwards. We highlight the importance of collecting well-preserved and fresh fecal samples, preferably on days without rain and on snow-covered ground. We then demonstrated that laboratory procedures could also be optimized for fecal samples containing uric acid, in particular by using a well-adapted extraction protocol. In addition, the panel of microsatellite markers can be reduced to the 14 best quality markers in order to obtain higher quality final data without loss of information. This study provides a series of recommendations aimed at improving genotyping success and ensuring the quality of results, while reducing costs. We aim to encourage genetic studies using non-invasive samples for monitoring rock ptarmigan or mountain Galliformes species, and are confident that following these procedures will facilitate the transition to genomic monitoring. Original Articles Introduction Effective management of wild populations requires reliable assessments of their viability. This relies on various monitoring methods, including genetics that allows for the estimation of various biological parameters (e.g., genetic diversity, genetic structure, hybridization, abundance, effective population size, response to selective pressures, etc.) and their evolution (Schwartz et al. 2007, Allendorf 2017). However, collecting genetic material for rare, protected or elusive species is not an easy task. The use of non-invasive genetic sampling methods is therefore highly relevant for monitoring these species (Taberlet et al. 1999, Waits and Paetkau 2005, Beja-Pereira et al. 2009, Carroll et al. 2018). These methods, based on collecting material left by individuals in the field (feces, feathers or hair), avoid capturing and handling animals and cause little disturbance to the target species and their environment (Beja-Pereira et al. 2009, Ferreira et al. 2018). Samples can be collected even if the animals are not observed, regardless of their sex, age or behavior (Bañuelos et al. 2019). Consequently, non-invasive genetic sampling can provide information on a larger number of individuals compared to physical captures or direct monitoring methods. This makes these methods a cost-effective tool for population monitoring (Solberg et al. 2006, De Barba et al. 2010, Ferreira et al. 2018). Despite their great potential, non-invasive genetic samples often have a low concentration and degraded host DNA, and may contain numerous PCR inhibitors (Taberlet et al. 1996, Broquet and Petit 2004, Waits and Paetkau 2005, Miquel et al. 2006, Beja-Pereira et al. 2009). This poor sample quality can lead to low genotyping success and high genotyping error rates and, consequently, erroneous estimates of biological parameters for populations. The genotyping errors may lead to an overestimation of the number of individuals (“ghost” individuals) or, conversely, to an underestimation as a result of amplification failure (Lampa et al. 2013). Nevertheless, the use of appropriate sampling methods and laboratory protocols can improve the genotyping success and effectively reduce error rates (see the reviews by Beja-Pereira et al. 2009, Morin et al. 2010, Lampa et al. 2013). In the field, collecting only fresh samples, preferably during dry weather conditions, can substantially enhance DNA quality and amplification success (Beja-Pereira et al. 2009, Lampa et al. 2013). In laboratory, applying standardized workflows and using software designed to detect genotyping errors or to cluster multilocus genotypes ensures data reliability, minimizes error propagation, and thus optimizes study cost (Morin et al. 2010, Lampa et al. 2013). In our study, non-invasive genetic samples of rock ptarmigan have been analyzed. The rock ptarmigan, Lagopus muta (subfamily Tetraoninae), is listed as a species of special conservation concern and as a hunted species under nationals legislations in Annexes I and II of the EU Birds Directive 2009/147/EC. French populations of rock ptarmigan are located in the Pyrenees and Alps which represent the southernmost distribution of the species in Western Europe. Their isolation and fragmentation make them vulnerable to genetic isolation (Caizergues et al. 2003, Bech et al. 2013). The species is classified as “Near Threatened” by the IUCN at the national level in France, and in its two main areas of distribution, i.e., the Pyrenees and the Alps (UMS Patrinat (coord.) 2019). Rock ptarmigan populations suffer from habitat changes caused by human activities and global warming (Storch 2007, Bech et al. 2013, Novoa et al. 2014), and should in future benefit from genetic studies to better understand their threats. In Southwestern Europe, rock ptarmigan is present in the highest habitats (Favaron et al. 2006, Nelli et al. 2013), above 1800m in French Alps (Novoa et al. 2014), that are sometimes difficult to access. The specificities of its behavior and molting pattern make the rock ptarmigan a very elusive species. Its two plumages provide camouflage in habitats – the reddish-brown plumage on rocks in summer and the white plumage on snow in winter (Visinoni et al. 2015, Nord et al. 2023). The absence of clear sexual dimorphism makes differentiation between individuals of the two sexes difficult (Novoa et al. 2014). To that extent, Bergan et al. (2016) tested the stability of DNA from fecal samples of rock ptarmigan collected from snow roosts in Norway and validated its use as a reliable source of DNA for future genetic studies. These authors suggest collecting samples in winter, when snow covers the ground, and before the spring rains out-wash feces. They reveal that sample quality is affected by freezing and thawing cycles and that the winter diet promotes the presence of intestinal epithelial cells and, therefore, host DNA in feces. However, this study has focused on DNA quality, leaving the effects of these parameters on genotyping-based individual identification untested. In addition, we do not know how stable fecal DNA is in the less snowy and warmer regions of southwestern Europe. Overall, we still lack information on non-invasive sampling of rock ptarmigan to be able to follow a precise and optimized protocol that would enable us to produce high-quality data with minimal losses and errors, and optimized costs. Indeed, to date, genetic studies of the Alpine rock ptarmigan using non-invasive samples are scarce (Costanzi and Steifetten 2019, Aleix-Mata et al. 2021). In this study, we analyzed the success and error rates of genotyping on a set of rock ptarmigan fecal samples collected in the French Alps. We discussed field and laboratory improvements to genotyping success, and cost optimization in order to encourage genetic studies using non-invasive samples from mountain Galliformes. From there, we proposed a combination of “Guidelines for sampling mountain Galliformes feces for molecular analysis” available online in open access on the protocols.io platform (Charbonnel et al. 2025). Original Articles Materials and Methods Fieldwork The study was carried out at the Bramant site (45º12’ - 45º11’N, 6º09’ - 6º11’E) in the Northern Internal Alps in Isère (France), where rock ptarmigans are regularly observed. It is a reference site for counting singing males in spring (Dos Santos et al. 2021). The site was divided into four sampling zones, each measuring 250m in radius (i.e., samples (i.e., feces) by walking parallel transects 20m apart, except in dangerous landscapes (Aleix-Mata et al. 2021). When it was possible, the sectors were surveyed from bottom to top. Location coordinates were recorded for each sample using a GPS system. Fecal samples were collected during two consecutive years, from 2021 to 2022. The sampling was carried out three times each year in late spring, between 26th May and 10th June. As far as possible, the sampling zones were covered identically for each survey. To assess the impact of weather conditions on sample quality, we compiled historical meteorological data (temperature, rain precipitations and snow depth) from the Open-Meteo platform (https://open-meteo.com), from May 1 to June 30 for the years 2021 and 2022. In 2021, only fresh fecal samples, described as green, whole, solid-textured feces with acid uric at the tips (white coloration), were collected. In 2022, we collected lower-quality samples to determine whether these parameters impact genotyping. To this end, three elements describing sample freshness were recorded: coloration (green or brown), substrate (snow or grass and rock) and presence or absence of acid uric mark. To avoid genetic contamination, gloves or twigs were used to collect samples. After collecting one feces, the remaining ones were destroyed in order to prevent duplication and to maximize the collection of fresh feces at each sampling (Lampa et al. 2013). Only one feces was collected in each tube containing absolute ethanol and they were conserved at -20°C until the DNA extraction in the laboratory. DNA extraction and Genotyping DNA extraction was carried out at the Antagene Laboratory (La Tour de Salvagny, France, https://antagene.com/), in a DNA-free extraction lab. Ethanol was removed from samples by transferring 1mL of supernatant to a tube and centrifuging. DNA was extracted using the Nucleospin 96 Tissue kit, Macherey-Nagel®. The supplier’s protocol for vacuum extraction using a silica column was respected, but lysis was carried out overnight at 56°C. DNA samples were genotyped at the Antagene Laboratory using a set of 19 microsatellite markers designed primarily on rock ptarmigan (14 markers; Costanzi et al. 2018) or, alternatively, on closely related species in the Tetraoninae subfamily (Piertney and Dallas 1997, Caizergues et al. 2001, Piertney and Höglund 2001) (Supporting information). All the microsatellites have been previously cross-amplified in rock ptarmigan (Supporting information). We used two multiplex PCRs (Polymerase Chain Reaction) to genotype the samples, following the PCR reactions and program detailed in Supporting information. As recommended for non-invasive samples, we used a multi-tube approach for genotyping (Navidi et al. 1992, Taberlet et al. 1996, Miquel et al. 2006, Beja-Pereira et al. 2009). Samples were genotyped three times to establish a consensus sequence for each one. A sample is considered as homozygous for a locus if a single allele is observed in at least two replicates and no other allele is observed in the other replicates. Conversely, it is considered as heterozygous if the same two different alleles are observed in at least two replicates. To avoid allele dropout or false allele, we noted ‘000/aaa’ for loci where the same allele was observed in all three replicates, but another allele was present in one of the replicates (Aleix‐Mata et al. 2025). Samples suspected of being contaminated were removed. To assess genotype quality, and whether the freshness of feces has an impact on the quality of genetic information, we calculated for each sample a Quality Index (QI) similar to that of Miquel et al. (2006) and estimated missing data using Gimlet software v.1.3.3 (Valière 2002). For subsequent analyses, we retained only exploitable samples, i.e., those with an QI greater than or equal to 0.6. We assess data quality by estimating genotyping errors (allele dropout, false alleles and null alleles) at each locus using Gimlet software v.1.3.3 (Valière 2002), and checked the presence of null alleles in loci using Micro-checker software v.2.2.3 (Van Oosterhout et al. 2004). Markers with null alleles were considered poor quality markers and were removed for further analysis. Then, we compared all genotypes in pairs using the option ‘regroup genotypes’ of the Gimlet software v.1.3.3 (Valière 2002). Samples with identical genotypes or with one or two different alleles, if the mismatch could be explained by an allele dropout or a false allele, are attributed to the same individual. We calculated the probability of identity unbiased (P (ID) ) and between siblings (P (ID)sib ) - key indicators for validating the quality of the dataset (Waits et al. 2001) - using Cervus software v.3.0.7 (Marshall et al. 1998, Kalinowski et al. 2007). Finally, to assess Euclidian distances between each sample in each sampling series, we used the sf package in R (Pebesma 2018). Original Articles Results During the study, we collected 338 rock ptarmigan feces, but only 103 (30.5%) were of sufficient quality (i.e., not contaminated samples and QI ≥ 0.6) (Table 1). Using elements describing sample freshness recorded in 2022, we observed that green samples or samples with acid uric mark or samples collected on snow had better genotype quality (Table 2). The difference was significant for the substrate (snow vs grass or rock) and color (green vs brown) (pval 5%). Using the remaining 14 markers, we characterized 26 different genotypes. The final data contained 4.1% missing data, few genotyping errors (3.8% ADO and no FA), and had an average QI of 0.90 (Table 1). The probabilities of identity between unrelated individuals (P (ID) = 9.5 × 10 -15 ) or between siblings (P (ID)sib = 4.7 × 10 -6 ), which means that the probability of two unrelated individuals or two siblings having the same genetic identity is almost zero. Original Articles Discussion Improving genotyping success The global genotyping success was particularly low (30.5%, Table 1) compared with similar studies. In previous studies involving genetic analysis of non-invasive samples from rock ptarmigan by Aleix-Mata et al. (2021) and Costanzi and Steifetten (2019) who used similar sets of microsatellites, genotyping success was much higher (67% and 65%, respectively). The main reason for this could be that we used a DNA extraction protocol (keeping 1mL of ethanol supernatant) and/or kit (Nucleospin 96 Tissue kit, Macherey-Nagel®) that were not suitable. To the best of our knowledge, this protocol had never been used before on fecal samples from Galliformes or birds in general, but had proven effective on fecal samples from mammals (e.g., Rode et al. 2024, Pirog et al. 2025). This difference can be explained by the presence of urea in bird feces. In line with this, we also observed that genotyping success significantly decreased from 41.2% in 2021 to 24.7% in 2022 (Table1; pval < 0.05, Fisher’s Exact Test for Count Data), which was explained by a significant decrease in QI and increase in missing data (Table1; pval < 0.05, Kruskal-Wallis rank sum test). This negative result highlights the need for molecular tools that can be used in the presence of uric acid, which is found in avian and reptile fecal samples, as uric acid inhibits molecular processes (e.g., Eriksson et al. 2017). We thus encourage using DNA extraction protocols that have already proven their effectiveness on rock ptarmigan or Galliformes fecal samples, and particularly the QIAamp® Fast DNA Stool Mini kit (Qiagen) recommended by Aleix-Mata et al. (2021). This kit has been specifically designed to extract DNA from fresh or frozen feces samples and contains a specific buffer (InhibitEX Buffer) to separate inhibitory substances present in fecal samples. The DNA extraction protocol can be optimized by first removing the uric acid mark (Segelbacher and Steinbruck 2001, Vallant et al. 2018, Haider et al. 2025), and then by swabbing the surface of the feces (Jacob et al. 2010, Vallant et al. 2018, Haider et al. 2025) or soaking the sample in a large volume of buffer (Beja-Pereira et al. 2009; see method details in online protocol). Both methods may result in a higher concentration of epithelial cells, reduce the number of PCR inhibitors, require a smaller amount of sample, leaving the majority of samples available, and are less time consuming. They yield large quantities of high molecular weight DNA and data with high amplification success and low genotyping error rates. Ensuring the quality of results We found that green samples, samples with a uric acid mark, and those collected on snow yielded better genotype quality (Table 2). This can also explain why “fresh” samples collected in 2021, partly selected for their green color, are of better quality (i.e., lower ADO, FA and missing rates) and have a higher genotyping success (i.e., higher QI) than samples collected in 2022 (Table 1). In fact, the criteria used by a trained observer to choose a fresh sample is a good criteria. Therefore, to ensure good data quality for future studies using non-invasive samples from rock ptarmigan or mountain Galliformes species, we recommend the systematic collection of well-preserved fecal samples. That are whole, morphologically intact feces, as well as solid-textured and stool-shaped feces (see the illustrations in the online protocol). We also recommend collecting recent samples, i.e., not discolored (green) feces with uric acid mark (white coloration at the tips) and no signs of decomposition, rot or mold (Costanzi and Steifetten 2019, Aleix-Mata et al. 2021). Storage conditions may also affect DNA quality. We suggest to store samples with silica gel at -20°C (Jacob et al. 2010, Bañuelos et al. 2019, Aleix-Mata et al. 2021, Aleix‐Mata et al. 2025). Silica gel ensures rapid drying, absence of moisture in the sampling tube, and seems easier to use than ethanol in the field (personal observation). Nevertheless, the multi-tube approach we have used greatly reduces the risk of genotyping errors (Navidi et al. 1992, Taberlet et al. 1996, Beja-Pereira et al. 2009, Vallant et al. 2018). Thanks to this approach, we were able to calculate the QI (Miquel et al. 2006) and select the highest quality samples, i.e., 103 samples. Using this quality filter, we also removed samples that contained more than 26% missing data, improving the reliability of our results. Cost optimization By improving sample quality, both during the field phase and the laboratory phase, study costs can be optimized by reducing sample loss and limiting the genotyping of poor-quality markers, while still retaining the three replicates of each sample required for the multitube approach. First, in the field, having fecal samples collected by experienced collectors trained in the criteria for the best quality samples (i.e., well-preserved and fresh feces) can be a way to increase genotyping success, improve repeatability, and thus reduce study costs (Rösner et al. 2014, Vallant et al. 2018, Augustine et al. 2020, Aleix-Mata et al. 2021, Scridel et al. 2022). We can see that choosing to collect only fresh samples reduces the probability of detecting new genotypes. Indeed, the accumulation curve for newly identified individuals, based on sampling intensity, suggests that almost all genotypes would have been collected by 2022, as a plateau appears to have been reached (Figure 1.A). This trend appears to be less pronounced in 2021. However, it is reasonable to assume that improving data quality in other ways (DNA extraction, marker selection, sample storage, etc.) could offset this effect. Secondly, it is advisable to collect samples when weather conditions are favorable. We observed that the collection day with the lowest genotyping success rate (third day in 2021, 19.4%) was the collection day with the highest rainfall (Figure 1.B). The day with the highest genotyping success (first day in 2021, 54.3%) was the cooler collection day, without precipitation and highest snow depth (Figure 1.B). That is why, collecting samples in winter or early spring, on a snow-covered substrate, could present advantages: (1) cold and dry period prevents DNA degradation, (2) subsequent heavy spring rains may damage feces, (3) the snow covering the ground offers ideal preservation conditions (cold), (4) collection samples after a snowfall allows them to be dated, (5) the presence of snow makes it easier to find bird tracks, and (6) dry and fiber-rich rock ptarmigan’s winter diet (Watson and Moss 2004, García-González et al. 2016), may increase the number of intestinal epithelial cells and therefore the amount of host DNA in feces (Bergan et al. 2016, Shyvers et al. 2020). Previous studies articles in genetic studies of Galliformes recommend winter sampling (Regnaut et al. 2006, Rösner et al. 2014, Mollet et al. 2015, Sittenthaler et al. 2018, Vallant et al. 2018, Costanzi and Steifetten 2019, Shyvers et al. 2020, Brøseth et al. 2025, Haider et al. 2025). Thirdly, the cost of the study can be reduced by avoiding sampling the same individual multiple times. We observed that samples collected from the same roost, i.e., within a radius of less than two meters from one sample (Bergan et al. 2016), belonged to the same individual (3 roosts analyzed). In addition, we observed that the minimum distance between two samples collected during the same survey and belonging to the same individual outside the roost varied from 5.81m to 474.8m (Figure 1.C). For samples belonging to two distinct individuals this distance varied from 7.8m to 83.9m. Given the overlap in minimum distances observed for the same individual and observed for different individuals (Figure 1.C), we recommend collecting only one fecal sample per roost and all other samples more than two meters away. Fourth, during the laboratory process, the quality of samples should be checked before genotyping (Morin et al. 2010). For example, performing a ‘PCR control’ to test amplification for one microsatellite marker, and discarding samples without PCR amplification (Aleix-Mata et al. 2021, Aleix‐Mata et al. 2025). Sample selection could also be based on the results of mitochondrial DNA amplification, as proposed by Taberlet et al. (1999) and applied by Costanzi and Steifetten (2019), or on the measurement of host DNA concentration. Finally, we observed that removing bad quality markers (Mut02, Mut12, Mut17, Mut22 and TTT1) allows us to increase the quality of final data (Table 1), without consequences on individual identification. We obtained the same number of unique genotypes identified with 19 or 14 microsatellite markers (results not shown). The P (ID) and P (ID)sib obtained are particularly low, below the recommendation of Waits et al. (2001). This indicates that our remaining microsatellite panel was sufficient for discriminating specimens from the whole dataset with a high degree of confidence. For future studies, we recommend no further use of Mut12 and Mut20 markers that often appear problematic (Costanzi et al. 2018, Costanzi and Steifetten 2019, Aleix-Mata et al. 2021). All other markers, around fifteen being sufficient, must meet high quality criteria that reduce the risk of genotyping errors and data loss, such as design for the species of interest or a closely related species with validated cross-amplification (Supporting information), with tetranucleotide repeat motifs less prone to slippage (Costanzi et al. 2018). Conclusion We have shown that the use of non-invasive samples looks highly promising for future genetic monitoring of rock ptarmigan populations. By following simple collection guidelines and an appropriate laboratory protocol, high-quality data can be obtained without additional costs. To this end, we have compiled a set of recommendations in an open access online protocol, in order to comply with the FAIR principles (Findable, Accessible, Interoperable, Reusable): “Guidelines for sampling mountain Galliformes feces for molecular analysis” (Charbonnel et al. 2025). We are also confident that following these protocols will facilitate the transition to genomic monitoring of populations (Andrews et al. 2018, Carroll et al. 2018). Genomic studies on rock ptarmigan are becoming increasingly common (Kozma et al. 2016, 2018, Costanzi et al. 2018, Sonsthagen et al. 2022). In the future, they will be able to be carried out using non-invasive samples, notably thanks to the recently published reference genome (Squires et al. 2023) and compliance with collection guidelines. Original Articles Supplementary Material File (jab-tables-for-improvingnoninvasivesamplingforthegeneticsurveyoftherockptarmigan.docx) Download 17.80 KB References 1. Aleix-Mata, G., Pérez, J. 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Keywords genetics non-invasive ptarmigan Authors Affiliations Emeline Charbonnel 0000-0002-8561-7610 [email protected] Independent Researcher View all articles by this author Estelle Lauer Fédération Départementale des Chasseurs de l’Isère View all articles by this author Sébastien Zimmermann Fédération Départementale des Chasseurs de l’Isère View all articles by this author Pascal Lapebie Pôle Scientifique, Fédération Nationale des Chasseurs View all articles by this author Gaël Aleix-Mata Cos de Banders, Govern d’Andorra View all articles by this author Metrics & Citations Metrics Article Usage 167 views 66 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Emeline Charbonnel, Estelle Lauer, Sébastien Zimmermann, et al. \articletype Original Articles Improving non-invasive sampling for the genetic survey of the rock ptarmigan (Lagopus muta). Authorea . 09 January 2026. 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